k. In statistics, a bias is a systematic error or deviation from the correct value. But in the
context of fairness, it refers to a tendency in favor or against a certain group or individual
characteristic, usually in a way that is considered unfair or harmful. A statistically unbiased
AI system that produces disparate outcomes for different demographic groups may thus be
viewed as biased in the ethical sense.[219]
l. Including Jon Kleinberg (Cornell University), Sendhil Mullainathan (University of Chicago),
Cynthia Chouldechova (Carnegie Mellon) and Sam Corbett-Davis (Stanford)[228]
m. Moritz Hardt (a director at the Max Planck Institute for Intelligent Systems) argues that
machine learning "is fundamentally the wrong tool for a lot of domains, where you're trying
to design interventions and mechanisms that change the world."[233]
n. When the law was passed in 2018, it still contained a form of this provision.
o. This is the United Nations' definition, and includes things like land mines as well.[247]
p. See table 4; 9% is both the OECD average and the U.S. average.[258]
q. Sometimes called a "robopocalypse"[266]
r. "Electronic brain" was the term used by the press around this time.[319][321]
s. Daniel Crevier wrote, "the conference is generally recognized as the official birthdate of the
new science."[324] Russell and Norvig called the conference "the inception of artificial
intelligence."[115]
t. Russell and Norvig wrote "for the next 20 years the field would be dominated by these
people and their students."[325]
u. Russell and Norvig wrote, "it was astonishing whenever a computer did anything kind of
smartish".[326]
v. The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel
Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU.
w. Russell and Norvig write: "in almost all cases, these early systems failed on more difficult
problems"[330]
x. Embodied approaches to AI[337] were championed by Hans Moravec[338] and Rodney
Brooks[339] and went by many names: Nouvelle AI.[339] Developmental robotics.[340]
y. Matteo Wong wrote in The Atlantic: "Whereas for decades, computer-science fields such as
natural-language processing, computer vision, and robotics used extremely different
methods, now they all use a programming method called "deep learning". As a result, their
code and approaches have become more similar, and their models are easier to integrate
into one another."[346]
z. Jack Clark wrote in Bloomberg: "After a half-decade of quiet breakthroughs in artificial
intelligence, 2015 has been a landmark year. Computers are smarter and learning faster
than ever", and noted that the number of software projects that use machine learning at
Google increased from a "sporadic usage" in 2012 to more than 2,700 projects in 2015.[348]
aa. Nils Nilsson wrote in 1983: "Simply put, there is wide disagreement in the field about what AI
is all about."[369]
ab. Daniel Crevier wrote that "time has proven the accuracy and perceptiveness of some of
Dreyfus's comments. Had he formulated them less aggressively, constructive actions they
suggested might have been taken much earlier."[374]
ac. Searle presented this definition of "Strong AI" in 1999.[384] Searle's original formulation was
"The appropriately programmed computer really is a mind, in the sense that computers
given the right programs can be literally said to understand and have other cognitive
states."[385] Strong AI is defined similarly by Russell and Norvig: "Stong AI – the assertion
that machines that do so are actually thinking (as opposed to simulating thinking)."[386]