Whose Idea Was AI? Here’s The Full History of AI!
Welcome back to our corner of the internet!
Today we’ll be taking a brief look at the history of AI! There are 10 main turning points in the history of this revolutionary technology. Let’s begin!
Note: There are innumerable feats in the field of AI, and I have selected ones which I personally believe are major turning points. I hope to inspire some curiosity into you after you have read this article.
1950 — The Turing Test
Alan Turing, a man well-known for being the mastermind behind the Enigma, proposed a thing called the Turing Test in 1950. The Turing Test was actually originally known as “the Imitation Game” as per the 2014 movie, which you may have heard of.
The “test” is actually a three player game where a computer or machine attempts to fool a human interrogator into thinking that it is a human. It is in essence a high level inquiry to test whether a machine is able to perfectly replicate human intelligence or not.
The test is known for asking questions resembling something of this sort: “Describe why time flies like an arrow but fruit flies like a banana?”.
“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.”
— Alan Turing
1955 — Artificial Intelligence?
The term “Artificial Intelligence” (or later abbreviated to simply AI as we refer to it on this blog) was officially coined in 1955 thanks to John McCarthy, Nathaniel Rochester, Marvin Minsky and Claude Shannon (two of which came from Ivy League schools and one of which came from IBM), who submitted a proposal for a two month experience which required 10 people, and explored further down the rabbit hole of AI.
August 31, 1955 (the date of the proposal’s submission) is actually now the officially acknowledged birth date of Artificial Intelligence as a field of study.
Despite all of those names listed above being reputable, John McCarthy is the known “father of AI” and is famously the prime creator of the term Artificial Intelligence. McCarthy later went on to create the programming language LISP (which was primarily used for advancements in AI) in 1958 as well.
Marvin Minsky went on successfully as well, writing around 7 reputable books on Turing’s theories and AI in Computer Science. Before his “debut” with the proposal group, Nathaniel Rochester had worked at IBM as chief architect of the IBM 701, the first mass produced scientific computer. Claude Shannon was also known as the “father of Information Theory”, also leading the industry when it came to development of digital circuits.
1964 — ELIZA
ELIZA was an interesting feat in the field of AI, which introduced people of the past to what could have been like their ChatGPT in many ways! It used NLP to function, and was created from 1964–1966 by Joseph Weizenbaum, a German-American computer scientist and professor at the Massachusetts Institute of Technology (abbreviated to simply MIT). ELIZA was invented to feel human, and this effect was achieved — even reportedly scaring Weizenbaum himself! ELIZA was also an early contestant in the Turing Test. ELIZA didn’t exactly pass the entire thing, she passed a restricted part of machine learning in the test. She was a firm contestant whose results are still compared to AI today!
ELIZA functioned through looking for specific keywords in typed comments to transform them into sentences.
1965 — DENDRAL
At Stanford University, a collaborative effort involving Edward Feigenbaum, Bruce G. Buchanan, Joshua Lederberg, and Carl Djerassi created DENDRAL. This project marked the beginning of expert systems and bore the significant distinction of being the first of its kind. Tasked with automating the cognitive processes of organic chemists, DENDRAL aimed not only to streamline decision-making but also to delve into the realm of the scientific method (which is a topic even schools cover).
DENDRAL’s primary focus lay on the concept of empirical induction. Empirical induction is the process of linking principles with observations. In organic chemistry, this could be looking at the outcome of a chemical reaction and then learning and linking principles to it. By harnessing the power of technology to emulate this reasoning and method, DENDRAL aimed to revolutionize the way chemists approach problem-solving, which significantly contributed to AI’s connection with scientific inquiry in the tech industry.
1974–1980 — AI Winter
AI Winter is a general space in time (which is constantly repeating) where interest in AI publicly calms down thanks to lack of funding, and little to no public attention. 1974–1980 marked the first significant AI Winter, spanning 6 years! As a result of this “hibernation of AI” very few technologies in the field advance and little is learnt. Most initiatives and proposals (like the one we mentioned before) died down and research was temporarily paused.
In a time like this, the opinion of the general public on AI was something on the lines of “where is this going in the long run?” or “massively overhyped, complex tech” or even “going nowhere”.
The next AI Winter was around the 1990s, meaning that if they happened chronologically every 10 years, we’re well overdue for one.
Stefan Haas, another Medium writer, reckons that another AI Winter is coming for us, and that “it has happened before and will happen again — very soon”.
Haas also thinks that if no major paradigm shift occurs in the industry, there’s nothing to prevent the next AI Winter coming soon.
1997 — Computer Chess Player vs Human Chess Champion
It’s a competition that’s been hyped for a very long time, even over 26 years later — the 1997 match which promised a game between IBM’s Deep Blue Computer versus global chess champion Gary Kasaparov. However, this was barely a surprise after the stats were revealed. IBM’s Deep Blue processed around 100–200 million chess moves per second. It also ran IBM’s AIX Operating System, with a multitude of chess chips which ran 2 million moves per second.
This matter was soon deemed a major turning point — the first globally known machine-victory-over-man scenario.
However, Deep Blue did in fact lose to Kasaparov in 1989. So could this truly be deemed a major victory?
1999 — The First Robotic Dog
You’ve probably heard of Spot, Boston Dynamics’ prized robotic dog which is known for its potential aid in the military, and its ability to move with great flexibility. But Sony was the first to enter this era, with Aibo (or 相棒 in Japanese).
It is still available on the market today, with updated generations having been released in recent years. Apparently it’s very expensive due to the high level of artificial intelligence that goes behind it.
An apparent use of this impressive artificial intelligence is its reaction to human gestures that are usually used on dogs. For example, upon rubbing its stomach it will bark or show an expression of joy or happiness.
Its personality and behaviour also changes over time according to how you treat it, which is a clever and entertaining way of showing its pet-likeness.
Watch different teams of Aibo Robotic Dogs play soccer/football here.
2011 — Siri
Now you can see that we’ve taken a significant stride into the future, skipping all the way to the famous announcement of Siri, the iOS virtual assistant powered by AI that was created by Apple.
Nowadays, Siri comes in all different forms, such as in a HomePod or built into you Mac, iPad or iPhone. It used what was then revolutionary technology known as Conversational AI to replicate human responses to your questions or commands.
Siri uses advanced Machine Learning technologies and Natural Language Processing to function in the way that we know today. Whilst Wildfire was a similar idea formed in 2001 (an entire decade earlier), Siri was a more advanced, successful and well-known attempt.
June 2014 — GANs Are Created
The idea of Generative Adversarial Networks (GANs) was first created by Ian Goodfellow and his team of colleagues in June of 2014. A General Adversarial Network is a special type of computer system made up of two neural networks. These networks act like opponents in a game, where they try to outdo each other. It’s a bit like a competition where when one side wins, the other side loses.
Imagine you’re drawing pictures and you want to get better at it. In this case, one of the neural networks would be like your drawing teacher. It tries to make pictures that look like real ones. The other network would be like a detective who tries to tell if a picture is drawn by you or your teacher. This detective network helps make sure the pictures your teacher creates are sufficient.
But here’s the twist: your teacher and the detective are in a contest. Your teacher tries to draw pictures so good that the detective can’t tell if they’re real or not. And the detective tries to get better at figuring out which pictures are real and which ones are drawn by your teacher. This contest pushes both sides to improve their skills, just like how players in a game get better when they compete with tough opponents. So in essence, that’s what a GAN is, and they’re beyond important to the computer science industry!
November 2014 — Alexa
Yup — we’re inching towards the modern day! You’ve probably heard of Alexa, right? Well Alexa, the advanced AI voice assistant from Amazon, was introduced in November 2014, (obviously a bit later than Siri).
The delay was due to the complexity of creating a system that could not only understand spoken language but also respond intelligently using Conversational AI (again, like Siri). This required extensive research and development to ensure that Alexa could offer meaningful interactions and useful responses.
Despite the delay, Alexa’s introduction marked a significant step forward in AI technology, bringing a new level of voice-based interaction to our homes.
Today, similarly to Siri, Alexa comes in a multitude of forms, like in the classic Echo Dot, with an HD Smart Display and more!
Do you think Alexa or Siri is better? Comment below!
Note: Some information collected in this article is debated across the internet. This includes dates, times and specific reasoning behind things. I’ve tried to collect a gathered opinion, but I wanted to let my readers know this afterwards. Thank you.
I appreciate that this article is quite a bit longer than my others, but I wanted to touch upon each of the 10 turning points.
I hope you enjoyed and learned something from this one (leave a few claps, a follow and a comment if you did).
See you in the next one, thank you!
