Understanding Artificial Intelligence
Everyone has heard the term artificial intelligence or ‘AI’, but terminology and variations such as ‘machine learning’, ‘deep learning’, and ‘neural networks’ can be difficult to distinguish. Many of these terms are used interchangeably with ‘artificial intelligence’ leading to confusion and misunderstandings about the differences.
Furthermore, Hollywood has a tendency to conflate AI with robots. Movies like Terminator, I, Robot, and Blade Runner perpetuate this stereotype that AI is typically embedded within a human-like robot, but the two are definitely not one and the same.
The term ‘artificial intelligence’ was first by John McCarthy way back in 1956 to describe machines that can perform tasks that are characteristic of human intelligence. It was a very general definition that is broad enough to include many of the things AIs can and cannot do today: problem solving, recognizing objects or faces, recognizing sounds, planning, learning, and understanding language.
The term, ‘machine learning’ was coined shortly thereafter in 1959. Arthur Samuel used the term to define the ability to learn without being explicitly programmed. Today, many of the rules- and logic-based systems that were previously referred to as artificial intelligence are no longer considered to be AI, making things even more confusing. However, in general, artificial intelligences can be broken down into three main categories:
Artificial Narrow Intelligence
Narrow or ‘weak’ AI are brute force systems that associate patterns in the input data to produce outputs that approximate those data. Some examples are IBM’s Watson, Apple’s Siri, and other neural networks.
A good specific-use example would be a natural language translation system based on deep learning (e.g., Google Translate). Google Translate is trained by rote for millions of iterations on corpora of human-translated texts so that if given as input an English sentence, it can look-up the corresponding French sentence, or produce an approximation by rigid, unintelligent template-matching.
Narrow or weak AI methods generally have none of the conceptual fluidity and generativity of human cognition necessary for a human-level, human-style virtual assistant. IBM recently conceded the limitations of narrow AI in their decision “to shift focus [away from] machines that fully replicate human general intelligence,” basically accepting that Watson will never attain human-level linguistic competence.
“The problem with Watson,” said the editor-in-chief of the Journal of Artificial Intelligence in Medicine, who has tried to apply Watson to medical literature to recommend treatments as a virtual medical assistant, “is that it’s essentially a really good search engine… The sort of knowledge Watson [has] is very flat and very broad.”
Artificial General Intelligence
Artificial General Intelligence (AGI) is the holy grail for AI research today. Narrow AI systems can perform internet searches, translate natural language (somewhat), drive automobiles (somewhat), or play some video games, but none of these systems can perform all of these tasks with human fluidity and generality. In theory, an AGI system would be able to do all of these cognitive tasks as a human could.
AGI would posses the ability to think generally; to make decisions that go beyond previous experience. Decisions would be made based on the AGI’sinnate and acquired knowledge.
There remain obstacles to overcome before achieving a true AGI. A key challenge is human creativity. Part of the reason machine learning and deep learning both require large amounts of data to do tasks that a human can do (with far less data) is that human brains make creative leaps. If AGI solves the creativity challenge, computers will be able to do the same thing that we do everyday, skip over the minutia (needless data), using our creative intuition.
University of Oxford philosopher, Nick Bostrom, defines superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”.
It is up for debate whether human intelligence can be surpassed, but Bostrom argues that a super intelligent machine could be built in the next century. If that were so, biological brains would be surpassed in practically every field, including scientific creativity, general wisdom, and social skills.
"Kufahamu akili bandia Kila mtu amesikia neno "" akili bandia "" au "" AI "" lakini maneno na tofauti kama vile "" kujifunza mashine "", "" kujifunza kina "" na "" mitandao ya neva "" inaweza kuwa vigumu kutofautisha." Wengi wa maneno haya ni kutumika interchangeably na "akili bandia" kuongoza kwa kuchanganyikiwa na misunderstandings kuhusu tofauti. Kwa kuongezea, Hollywood ina mwelekeo wa kuchanganya AI na roboti. Filamu kama Terminator, I, Robot, na Blade Runner zinaendeleza wazo hili kwamba AI ni kawaida iliyoingizwa ndani ya roboti kama ya binadamu, lakini hizi mbili ni dhahiri si moja na sawa. "Jina ""Intelligence Artificial"" lilitumiwa na John McCarthy mnamo mwaka 1956 kuelezea mashine ambazo zinaweza kufanya kazi ambazo ni sifa ya akili ya binadamu." "Hiyo ilikuwa ufafanuzi wa jumla sana ambao ni mpana wa kutosha kujumuisha mambo mengi ambayo AI inaweza na haiwezi kufanya leo: kutatua tatizo, kutambua vitu au nyuso, kutambua sauti, kupanga, kujifunza, na kuelewa lugha.""" "Jina ""machine learning"" lilitumiwa mara ya kwanza mwaka wa 1959." Arthur Samuel alitumia neno hilo kufafanua uwezo wa kujifunza bila kupangwa waziwazi. "Leo, mifumo mingi ya sheria na mantiki ambayo hapo awali ilitajwa kama akili bandia haichukuliwi tena kuwa AI, na kufanya mambo kuwa ya kuchanganyikiwa zaidi. """ Kwa ujumla, akili bandia inaweza kugawanywa katika makundi matatu: akili nyembamba ya bandia (AI) ni mfumo wa nguvu mbaya ambao unashiriki mifumo katika data ya kuingia ili kuzalisha matokeo ambayo yanakaribia data hizo. Mifano fulani ni Watson ya IBM, Siri ya Apple, na mitandao mingine ya neva. Mfano mzuri wa matumizi maalum itakuwa mfumo wa tafsiri ya lugha ya asili kulingana na kujifunza kina (kwa mfano, Google Tafsiri). Google Translate ni mafunzo kwa rote kwa mamilioni ya iterations juu ya corpus ya maandishi ya binadamu kutafsiriwa, hivyo kwamba kama kupewa kama pembejeo sentensi Kiingereza, inaweza kuangalia juu ya sentensi ya Kifaransa sambamba, au kuzalisha approximation na rigid, unintelligent template-kufanana. Mbinu za AI nyembamba au dhaifu kwa ujumla hazina mtiririko wa dhana na generativity ya utambuzi wa binadamu unaohitajika kwa msaidizi wa kawaida wa kiwango cha binadamu. IBM hivi karibuni ilikubali mapungufu ya narrow AI katika uamuzi wao wa kuhamisha lengo [kuko mbali na] mashine ambazo zinaiga kikamilifu akili ya jumla ya binadamu, kimsingi kukubali kwamba Watson kamwe haitafikia uwezo wa lugha ya kiwango cha binadamu. "Mchambuzi mkuu wa jarida la ""Journal of Artificial Intelligence in Medicine"" alisema: ""Watson ni injini nzuri ya utafutaji, lakini ina ujuzi wa kina na wa kina." Mifumo ndogo ya AI inaweza kufanya utafutaji wa mtandao, kutafsiri lugha ya asili (kwa kiasi fulani), kuendesha magari (kwa kiasi fulani), au kucheza michezo ya video, lakini hakuna moja ya mifumo hii inaweza kufanya kazi hizi zote kwa uwiano wa binadamu na jumla. Kwa nadharia, mfumo wa AGI ungeweza kufanya kazi hizi zote za utambuzi kama binadamu angeweza. AGI ingekuwa na uwezo wa kufikiri kwa ujumla; kufanya maamuzi ambayo kwenda zaidi ya uzoefu wa awali. Maamuzi yangefanywa kulingana na AGI <unk> innate na maarifa yaliyopatikana. Bado kuna vikwazo vya kushinda kabla ya kufikia AGI ya kweli. Changamoto kuu ni ubunifu wa kibinadamu. Sehemu ya sababu ya kujifunza mashine na kujifunza kina wote wanahitaji kiasi kikubwa cha data kufanya kazi ambazo binadamu anaweza kufanya (kwa data kidogo sana) ni kwamba ubongo wa binadamu hufanya kuruka ubunifu. "Ikiwa AGI itatatua changamoto ya ubunifu, kompyuta zitaweza kufanya kitu kimoja ambacho sisi hufanya kila siku, kuruka juu ya data isiyo ya lazima, kwa kutumia intuition yetu ya ubunifu. """ "Nick Bostrom, mwanafalsafa wa Chuo Kikuu cha Oxford, alifafanua akili ya juu kama ""akili yoyote ambayo inazidi utendaji wa utambuzi wa binadamu katika karibu nyanja zote za maslahi.""" Bostrom anasema kwamba hakuna kitu kinachoweza kuhimiza akili ya binadamu, lakini anaamini kwamba mashine yenye akili nyingi inaweza kuundwa katika karne ijayo. Kama ingekuwa hivyo, ubongo wa kibiolojia ungeweza kuzidiwa katika karibu kila uwanja, kutia ndani ubunifu wa kisayansi, hekima ya jumla, na ustadi wa kijamii.