## 1. The History of AI



The field of artificial intelligence (AI) officially started in 1956,
launched by a small but now-famous
 DARPA-sponsored
 summer conference at Dartmouth College, in Hanover, New Hampshire.
(The 50-year celebration of this conference,
 AI@50,
 was held in July 2006 at Dartmouth, with five of the original
participants making it
 back.[2]
 What happened at this historic conference figures in the final
section of this entry.) Ten thinkers attended, including John McCarthy
(who was working at Dartmouth in 1956), Claude Shannon, Marvin Minsky,
Arthur Samuel, Trenchard Moore (apparently the lone note-taker at the
original conference), Ray Solomonoff, Oliver Selfridge, Allen Newell,
and Herbert Simon. From where we stand now, into the start of the new
millennium, the Dartmouth conference is memorable for many reasons,
including this pair: one, the term 'artificial
intelligence' was coined there (and has long been firmly
entrenched, despite being disliked by some of the attendees, e.g.,
Moore); two, Newell and Simon revealed a program - Logic
Theorist (LT) - agreed by the attendees (and, indeed, by nearly
all those who learned of and about it soon after the conference) to be
a remarkable achievement. LT was capable of proving elementary
theorems in the propositional
 calculus.[3][4]



Though the *term* 'artificial intelligence' made
its advent at the 1956 conference, certainly the *field* of AI,
operationally defined (defined, i.e., as a field constituted by
practitioners who think and act in certain ways), was in operation
before 1956. For example, in a famous *Mind* paper of 1950,
Alan Turing argues that the question "Can a machine
think?" (and here Turing is talking about standard computing
machines: machines capable of computing functions from the natural
numbers (or pairs, triples, ... thereof) to the natural numbers
that a Turing machine or equivalent can handle) should be replaced
with the question "Can a machine be linguistically
indistinguishable from a human?." Specifically, he proposes a
test, the
 "Turing Test"
 (TT) as it's now known. In the TT, a woman and a computer are
sequestered in sealed rooms, and a human judge, in the dark as to
which of the two rooms contains which contestant, asks questions by
email (actually, by *teletype*, to use the original term) of the
two. If, on the strength of returned answers, the judge can do no
better than 50/50 when delivering a verdict as to which room houses
which player, we say that the computer in question has **passed**
the TT. Passing in this sense operationalizes linguistic
indistinguishability. Later, we shall discuss the role that TT has
played, and indeed continues to play, in attempts to define AI. At the
moment, though, the point is that in his paper, Turing explicitly lays
down the call for building machines that would provide an existence
proof of an affirmative answer to his question. The call even includes
a suggestion for how such construction should proceed. (He suggests
that "child machines" be built, and that these machines
could then gradually grow up on their own to learn to communicate in
natural language at the level of adult humans. This suggestion has
arguably been followed by Rodney Brooks and the philosopher Daniel
Dennett (1994) in the Cog Project. In addition, the Spielberg/Kubrick
movie *A.I.* is at least in part a cinematic exploration of
Turing's
 suggestion.[5])
 The TT continues to be at the heart of AI and discussions of its
foundations, as confirmed by the appearance of (Moor 2003). In fact,
the TT continues to be used to *define* the field, as in
Nilsson's (1998) position, expressed in his textbook for the
field, that AI simply is the field devoted to building an artifact
able to negotiate this test. Energy supplied by the dream of
engineering a computer that can pass TT, or by controversy surrounding
claims that it has *already* been passed, is if anything
stronger than ever, and the reader has only to do an internet search
via the string 



turing test passed 



to find up-to-the-minute attempts at reaching this dream, and attempts
(sometimes made by philosophers) to debunk claims that some such
attempt has succeeded.



Returning to the issue of the historical record, even if one bolsters
the claim that AI started at the 1956 conference by adding the proviso
that 'artificial intelligence' refers to a nuts-and-bolts
*engineering* pursuit (in which case Turing's
philosophical discussion, despite calls for a child machine,
wouldn't exactly count as AI per se), one must confront the fact
that Turing, and indeed many predecessors, did attempt to build
intelligent artifacts. In Turing's case, such building was
surprisingly well-understood before the advent of programmable
computers: Turing wrote a program for playing chess before there were
computers to run such programs on, by slavishly following the code
himself. He did this well before 1950, and long before Newell (1973)
gave thought in print to the possibility of a sustained, serious
attempt at building a good chess-playing
 computer.[6]



From the perspective of philosophy, which views the systematic
investigation of mechanical intelligence as meaningful and productive
separate from the specific logicist formalisms (e.g., first-order
logic) and problems (e.g., the *Entscheidungsproblem*) that gave
birth to computer science, neither the 1956 conference, nor
Turing's *Mind* paper, come close to marking the start of
AI. This is easy enough to see. For example, Descartes proposed TT
(not the TT by name, of course) long before Turing was
 born.[7]
 Here's the relevant passage: 



> 
> If there were machines which bore a resemblance to our body and
> imitated our actions as far as it was morally possible to do so, we
> should always have two very certain tests by which to recognise that,
> for all that, they were not real men. The first is, that they could
> never use speech or other signs as we do when placing our thoughts on
> record for the benefit of others. For we can easily understand a
> machine's being constituted so that it can utter words, and even
> emit some responses to action on it of a corporeal kind, which brings
> about a change in its organs; for instance, if it is touched in a
> particular part it may ask what we wish to say to it; if in another
> part it may exclaim that it is being hurt, and so on. But it never
> happens that it arranges its speech in various ways, in order to reply
> appropriately to everything that may be said in its presence, as even
> the lowest type of man can do. And the second difference is, that
> although machines can perform certain things as well as or perhaps
> better than any of us can do, they infallibly fall short in others, by
> which means we may discover that they did not act from knowledge, but
> only for the disposition of their organs. For while reason is a
> universal instrument which can serve for all contingencies, these
> organs have need of some special adaptation for every particular
> action. From this it follows that it is morally impossible that there
> should be sufficient diversity in any machine to allow it to act in
> all the events of life in the same way as our reason causes us to act.
> (Descartes 1637, p. 116)
> 



At the moment, Descartes is certainly carrying the
 day.[8]
 Turing predicted that his test would be passed by 2000, but the
fireworks across the globe at the start of the new millennium have
long since died down, and the most articulate of computers still
can't meaningfully debate a sharp toddler. Moreover, while in
certain focussed areas machines out-perform minds (IBM's famous
Deep Blue prevailed in chess over Gary Kasparov, e.g.; and more
recently, AI systems have prevailed in other games, e.g.
*Jeopardy!* and Go, about which more will momentarily be said),
minds have a (Cartesian) capacity for cultivating their expertise in
virtually *any* sphere. (If it were announced to Deep Blue, or
any current successor, that chess was no longer to be the game of
choice, but rather a heretofore unplayed variant of chess, the machine
would be trounced by human children of average intelligence having no
chess expertise.) AI simply hasn't managed to create
*general* intelligence; it hasn't even managed to produce
an artifact indicating that *eventually* it will create such a
thing. 



But what about IBM Watson's famous nail-biting victory in the
*Jeopardy!* game-show
 contest?[9]
 That certainly seems to be a machine triumph over humans on their
"home field," since *Jeopardy!* delivers a
human-level linguistic challenge ranging across many domains. Indeed,
among many AI cognoscenti, Watson's success is considered to be
much more impressive than Deep Blue's, for numerous reasons. One
reason is that while chess is generally considered to be
well-understood from the formal-computational perspective (after all,
it's well-known that there exists a perfect strategy for playing
chess), in open-domain **question-answering** (QA), as in any
significant natural-language processing task, there is no consensus as
to what problem, formally speaking, one is trying to solve. Briefly,
question-answering (QA) is what the reader would think it is: one asks
a question of a machine, and gets an answer, where the answer has to
be produced via some "significant" computational process.
(See Strzalkowski & Harabagiu (2006) for an overview of what QA,
historically, has been as a field.) A bit more precisely, there is no
agreement as to what underlying function, formally speaking,
question-answering capability computes. This lack of agreement stems
quite naturally from the fact that there is of course no consensus as
to what natural languages *are*, formally
 speaking.[10]
 Despite this murkiness, and in the face of an almost universal belief
that open-domain question-answering would remain unsolved for a decade
or more, Watson decisively beat the two top human *Jeopardy!*
champions on the planet. During the contest, Watson had to answer
questions that required not only command of simple factoids
(**Question**1), but also of some amount of rudimentary
reasoning (in the form of temporal reasoning) and commonsense
(**Question**2):



**Question**1: The only two consecutive U.S. presidents
with the same first name. 



**Question**2: In May 1898, Portugal celebrated the
400th anniversary of this explorer's arrival in India. 



While Watson is demonstrably better than humans in
*Jeopardy!*-style quizzing (a new human *Jeopardy!* master
could arrive on the scene, but as for chess, AI now assumes that a
second round of IBM-level investment would vanquish the new human
opponent), this approach does not work for the kind of NLP challenge
that Descartes described; that is, Watson can't converse on the
fly. After all, some questions don't hinge on sophisticated
information retrieval and machine learning over pre-existing data, but
rather on intricate reasoning right on the spot. Such questions may
for instance involve anaphora resolution, which require even deeper
degrees of commonsensical understanding of time, space, history, folk
psychology, and so on. Levesque (2013) has catalogued some alarmingly
simple questions which fall in this category. (Marcus, 2013, gives an
account of Levesque's challenges that is accessible to a wider
audience.) The other class of question-answering tasks on which Watson
fails can be characterized as *dynamic* question-answering. These
are questions for which answers may not be recorded in textual form
anywhere at the time of questioning, or for which answers are
dependent on factors that change with time. Two questions that fall in
this category are given below (Govindarajulu et al. 2013):



**Question**3: If I have 4 foos and 5 bars, and if foos
are not the same as bars, how many foos will I have if I get 3 bazes
which just happen to be foos?



**Question**4: What was IBM's Sharpe ratio in the
last 60 days of trading? 



Closely following Watson's victory, in March 2016,
 Google DeepMind's AlphaGo
 defeated one of Go's top-ranked players, Lee Seedol, in four
out of five matches. This was considered a landmark achievement within
AI, as it was widely believed in the AI community that computer
victory in Go was at least a few decades away, partly due to the
enormous number of valid sequences of moves in Go compared to that in
 Chess.[11]
 While this is a remarkable achievement, it should be noted that,
despite breathless coverage in the popular
 press,[12]
 AlphaGo, while indisputably a great Go player, is just that. For
example, neither AlphaGo nor Watson can understand the rules of Go
written in plain-and-simple English and produce a computer program
that can play the game. It's interesting that there is one
endeavor in AI that tackles a narrow version of this very problem: In
**general game playing**, a machine is given a description of a
brand new game just before it has to play the game (Genesereth et al.
2005). However, the description in question is expressed in a formal
language, and the machine has to manage to play the game from this
description. Note that this is still far from understanding even a
simple description of a game in English well enough to play it. 



But what if we consider the history of AI not from the perspective of
philosophy, but rather from the perspective of the field with which,
today, it is most closely connected? The reference here is to computer
science. From this perspective, does AI run back to well before
Turing? Interestingly enough, the results are the same: we find that
AI runs deep into the past, and has always had philosophy in its
veins. This is true for the simple reason that computer science grew
out of logic and probability
 theory,[13]
 which in turn grew out of (and is still intertwined with) philosophy.
Computer science, today, is shot through and through with logic; the
two fields cannot be separated. This phenomenon has become an object
of study unto itself (Halpern et al. 2001). The situation is no
different when we are talking not about traditional logic, but rather
about probabilistic formalisms, also a significant component of
modern-day AI: These formalisms also grew out of philosophy, as nicely
chronicled, in part, by Glymour (1992). For example, in the one mind
of Pascal was born a method of rigorously calculating probabilities,
conditional probability (which plays a particularly large role in AI,
currently), and such fertile philosophico-probabilistic arguments as
 Pascal's wager,
 according to which it is irrational not to become a Christian. 



That modern-day AI has its roots in philosophy, and in fact that these
historical roots are temporally deeper than even Descartes'
distant day, can be seen by looking to the clever, revealing cover of
the second edition (the third edition is the current one) of the
comprehensive textbook
 *Artificial Intelligence: A Modern Approach*
 (known in the AI community as simply *AIMA2e* for Russell &
Norvig, 2002).



![cover of AIMA2e](2ebig.jpg)

*Cover of* AIMA2e *(Russell & Norvig 2002)*




What you see there is an eclectic collection of memorabilia that might
be on and around the desk of some imaginary AI researcher. For
example, if you look carefully, you will specifically see: a picture
of Turing, a view of Big Ben through a window (perhaps R&N are
aware of the fact that Turing famously held at one point that a
physical machine with the power of a universal Turing machine is
physically impossible: he quipped that it would have to be the size of
Big Ben), a planning algorithm described in Aristotle's *De
Motu Animalium*,
 Frege's fascinating notation for first-order logic,
 a glimpse of Lewis Carroll's (1958) pictorial representation of
syllogistic reasoning, Ramon Lull's concept-generating wheel
from his 13th-century *Ars Magna*, and a number of
other pregnant items (including, in a clever, recursive, and
bordering-on-self-congratulatory touch, a copy of *AIMA* itself).
Though there is insufficient space here to make all the historical
connections, we can safely infer from the appearance of these items
(and here we of course refer to the ancient ones: Aristotle conceived
of planning as information-processing over two-and-a-half millennia
back; and in addition, as Glymour (1992) notes, Artistotle can also be
credited with devising the first knowledge-bases and ontologies, two
types of representation schemes that have long been central to AI)
that AI is indeed very, very old. Even those who insist that AI is at
least in part an artifact-building enterprise must concede that, in
light of these objects, AI is ancient, for it isn't just
theorizing from the perspective that intelligence is at bottom
computational that runs back into the remote past of human history:
Lull's wheel, for example, marks an attempt to capture
intelligence not only in computation, but in a physical artifact that
*embodies* that
 computation.[14]




AIMA has now reached its the third edition, and those interested in
the history of AI, and for that matter the history of philosophy of
mind, will not be disappointed by examination of the cover of the
third installment (the cover of the second edition is almost exactly
like the first edition). (All the elements of the cover, separately
listed and annotated, can be found
 online.)
 One significant addition to the cover of the third edition is a
drawing of Thomas Bayes; his appearance reflects the recent rise in
the popularity of probabilistic techniques in AI, which we discuss
later.



One final point about the history of AI seems worth making. 



It is generally assumed that the birth of modern-day AI in the 1950s
came in large part because of and through the advent of the modern
high-speed digital computer. This assumption accords with
common-sense. After all, AI (and, for that matter, to some degree its
cousin, cognitive science, particularly computational cognitive
modeling, the sub-field of cognitive science devoted to producing
computational simulations of human cognition) is aimed at implementing
intelligence in a computer, and it stands to reason that such a goal
would be inseparably linked with the advent of such devices. However,
this is only part of the story: the part that reaches back but to
Turing and others (e.g., von Neuman) responsible for the first
electronic computers. The other part is that, as already mentioned, AI
has a particularly strong tie, historically speaking, to reasoning
(logic-based and, in the need to deal with uncertainty,
inductive/probabilistic reasoning). In this story, nicely told by
Glymour (1992), a search for an answer to the question "What is
a proof?" eventually led to an answer based on Frege's
version of first-order logic (FOL): a (finitary) mathematical proof
consists in a series of step-by-step inferences from one formula of
first-order logic to the next. The obvious extension of this answer
(and it isn't a complete answer, given that lots of classical
mathematics, despite conventional wisdom, clearly can't be
expressed in FOL; even the Peano Axioms, to be expressed as a finite
set of formulae, require *S*OL) is to say that not only
mathematical thinking, but thinking, period, can be expressed in FOL.
(This extension was entertained by many logicians long before the
start of information-processing psychology and cognitive science
- a fact some cognitive psychologists and cognitive scientists
often seem to forget.) Today, logic-based AI is only *part* of
AI, but the point is that this part still lives (with help from logics
much more powerful, but much more complicated, than FOL), and it can
be traced all the way back to Aristotle's theory of the
 syllogism.[15]
 In the case of uncertain reasoning, the question isn't
"What is a proof?", but rather questions such as
"What is it rational to believe, in light of certain
observations and probabilities?" This is a question posed and
tackled long before the arrival of digital computers. 


## 2. What Exactly *is* AI?



So far we have been proceeding as if we have a firm and precise grasp
of the nature of AI. But what exactly *is* AI? Philosophers
arguably know better than anyone that precisely defining a particular
discipline to the satisfaction of all relevant parties (including
those working in the discipline itself) can be acutely challenging.
Philosophers of science certainly have proposed credible accounts of
what constitutes at least the general shape and texture of a given
field of science and/or engineering, but what exactly is the
agreed-upon definition of physics? What about biology? What, for that
matter, is philosophy, exactly? These are remarkably difficult, maybe
even eternally unanswerable, questions, especially if the target is a
*consensus* definition. Perhaps the most prudent course we can
manage here under obvious space constraints is to present in
encapsulated form some *proposed* definitions of AI. We do
include a glimpse of recent attempts to define AI in detailed,
rigorous fashion (and we suspect that such attempts will be of
interest to philosophers of science, and those interested in this
sub-area of philosophy). 



Russell and Norvig (1995, 2002, 2009), in their aforementioned
*AIMA* text, provide a set of possible answers to the "What
is AI?" question that has considerable currency in the field
itself. These answers all assume that AI should be defined in terms of
its goals: a candidate definition thus has the form "AI is the
field that aims at building ..." The answers all fall under
a quartet of types placed along two dimensions. One dimension is
whether the goal is to match human performance, or, instead, ideal
rationality. The other dimension is whether the goal is to build
systems that reason/think, or rather systems that act. The situation
is summed up in this table:





|  |  |  |
| --- | --- | --- |
|  | **Human-Based** | **Ideal Rationality** |
| **Reasoning-Based:** |  Systems that think like humans.  |  Systems that think rationally. |
| **Behavior-Based:** |  Systems that act like humans.  |  Systems that act rationally. |



*Four Possible Goals for AI According to* AIMA 




Please note that this quartet of possibilities does reflect (at least
a significant portion of) the relevant literature. For example,
philosopher John Haugeland (1985) falls into the Human/Reasoning
quadrant when he says that AI is "The exciting new effort to
make computers think ... *machines with minds*, in the
full and literal sense." (By far, this is the quadrant that most
popular narratives affirm and explore. The recent
 Westworld
 TV series is a powerful case in point.) Luger and Stubblefield (1993)
seem to fall into the Ideal/Act quadrant when they write: "The
branch of computer science that is concerned with the automation of
intelligent behavior." The Human/Act position is occupied most
prominently by Turing, whose test is passed only by those systems able
to act sufficiently like a human. The "thinking
rationally" position is defended (e.g.) by Winston (1992). While
it might not be entirely uncontroversial to assert that the four bins
given here are exhaustive, such an assertion appears to be quite
plausible, even when the literature up to the present moment is
canvassed. 



It's important to know that the contrast between the focus on
systems that think/reason versus systems that act, while found, as we
have seen, at the heart of the *AIMA* texts, and at the heart of
AI itself, should not be interpreted as implying that AI researchers
view their work as falling all and only within one of these two
compartments. Researchers who focus more or less exclusively on
knowledge representation and reasoning, are also quite prepared to
acknowledge that they are working on (what they take to be) a central
component or capability within any one of a family of larger systems
spanning the reason/act distinction. The clearest case may come from
the work on planning - an AI area traditionally making central
use of representation and reasoning. For good or ill, much of this
research is done in abstraction (in vitro, as opposed to in vivo), but
the researchers involved certainly intend or at least hope that the
results of their work can be embedded into systems that actually do
things, such as, for example, execute the plans. 



What about Russell and Norvig themselves? What is their answer to the
What is AI? question? They are firmly in the the "acting
rationally" camp. In fact, it's safe to say both that they
are the chief proponents of this answer, and that they have been
remarkably successful evangelists. Their extremely influential
*AIMA* series can be viewed as a book-length defense and
specification of the Ideal/Act category. We will look a bit later at
how Russell and Norvig lay out all of AI in terms of **intelligent
agents**, which are systems that act in accordance with various
ideal standards for rationality. But first let's look a bit
closer at the view of intelligence underlying the *AIMA* text. We
can do so by turning to Russell (1997). Here Russell recasts the
"What is AI?" question as the question "What is
intelligence?" (presumably under the assumption that we have a
good grasp of what an artifact is), and then he identifies
intelligence with **rationality**. More specifically, Russell sees
AI as the field devoted to building **intelligent agents**, which
are functions taking as input tuples of percepts from the external
environment, and producing behavior (actions) on the basis of these
percepts. Russell's overall picture is this one:



!['Percept history' to 'Agent function' to 'Behavior' to 'Environment' to 'Percept history'; also 'Environment' to/from 'State history' to 'Performance Measure'](rationality_diagram_3.png)

*The Basic Picture Underlying Russell's Account of
Intelligence/Rationality* 




Let's unpack this diagram a bit, and take a look, first, at the
account of **perfect rationality** that can be derived from it. The
behavior of the agent in the environment \(E\) (from a class \(\bE\)
of environments) produces a sequence of states or snapshots of that
environment. A performance measure \(U\) evaluates this sequence;
notice the box labeled "Performance Measure" in the above
figure. We let \(V(f,\bE,U)\) denote the *expected* utility
according to \(U\) of the agent function \(f\) operating on
 \(\bE\).[16]
 Now we identify a perfectly rational agent with the agent function:


 

\[\tag{1}\label{eq1}
f\_{\opt} = \argmax\_f V(f,\bE,U)
\]


According to the above equation, a perfectly rational agent can be
taken to be the function \(f\_{opt}\) which produces the maximum
expected utility in the environment under consideration. Of course, as
Russell points out, it's usually not possible to actually build
perfectly rational agents. For example, though it's easy enough
to specify an algorithm for playing invincible chess, it's not
feasible to implement this algorithm. What traditionally happens in AI
is that programs that are - to use Russell's apt
terminology - **calculatively rational** are constructed
instead: these are programs that, *if executed infinitely fast*,
would result in perfectly rational behavior. In the case of chess,
this would mean that we strive to write a program that runs an
algorithm capable, in principle, of finding a flawless move, but we
add features that truncate the search for this move in order to play
within intervals of digestible duration. 



Russell himself champions a new brand of intelligence/rationality for
AI; he calls this brand **bounded optimality**. To understand
Russell's view, first we follow him in introducing a
distinction: We say that agents have two components: a program, and a
machine upon which the program runs. We write \(Agent(P, M)\) to
denote the agent function implemented by program \(P\) running on
machine \(M\). Now, let \(\mathcal{P}(M)\) denote the set of all
programs \(P\) that can run on machine \(M\). The **bounded
optimal** program \(P\_{\opt,M}\) then is:

 

\[
P\_{\opt,M}=\argmax\_{P\in\mathcal{P}(M)}V(\mathit{Agent}(P,M),\bE,U)
\]


You can understand this equation in terms of any of the mathematical
idealizations for standard computation. For example, machines can be
identified with Turing machines minus instructions (i.e., TMs are here
viewed architecturally only: as having tapes divided into squares upon
which symbols can be written, read/write heads capable of moving up
and down the tape to write and erase, and control units which are in
one of a finite number of states at any time), and programs can be
identified with instructions in the Turing-machine model (telling the
machine to write and erase symbols, depending upon what state the
machine is in). So, if you are told that you must
"program" within the constraints of a 22-state Turing
machine, you could search for the "best" program given
those constraints. In other words, you could strive to find the
optimal program within the bounds of the 22-state architecture.
Russell's (1997) view is thus that AI is the field devoted to
creating optimal programs for intelligent agents, under time and space
constraints on the machines implementing these
 programs.[17]




The reader must have noticed that in the equation for \(P\_{\opt,M}\)
we have not elaborated on \(\bE\) and \(U\) and how equation
\eqref{eq1} might be used to construct an agent if the class of
environments \(\bE\) is quite general, or if the true environment
\(E\) is simply unknown. Depending on the task for which one is
constructing an artificial agent, \(E\) and \(U\) would vary. The
mathematical form of the environment \(E\) and the utility function
\(U\) would vary wildly from, say, chess to *Jeopardy!*. Of
course, if we were to design a globally intelligent agent, and not
just a chess-playing agent, we could get away with having just one
pair of \(E\) and \(U\). What would \(E\) look like if we were
building a generally intelligent agent and not just an agent that is
good at a single task? \(E\) would be a model of not just a single
game or a task, but the entire physical-social-virtual universe
consisting of many games, tasks, situations, problems, etc. This
project is (at least currently) hopelessly difficult as, obviously, we
are nowhere near to having such a comprehensive theory-of-everything
model. For further discussion of a theoretical architecture put
forward for this problem, see the
 Supplement on the AIXI architecture.
 



It should be mentioned that there is a different, much more
straightforward answer to the "What is AI?" question. This
answer, which goes back to the days of the original Dartmouth
conference, was expressed by, among others, Newell (1973), one of the
grandfathers of modern-day AI (recall that he attended the 1956
conference); it is: 



> 
> AI is the field devoted to building artifacts that are intelligent,
> where 'intelligent' is operationalized through
> intelligence tests (such as the Wechsler Adult Intelligence Scale),
> and other tests of mental ability (including, e.g., tests of
> mechanical ability, creativity, and so on).
> 



The above definition can be seen as fully specifying a concrete
version of Russell and Norvig's four possible goals. Though few
are aware of this now, this answer was taken quite seriously for a
while, and in fact underlied one of the most famous programs in the
history of AI: the ANALOGY program of Evans (1968), which solved
geometric analogy problems of a type seen in many intelligence tests.
An attempt to rigorously define this forgotten form of AI (as what
they dub **Psychometric AI**), and to resurrect it from the days of
Newell and Evans, is provided by Bringsjord and Schimanski (2003) [see
also e.g. (Bringsjord 2011)]. A sizable private investment has been
made in the ongoing attempt, now known as
 Project Aristo,
 to build a "digital Aristotle", in the form of a machine
able to excel on standardized tests such at the AP exams tackled by US
high school students (Friedland et al. 2004). (Vibrant work in this
direction continues today at the
 Allen Institute for Artificial Intelligence.)[18]
 In addition, researchers at Northwestern have forged a connection
between AI and tests of mechanical ability (Klenk et al. 2005). 



In the end, as is the case with any discipline, to really know
precisely what that discipline is requires you to, at least to some
degree, dive in and do, or at least dive in and read. Two decades ago
such a dive was quite manageable. Today, because the content that has
come to constitute AI has mushroomed, the dive (or at least the swim
after it) is a bit more demanding.


## 3. Approaches to AI



There are a number of ways of "carving up" AI. By far the
most prudent and productive way to summarize the field is to turn yet
again to the *AIMA* text given its comprehensive overview of the
field. 


### 3.1 The Intelligent Agent Continuum



As Russell and Norvig (2009) tell us in the Preface of *AIMA*:




> 
> The main unifying theme is the idea of an intelligent agent. We define
> AI as the study of agents that receive percepts from the environment
> and perform actions. Each such agent implements a function that maps
> percept sequences to actions, and we cover different ways to represent
> these functions... (Russell & Norvig 2009, vii)
> 



The basic picture is thus summed up in this figure:



![agent with sensors and actuators receiving percepts and performing actions](agent-environment.png)

*Impressionistic Overview of an Intelligent Agent*




The content of *AIMA* derives, essentially, from fleshing out
this picture; that is, the above figure corresponds to the different
ways of representing the overall function that intelligent agents
implement. And there is a progression from the least powerful agents
up to the more powerful ones. The following figure gives a high-level
view of a simple kind of agent discussed early in the book. (Though
simple, this sort of agent corresponds to the architecture of
representation-free agents designed and implemented by Rodney Brooks,
1991.)



![simple reflex agent with no internal model of the world interacting with environment](simple-reflex-agent.png)

*A Simple Reflex Agent*




As the book progresses, agents get increasingly sophisticated, and the
implementation of the function they represent thus draws from more and
more of what AI can currently muster. The following figure gives an
overview of an agent that is a bit smarter than the simple reflex
agent. This smarter agent has the ability to internally model the
outside world, and is therefore not simply at the mercy of what can at
the moment be directly sensed.



![reflex agent with model of world interacting with environment](reflexstate-agent.png)

*A More Sophisticated Reflex Agent*




There are seven parts to *AIMA*. As the reader passes through
these parts, she is introduced to agents that take on the powers
discussed in each part. Part I is an introduction to the agent-based
view. Part II is concerned with giving an intelligent agent the
capacity to think ahead a few steps in clearly defined environments.
Examples here include agents able to successfully play games of
perfect information, such as chess. Part III deals with agents that
have declarative knowledge and can reason in ways that will be quite
familiar to most philosophers and logicians (e.g., knowledge-based
agents deduce what actions should be taken to secure their goals).
Part IV of the book outfits agents with the power to handle
uncertainty by reasoning in probabilistic
 fashion.[19]
 In Part V, agents are given a capacity to learn. The following figure
shows the overall structure of a learning agent.



![agent that can learn interacting with environment](learning-agent.png)

*A Learning Agent*




The final set of powers agents are given allow them to communicate.
These powers are covered in Part VI. 



Philosophers who patiently travel the entire progression of
increasingly smart agents will no doubt ask, when reaching the end of
Part VII, if anything is missing. Are we given enough, in general, to
build an artificial person, or is there enough only to build a mere
animal? This question is implicit in the following from Charniak and
McDermott (1985): 



> 
> The ultimate goal of AI (which we are very far from achieving) is to
> build a person, or, more humbly, an animal. (Charniak & McDermott
> 1985, 7)
> 



To their credit, Russell & Norvig, in *AIMA*'s Chapter
27, "AI: Present and Future," consider this question, at
least to some
 degree.[]
 They do so by considering some challenges to AI that have hitherto
not been met. One of these challenges is described by R&N as
follows: 



> 
> [M]achine learning has made very little progress on the important
> problem of constructing new representations at levels of abstraction
> higher than the input vocabulary. In computer vision, for example,
> learning complex concepts such as Classroom and Cafeteria would be
> made unnecessarily difficult if the agent were forced to work from
> pixels as the input representation; instead, the agent needs to be
> able to form intermediate concepts first, such as Desk and Tray,
> without explicit human supervision. Similar concepts apply to learning
> behavior: *HavingACupOfTea* is a very important high-level step
> in many plans, but how does it get into an action library that
> initially contains much simpler actions such as RaiseArm and Swallow?
> Perhaps this will incorporate **deep belief networks** -
> Bayesian networks that have multiple layers of hidden variables, as in
> the work of Hinton *et al.* (2006), Hawkins and Blakeslee (2004),
> and Bengio and LeCun (2007). ... Unless we understand such
> issues, we are faced with the daunting task of constructing large
> commonsense knowledge bases by hand, and approach that has not fared
> well to date. (Russell & Norvig 2009, Ch. 27.1)
> 



While there has seen some advances in addressing this challenge (in
the form of *deep learning* or *representation learning*),
this specific challenge is actually merely a foothill before a range
of dizzyingly high mountains that AI must eventually somehow manage to
climb. One of those mountains, put simply, is
 *reading*.[21]
 Despite the fact that, as noted, Part V of *AIMA* is devoted to
machine learning, AI, as it stands, offers next to nothing in the way
of a mechanization of learning by reading. Yet when you think about
it, reading is probably the dominant way you learn at this stage in
your life. Consider what you're doing at this very moment.
It's a good bet that you are reading this sentence because,
earlier, you set yourself the goal of learning about the field of AI.
 Yet
 the formal models of learning provided in *AIMA*'s Part IV
(which are all and only the models at play in AI) cannot be applied to
learning by
 reading.[22]
 These models all start with a *function-based* view of learning.
According to this view, to learn is almost invariably to produce an
underlying function \(\ff\) on the basis of a restricted set of
pairs

 

\[
 \left\{\left\langle x\_1, \ff(x\_1)\right\rangle,\left\langle x\_2, \ff(x\_2)\right\rangle, \ldots, \left\langle x\_n, \ff(x\_n)\right\rangle\right\}.
\]


For example, consider receiving inputs consisting of 1, 2, 3, 4, and
5, and corresponding range values of 1, 4, 9, 16, and 25; the goal is
to "learn" the underlying mapping from natural numbers to
natural numbers. In this case, assume that the underlying function is
\(n^2\), and that you do "learn" it. While this narrow
model of learning can be productively applied to a number of
processes, the process of reading isn't one of them. Learning by
reading cannot (at least for the foreseeable future) be modeled as
divining a function that produces argument-value pairs. Instead, your
reading about AI can pay dividends only if your knowledge has
increased in the right way, *and* if that knowledge leaves you
poised to be able to produce behavior taken to confirm sufficient
mastery of the subject area in question. This behavior can range from
correctly answering and justifying test questions regarding AI, to
producing a robust, compelling presentation or paper that signals your
achievement. 



Two points deserve to be made about machine reading. First, it may not
be clear to all readers that reading is an ability that is central to
intelligence. The centrality derives from the fact that intelligence
requires vast knowledge. We have no other means of getting systematic
knowledge into a system than to get it in from text, whether text on
the web, text in libraries, newspapers, and so on. You might even say
that the big problem with AI has been that machines really don't
know much compared to humans. That can only be because of the fact
that humans read (or hear: illiterate people can listen to text being
uttered and learn that way). Either machines gain knowledge by humans
manually encoding and inserting knowledge, or by reading and
listening. These are brute facts. (We leave aside supernatural
techniques, of course. Oddly enough, Turing didn't: he seemed to
think ESP should be discussed in connection with the powers of minds
and machines. See Turing,
 1950.)[23]



Now for the second point. Humans able to read have invariably also
learned a language, and learning languages has been modeled in
conformity to the function-based approach adumbrated just above
(Osherson et al. 1986). However, this doesn't entail that an
artificial agent able to read, at least to a significant degree, must
have really and truly learned a natural language. AI is first and
foremost concerned with engineering computational artifacts that
measure up to some test (where, yes, sometimes that test is from the
human sphere), not with whether these artifacts process information in
ways that match those present in the human case. It may or may not be
necessary, when engineering a machine that can read, to imbue that
machine with human-level linguistic competence. The issue is
empirical, and as time unfolds, and the engineering is pursued, we
shall no doubt see the issue settled. 



Two additional high mountains facing AI are subjective consciousness
and creativity, yet it would seem that these great challenges are ones
the field apparently hasn't even come to grips with. Mental
phenomena of paramount importance to many philosophers of mind and
neuroscience are simply missing from *AIMA*. For example,
consciousness is only mentioned in passing in *AIMA*, but
subjective consciousness is the most important thing in our lives
- indeed we only desire to go on living because we wish to go on
enjoying subjective states of certain types. Moreover, if human minds
are the product of evolution, then presumably phenomenal consciousness
has great survival value, and would be of tremendous help to a robot
intended to have at least the behavioral repertoire of the first
creatures with brains that match our own (hunter-gatherers; see Pinker
1997). Of course, subjective consciousness is largely missing from the
sister fields of cognitive psychology and computational cognitive
modeling as well. We discuss some of these challenges in the
 Philosophy of Artificial Intelligence
 section below. For a list of similar challenges to cognitive science,
see the relevant
 section of the entry on cognitive science.[24]




To some readers, it might seem in the very least tendentious to point
to subjective consciousness as a major challenge to AI that it has yet
to address. These readers might be of the view that pointing to this
problem is to look at AI through a distinctively philosophical prism,
and indeed a controversial philosophical standpoint. 



But as its literature makes clear, AI measures itself by looking to
animals and humans and picking out in them remarkable mental powers,
and by then seeing if these powers can be mechanized. Arguably the
power most important to humans (the capacity to experience) is nowhere
to be found on the target list of most AI researchers. There may be a
good reason for this (no formalism is at hand, perhaps), but there is
no denying the state of affairs in question obtains, and that, in
light of how AI measures itself, that it's worrisome. 



As to creativity, it's quite remarkable that the power we most
praise in human minds is nowhere to be found in *AIMA*. Just as
in (Charniak & McDermott 1985) one cannot find
'neural' in the index, 'creativity'
can't be found in the index of *AIMA*. This is particularly
odd because many AI researchers have in fact worked on creativity
(especially those coming out of philosophy; e.g., Boden 1994,
Bringsjord & Ferrucci 2000). 



Although the focus has been on *AIMA*, any of its counterparts
could have been used. As an example, consider *Artificial
Intelligence: A New Synthesis*, by Nils Nilsson. As in the case of
*AIMA*, everything here revolves around a gradual progression
from the simplest of agents (in Nilsson's case, *reactive
agents*), to ones having more and more of those powers that
distinguish persons. Energetic readers can verify that there is a
striking parallel between the main sections of Nilsson's book
and *AIMA*. In addition, Nilsson, like Russell and Norvig,
ignores phenomenal consciousness, reading, and creativity. None of the
three are even mentioned. Likewise, a recent comprehensive AI textbook
by Luger (2008) follows the same pattern. 



A final point to wrap up this section. It seems quite plausible to
hold that there is a certain inevitability to the structure of an AI
textbook, and the apparent reason is perhaps rather interesting. In
personal conversation, Jim Hendler, a well-known AI researcher who is
one of the main innovators behind Semantic Web (Berners-Lee, Hendler,
Lassila 2001), an under-development "AI-ready" version of
the World Wide Web, has said that this inevitability can be rather
easily displayed when teaching Introduction to AI; here's how.
Begin by asking students what they think AI is. Invariably, many
students will volunteer that AI is the field devoted to building
artificial creatures that are intelligent. Next, ask for examples of
intelligent creatures. Students always respond by giving examples
across a continuum: simple multi-cellular organisms, insects, rodents,
lower mammals, higher mammals (culminating in the great apes), and
finally human persons. When students are asked to describe the
differences between the creatures they have cited, they end up
essentially describing the progression from simple agents to ones
having our (e.g.) communicative powers. This progression gives the
skeleton of every comprehensive AI textbook. Why does this happen? The
answer seems clear: it happens because we can't resist
conceiving of AI in terms of the powers of extant creatures with which
we are familiar. At least at present, persons, and the creatures who
enjoy only bits and pieces of personhood, are - to repeat
- the measure of
 AI.[25]



### 3.2 Logic-Based AI: Some Surgical Points



Reasoning based on classical deductive logic is monotonic; that is, if
\(\Phi\vdash\phi\), then for all \(\psi\), \(\Phi\cup
\{\psi\}\vdash\phi\). Commonsense reasoning is not monotonic. While
you may currently believe on the basis of reasoning that your house is
still standing, if while at work you see on your computer screen that
a vast tornado is moving through the location of your house, you will
drop this belief. The addition of new information causes previous
inferences to fail. In the simpler example that has become an AI
staple, if I tell you that Tweety is a bird, you will infer that
Tweety can fly, but if I then inform you that Tweety is a penguin, the
inference evaporates, as well it should. Nonmonotonic (or defeasible)
logic includes formalisms designed to capture the mechanisms
underlying these kinds of examples. See the separate entry on
 logic and artificial intelligence,
 which is focused on nonmonotonic reasoning, and reasoning about time
and change. It also provides a history of the early days of
logic-based AI, making clear the contributions of those who founded
the tradition (e.g., John McCarthy and Pat Hayes; see their seminal
1969 paper). 



The formalisms and techniques of logic-based AI have reached a level
of impressive maturity - so much so that in various academic and
corporate laboratories, implementations of these formalisms and
techniques can be used to engineer robust, real-world software. It is
strongly recommend that readers who have an interest to learn where AI
stands in these areas consult (Mueller 2006), which provides, in one
volume, integrated coverage of nonmonotonic reasoning (in the form,
specifically, of circumscription), and reasoning about time and change
in the situation and event calculi. (The former calculus is also
introduced by Thomason. In the second, timepoints are included, among
other things.) The other nice thing about (Mueller 2006) is that the
logic used is multi-sorted first-order logic (MSL), which has
unificatory power that will be known to and appreciated by many
technical philosophers and logicians (Manzano 1996). 



We now turn to three further topics of importance in AI. They are:


1. The overarching scheme of logicist AI, in the context of the
attempt to build intelligent artificial agents.
2. Common Logic and the intensifying quest for interoperability.
3. A technique that can be called **encoding down**, which can
allow machines to reason efficiently over knowledge that, were it not
encoded down, would, when reasoned over, lead to paralyzing
inefficiency.



This trio is covered in order, beginning with the first.



Detailed accounts of logicist AI that fall under the agent-based
scheme can be found in (Lenat 1983, Lenat & Guha 1990, Nilsson
1991, Bringsjord & Ferrucci
 1998).[26].
 The core idea is that an intelligent agent receives percepts from the
external world in the form of formulae in some logical system (e.g.,
first-order logic), and infers, on the basis of these percepts and its
knowledge base, what actions should be performed to secure the
agent's goals. (This is of course a barbaric simplification.
Information from the external world is *encoded* in formulae,
and transducers to accomplish this feat may be components of the
agent.) 



To clarify things a bit, we consider, briefly, the logicist view in
connection with arbitrary **logical systems**
 \(\mathcal{L}\_{X}\).[27]
 We obtain a particular logical system by setting \(X\) in the
appropriate way. Some examples: If \(X=I\), then we have a system at
the level of FOL [following the standard notation from model theory;
see e.g. (Ebbinghaus et al. 1984)]. \(\mathcal{L}\_{II}\) is
second-order logic, and \(\mathcal{L}\_{\omega\_I\omega}\) is a
"small system" of infinitary logic (countably infinite
conjunctions and disjunctions are permitted). These logical systems
are all **extensional**, but there are **intensional** ones as
well. For example, we can have logical systems corresponding to those
seen in standard propositional modal logic (Chellas 1980). One
possibility, familiar to many philosophers, would be propositional
KT45, or
 \(\mathcal{L}\_{KT45}\).[28]
 In each case, the system in question includes a relevant alphabet
from which well-formed formulae are constructed by way of a formal
grammar, a reasoning (or proof) theory, a formal semantics, and at
least some meta-theoretical results (soundness, completeness, etc.).
Taking off from standard notation, we can thus say that a set of
formulas in some particular logical system \(\mathcal{L}\_X\),
\(\Phi\_{\mathcal{L}\_X}\), can be used, in conjunction with some
reasoning theory, to infer some particular formula
\(\phi\_{\mathcal{L}\_X}\). (The reasoning may be deductive, inductive,
abductive, and so on. Logicist AI isn't in the least restricted
to any particular mode of reasoning.) To say that such a situation
holds, we write 

 \[
 \Phi\_{\mathcal{L}\_X} \vdash\_{\mathcal{L}\_X} \phi\_{\mathcal{L}\_X}
 \]

 



When the logical system referred to is clear from context, or when we
don't care about which logical system is involved, we can simply
write 

 \[
 \Phi \vdash \phi
 \]

 



Each logical system, in its formal semantics, will include objects
designed to represent ways the world pointed to by formulae in this
system can be. Let these ways be denoted by \(W^i\_{{\mathcal{L}\_X}}\).
When we aren't concerned with which logical system is involved,
we can simply write \(W^i\). To say that such a way models a formula
\(\phi\) we write 

 \[
 W\_i \models \phi
 \]

 



We extend this to a set of formulas in the natural way:
\(W^i\models\Phi\) means that all the elements of \(\Phi\) are true on
\(W^i\). Now, using the simple machinery we've established, we
can describe, in broad strokes, the life of an intelligent agent that
conforms to the logicist point of view. This life conforms to the
basic cycle that undergirds intelligent agents in the *AIMA*
sense. 



To begin, we assume that the human designer, after studying the world,
uses the language of a particular logical system to give to our agent
an initial set of beliefs \(\Delta\_0\) about what this world is like.
In doing so, the designer works with a formal model of this world,
\(W\), and ensures that \(W\models\Delta\_0\). Following tradition, we
refer to \(\Delta\_0\) as the agent's (starting) **knowledge
base**. (This terminology, given that we are talking about the
agent's *beliefs*, is known to be peculiar, but it
persists.) Next, the agent **ADJUSTS** its knowlege base to produce
a new one, \(\Delta\_1\). We say that adjustment is carried out by way
of an operation \(\mathcal{A}\); so
\(\mathcal{A}[\Delta\_0]=\Delta\_1\). How does the adjustment process,
\(\mathcal{A}\), work? There are many possibilities. Unfortunately,
many believe that the simplest possibility (viz.,
\(\mathcal{A}[\Delta\_i]\) equals the set of all formulas that can be
deduced in some elementary manner from \(\Delta\_i\)) exhausts
*all* the possibilities. The reality is that adjustment, as
indicated above, can come by way of *any* mode of reasoning
- induction, abduction, and yes, various forms of deduction
corresponding to the logical system in play. For present purposes,
it's not important that we carefully enumerate all the options.




The cycle continues when the agent **ACTS** on the environment, in
an attempt to secure its goals. Acting, of course, can cause changes
to the environment. At this point, the agent **SENSES** the
environment, and this new information \(\Gamma\_1\) factors into the
process of adjustment, so that
\(\mathcal{A}[\Delta\_1\cup\Gamma\_1]=\Delta\_2\). The cycle of **SENSES
\(\Rightarrow\) ADJUSTS \(\Rightarrow\) ACTS** continues to produce
the life \(\Delta\_0,\Delta\_1,\Delta\_2,\Delta\_3,\ldots,\) ... of
our agent. 



It may strike you as preposterous that logicist AI be touted as an
approach taken to replicate *all* of cognition. Reasoning over
formulae in some logical system might be appropriate for
computationally capturing high-level tasks like trying to solve a math
problem (or devising an outline for an entry in the Stanford
Encyclopedia of Philosophy), but how could such reasoning apply to
tasks like those a hawk tackles when swooping down to capture
scurrying prey? In the human sphere, the task successfully negotiated
by athletes would seem to be in the same category. Surely, some will
declare, an outfielder chasing down a fly ball doesn't prove
theorems to figure out how to pull off a diving catch to save the
game! Two brutally reductionistic arguments can be given in support of
this "logicist theory of everything" approach towards
cognition. The first stems from the fact that a complete proof
calculus for just first-order logic can simulate all of Turing-level
computation (Chapter 11, Boolos et al. 2007). The second justification
comes from the role logic plays in foundational theories of
mathematics and mathematical reasoning. Not only are foundational
theories of mathematics cast in logic (Potter 2004), but there have
been successful projects resulting in machine verification of ordinary
non-trivial theorems, e.g., in the
 Mizar project
 alone around 50,000 theorems have been verified (Naumowicz and
Kornilowicz 2009). The argument goes that if any approach to AI can be
cast mathematically, then it can be cast in a logicist form. 



Needless to say, such a declaration has been carefully considered by
logicists beyond the reductionistic argument given above. For example,
Rosenschein and Kaelbling (1986) describe a method in which logic is
used to specify finite state machines. These machines are used at
"run time" for rapid, reactive processing. In this
approach, though the finite state machines contain no logic in the
traditional sense, they are produced by logic and inference. Real
robot control via first-order theorem proving has been demonstrated by
Amir and Maynard-Reid (1999, 2000, 2001). In fact, you can
 download
 version 2.0 of the software that makes this approach real for a Nomad
200 mobile robot in an office environment. Of course, negotiating an
office environment is a far cry from the rapid adjustments an
outfielder for the Yankees routinely puts on display, but certainly
it's an open question as to whether future machines will be able
to mimic such feats through rapid reasoning. The question is open if
for no other reason than that all must concede that the constant
increase in reasoning speed of first-order theorem provers is
breathtaking. (For up-to-date news on this increase, visit and monitor
the
 TPTP site.)
 There is no known reason why the software engineering in question
cannot continue to produce speed gains that would eventually allow an
artificial creature to catch a fly ball by processing information in
purely logicist fashion. 



Now we come to the second topic related to logicist AI that warrants
mention herein: common logic and the intensifying quest for
interoperability between logic-based systems using different logics.
Only a few brief comments are
 offered.[29]
 Readers wanting more can explore the links provided in the course of
the summary. 



One standardization is through what is known as
 Common Logic
 (CL), and variants thereof. (CL is published as an
 ISO standard
 - ISO is the International Standards Organization.)
Philosophers interested in logic, and of course logicians, will find
CL to be quite fascinating. From an historical perspective, the advent
of CL is interesting in no small part because the person spearheading
it is none other than Pat Hayes, the same Hayes who, as we have seen,
worked with McCarthy to establish logicist AI in the 1960s. Though
Hayes was not at the original 1956 Dartmouth conference, he certainly
must be regarded as one of the founders of contemporary AI.) One of
the interesting things about CL, at least as we see it, is that it
signifies a trend toward the marriage of logics, and programming
languages and environments. Another system that is a logic/programming
hybrid is
 Athena,
 which can be used as a programming language, and is at the same time
a form of MSL. Athena is based on formal systems known as
**denotational proof languages** (Arkoudas 2000). 



How is interoperability between two systems to be enabled by CL?
Suppose one of these systems is based on logic \(L\), and the other on
\(L'\). (To ease exposition, assume that both logics are first-order.)
The idea is that a theory \(\Phi\_L\), that is, a set of formulae in
\(L\), can be translated into CL, producing \(\Phi\_{CL}\), and then
this theory can be translated into \(\Phi\_L'\). CL thus becomes an
*inter lingua*. Note that what counts as a well-formed formula in
\(L\) can be different than what counts as one in \(L'\). The two
logics might also have different proof theories. For example,
inference in \(L\) might be based on resolution, while inference in
\(L'\) is of the natural deduction variety. Finally, the symbol sets
will be different. Despite these differences, courtesy of the
translations, desired behavior can be produced across the translation.
That, at any rate, is the hope. The technical challenges here are
immense, but federal monies are increasingly available for attacks on
the problem of interoperability.



Now for the third topic in this section: what can be called
**encoding down**. The technique is easy to understand. Suppose
that we have on hand a set \(\Phi\) of first-order axioms. As is
well-known, the problem of deciding, for arbitrary formula \(\phi\),
whether or not it's deducible from \(\Phi\) is
Turing-undecidable: there is no Turing machine or equivalent that can
correctly return "Yes" or "No" in the general
case. However, if the domain in question is finite, we can encode this
problem down to the propositional calculus. An assertion that all
things have \(F\) is of course equivalent to the assertion that
\(Fa\), \(Fb\), \(Fc\), as long as the domain contains only these
three objects. So here a first-order quantified formula becomes a
conjunction in the propositional calculus. Determining whether such
conjunctions are provable from axioms themselves expressed in the
propositional calculus is Turing-decidable, and in addition, in
certain clusters of cases, the check can be done very quickly in the
propositional case; *very quickly*. Readers interested in
encoding down to the propositional calculus should consult recent
 DARPA-sponsored work by Bart Selman.
 Please note that the target of encoding down doesn't need to be
the propositional calculus. Because it's generally harder for
machines to find proofs in an intensional logic than in straight
first-order logic, it is often expedient to encode down the former to
the latter. For example, propositional modal logic can be encoded in
multi-sorted logic (a variant of FOL); see (Arkoudas & Bringsjord
2005). Prominent usage of such an encoding down can be found in a set
of systems known as *Description Logics*, which are a set of
logics less expressive than first-order logic but more expressive than
propositional logic (Baader et al. 2003). Description logics are used
to reason about ontologies in a given domain and have been
successfully used, for example, in the biomedical domain (Smith et al.
2007). 


### 3.3 Non-Logicist AI: A Summary



It's tempting to define non-logicist AI by negation: an approach
to building intelligent agents that rejects the distinguishing
features of logicist AI. Such a shortcut would imply that the agents
engineered by non-logicist AI researchers and developers, whatever the
virtues of such agents might be, cannot be said to know that \(\phi\);
- for the simple reason that, by negation, the non-logicist
paradigm would have not even a single declarative proposition that is
a candidate for \(\phi\);. However, this isn't a particularly
enlightening way to define non-symbolic AI. A more productive approach
is to say that non-symbolic AI is AI carried out on the basis of
particular formalisms other than logical systems, and to then
enumerate those formalisms. It will turn out, of course, that these
formalisms fail to include knowledge in the normal sense. (In
philosophy, as is well-known, the normal sense is one according to
which if \(p\) is known, \(p\) is a declarative statement.) 



From the standpoint of formalisms other than logical systems,
non-logicist AI can be partitioned into symbolic but non-logicist
approaches, and connectionist/neurocomputational approaches. (AI
carried out on the basis of symbolic, declarative structures that, for
readability and ease of use, are not treated directly by researchers
as elements of formal logics, does not count. In this category fall
traditional semantic networks, Schank's (1972) conceptual
dependency scheme, frame-based schemes, and other such schemes.) The
former approaches, today, are probabilistic, and are based on the
formalisms (Bayesian networks) covered
 below.
 The latter approaches are based, as we have noted, on formalisms that
can be broadly termed "neurocomputational." Given our
space constraints, only one of the formalisms in this category is
described here (and briefly at that): the aforementioned **artificial
neural
 networks**.[30].
 Though artificial neural networks, with an appropriate architecture,
could be used for arbitrary computation, they are almost exclusively
used for building learning systems. 



Neural nets are composed of **units** or **nodes** designed to
represent neurons, which are connected by **links** designed to
represent dendrites, each of which has a numeric **weight**.



![sum of weighted inputs passed to an activation function that generates output](neuron-unit_1.png)

*A "Neuron" Within an Artificial Neural Network (from
AIMA3e)*




It is usually assumed that some of the units work in symbiosis with
the external environment; these units form the sets of **input**
and **output** units. Each unit has a current **activation
level**, which is its output, and can compute, based on its inputs
and weights on those inputs, its activation level at the next moment
in time. This computation is entirely local: a unit takes account of
but its neighbors in the net. This local computation is calculated in
two stages. First, the **input function**, \(in\_i\), gives the
weighted sum of the unit's input values, that is, the sum of the
input activations multiplied by their weights: 

 

\[
in\_i = \displaystyle\sum\_j W\_{ji} a\_j
\]


In the second stage, the **activation function**, \(g\), takes the
input from the first stage as argument and generates the output, or
activation level, \(a\_i\): 

 

\[
a\_i = g(in\_i) = g \left(\displaystyle\sum\_j W\_{ji}a\_j\right)
\]


One common (and confessedly elementary) choice for the activation
function (which usually governs all units in a given net) is the step
function, which usually has a threshold \(t\) that sees to it that a 1
is output when the input is greater than \(t\), and that 0 is output
otherwise. This is supposed to be "brain-like" to some
degree, given that 1 represents the firing of a pulse from a neuron
through an axon, and 0 represents no firing. A simple three-layer
neural net is shown in the following picture.



![neural network with 3 layers](neural-net_1.png)

*A Simple Three-Layer Artificial Neural Network (from
AIMA3e)*




As you might imagine, there are many different kinds of neural
networks. The main distinction is between **feed-forward** and
**recurrent** networks. In feed-forward networks like the one
pictured immediately above, as their name suggests, links move
information in one direction, and there are no cycles; recurrent
networks allow for cycling back, and can become rather complicated.
For a more detailed presentation, see the



Supplement on Neural Nets.
 



Neural networks were fundamentally plagued by the fact that while they
are simple and have theoretically efficient learning algorithms, when
they are multi-layered and thus sufficiently expressive to represent
non-linear functions, they were very hard to train in practice. This
changed in the mid 2000s with the advent of methods that exploit
state-of-the-art hardware better (Rajat et al. 2009). The
backpropagation method for training multi-layered neural networks can
be translated into a sequence of repeated simple arithmetic operations
on a large set of numbers. The general trend in computing hardware has
favored algorithms that are able to do a large of number of simple
operations that are not that dependent on each other, versus a small
of number of complex and intricate operations.



Another key recent observation is that deep neural networks can be
pre-trained first in an unsupervised phase where they are just fed
data without any labels for the data. Each hidden layer is forced to
represent the outputs of the layer below. The outcome of this training
is a series of layers which represent the input domain with increasing
levels of abstraction. For example, if we pre-train the network with
images of faces, we would get a first layer which is good at detecting
edges in images, a second layer which can combine edges to form facial
features such as eyes, noses etc., a third layer which responds to
groups of features, and so on (LeCun et al. 2015).



Perhaps the best technique for teaching students about neural networks
in the context of other statistical learning formalisms and methods is
to focus on a specific problem, preferably one that seems unnatural to
tackle using logicist techniques. The task is then to seek to engineer
a solution to the problem, *using any and all techniques
available*. One nice problem is handwriting recognition (which
also happens to have a rich philosophical dimension; see e.g.
Hofstadter & McGraw 1995). For example, consider the problem of
assigning, given as input a handwritten digit \(d\), the correct
digit, 0 through 9. Because there is a database of 60,000 labeled
digits available to researchers (from the National Institute of
Science and Technology), this problem has evolved into a benchmark
problem for comparing learning algorithms. It turns out that neural
networks currently reign as the best approach to the problem according
to a recent ranking by Benenson (2016).



Readers interested in AI (and computational cognitive science) pursued
from an overtly brain-based orientation are encouraged to explore the
work of Rick Granger (2004a, 2004b) and researchers in his
 Brain Engineering Laboratory
 and
 W. H. Neukom Institute for Computational Sciences.
 The contrast between the "dry", logicist AI started at
the original 1956 conference, and the approach taken here by Granger
and associates (in which brain circuitry is directly modeled) is
remarkable. For those interested in computational properties of neural
networks, Hornik et al. (1989) address the general representation
capability of neural networks independent of learning. 


### 3.4 AI Beyond the Clash of Paradigms



At this point the reader has been exposed to the chief formalisms in
AI, and may wonder about heterogeneous approaches that bridge them. Is
there such research and development in AI? Yes. From an
*engineering* standpoint, such work makes irresistibly good
sense. There is now an understanding that, in order to build
applications that get the job done, one should choose from a toolbox
that includes logicist, probabilistic/Bayesian, and neurocomputational
techniques. Given that the original top-down logicist paradigm is
alive and thriving (e.g., see Brachman & Levesque 2004, Mueller
2006), and that, as noted, a resurgence of Bayesian and
neurocomputational approaches has placed these two paradigms on solid,
fertile footing as well, AI now moves forward, armed with this
fundamental triad, and it is a virtual certainty that applications
(e.g., robots) will be engineered by drawing from elements of all
three. Watson's DeepQA architecture is one recent example of an
engineering system that leverages multiple paradigms. For a detailed
discussion, see the



Supplement on Watson's DeepQA Architecture.
 



Google DeepMind's AlphaGo is another example of a multi-paradigm
system, although in a much narrower form than Watson. The central
algorithmic problem in games such as Go or Chess is to search through
a vast sequence of valid moves. For most non-trivial games, this is
not feasible to do so exhaustively. The Monte Carlo tree search (MCTS)
algorithm gets around this obstacle by searching through an enormous
space of valid moves in a statistical fashion (Browne et al. 2012).
While MCTS is the central algorithm in AlpaGo, there are two neural
networks which help evaluate states in the game and help model how
expert opponents play (Silver et al. 2016). It should be noted that
MCTS is behind almost all the winning submissions in general game
playing (Finnsson 2012).



What, though, about deep, theoretical integration of the main
paradigms in AI? Such integration is at present only a possibility for
the future, but readers are directed to the research of some striving
for such integration. For example: Sun (1994, 2002) has been working
to demonstrate that human cognition that is on its face symbolic in
nature (e.g., professional philosophizing in the analytic tradition,
which deals explicitly with arguments and definitions carefully
symbolized) can arise from cognition that is neurocomputational in
nature. Koller (1997) has investigated the marriage between
probability theory and logic. And, in general, the very recent arrival
of so-called *human-level* AI is being led by theorists seeking
to genuinely integrate the three paradigms set out above (e.g.,
Cassimatis 2006).



Finally, we note that **cognitive architectures** such as Soar
(Laird 2012) and PolyScheme (Cassimatis 2006) are another area where
integration of different fields of AI can be found. For example, one
such endeavor striving to build human-level AI is the Companions
project (Forbus and Hinrichs 2006). Companions are long-lived systems
that strive to be human-level AI systems that function as
collaborators with humans. The Companions architecture tries to solve
multiple AI problems such as reasoning and learning, interactivity,
and longevity in one unifying system.


## 4. The Explosive Growth of AI



As we noted above, work on AI has mushroomed over the past couple of
decades. Now that we have looked a bit at the content that composes
AI, we take a quick look at the explosive growth of AI.



First, a point of clarification. The growth of which we speak is not a
shallow sort correlated with amount of funding provided for a given
sub-field of AI. That kind of thing happens all the time in all
fields, and can be triggered by entirely political and financial
changes designed to grow certain areas, and diminish others. Along the
same line, the growth of which we speak is not correlated with the
amount of industrial activity revolving around AI (or a sub-field
thereof); for this sort of growth too can be driven by forces quite
outside an expansion in the scientific breadth of
 AI.[31]
 Rather, we are speaking of an explosion of deep *content*: new
material which someone intending to be conversant with the field needs
to know. Relative to other fields, the size of the explosion may or
may not be unprecedented. (Though it should perhaps be noted that an
analogous increase in philosophy would be marked by the development of
entirely new formalisms for reasoning, reflected in the fact that,
say, longstanding philosophy textbooks like Copi's (2004)
*Introduction to Logic* are dramatically rewritten and enlarged
to include these formalisms, rather than remaining anchored to
essentially immutable core formalisms, with incremental refinement
around the edges through the years.) But it certainly appears to be
quite remarkable, and is worth taking note of here, if for no other
reason than that AI's near-future will revolve in significant
part around whether or not the new content in question forms a
foundation for new long-lived research and development that would not
otherwise
 obtain.[32]



AI has also witnessed an explosion in its usage in various artifacts
and applications. While we are nowhere near building a machine with
capabilities of a human or one that acts rationally in all scenarios
according to the Russell/Hutter definition above, algorithms that have
their origins in AI research are now widely deployed for many tasks in
a variety of domains. 


### 4.1 Bloom in Machine Learning



A huge part of AI's growth in applications has been made
possible through invention of new algorithms in the subfield of
**machine learning**. Machine learning is concerned with building
systems that improve their performance on a task when given examples
of ideal performance on the task, or improve their performance with
repeated experience on the task. Algorithms from machine learning have
been used in speech recognition systems, spam filters, online
fraud-detection systems, product-recommendation systems, etc. The
current state-of-the-art in machine learning can be divided into three
areas (Murphy 2013, Alpaydin 2014):


1. **Supervised Learning**: A form of learning in which a computer
tries to learn a function \(\ff\) given examples, the training data
\(T\), of its values at various points in its domain 

\[
T=\left\{\left\langle x\_1, \ff(x\_1)\right\rangle,\left\langle x\_2, \ff(x\_2)\right\rangle, \ldots, \left\langle x\_n, \ff(x\_n)\right\rangle\right\}.
\] 

 A
sample task would be trying to label images of faces with a
person's name. The supervision in supervised learning comes in
the form of the value of the function \(\ff(x)\) at various points
\(x\) in some part of the domain of the function. This is usually
given in the form of a fixed set of input and output pairs for the
function. Let \(\hh\) be the "learned function." The goal
of supervised learning is have \({\hh}\) match as closely as possible
the true function \({\ff}\) over the same domain. The **error** is
usually defined in terms of an error function, for instance, \(error =
\sum\_{x\in T} \delta(\ff(x) - \hh(x))\), over the training data \(T\).
Other forms of supervision and goals for learning are possible. For
example, in **active learning** the learning algorithm can request
the value of the function for arbitrary inputs. Supervised learning
dominates the field of machine learning and has been used in almost
all practical applications mentioned just above.
2. **Unsupervised Learning**: Here the machine tries to find
useful knowledge or information when given some raw data \(\left\{
x\_1,x\_2, \ldots, x\_n \right\}\). There is no function associated with
the input that has to be learned. The idea is that the machine helps
uncover interesting patterns or information that could be hidden in
the data. One use of unsupervised learning is **data mining**,
where large volumes of data are searched for interesting information.
*PageRank*, one of the earliest algorithms used by the Google
search engine, can be considered to be an unsupervised learning system
that ranks pages without any human supervision (Chapter 14.10, Hastie
et al. 2009).
3. **Reinforcement Learning**: Here a machine is set loose in an
environment where it constantly acts and perceives (similar to the
Russell/Hutter view above) and only *occasionally* receives
feedback on its behavior in the form of rewards or punishments. The
machine has to learn to behave rationally from this feedback. One use
of reinforcement learning has been in building agents to play computer
games. The objective here is to build agents that map sensory data
from the game at every time instant to an action that would help win
in the game or maximize a human player's enjoyment of the game.
In most games, we know how well we are playing only at the end of the
game or only at infrequent intervals throughout the game (e.g., a
chess game that we feel we are winning could quickly turn against us
at the end). In supervised learning, the training data has ideal
input-output pairs. This form of learning is not suitable for building
agents that have to operate across a length of time and are judged not
on one action but a series of actions and their effects on the
environment. The field of Reinforcement Learning tries to tackle this
problem through a variety of methods. Though a bit dated, Sutton and
Barto (1998) provide a comprehensive introduction to the field.



In addition to being used in domains that are traditionally the ken of
AI, machine-learning algorithms have also been used in all stages of
the scientific process. For example, machine-learning techniques are
now routinely applied to analyze large volumes of data generated from
particle accelerators. CERN, for instance, generates a petabyte
(\(10^{15}\) bytes) per second, and statistical algorithms that have
their origins in AI are used to filter and analyze this data. Particle
accelerators are used in fundamental experimental research in physics
to probe the structure of our physical universe. They work by
colliding larger particles together to create much finer particles.
Not all such events are fruitful. Machine-learning methods have been
used to select events which are then analyzed further (Whiteson &
Whiteson 2009 and Baldi et al. 2014). More recently, researchers at
CERN launched a machine learning
  competition
 to aid in the analysis of the Higgs Boson. The goal of this challenge
was to develop algorithms that separate meaningful events from
background noise given data from the Large Hadron Collider, a particle
accelerator at CERN.



In the past few decades, there has been an explosion in data that does
not have any explicit semantics attached to it. This data is generated
by both humans and machines. Most of this data is not easily
machine-processable; for example, images, text, video (as opposed to
carefully curated data in a knowledge- or data-base). This has given
rise to a huge industry that applies AI techniques to get usable
information from such enormous data. This field of applying techniques
derived from AI to large volumes of data goes by names such as
"data mining," "big data,"
"analytics," etc. This field is too vast to even
moderately cover in the present article, but we note that there is no
full agreement on what constitutes such a "big-data"
problem. One definition, from Madden (2012), is that big data differs
from traditional machine-processable data in that it is too big (for
most of the existing state-of-the-art hardware), too quick (generated
at a fast rate, e.g. online email transactions), or too hard. It is in
the too-hard part that AI techniques work quite well. While this
universe is quite varied, we use the Watson's system later in
this article as an AI-relevant exemplar. As we will see later, while
most of this new explosion is powered by learning, it isn't
entirely limited to just learning. This bloom in learning algorithms
has been supported by both a resurgence in neurocomputational
techniques and probabilistic techniques.


### 4.2 The Resurgence of Neurocomputational Techniques



One of the remarkable aspects of (Charniak & McDermott 1985) is
this: The authors say the central dogma of AI is that "What the
brain does may be thought of at some level as a kind of
computation" (p. 6). And yet nowhere in the book is brain-like
computation discussed. In fact, you will search the index in vain for
the term 'neural' and its variants. Please note that the
authors are not to blame for this. A large part of AI's growth
has come from formalisms, tools, and techniques that are, in some
sense, brain-based, not logic-based. A paper that conveys the
importance and maturity of neurocomputation is (Litt et al. 2006).
(Growth has also come from a return of probabilistic techniques that
had withered by the mid-70s and 80s. More about that momentarily, in
the next "resurgence"
 section.)
 



One very prominent class of non-logicist formalism does make an
explicit nod in the direction of the brain: viz., **artificial neural
networks** (or as they are often simply called, **neural
networks**, or even just **neural nets**). (The structure of
neural networks and more recent developments are discussed
 above).
 Because Minsky and Pappert's (1969) *Perceptrons* led many
(including, specifically, many sponsors of AI research and
development) to conclude that neural networks didn't have
sufficient information-processing power to model human cognition, the
formalism was pretty much universally dropped from AI. However, Minsky
and Pappert had only considered very limited neural networks.
**Connectionism**, the view that intelligence consists not in
symbolic processing, but rather *non*-symbolic processing at
least somewhat like what we find in the brain (at least at the
cellular level), approximated specifically by artificial neural
networks, came roaring back in the early 1980s on the strength of more
sophisticated forms of such networks, and soon the situation was (to
use a metaphor introduced by John McCarthy) that of two horses in a
race toward building truly intelligent agents.



If one had to pick a year at which connectionism was resurrected, it
would certainly be 1986, the year *Parallel Distributed
Processing* (Rumelhart & McClelland 1986) appeared in print.
The rebirth of connectionism was specifically fueled by the
back-propagation (backpropagation) algorithm over neural networks,
nicely covered in Chapter 20 of *AIMA*. The
symbolicist/connectionist race led to a spate of lively debate in the
literature (e.g., Smolensky 1988, Bringsjord 1991), and some AI
engineers have explicitly championed a methodology marked by a
rejection of knowledge representation and reasoning. For example,
Rodney Brooks was such an engineer; he wrote the well-known
"Intelligence Without Representation" (1991), and his Cog
Project, to which we referred above, is arguably an incarnation of the
premeditatedly non-logicist approach. Increasingly, however, those in
the business of building sophisticated systems find that *both*
logicist and more neurocomputational techniques are required (Wermter
& Sun
 2001).[33]
 In addition, the neurocomputational paradigm today includes
connectionism only as a proper part, in light of the fact that some of
those working on building intelligent systems strive to do so by
engineering brain-based computation outside the neural network-based
approach (e.g., Granger 2004a, 2004b). 



Another recent resurgence in neurocomputational techniques has
occurred in machine learning. The modus operandi in machine learning
is that given a problem, say recognizing handwritten digits
\(\{0,1,\ldots,9\}\) or faces, from a 2D matrix representing an image
of the digits or faces, a machine learning or a domain expert would
construct a **feature vector representation** function for the
task. This function is a transformation of the input into a format
that tries to throw away irrelevant information in the input and keep
only information useful for the task. Inputs transformed by \(\rr\)
are termed **features**. For recognizing faces, irrelevant
information could be the amount of lighting in the scene and relevant
information could be information about facial features. The machine is
then fed a sequence of inputs represented by the features and the
ideal or ground truth output values for those inputs. This converts
the learning challenge from that of having to learn the function
\(\ff\) from the examples: \(\left\{\left\langle x\_1,
\ff(x\_1)\right\rangle,\left\langle x\_2, \ff(x\_2)\right\rangle, \ldots,
\left\langle x\_n, \ff(x\_n)\right\rangle \right\}\) to having to learn
from possibly easier data: \(\left\{\left\langle \rr(x\_1),
\ff(x\_1)\right\rangle,\left\langle \rr(x\_2), \ff(x\_2)\right\rangle,
\ldots, \left\langle \rr(x\_n), \ff(x\_n)\right\rangle \right\}\). Here
the function \(\rr\) is the function that computes the feature vector
representation of the input. Formally, \(\ff\) is assumed to be a
composition of the functions \(\gg\) and \(\rr\). That is, for any
input \(x\), \(f(x) = \gg\left(\rr\left(x\right)\right)\). This is
denoted by \(\ff=\gg\circ \rr\). For any input, the features are first
computed, and then the function \(\gg\) is applied. If the feature
representation \(\rr\) is provided by the domain expert, the learning
problem becomes simpler to the extent the feature representation takes
on the difficulty of the task. At one extreme, the feature vector
could hide an easily extractable form of the answer in the input and
in the other extreme the feature representation could be just the
plain input.



For non-trivial problems, choosing the right representation is vital.
For instance, one of the drastic changes in the AI landscape was due
to Minsky and Papert's (1969) demonstration that the perceptron
cannot learn even the binary **XOR** function, but this function
can be learnt by the perceptron if we have the right representation.
Feature engineering has grown to be one of the most labor intensive
tasks of machine learning, so much so that it is considered to be one
of the *"black arts"* of machine learning. The other
significant black art of learning methods is choosing the right
parameters. These black arts require significant human expertise and
experience, which can be quite difficult to obtain without significant
apprenticeship (Domingos 2012). Another bigger issue is that the task
of feature engineering is just knowledge representation in a new skin.




Given this state of affairs, there has been a recent resurgence in
methods for automatically learning a feature representation function
\(\rr\); such methods potentially bypass a large part of human labor
that is traditionally required. Such methods are based mostly on what
are now termed **deep neural networks**. Such networks are simply
neural networks with two or more hidden layers. These networks allow
us to learn a feature function \(\rr\) by using one or more of the
hidden layers to learn \(\rr\). The general form of learning in which
one learns from the raw sensory data without much hand-based feature
engineering has now its own term: **deep learning**. A general and
yet concise definition (Bengio et al. 2015) is: 



> 
> Deep learning can safely be regarded as the study of models that
> either involve a greater amount of composition of learned functions or
> learned concepts than traditional machine learning does. (Bengio et
> al. 2015, Chapter 1)
> 



Though the idea has been around for decades, recent innovations
leading to more efficient learning techniques have made the approach
more feasible (Bengio et al. 2013). Deep-learning methods have
recently produced state-of-the-art results in image recognition (given
an image containing various objects, label the objects from a given
set of labels), speech recognition (from audio input, generate a
textual representation), and the analysis of data from particle
accelerators (LeCun et al. 2015). Despite impressive results in tasks
such as these, minor and major issues remain unresolved. A minor issue
is that significant human expertise is still needed to choose an
architecture and set up the right parameters for the architecture; a
major issue is the existence of so-called **adversarial inputs**,
which are indistinguishable from normal inputs to humans but are
computed in a special manner that makes a neural network regard them
as different than similar inputs in the training data. The existence
of such adversarial inputs, which remain stable across training data,
has raised doubts about how well performance on benchmarks can
translate into performance in real-world systems with sensory noise
(Szegedy et al. 2014).


### 4.3 The Resurgence of Probabilistic Techniques



There is a second dimension to the explosive growth of AI: the
explosion in popularity of probabilistic methods that aren't
neurocomputational in nature, in order to formalize and mechanize a
form of non-logicist reasoning in the face of uncertainty.
Interestingly enough, it is Eugene Charniak himself who can be safely
considered one of the leading proponents of an explicit, premeditated
turn away from logic to statistical techniques. His area of
specialization is natural language processing, and whereas his
introductory textbook of 1985 gave an accurate sense of his approach
to parsing at the time (as we have seen, write computer programs that,
given English text as input, ultimately infer meaning expressed in
FOL), this approach was abandoned in favor of purely statistical
approaches (Charniak 1993). At the
 AI@50
 conference, Charniak boldly proclaimed, in a talk tellingly entitled
"Why Natural Language Processing is Now Statistical Natural
Language Processing," that logicist AI is moribund, and that the
statistical approach is the only promising game in town - for
the next 50
 years.[34]



The chief source of energy and debate at the conference flowed from
the clash between Charniak's probabilistic orientation, and the
original logicist orientation, upheld at the conference in question by
John McCarthy and others.



AI's use of probability theory grows out of the standard form of
this theory, which grew directly out of technical philosophy and
logic. This form will be familiar to many philosophers, but
let's review it quickly now, in order to set a firm stage for
making points about the new probabilistic techniques that have
energized AI. 



Just as in the case of FOL, in probability theory we are concerned
with declarative statements, or **propositions**, to which degrees
of belief are applied; we can thus say that both logicist and
probabilistic approaches are symbolic in nature. Both approaches also
agree that statements can either be true or false in the world. In
building agents, a simplistic logic-based approach requires agents to
know the truth-value of all possible statements. This is not
realistic, as an agent may not know the truth-value of some
proposition \(p\) due to either ignorance, non-determinism in the
physical world, or just plain vagueness in the meaning of the
statement. More specifically, the fundamental proposition in
probability theory is a **random variable**, which can be conceived
of as an aspect of the world whose status is initially unknown to the
agent. We usually capitalize the names of random variables, though we
reserve \(p,q,r, \ldots\) as such names as well. For example, in a
particular murder investigation centered on whether or not Mr. Barolo
committed the crime, the random variable \(Guilty\) might be of
concern. The detective may be interested as well in whether or not the
murder weapon - a particular knife, let us assume -
belongs to Barolo. In light of this, we might say that \(\Weapon =
\true\) if it does, and \(\Weapon = \false\) if it doesn't. As a
notational convenience, we can write \(weapon\) and \(\lnot weapon\)
and for these two cases, respectively; and we can use this convention
for other variables of this type. 



The kind of variables we have described so far are
\(\mathbf{Boolean}\), because their \(\mathbf{domain}\) is simply
\(\{true,false\}.\) But we can generalize and allow
\(\mathbf{discrete}\) random variables, whose values are from any
countable domain. For example, \(\PriceTChina\) might be a variable
for the price of (a particular, presumably) tea in China, and its
domain might be \(\{1,2,3,4,5\}\), where each number here is in US
dollars. A third type of variable is \(\mathbf{continous}\); its
domain is either the reals, or some subset thereof. 



We say that an **atomic event** is an assignment of particular
values from the appropriate domains to all the variables composing the
(idealized) world. For example, in the simple murder investigation
world introduced just above, we have two Boolean variables,
\(\Guilty\) and \(\Weapon\), and there are just four atomic events.
Note that atomic events have some obvious properties. For example,
they are mutually exclusive, exhaustive, and logically entail the
truth or falsity of every proposition. Usually not obvious to
beginning students is a fourth property, namely, any proposition is
logically equivalent to the disjunction of all atomic events that
entail that proposition. 



Prior probabilities correspond to a degree of belief accorded to a
proposition in the complete absence of any other information. For
example, if the prior probability of Barolo's guilt is \(0.2\),
we write 

 \[
 P\left(\Guilty=true\right)=0.2
 \]

 



or simply \(\P(guilty)=0.2\). It is often convenient to have a
notation allowing one to refer economically to the probabilities of
*all* the possible values for a random variable. For example,
we can write 

 \[
 \P\left(\PriceTChina\right)
 \]

 



as an abbreviation for the five equations listing all the possible
prices for tea in China. We can also write 

 \[
 \P\left(\PriceTChina\right)=\langle 1,2,3,4,5\rangle
 \]

 



In addition, as further convenient notation, we can write \(
\mathbf{P}\left(\Guilty, \Weapon\right)\) to denote the probabilities
of all combinations of values of the relevant set of random variables.
This is referred to as the **joint probability distribution** of
\(\Guilty\) and \(\Weapon\). The **full** joint probability
distribution covers the distribution for all the random variables used
to describe a world. Given our simple murder world, we have 20 atomic
events summed up in the equation 

 \[
 \mathbf{P}\left(\Guilty, \Weapon, \PriceTChina\right)
 \]

 



The final piece of the basic language of probability theory
corresponds to **conditional** probabilities. Where \(p\) and \(q\)
are any propositions, the relevant expression is \(P\!\left(p\given
q\right)\), which can be interpreted as "the probability of
\(p\), given that all we know is \(q\)." For example,

 \[
 P\left(guilty\ggiven weapon\right)=0.7
 \]

 



says that if the murder weapon belongs to Barolo, and no other
information is available, the probability that Barolo is guilty is
\(0.7.\) 



Andrei Kolmogorov showed how to construct probability theory from
three axioms that make use of the machinery now introduced, viz., 


1. All probabilities fall between \(0\) and \(1.\) I.e., \(\forall
p. 0 \leq P(p) \leq 1\).
2. Valid (in the traditional logicist sense) propositions have a
probability of \(1\); unsatisfiable (in the traditional logicist
sense) propositions have a probability of \(0\).
3. \(P(p\lor q) = P(p) +P(q) - P(p\land q)\)



These axioms are clearly at bottom logicist. The remainder of
probability theory can be erected from this foundation (conditional
probabilities are easily defined in terms of prior probabilities). We
can thus say that logic is in some fundamental sense still being used
to characterize the set of beliefs that a rational agent can have. But
where does probabilistic *inference* enter the picture on this
account, since traditional deduction is not used for inference in
probability theory?



Probabilistic inference consists in computing, from observed evidence
expressed in terms of probability theory, posterior probabilities of
propositions of interest. For a good long while, there have been
algorithms for carrying out such computation. These algorithms precede
the resurgence of probabilistic techniques in the 1990s. (Chapter 13
of *AIMA* presents a number of them.) For example, given the
Kolmogorov axioms, here is a straightforward way of computing the
probability of any proposition, using the full joint distribution
giving the probabilities of all atomic events: Where \(p\) is some
proposition, let \(\alpha(p)\) be the disjunction of all atomic events
in which \(p\) holds. Since the probability of a proposition (i.e.,
\(P(p)\)) is equal to the sum of the probabilities of the atomic
events in which it holds, we have an equation that provides a method
for computing the probability of any proposition \(p\), viz., 



\[
 P(p) = \sum\_{e\_i\in\alpha(p)} P(e\_i)
\]


Unfortunately, there were two serious problems infecting this original
probabilistic approach: One, the processing in question needed to take
place over paralyzingly large amounts of information (enumeration over
the entire distribution is required). And two, the expressivity of the
approach was merely propositional. (It was by the way the philosopher
Hilary Putnam (1963) who pointed out that there was a price to pay in
moving to the first-order level. The issue is not discussed herein.)
Everything changed with the advent of a new formalism that marks the
marriage of probabilism and graph theory: **Bayesian networks**
(also called **belief nets**). The pivotal text was (Pearl 1988).
For a more detailed discussion, see the



Supplement on Bayesian Networks.
 



Before concluding this section, it is probably worth noting that, from
the standpoint of philosophy, a situation such as the murder
investigation we have exploited above would often be analyzed into
*arguments*, and strength factors, not into numbers to be
crunched by purely arithmetical procedures. For example, in the
epistemology of Roderick Chisholm, as presented his *Theory of
Knowledge* (1966, 1977), Detective Holmes might classify a
proposition like  *Barolo committed the murder.* as
**counterbalanced** if he was unable to find a compelling argument
either way, or perhaps **probable** if the murder weapon turned out
to belong to Barolo. Such categories cannot be found on a continuum
from 0 to 1, and they are used in articulating arguments for or
against Barolo's guilt. Argument-based approaches to uncertain
and defeasible reasoning are virtually non-existent in AI. One
exception is Pollock's approach, covered below. This approach is
Chisholmian in nature. 



It should also be noted that there have been well-established
formalisms for dealing with probabilistic reasoning as an instance of
logic-based reasoning. E.g., the activity a researcher in
probabilistic reasoning undertakes when she proves a theorem \(\phi\)
about their domain (e.g. any theorem in (Pearl 1988)) is purely within
the realm of traditional logic. Readers interested in logic-flavored
approaches to probabilistic reasoning can consult (Adams 1996,
Hailperin 1996 & 2010, Halpern 1998). Formalisms marrying
probability theory, induction and deductive reasoning, placing them on
an equal footing, have been on the rise, with Markov logic (Richardson
and Domingos 2006) being salient among these approaches. 



**Probabilistic Machine Learning**



Machine learning, in the sense given
 above,
 has been associated with probabilistic techniques. Probabilistic
techniques have been associated with both the learning of functions
(e.g. Naive Bayes classification) and the modeling of theoretical
properties of learning algorithms. For example, a standard
reformulation of supervised learning casts it as a **Bayesian
problem**. Assume that we are looking at recognizing digits
\([0{-}9]\) from a given image. One way to cast this problem is to ask
what the probability that the hypothesis \(H\_x\): "*the digit
is \(x\)*" is true given the image \(d\) from a sensor. Bayes
theorem gives us: 

 

\[
 P\left(H\_x\ggiven d\right) = \frac{P\left(d\ggiven H\_x\right)\*P\left(H\_x\right)}{P\left(d\right)}
\]


\(P(d\given H\_x)\) and \(P(H\_x)\) can be estimated from the given
training dataset. Then the hypothesis with the highest posterior
probability is then given as the answer and is given by:
\(\argmax\_{x}P\left(d\ggiven H\_x\right)\*P\left(H\_x\right) \) In
addition to probabilistic methods being used to build algorithms,
probability theory has also been used to analyze algorithms which
might not have an overt probabilistic or logical formulation. For
example, one of the central classes of meta-theorems in learning,
**probably approximately correct (PAC)** theorems, are cast in
terms of lower bounds of the probability that the mismatch between the
induced/learnt **fL** function and the true function
**fT** being less than a certain amount, given that the
learnt function **fL** works well for a certain number
of cases (see Chapter 18, AIMA). 


## 5. AI in the Wild



From at least its modern inception, AI has always been connected to
gadgets, often ones produced by corporations, and it would be remiss
of us not to say a few words about this phenomenon. While there have
been a large number of commercial in-the-wild success stories for AI
and its sister fields, such as optimization and decision-making, some
applications are more visible and have been thoroughly battle-tested
in the wild. In 2014, one of the most visible such domains (one in
which AI has been strikingly successful) is information retrieval,
incarnated as web search. Another recent success story is pattern
recognition. The state-of-the-art in applied pattern recognition
(e.g., fingerprint/face verification, speech recognition, and
handwriting recognition) is robust enough to allow
"high-stakes" deployment outside the laboratory. As of mid
2018, several corporations and research laboratories have begun
testing autonomous vehicles on public roads, with even a handful of
jurisdictions making self-driving cars legal to operate. For example,
Google's autonomous cars have navigated hundreds of thousands of
miles in California with minimal human help under non-trivial
conditions (Guizzo 2011). 



Computer games provide a robust test bed for AI techniques as they can
capture important parts that might be necessary to test an AI
technique while abstracting or removing details that might beyond the
scope of core AI research, for example, designing better hardware or
dealing with legal issues (Laird and VanLent 2001). One subclass of
games that has seen quite fruitful for commercial deployment of AI is
real-time strategy games. Real-time strategy games are games in which
players manage an army given limited resources. One objective is to
constantly battle other players and reduce an opponent's forces.
Real-time strategy games differ from strategy games in that players
plan their actions simultaneously in real-time and do not have to take
turns playing. Such games have a number of challenges that are
tantalizing within the grasp of the state-of-the-art. This makes such
games an attractive venue in which to deploy simple AI agents. An
overview of AI used in real-time strategy games can be found in
(Robertson and Watson 2015). 



Some other ventures in AI, despite significant success, have been only
chugging slowly and humbly along, quietly. For instance, AI-related
methods have achieved triumphs in solving open problems in mathematics
that have resisted any solution for decades. The most noteworthy
instance of such a problem is perhaps a proof of the statement that
"*All Robbins algebras are Boolean algebras.*" This
was conjectured in the 1930s, and the proof was finally discovered by
the Otter automatic theorem-prover in 1996 after just a few months of
effort (Kolata 1996, Wos 2013). Sister fields like formal verification
have also bloomed to the extent that it is now not too difficult to
semi-automatically verify vital hardware/software components (Kaufmann
et al. 2000 and Chajed et al. 2017).



Other related areas, such as (natural) language translation, still
have a long way to go, but are good enough to let us use them under
restricted conditions. The jury is out on tasks such as machine
translation, which seems to require both statistical methods (Lopez
2008) *and* symbolic methods (Espana-Bonet 2011). Both
methods now have comparable but limited success in the wild. A
deployed translation system at Ford that was initially developed for
translating manufacturing process instructions from English to other
languages initially started out as rule-based system with Ford and
domain-specific vocabulary and language. This system then evolved to
incorporate statistical techniques along with rule-based techniques as
it gained new uses beyond translating manuals, for example, lay users
within Ford translating their own documents (Rychtyckyj and Plesco
2012). 



AI's great achievements mentioned above so far have all been in
limited, narrow domains. This lack of any success in the unrestricted
general case has caused a small set of researchers to break away into
what is now called
  artificial general intelligence
 (Goertzel and Pennachin 2007). The stated goals of this movement
include shifting the focus again to building artifacts that are
generally intelligent and not just capable in one narrow domain. 


## 6. Moral AI



*Computer Ethics* has been around for a long time. In this
sub-field, typically one would consider how one ought to act in a
certain class of situations involving computer technology, where the
"one" here refers to a human being (Moor 1985). So-called
"robot ethics" is different. In this sub-field (which goes
by names such as "moral AI," "ethical AI,"
"machine ethics," "moral robots," etc.) one is
confronted with such prospects as robots being able to make autonomous
and weighty decisions - decisions that might or might not be
morally permissible (Wallach & Allen 2010). If one were to attempt
to engineer a robot with a capacity for sophisticated ethical
reasoning and decision-making, one would also be doing Philosophical
AI, as that concept is characterized
 elsewhere
 in the present entry. There can be many different flavors of
approaches toward Moral AI. Wallach and Allen (2010) provide a
high-level overview of the different approaches. Moral reasoning is
obviously needed in robots that have the capability for lethal action.
Arkin (2009) provides an introduction to how we can control and
regulate machines that have the capacity for lethal behavior. Moral AI
goes beyond obviously lethal situations, and we can have a spectrum of
moral machines. Moor (2006) provides one such spectrum of possible
moral agents. An example of a non-lethal but ethically-charged machine
would be a lying machine. Clark (2010) uses a **computational theory
of the mind**, the ability to represent and reason about other
agents, to build a lying machine that successfully persuades people
into believing falsehoods. Bello & Bringsjord (2013) give a
general overview of what might be required to build a moral machine,
one of the ingredients being a theory of mind. 



The most general framework for building machines that can reason
ethically consists in endowing the machines with a **moral code**.
This requires that the formal framework used for reasoning by the
machine be expressive enough to receive such codes. The field of Moral
AI, for now, is not concerned with the source or provenance of such
codes. The source could be humans, and the machine could receive the
code directly (via explicit encoding) or indirectly (reading). Another
possibility is that the code is inferred by the machine from a more
basic set of laws. We assume that the robot has access to some such
code, and we then try to engineer the robot to follow that code under
all circumstances while making sure that the moral code and its
representation do not lead to unintended consequences. **Deontic
logics** are a class of formal logics that have been studied the
most for this purpose. Abstractly, such logics are concerned mainly
with what follows from a given moral code. Engineering then studies
the match of a given deontic logic to a moral code (i.e., is the logic
expressive enough) which has to be balanced with the ease of
automation. Bringsjord et al. (2006) provide a blueprint for using
deontic logics to build systems that can perform actions in accordance
with a moral code. The role deontic logics play in the framework
offered by Bringsjord et al (which can be considered to be
representative of the field of deontic logic for moral AI) can be best
understood as striving towards Leibniz's dream of a universal
moral calculus: 



> 
> When controversies arise, there will be no more need for a disputation
> between two philosophers than there would be between two accountants
> [computistas]. It would be enough for them to pick up their pens and
> sit at their abacuses, and say to each other (perhaps having summoned
> a mutual friend): 'Let us calculate.'
> 



Deontic logic-based frameworks can also be used in a fashion that is
analogous to moral self-reflection. In this mode, logic-based
verification of the robot's internal modules can done before the
robot ventures out into the real world. Govindarajulu and Bringsjord
(2015) present an approach, drawing from **formal-program
verification**, in which a deontic-logic based system could be used
to verify that a robot acts in a certain ethically-sanctioned manner
under certain conditions. Since formal-verification approaches can be
used to assert statements about an infinite number of situations and
conditions, such approaches might be preferred to having the robot
roam around in an ethically-charged test environment and make a finite
set of decisions that are then judged for their ethical correctness.
More recently, Govindarajulu and Bringsjord (2017) use a deontic logic
to present a computational model of the
 Doctrine of Double Effect,
 an ethical principle for moral dilemmas that has been studied
empirically and analyzed extensively by
 philosophers.[35]
 The principle is usually presented and motivated via dilemmas using
trolleys and was first presented in this fashion by Foot (1967). 



While there has been substantial theoretical and philosophical work,
the field of machine ethics is still in its infancy. There has been
some embryonic work in building ethical machines. One recent such
example would be Pereira and Saptawijaya (2016) who use logic
programming and base their work in machine ethics on the ethical
theory known as **contractualism**, set out by Scanlon (1982). And
what about the future? Since artificial agents are bound to get
smarter and smarter, and to have more and more autonomy and
responsibility, robot ethics is almost certainly going to grow in
importance. This endeavor might not be a straightforward application
of classical ethics. For example, experimental results suggest that
humans hold robots to different ethical standards than they expect
from humans under similar conditions (Malle et al.
 2015).[36]



## 7. Philosophical AI



Notice that the heading for this section isn't Philosophy
*of* AI. We'll get to that category momentarily. (For now
it can be identified with the attempt to answer such questions as
whether artificial agents created in AI can ever reach the full
heights of human intelligence.) Philosophical AI is AI, not
philosophy; but it's AI rooted in and flowing from, philosophy.
For example, one could engage, using the tools and techniques of
philosophy, a paradox, work out a proposed solution, and then proceed
to a step that is surely optional for philosophers: expressing the
solution in terms that can be translated into a computer program that,
when executed, allows an artificial agent to surmount concrete
instances of the original
 paradox.[37]
 Before we ostensively characterize Philosophical AI of this sort
courtesy of a particular research program, let us consider first the
view that AI is in fact simply philosophy, or a part thereof. 



Daniel Dennett (1979) has famously claimed not just that there are
parts of AI intimately bound up with philosophy, but that AI
*is* philosophy (and psychology, at least of the cognitive
sort). (He has made a parallel claim about Artificial Life (Dennett
1998)). This view will turn out to be incorrect, but the reasons why
it's wrong will prove illuminating, and our discussion will pave
the way for a discussion of Philosophical AI. 



What does Dennett say, exactly? This: 



> 
> I want to claim that AI is better viewed as sharing with traditional
> epistemology the status of being a most general, most abstract asking
> of the top-down question: how is knowledge possible? (Dennett 1979,
> 60)
> 



Elsewhere he says his view is that AI should be viewed "as a
most abstract inquiry into the possibility of intelligence or
knowledge" (Dennett 1979, 64). 



In short, Dennett holds that AI is the attempt to explain
intelligence, not by studying the brain in the hopes of identifying
components to which cognition can be reduced, and not by engineering
small information-processing units from which one can build in
bottom-up fashion to high-level cognitive processes, but rather by
- and this is why he says the approach is *top-down*
- designing and implementing abstract algorithms that capture
cognition. Leaving aside the fact that, at least starting in the early
1980s, AI includes an approach that is in some sense bottom-up (see
the neurocomputational paradigm discussed above, in
 Non-Logicist AI: A Summary;
 and see, specifically, Granger's (2004a, 2004b) work,
hyperlinked in text immediately above, a specific counterexample), a
fatal flaw infects Dennett's view. Dennett sees the potential
flaw, as reflected in: 



> 
> It has seemed to some philosophers that AI cannot plausibly be so
> construed because it takes on an additional burden: it restricts
> itself to *mechanistic* solutions, and hence its domain is not
> the Kantian domain of all possible modes of intelligence, but just all
> possible mechanistically realizable modes of intelligence. This, it is
> claimed, would beg the question against vitalists, dualists, and other
> anti-mechanists. (Dennett 1979, 61)
> 



Dennett has a ready answer to this objection. He writes: 



> 
> But ... the mechanism requirement of AI is not an additional
> constraint of any moment, for if psychology is possible at all, and if
> Church's thesis is true, the constraint of mechanism is no more
> severe than the constraint against begging the question in psychology,
> and who would wish to evade that? (Dennett 1979, 61)
> 



Unfortunately, this is acutely problematic; and examination of the
problems throws light on the nature of AI. 



First, insofar as philosophy and psychology are concerned with the
nature of mind, they aren't in the least trammeled by the
presupposition that mentation consists in computation. AI, at least of
the "Strong" variety (we'll discuss
"Strong" versus "Weak" AI
 below)
 is indeed an attempt to substantiate, through engineering certain
impressive artifacts, the thesis that intelligence is at bottom
computational (at the level of Turing machines and their equivalents,
e.g., Register machines). So there is a philosophical claim, for sure.
But this doesn't make AI philosophy, any more than some of the
deeper, more aggressive claims of some physicists (e.g., that the
 universe is ultimately digital in nature)
 make their field philosophy. Philosophy of physics certainly
*entertains* the proposition that the physical universe can be
perfectly modeled in digital terms (in a series of cellular automata,
e.g.), but of course philosophy of physics can't be
*identified* with this doctrine. 



Second, we now know well (and those familiar with the relevant formal
terrain knew at the time of Dennett's writing) that information
processing can exceed standard computation, that is, can exceed
computation at and below the level of what a Turing machine can muster
(**Turing-computation**, we shall say). (Such information
processing is known as *hypercomputation*, a term coined by
philosopher Jack Copeland, who has himself defined such machines
(e.g., Copeland 1998). The first machines capable of hypercomputation
were *trial-and-error machines*, introduced in the same famous
issue of the *Journal of Symbolic Logic* (Gold 1965; Putnam
1965). A new hypercomputer is the infinite time Turing machine
(Hamkins & Lewis 2000).) Dennett's appeal to Church's
thesis thus flies in the face of the mathematical facts: some
varieties of information processing exceed standard computation (or
Turing-computation). Church's thesis, or more precisely, the
Church-Turing thesis, is the view that a function \(f\) is effectively
computable if and only if \(f\) is Turing-computable (i.e., some
Turing machine can compute \(f\)). Thus, this thesis has nothing to
say about information processing that is more demanding than what a
Turing machine can achieve. (Put another way, there is no
counter-example to CTT to be automatically found in an
information-processing device capable of feats beyond the reach of
TMs.) For all philosophy and psychology know, intelligence, even if
tied to information processing, exceeds what is Turing-computational
or
 Turing-mechanical.[38]
 This is especially true because philosophy and psychology, unlike AI,
are in no way fundamentally charged with engineering artifacts, which
makes the physical realizability of hypercomputation irrelevant from
their perspectives. Therefore, *contra* Dennett, to consider AI
as psychology or philosophy is to commit a serious error, precisely
because so doing would box these fields into only a speck of the
entire space of functions from the natural numbers (including tuples
therefrom) to the natural numbers. (Only a tiny portion of the
functions in this space are Turing-computable.) AI is without question
much, much narrower than this pair of fields. Of course, it's
possible that AI could be replaced by a field devoted not to building
computational artifacts by writing computer programs and running them
on embodied Turing machines. But this new field, by definition, would
not be AI. Our exploration of *AIMA* and other textbooks provide
direct empirical confirmation of this. 



Third, most AI researchers and developers, in point of fact, are
simply concerned with building useful, profitable artifacts, and
don't spend much time reflecting upon the kinds of abstract
definitions of intelligence explored in this entry (e.g.,
 What Exactly *is* AI?).
 



Though AI isn't philosophy, there are certainly ways of doing
real implementation-focussed AI of the highest caliber that are
intimately bound up with philosophy. The best way to demonstrate this
is to simply present such research and development, or at least a
representative example thereof. While there have been many examples of
such work, the most prominent example in AI is John Pollock's
OSCAR project, which stretched over a considerable portion of his
lifetime. For a detailed presentation and further discussion, see
the



Supplement on the OSCAR Project.




It's important to note at this juncture that the OSCAR project,
and the information processing that underlies it, are without question
at once philosophy *and* technical AI. Given that the work in
question has appeared in the pages of *Artificial Intelligence*,
a first-rank journal devoted to that field, and not to philosophy,
this is undeniable (see, e.g., Pollock 2001, 1992). This point is
important because while it's certainly appropriate, in the
present venue, to emphasize connections between AI and philosophy,
some readers may suspect that this emphasis is contrived: they may
suspect that the truth of the matter is that page after page of AI
journals are filled with narrow, technical content far from
philosophy. Many such papers do exist. But we must distinguish between
writings designed to present the nature of AI, and its core methods
and goals, versus writings designed to present progress on specific
technical issues. 



Writings in the latter category are more often than not quite narrow,
but, as the example of Pollock shows, sometimes these specific issues
are inextricably linked to philosophy. And of course Pollock's
work is a representative example (albeit the most substantive one).
One could just as easily have selected work by folks who don't
happen to also produce straight philosophy. For example, for an entire
book written within the confines of AI and computer science, but which
is epistemic logic in action in many ways, suitable for use in
seminars on that topic, see (Fagin et al. 2004). (It is hard to find
technical work that isn't bound up with philosophy in some
direct way. E.g., AI research on learning is all intimately bound up
with philosophical treatments of induction, of how genuinely new
concepts not simply defined in terms of prior ones can be learned. One
possible partial answer offered by AI is **inductive logic
programming**, discussed in Chapter 19 of *AIMA*.) 



What of writings in the former category? Writings in this category,
while by definition in AI venues, not philosophy ones, are nonetheless
philosophical. Most textbooks include plenty of material that falls
into this latter category, and hence they include discussion of the
philosophical nature of AI (e.g., that AI is aimed at building
artificial intelligences, and that's why, after all, it's
called 'AI'). 


## 8. Philosophy of Artificial Intelligence


### 8.1 "Strong" versus "Weak" AI



Recall that we earlier discussed proposed definitions of AI, and
recall specifically that these proposals were couched in terms of the
*goals* of the field. We can follow this pattern here: We can
distinguish between "Strong" and "Weak" AI by
taking note of the different goals that these two versions of AI
strive to reach. "Strong" AI seeks to create artificial
persons: machines that have all the mental powers we have, including
phenomenal consciousness. "Weak" AI, on the other hand,
seeks to build information-processing machines that *appear* to
have the full mental repertoire of human persons (Searle 1997).
"Weak" AI can also be defined as the form of AI that aims
at a system able to pass not just the Turing Test (again, abbreviated
as TT), but the *Total* Turing Test (Harnad 1991). In TTT, a
machine must muster more than linguistic indistinguishability: it must
pass for a human in all behaviors - throwing a baseball, eating,
teaching a class, etc. 



It would certainly seem to be exceedingly difficult for philosophers
to overthrow "Weak" AI (Bringsjord and Xiao 2000). After
all, what *philosophical* reason stands in the way of AI
producing artifacts that *appear* to be animals or even humans?
However, some philosophers have aimed to do in "Strong"
AI, and we turn now to the most prominent case in point. 


### 8.2 The Chinese Room Argument Against "Strong AI"



Without question, the most famous argument in the philosophy of AI is
John Searle's (1980) Chinese Room Argument (CRA), designed to
overthrow "Strong" AI. We present a quick summary here and
a "report from the trenches" as to how AI practitioners
regard the argument. Readers wanting to further study CRA will find an
excellent next step in the entry on
 the Chinese Room Argument
 and (Bishop & Preston 2002). 



CRA is based on a thought-experiment in which Searle himself stars. He
is inside a room; outside the room are native Chinese speakers who
don't know that Searle is inside it. Searle-in-the-box, like
Searle-in-real-life, doesn't know any Chinese, but is fluent in
English. The Chinese speakers send cards into the room through a slot;
on these cards are written questions in Chinese. The box, courtesy of
Searle's secret work therein, returns cards to the native
Chinese speakers as output. Searle's output is produced by
consulting a rulebook: this book is a lookup table that tells him what
Chinese to produce based on what is sent in. To Searle, the Chinese is
all just a bunch of - to use Searle's language -
squiggle-squoggles. The following schematic picture sums up the
situation. The labels should be obvious. \(O\) denotes the outside
observers, in this case the Chinese speakers. Input is denoted by
\(i\) and output by \(o\). As you can see, there is an icon for the
rulebook, and Searle himself is denoted by \(P\).



![input/output diagram of chinese room](cr1.png)

*The Chinese Room, Schematic View*




Now, what is the argument based on this thought-experiment? Even if
you've never heard of CRA before, you doubtless can see the
basic idea: that Searle (in the box) is supposed to be everything a
computer can be, and because he doesn't understand Chinese, no
computer could have such understanding. Searle is mindlessly moving
squiggle-squoggles around, and (according to the argument)
that's all computers do,
 fundamentally.[39]



Where does CRA stand today? As we've already indicated, the
argument would still seem to be alive and well; witness (Bishop &
Preston 2002). However, there is little doubt that at least among AI
*practitioners*, CRA is generally rejected. (This is of course
thoroughly unsurprising.) Among these practitioners, the philosopher
who has offered the most formidable response out of AI itself is
Rapaport (1988), who argues that while AI systems are indeed
syntactic, the right syntax can constitute semantics. It should be
said that a common attitude among proponents of "Strong"
AI is that CRA is not only unsound, but silly, based as it is on a
fanciful story (CR) far removed from the *practice* of AI
- practice which is year by year moving ineluctably toward
sophisticated robots that will once and for all silence CRA and its
proponents. For example, John Pollock (as we've noted,
philosopher *and* practitioner of AI) writes: 



> 
> Once [my intelligent system] OSCAR is fully functional, the argument
> from analogy will lead us inexorably to attribute thoughts and
> feelings to OSCAR with precisely the same credentials with which we
> attribute them to human beings. Philosophical arguments to the
> contrary will be passe. (Pollock 1995, p. 6)
> 



To wrap up discussion of CRA, we make two quick points, to wit: 


1. Despite the confidence of the likes of Pollock about the eventual
irrelevance of CRA in the face of the eventual human-level prowess of
OSCAR (and, by extension, any number of other still-improving AI
systems), the brute fact is that deeply semantic natural-language
processing (NLP) is rarely even pursued these days, so proponents of
CRA are certainly not the ones feeling some discomfort in light of the
current state of AI. In short, Searle would rightly point to any of
the success stories of AI, including the Watson system we have
discussed, and still proclaim that understanding is nowhere to be
found - and he would be well within his philosophical rights in
saying this.
2. It would appear that the CRA is bubbling back to a level of
engagement not seen for a number of years, in light of the empirical
fact that certain thinkers are now issuing explicit warnings to the
effect that future conscious, malevolent machines may well wish to do
in our species. In reply, Searle (2014) points out that since CRA is
sound, there can't be conscious machines; and if there
can't be conscious machines, there can't be malevolent
machines that wish anything. We return to this at the end of our
entry; the chief point here is that CRA continues to be quite
relevant, and indeed we suspect that Searle's basis for
have-no-fear will be taken up energetically by not only philosophers,
but AI experts, futurists, lawyers, and policy-makers.



Readers may wonder if there are philosophical debates that AI
researchers engage in, in the course of working in their field (as
opposed to when they might attend a philosophy conference). Surely, AI
researchers have philosophical discussions amongst themselves, right?




Generally, one finds that AI researchers do discuss among themselves
topics in philosophy of AI, and these topics are usually the very same
ones that occupy philosophers of AI. However, the attitude reflected
in the quote from Pollock immediately above is by far the dominant
one. That is, in general, the attitude of AI researchers is that
philosophizing is sometimes fun, but the upward march of AI
engineering cannot be stopped, will not fail, and will eventually
render such philosophizing otiose. 



We will return to the issue of the future of AI in the
 final section
 of this entry. 


### 8.3 The Godelian Argument Against "Strong AI"



Four decades ago, J.R. Lucas (1964) argued that Godel's
first incompleteness theorem entails that no machine can ever reach
human-level intelligence. His argument has not proved to be
compelling, but Lucas initiated a debate that has produced more
formidable arguments. One of Lucas' indefatigable defenders is
the physicist Roger Penrose, whose first attempt to vindicate Lucas
was a Godelian attack on "Strong" AI articulated in
his *The Emperor's New Mind* (1989). This first attempt
fell short, and Penrose published a more elaborate and more fastidious
Godelian case, expressed in Chapters 2 and 3 of his *Shadows of
the Mind* (1994). 



In light of the fact that readers can turn to the
 entry on the Godel's Incompleteness Theorems,
 a full review here is not needed. Instead, readers will be given a
decent sense of the argument by turning to an online paper in which
Penrose, writing in response to critics (e.g., the philosopher David
Chalmers, the logician Solomon Feferman, and the computer scientist
Drew McDermott) of his *Shadows of the Mind*, distills the
argument to a couple of
 paragraphs.[40]
 Indeed, in this paper Penrose gives what he takes to be the perfected
version of the core Godelian case given in *SOTM*. Here is
this version, verbatim: 



> 
> We try to suppose that the totality of methods of (unassailable)
> mathematical reasoning that are in principle humanly accessible can be
> encapsulated in some (not necessarily computational) sound formal
> system \(F\). A human mathematician, if presented with \(F\), could
> argue as follows (bearing in mind that the phrase "I am
> \(F\)" is merely a shorthand for "\(F\) encapsulates all
> the humanly accessible methods of mathematical proof"):
> 
> 
> > 
> > (A) "Though I don't know that I necessarily am \(F\), I
> > conclude that if I were, then the system \(F\) would have to be sound
> > and, more to the point, \(F'\) would have to be sound, where \(F'\) is
> > \(F\) supplemented by the further assertion "I am \(F\)."
> > I perceive that it follows from the assumption that I am \(F\) that
> > the Godel statement \(G(F')\) would have to be true and,
> > furthermore, that it would not be a consequence of \(F'\). But I have
> > just perceived that "If I happened to be \(F\), then \(G(F')\)
> > would have to be true," and perceptions of this nature would be
> > precisely what \(F'\) is supposed to achieve. Since I am therefore
> > capable of perceiving something beyond the powers of \(F'\), I deduce
> > that I cannot be \(F\) after all. Moreover, this applies to any other
> > (Godelizable) system, in place of \(F\)." (Penrose 1996,
> > 3.2)
> > 
> 
> 
> 



Does this argument succeed? A firm answer to this question is not
appropriate to seek in the present entry. Interested readers are
encouraged to consult four full-scale treatments of the argument
(LaForte et. al 1998; Bringsjord and Xiao 2000; Shapiro 2003; Bowie
1982).


### 8.4 Additional Topics and Readings in Philosophy of AI



In addition to the Godelian and Searlean arguments covered
briefly above, a third attack on "Strong" AI (of the
symbolic variety) has been widely discussed (though with the rise of
statistical machine learning has come a corresponding decrease in the
attention paid to it), namely, one given by the philosopher Hubert
Dreyfus (1972, 1992), some incarnations of which have been
co-articulated with his brother, Stuart Dreyfus (1987), a computer
scientist. Put crudely, the core idea in this attack is that human
expertise is not based on the explicit, disembodied, mechanical
manipulation of symbolic information (such as formulae in some logic,
or probabilities in some Bayesian network), and that AI's
efforts to build machines with such expertise are doomed if based on
the symbolic paradigm. The genesis of the Dreyfusian attack was a
belief that the critique of (if you will) symbol-based philosophy
(e.g., philosophy in the logic-based, rationalist tradition, as
opposed to what is called the Continental tradition) from such
thinkers as Heidegger and Merleau-Ponty could be made against the
rationalist tradition in AI. After further reading and study of
Dreyfus' writings, readers may judge whether this critique is
compelling, in an information-driven world increasingly managed by
intelligent agents that carry out symbolic reasoning (albeit not even
close to the human level). 



For readers interested in exploring philosophy of AI beyond what Jim
Moor (in a recent address - "The Next Fifty Years of AI:
Future Scientific Research vs. Past Philosophical Criticisms"
- as the 2006 Barwise Award winner at the annual eastern
American Philosophical Association meeting) has called the "the
big three" criticisms of AI, there is no shortage of additional
material, much of it available on the Web. The last chapter of
*AIMA* provides a compressed overview of some additional
arguments against "Strong" AI, and is in general not a bad
next step. Needless to say, Philosophy of AI today involves much more
than the three well-known arguments discussed above, and, inevitably,
Philosophy of AI tomorrow will include new debates and problems we
can't see now. Because machines, inevitably, will get smarter
and smarter (regardless of just *how* smart they get),
Philosophy of AI, pure and simple, is a growth industry. With every
human activity that machines match, the "big" questions
will only attract more attention. 


## 9. The Future



If past predictions are any indication, the only thing we know today
about tomorrow's science and technology is that it will be
radically different than whatever we predict it will be like.
Arguably, in the case of AI, we may also specifically know today that
progress will be much slower than what most expect. After all, at the
1956 kickoff conference (discussed at the start of this entry), Herb
Simon predicted that thinking machines able to match the human mind
were "just around the corner" (for the relevant quotes and
informative discussion, see the first chapter of *AIMA*). As it
turned out, the new century would arrive without a single machine able
to converse at even the toddler level. (Recall that when it comes to
the building of machines capable of displaying human-level
intelligence, Descartes, not Turing, seems today to be the better
prophet.) Nonetheless, astonishing though it may be, serious thinkers
in the late 20th century have continued to issue incredibly optimistic
predictions regarding the progress of AI. For example, Hans Moravec
(1999), in his *Robot: Mere Machine to Transcendent Mind*,
informs us that because the speed of computer hardware doubles every
18 months (in accordance with Moore's Law, which has
apparently held in the past), "fourth generation"
robots will soon enough exceed humans in all respects, from running
companies to writing novels. These robots, so the story goes, will
evolve to such lofty cognitive heights that we will stand to them as
single-cell organisms stand to us
 today.[41]




Moravec is by no means singularly Pollyannaish: Many others in AI
predict the same sensational future unfolding on about the same rapid
schedule. In fact, at the aforementioned AI@50 conference, Jim Moor
posed the question "Will human-level AI be achieved within the
next 50 years?" to five thinkers who attended the original 1956
conference: John McCarthy, Marvin Minsky, Oliver Selfridge, Ray
Solomonoff, and Trenchard Moore. McCarthy and Minsky gave firm,
unhesitating affirmatives, and Solomonoff seemed to suggest that AI
provided the one ray of hope in the face of fact that our species
seems bent on destroying itself. (Selfridge's reply was a bit
cryptic. Moore returned a firm, unambiguous negative, and declared
that once his computer is smart enough to interact with him
conversationally about mathematical problems, he might take this whole
enterprise more seriously.) It is left to the reader to judge the
accuracy of such risky predictions as have been given by Moravec,
McCarthy, and
 Minsky.[42]




The judgment of the reader in this regard ought to factor in the
stunning resurgence, very recently, of serious reflection on what is
known as "The Singularity," (denoted by us simply as
**S**) the future point at which artificial intelligence exceeds
human intelligence, whereupon immediately thereafter (as the story
goes) the machines make themselves rapidly smarter and smarter and
smarter, reaching a superhuman level of intelligence that, stuck as we
are in the mud of our limited mentation, we can't fathom. For
extensive, balanced analysis of **S**, see Eden et al. (2013).



Readers unfamiliar with the literature on **S** may be quite
surprised to learn the degree to which, among learned folks, this
hypothetical event is not only taken seriously, but has in fact become
a target for extensive and frequent philosophizing [for a mordant tour
of the recent thought in question, see Floridi (2015)]. What
*arguments* support the belief that **S** is in our future?
There are two main arguments at this point: the familiar
hardware-based one [championed by Moravec, as noted above, and again
more recently by Kurzweil (2006)]; and the - as far as we know
- original argument given by mathematician I. J. Good (1965). In
addition, there is a recent and related doomsayer argument advanced by
Bostrom (2014), which seems to presuppose that **S** will occur.
Good's argument, nicely amplified and adjusted by Chalmers
(2010), who affirms the tidied-up version of the argument, runs as
follows:


* **Premise 1**: There will be AI (created by HI and such that AI
= HI).
* **Premise 2**: If there is AI, there will be AI\(^+\) (created
by AI).
* **Premise 3**: If there is AI\(^+\), there will be AI\(^{++}\)
(created by AI\(^+\)).
* **Conclusion**: There will be AI\(^{++}\) (= **S** will
occur).



In this argument, 'AI' is artificial intelligence at the
level of, and created by, human persons, 'AI\(^+\)'
artificial intelligence above the level of human persons, and
'AI\(^{++}\)' super-intelligence constitutive of **S**.
The key process is presumably the *creation* of one class of
machine by another. We have added for convenience 'HI' for
human intelligence; the central idea is then: HI will create AI, the
latter at the same level of intelligence as the former; AI will create
AI\(^+\); AI\(^+\) will create AI\(^{++}\); with the ascension
proceeding perhaps forever, but at any rate proceeding long enough for
us to be as ants outstripped by gods. 



The argument certainly appears to be formally valid. Are its three
premises true? Taking up such a question would fling us far beyond the
scope of this entry. We point out only that the concept of one class
of machines creating another, more powerful class of machines is not a
transparent one, and neither Good nor Chalmers provides a rigorous
account of the concept, which is ripe for philosophical analysis. (As
to mathematical analysis, some exists, of course. It is for example
well-known that a computing machine at level \(L\) cannot possibly
create another machine at a higher level \(L'\). For instance, a
linear-bounded automaton can't create a Turing machine.)



The Good-Chalmers argument has a rather clinical air about it; the
argument doesn't say anything regarding whether machines in
the AI\(^{++}\) category will be benign, malicious, or munificent.
Many others gladly fill this gap with dark, dark pessimism. The
*locus classicus* here is without question a widely read paper by
Bill Joy (2000): "Why The Future Doesn't Need Us."
Joy believes that the human race is doomed, in no small part because
it's busy building smart machines. He writes: 



> 
> 
> The 21st-century technologies - genetics, nanotechnology, and
> robotics (GNR) - are so powerful that they can spawn whole new
> classes of accidents and abuses. Most dangerously, for the first time,
> these accidents and abuses are widely within the reach of individuals
> or small groups. They will not require large facilities or rare raw
> materials. Knowledge alone will enable the use of them.
> 
> 
> 
> Thus we have the possibility not just of weapons of mass destruction
> but of knowledge-enabled mass destruction (KMD), this destructiveness
> hugely amplified by the power of self-replication. 
> 
> 
> 
> I think it is no exaggeration to say we are on the cusp of the further
> perfection of extreme evil, an evil whose possibility spreads well
> beyond that which weapons of mass destruction bequeathed to the
> nation-states, on to a surprising and terrible empowerment of extreme
>  individuals.[43]
> 
> 
> 
> 



Philosophers would be most interested in *arguments* for this
view. What are Joy's? Well, no small reason for the attention
lavished on his paper is that, like Raymond Kurzweil (2000), Joy
relies heavily on an argument given by none other than the Unabomber
(Theodore Kaczynski). The idea is that, assuming we succeed in
building intelligent machines, we will have them do most (if not all)
work for us. If we further allow the machines to make decisions for us
- even if we retain oversight over the machines -, we will
eventually depend on them to the point where we must simply accept
their decisions. But even if we don't allow the machines to make
decisions, the control of such machines is likely to be held by a
small elite who will view the rest of humanity as unnecessary -
since the machines can do any needed work (Joy 2000).



This isn't the place to assess this argument. (Having said that,
the pattern pushed by the Unabomber and his supporters certainly
*appears* to be flatly
 invalid.[44])
 In fact, many readers will doubtless feel that no such place exists
or will exist, because the reasoning here is amateurish. So then, what
about the reasoning of professional philosophers on the matter? 



Bostrom has recently painted an exceedingly dark picture of a possible
future. He points out that the "first superintelligence"
could have the capability



> 
> to shape the future of Earth-originating life, could easily have
> non-anthropomorphic final goals, and would likely have instrumental
> reasons to pursue open-ended resource acquisition. If we now reflect
> that human beings consist of useful resources (such as conveniently
> located atoms) and that we depend on many more local resources, we can
> see that the outcome could easily be one in which humanity quickly
> becomes extinct. (Bostrom 2014, p. 416)
> 



 Clearly, the most vulnerable premise in this sort of argument
is that the "first superintelligence" will arrive indeed
arrive. Here perhaps the Good-Chalmers argument provides a basis.




Searle (2014) thinks Bostrom's book is misguided and
fundamentally mistaken, and that we needn't worry. His rationale
is dirt-simple: Machines aren't conscious; Bostrom is alarmed at
the prospect of malicious machines who do us in; a malicious machine
is by definition a conscious machine; ergo, Bostrom's argument
doesn't work. Searle writes: 



> 
> If the computer can fly airplanes, drive cars, and win at chess, who
> cares if it is totally nonconscious? But if we are worried about a
> maliciously motivated superintelligence destroying us, then it is
> important that the malicious motivation should be real. Without
> consciousness, there is no possibiity of its being real.
> 



The positively remarkable thing here, it seems to us, is that Searle
appears to be unaware of the brute fact that most AI engineers are
perfectly content to build machines on the basis of the *AIMA*
view of AI we presented and explained above: the view according to
which machines simply map percepts to actions. On this view, it
doesn't matter whether the machine *really* has desires;
what matters is whether it acts suitably on the basis of how AI
scientists engineer formal *correlates* to desire. An
autonomous machine with overwhelming destructive power that
non-consciously "decides" to kill doesn't become
just a nuisance because genuine, human-level, subjective desire is
absent from the machine. If an AI can play the game of chess, and the
game of *Jeopardy!*, it can certainly play the game of war. Just
as it does little good for a human loser to point out that the
victorious machine in a game of chess isn't conscious, it will
do little good for humans being killed by machines to point out that
these machines aren't conscious. (It is interesting to note that
the genesis of Joy's paper was an informal conversation with
John Searle and Raymond Kurzweil. According to Joy, Searle
didn't think there was much to worry about, since he was (and
is) quite confident that tomorrow's robots can't be
 conscious.[45])
 



There are some things we can *safely* say about tomorrow.
Certainly, barring some cataclysmic events (nuclear or biological
warfare, global economic depression, a meteorite smashing into Earth,
etc.), we now know that AI will succeed in producing artificial
*animals*. Since even some natural animals (mules, e.g.) can be
easily trained to work for humans, it stands to reason that artificial
animals, designed from scratch with our purposes in mind, will be
deployed to work for us. In fact, many jobs currently done by humans
will certainly be done by appropriately programmed artificial animals.
To pick an arbitrary example, it is difficult to believe that
commercial drivers won't be artificial in the future. (Indeed,
Daimler is already running commercials in which they tout the ability
of their automobiles to drive "autonomously," allowing
human occupants of these vehicles to ignore the road and read.) Other
examples would include: cleaners, mail carriers, clerical workers,
military scouts, surgeons, and pilots. (As to cleaners, probably a
significant number of readers, at this very moment, have robots from
iRobot cleaning the carpets in their homes.) It is hard to see how
such jobs are inseparably bound up with the attributes often taken to
be at the core of personhood - attributes that would be the most
difficult for AI to
 replicate.[46]




Andy Clark (2003) has another prediction: Humans will gradually
become, at least to an appreciable degree, cyborgs, courtesy of
artificial limbs and sense organs, and implants. The main driver of
this trend will be that while standalone AIs are often desirable, they
are hard to engineer when the desired level of intelligence is high.
But to let humans "pilot" less intelligent machines is a
good deal easier, and still very attractive for concrete reasons.
Another related prediction is that AI would play the role of a
cognitive prosthesis for humans (Ford et al. 1997; Hoffman et al.
2001). The prosthesis view sees AI as a "great equalizer"
that would lead to less stratification in society, perhaps similar to
how the Hindu-Arabic numeral system made arithmetic available to the
masses, and to how the Guttenberg press contributed to literacy
becoming more universal. 



Even if the argument is formally invalid, it leaves us with a question
- the cornerstone question about AI and the future: Will AI
produce artificial creatures that replicate and exceed human cognition
(as Kurzweil and Joy believe)? Or is this merely an interesting
supposition? 



This is a question not just for scientists and engineers; it is also a
question for philosophers. This is so for two reasons. One, research
and development designed to validate an affirmative answer must
include philosophy - for reasons rooted in earlier parts of the
present entry. (E.g., philosophy is the place to turn to for robust
formalisms to model human propositional attitudes in machine terms.)
Two, philosophers might well be able to provide arguments that answer
the cornerstone question now, definitively. If a version of either of
the three arguments against "Strong" AI alluded to above
(Searle's CRA; the Godelian attack; the Dreyfus argument)
are sound, then of course AI will not manage to produce machines
having the mental powers of persons. No doubt the future holds not
only ever-smarter machines, but new arguments pro and con on the
question of whether this progress can reach the human level that
Descartes declared to be unreachable.