{"id":193,"date":"2022-02-03T23:17:59","date_gmt":"2022-02-04T07:17:59","guid":{"rendered":"https:\/\/blogs.ubc.ca\/lyontsang\/?p=193"},"modified":"2022-02-03T23:17:59","modified_gmt":"2022-02-04T07:17:59","slug":"ip-2","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/lyontsang\/2022\/02\/03\/ip-2\/","title":{"rendered":"IP 2 \u2014\u00a0ARTIFICIAL INTELLIGENCE"},"content":{"rendered":"\n

IMPORTANT PEOPLE<\/strong><\/h2>\n\n\n\n

Alan Matheson Turing<\/strong><\/p>\n\n\n\n

Turing played an important role in the British effort to crack the German Enigma cipher during WW2.<\/p>\n\n\n\n

His research after the war influenced many of the earliest principles in computer science and artificial intelligence. One of the most notable was the \u201cTuring test\u201d, which questioned whether a machine could imitate a human.<\/p>\n\n\n\n

Turing questioned what \u201cthinking\u201d really was, and wondered if machines could be ever be capable of it.\u00a0<\/p>\n\n\n\n

John McCarthy<\/strong><\/p>\n\n\n\n

In 1955, McCarthy was one of three to formally introduce the term \u201cartificial intelligence\u201d. <\/p>\n\n\n\n

He developed LISP in 1958 \u2014 a programming language. LISP would eventually set a standard for code to be (1) expressive (able to deal with different functions) and (2) reflective (able to adapt and self-modify). <\/p>\n\n\n\n

McCarthy identified human logic and ability of abstraction as skills that machines should strive to emulate.\u00a0<\/p>\n\n\n\n

Herb Simon<\/strong><\/p>\n\n\n\n

Simon helped create some of the first artificial intelligence programs ever.<\/p>\n\n\n\n

One was the \u201cLogic Theorist\u201d, which used automated reasoning to prove mathematical theorems. Another was the \u201cGeneral Problem Solver\u201d, which searched for possible actions to reach a defined goal. <\/p>\n\n\n\n

Simon considered problem solving and decision making as key factors of intelligence.\u00a0<\/p>\n\n\n\n

Marvin Minsky <\/strong><\/p>\n\n\n\n

Minsky and McCarthy co-founded MIT\u2019s AI lab in 1959.<\/p>\n\n\n\n

Much of Minsky\u2019s work involved hardware \u2014 head mounted displays and improved microscopes, for example. <\/p>\n\n\n\n

This all came together in his \u201cSociety of Mind\u201d theory, where he broke up the individual into separate components (like language and memory). Minsky argued that “intelligence” was the outcome of their interactions.<\/p>\n\n\n\n

Timnit Gebru<\/strong><\/p>\n\n\n\n

Gebru has emerged as one of today\u2019s leaders in artificial intelligence. <\/p>\n\n\n\n

Her work aims not only to illuminate risks like surveillance and algorithmic biases when AI is used, but also on broader issues of diversity, ethics, and control within big tech.<\/p>\n\n\n\n

One of Gebru\u2019s current projects at the DAIR involves collaborating with researchers around the world to explore how AI has affected African immigrants in the states \u2014\u00a0hoping to thoughtfully leverage artificial intelligence for social good.\u00a0<\/p>\n\n\n\n

\n\n\n\n

MACHINE VS HUMAN LANGUAGE<\/strong><\/h2>\n\n\n\n

Programming languages have a smaller margin of error than human ones.<\/p>\n\n\n\n

Their rules for syntax (how words are used) and semantics (what words mean) are predefined, and usually not very flexible. <\/p>\n\n\n\n

There is no room for emotion, slang, or non-verbal cues in code either \u2014\u00a0all of which heavily influence human-to-human interaction. While we can express one idea in many different ways, programming operators are far less likely to overlap.\u00a0<\/p>\n\n\n\n

Both machine and human languages change over time, but the former depends on authority developers to release something new before they can come into effect. <\/p>\n\n\n\n

Human language involves formal processes too \u2014\u00a0new words added to the dictionary, for example. Yet nothing was stopping us from saying \u201clol\u201d before it was added to the Oxford in 2011<\/a>\u2026\u00a0<\/p>\n\n\n\n

\n\n\n\n

ARTIFICIAL VS HUMAN INTELLIGENCE<\/strong>
<\/h2>\n\n\n\n

Many agree that artificial intelligence mimics human thought processes like logic and reasoning. After all, one of the benefits in getting machines to \u201cthink\u201d is their ability to process large amounts of data \u2014\u00a0much faster and more thoroughly than we can. <\/p>\n\n\n\n

But volume presents an issue too when AI is so laser-focused on finding patterns and trends \u2014 it struggles when dealing with change or anything “extraordinary”… <\/p>\n\n\n\n

Of course, AI is really an application of human intelligence at the end of the day. We decide what information machines should have, we set goals for what they should be able to accomplish, and we intervene to train and adjust these systems whenever we feel the need to.\u00a0<\/p>\n\n\n\n

\n\n\n\n

MACHINE VS HUMAN<\/strong> LEARNING<\/h2>\n\n\n\n

Machine learning is shaped by whatever data is available.<\/p>\n\n\n\n

I thought a long time about this question \u2014\u00a0you could make the argument that humans have the same constraint… <\/p>\n\n\n\n

Even though the internet gives us access to all the information in the world, we really only learn (1) what we’re taught \/ told and (2) what we think we’re interested in and willing to make an effort for. <\/p>\n\n\n\n

Having said that \u2014\u00a0and even if limited \u2014\u00a0it is our potential to independently access information and make decisions with it that sets human learning apart machine.<\/p>\n\n\n\n

The machine can’t do much about bias \u2014\u00a0it might not even recognize bias, whether the source is from flawed data or the very engineers responsible for building and programming these things.<\/p>\n\n\n\n

\n\n\n\n

WOULD THIS PAGE PASS A TURING TEST?<\/strong><\/h2>\n\n\n\n

I did feel \u2014 for lack of a better term \u2014\u00a0somewhat robotic throughout this assignment.<\/p>\n\n\n\n

I didn\u2019t know any of the “important people” for example, except for a tiny bit about Turing. I was doomed to simply repeat what others have already said about these individuals… <\/p>\n\n\n\n

So I controlled what I could \u2014\u00a0being selective with what to share, and being intentional with how I did it. If you revisit my summaries of the big players, you’ll notice they all include (1) a general fact, (2) a notable contribution in the field, and (3) how “intelligence” manifested in their work.<\/p>\n\n\n\n

During the other questions, I found myself wandering through the web for answers. Yet the more I read, the more I didn’t understand (Turing completeness<\/a> ???). <\/p>\n\n\n\n

Instead of trying to explain everything, I chose to focus on what actually made some sense to me. The theme of reflexivity \u2014\u00a0and the machine’s lack of it \u2014\u00a0served as a nice anchor. <\/p>\n\n\n\n

I write in oddly short paragraphs, and I’ve been accused of using em dashes too liberally \u2014\u00a0it’s my style, even if conventions and traditions within the art of writing frown upon it. <\/p>\n\n\n\n

There is no doubt in my mind that a machine could write just like me \u2014\u00a0but would it understand why it’s doing that?<\/p>\n\n\n\n

\n\n\n\n

REFERENCES<\/h2>\n\n\n\n

Alan Turing. (2022, January 26). In Wikipedi<\/em>a. https:\/\/en.wikipedia.org\/w\/index.php?title=Alan_Turing&oldid=1068099570<\/a> <\/strong> <\/p>\n\n\n\n

Artificial intelligence. (2022, February 3). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Artificial_intelligence&oldid=1069601384<\/a><\/strong><\/p>\n\n\n\n

Brittanica. (2022). John McCarthy. In britannica.com.<\/em> Retrieved February 3, 2022, from https:\/\/www.britannica.com\/biography\/John-McCarthy<\/a><\/strong><\/p>\n\n\n\n

General Problem Solver. (2021, March 6). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=General_Problem_Solver&oldid=1010719225<\/a><\/strong><\/p>\n\n\n\n

Gurskas, D. (2018, June 20). Computer and human languages. diessi.ca<\/em>. https:\/\/diessi.ca\/blog\/computer-and-human-languages\/#:~:text=When%20it%20comes%20to%20human,introductory%20books%20to%20Computer%20Science<\/a><\/strong><\/p>\n\n\n\n

Harris, A. (2018, October 31). Human languages vs. Programming languages. Medium<\/em>. https:\/\/medium.com\/@anaharris\/human-languages-vs-programming-languages-c89410f13252<\/a><\/strong><\/p>\n\n\n\n

Heilweil, R. (2020, February 18). Why algorithms can be racist and sexist. Recode, Vox<\/em>. https:\/\/www.vox.com\/recode\/2020\/2\/18\/21121286\/algorithms-bias-discrimination-facial-recognition-transparency<\/a><\/strong><\/p>\n\n\n\n

Herbert A. Simon. (2022, January 28). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Herbert_A._Simon&oldid=1068450790<\/a><\/strong><\/p>\n\n\n\n

John McCarthy. (2022, February 2). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=John_McCarthy_(computer_scientist)&oldid=1069550389<\/a><\/strong> <\/p>\n\n\n\n

Kumari, R. (2021, January 3). 7 differences between artificial intelligence and human intelligence. Analytics Steps<\/em>. https:\/\/www.analyticssteps.com\/blogs\/7-differences-artificial-intelligence-ai-human-intelligence<\/a><\/strong><\/p>\n\n\n\n

Lisp (Programming Language). (2022, January 31). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Lisp_(programming_language)&oldid=1068970338<\/a><\/strong><\/p>\n\n\n\n

Logic Theorist. (2022, Feburary 3). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Logic_Theorist&oldid=1069745360<\/a><\/strong><\/p>\n\n\n\n

Machine learning. (2022, February 4). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Machine_learning&oldid=1069785162<\/a><\/strong><\/p>\n\n\n\n

Marvin Minsky. (2022, January 15). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Marvin_Minsky&oldid=1065870561<\/a><\/strong><\/p>\n\n\n\n

Society of Mind. (2022, January 8). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Society_of_Mind&oldid=1064510404<\/a><\/strong><\/p>\n\n\n\n

Tam, J. (2015, August 11). RIP to LOL – the history of laughing out loud. BBC News<\/em>. https:\/\/www.bbc.com\/news\/newsbeat-33858624<\/a><\/strong><\/p>\n\n\n\n

Turing completeness. (2022, February 1). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Turing_completeness&oldid=1069191175<\/a><\/strong><\/p>\n\n\n\n

Turing test. (2022, February 3). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/w\/index.php?title=Turing_test&oldid=1069691794<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

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