IMPORTANT PEOPLE
Alan Matheson Turing
Turing played an important role in the British effort to crack the German Enigma cipher during WW2.
His research after the war influenced many of the earliest principles in computer science and artificial intelligence. One of the most notable was the “Turing test”, which questioned whether a machine could imitate a human.
Turing questioned what “thinking” really was, and wondered if machines could be ever be capable of it.
John McCarthy
In 1955, McCarthy was one of three to formally introduce the term “artificial intelligence”.
He developed LISP in 1958 — 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).
McCarthy identified human logic and ability of abstraction as skills that machines should strive to emulate.
Herb Simon
Simon helped create some of the first artificial intelligence programs ever.
One was the “Logic Theorist”, which used automated reasoning to prove mathematical theorems. Another was the “General Problem Solver”, which searched for possible actions to reach a defined goal.
Simon considered problem solving and decision making as key factors of intelligence.
Marvin Minsky
Minsky and McCarthy co-founded MIT’s AI lab in 1959.
Much of Minsky’s work involved hardware — head mounted displays and improved microscopes, for example.
This all came together in his “Society of Mind” theory, where he broke up the individual into separate components (like language and memory). Minsky argued that “intelligence” was the outcome of their interactions.
Timnit Gebru
Gebru has emerged as one of today’s leaders in artificial intelligence.
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.
One of Gebru’s current projects at the DAIR involves collaborating with researchers around the world to explore how AI has affected African immigrants in the states — hoping to thoughtfully leverage artificial intelligence for social good.
MACHINE VS HUMAN LANGUAGE
Programming languages have a smaller margin of error than human ones.
Their rules for syntax (how words are used) and semantics (what words mean) are predefined, and usually not very flexible.
There is no room for emotion, slang, or non-verbal cues in code either — all 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.
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.
Human language involves formal processes too — new words added to the dictionary, for example. Yet nothing was stopping us from saying “lol” before it was added to the Oxford in 2011…
ARTIFICIAL VS HUMAN INTELLIGENCE
Many agree that artificial intelligence mimics human thought processes like logic and reasoning. After all, one of the benefits in getting machines to “think” is their ability to process large amounts of data — much faster and more thoroughly than we can.
But volume presents an issue too when AI is so laser-focused on finding patterns and trends — it struggles when dealing with change or anything “extraordinary”…
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.
MACHINE VS HUMAN LEARNING
Machine learning is shaped by whatever data is available.
I thought a long time about this question — you could make the argument that humans have the same constraint…
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.
Having said that — and even if limited — it is our potential to independently access information and make decisions with it that sets human learning apart machine.
The machine can’t do much about bias — it might not even recognize bias, whether the source is from flawed data or the very engineers responsible for building and programming these things.
WOULD THIS PAGE PASS A TURING TEST?
I did feel — for lack of a better term — somewhat robotic throughout this assignment.
I didn’t 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…
So I controlled what I could — being 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.
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 ???).
Instead of trying to explain everything, I chose to focus on what actually made some sense to me. The theme of reflexivity — and the machine’s lack of it — served as a nice anchor.
I write in oddly short paragraphs, and I’ve been accused of using em dashes too liberally — it’s my style, even if conventions and traditions within the art of writing frown upon it.
There is no doubt in my mind that a machine could write just like me — but would it understand why it’s doing that?
REFERENCES
Alan Turing. (2022, January 26). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Alan_Turing&oldid=1068099570
Artificial intelligence. (2022, February 3). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Artificial_intelligence&oldid=1069601384
Brittanica. (2022). John McCarthy. In britannica.com. Retrieved February 3, 2022, from https://www.britannica.com/biography/John-McCarthy
General Problem Solver. (2021, March 6). In Wikipedia. https://en.wikipedia.org/w/index.php?title=General_Problem_Solver&oldid=1010719225
Gurskas, D. (2018, June 20). Computer and human languages. diessi.ca. https://diessi.ca/blog/computer-and-human-languages/#:~:text=When%20it%20comes%20to%20human,introductory%20books%20to%20Computer%20Science
Harris, A. (2018, October 31). Human languages vs. Programming languages. Medium. https://medium.com/@anaharris/human-languages-vs-programming-languages-c89410f13252
Heilweil, R. (2020, February 18). Why algorithms can be racist and sexist. Recode, Vox. https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency
Herbert A. Simon. (2022, January 28). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Herbert_A._Simon&oldid=1068450790
John McCarthy. (2022, February 2). In Wikipedia. https://en.wikipedia.org/w/index.php?title=John_McCarthy_(computer_scientist)&oldid=1069550389
Kumari, R. (2021, January 3). 7 differences between artificial intelligence and human intelligence. Analytics Steps. https://www.analyticssteps.com/blogs/7-differences-artificial-intelligence-ai-human-intelligence
Lisp (Programming Language). (2022, January 31). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Lisp_(programming_language)&oldid=1068970338
Logic Theorist. (2022, Feburary 3). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Logic_Theorist&oldid=1069745360
Machine learning. (2022, February 4). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Machine_learning&oldid=1069785162
Marvin Minsky. (2022, January 15). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Marvin_Minsky&oldid=1065870561
Society of Mind. (2022, January 8). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Society_of_Mind&oldid=1064510404
Tam, J. (2015, August 11). RIP to LOL – the history of laughing out loud. BBC News. https://www.bbc.com/news/newsbeat-33858624
Turing completeness. (2022, February 1). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Turing_completeness&oldid=1069191175
Turing test. (2022, February 3). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Turing_test&oldid=1069691794