The History of AI (What Is Artificial Intelligence)

You like milk with flavor straws, magic straws, flavors straws. Well here I am. The thing with almost human brain, Elektro the robot. You asked for it. I am Elektro, mightiest of all robots. Artificial intelligence has been a topic of growing prominence in the media and mainstream culture since 2015, as well as in the investment world, with companies that even mention the word in their business model, gaining massive amounts of funding. While to many, the hype around AI may appear sudden, the concepts of modern artificial intelligence have been around for over a century and extending further, the concept of artificial intelligence and artificial beings have been in the minds of humans for thousands of years. To better understand and appreciate this technology and those who brought it to us as well as to gain insight into where it will take us: sit back, relax and join me in an exploration on the history of artificial intelligence. [Music] Since at least the times of Ancient Greece, mechanical men and artificial beings have been dreamt about, such as a Greek myths of Hephaestus, the Greek god of smithing, and his designs of mechanical men and other autonomous machines. Progressing forward toward the Middle Ages and away from fables and myths of ancient times, realistic humanoid automatons and other self-operating machines were built by craftsmen from various civilizations. Some of the more prominently known are of Ismail Al- Jazari of the Turkish Artuqid Dynasty in 1206 and Leonardo Da Vinci in the 1500s. Al-Jazari designed what is believed to be the first programmable humanoid robots, a boat carrying 4 mechanical musicians, powered by the flow of water and Da Vinci of his various mechanical inventions, built a knight automaton that could wave its arm and
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move its mouth. Moving forward to the 1600s, brilliant philosophers and mathematicians, Thomas Hobbes, Gottfried Leibniz and Rene Descartes believed in the concept that all rational thought could be made as symmetric as algebra or geometry. This concept was originally birthed by Aristotle in the 4th century, referred to as syllogistic logic, where a conclusion is drawn based on 2 or more propositions. As Thomas Hobbes stated in his book, Leviathan, “When a man [reasons], he does nothing else but conceive a sum total, from addition of parcels; or conceive a remainder, from subtraction of one sum from another… these operations are not incident to numbers only, but to all manner of things that can be added together and taken one out of another…the logicians teach the same in consequence of words; adding together two [words] to make an affirmation, and two affirmations to make a syllogism; and many syllogisms to make a demonstration. Leibniz took Hobbes philosophies a step further and laid the foundations for the ‘language’ machines communicate in today, binary. His motivation for doing so was because he realized that mathematical computing processes could be done much easier in a number encoding with less digits. Descartes examined the concept of, ‘thinking machines’, and even proposed a test to determine intelligence, in his 1637 book, Discourse on the Method, where Descartes famously stated the line, “I think therefore I am”, he also stated in that book, “If there were machines that bore a resemblance to our bodies and imitated our actions as closely as possible…we should still have two very certain means of recognizing that they [are] not real humans. The first is that…such a machine should produce arrangements of words as to give an appropriately meaningful answer to whatever is said in its presence. Secondly, even though some machines might do things as well as we do them, or perhaps even better, they would inevitably fail in others, which would reveal that they are not acting from understanding. Also, in the 1600s and throughout the Middle Ages, on the other side of the spectrum, entertainment and spirituality, growing from Greek myth, the concept of artificial beings continued to be explored, such as in fields like ancient chemistry, in other words, alchemy, which was more of a pseudoscience, with the goal to transform the pure into the rare, transforming mind into matter. Countless stories during this time-period also portray this concept, such as the Golem in Jewish folklore, which is a being created from inanimate matter. Progressing forward, we see this trope again in stories such as Frankenstein, first published in 1818, with a being reanimated from inanimate flesh. After the height of the first Industrial Revolution in the mid-1800s, where machines began replacing human muscle and the beginnings of the field of modern computing, we see these stories take a turn towards modern sci-fi elements and portraying technology as evolving into human form. Take for example this clip from the silent film, Metropolis. [Music] [Music] [Music] The field of modern computing was officially born with Charles Babbage’s mechanical analytical engine in the 1840s. Although it was never built due to a variety of reasons, rebuilding of his designs in present day show that they would have worked. This then means that Ada Lovelace was the worlds first programmer, with her algorithm on calculating Bernoulli numbers on Babbage’s machine. Early computers had to be hardcoded to solve problems and Lovelace being the first programmer had serious doubts on the feasibility of artificial intelligence, nearly 200 years after Descartes she shared similar sentiments stating about the analytical engine, “It has no pretentions whatsoever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths”. This is referred to as Lovelace’s Objection. As a side note, be sure to check my video on the, History of Computing, if you want more background knowledge on the evolution of the field of computing. Back on topic, a decade after Babbage’s analytical engine, in the 1850s, George Boole, an English mathematician and philosopher, revolutionized the field of computing and laid the first true steps for a computing based artificial intelligence. Boole like those before him also believed human thinking could be mastered by laws described by the means of mathematics. He took the principles of syllogistic reasoning from Aristotle and expanded much deeper on the relationship between logic and math that Leibniz had set, thus resulting in the birth of Boolean logic, essentially replacing multiplication with AND, and, addition with OR, with the output being either TRUE or FALSE. This abstraction of logic by Boole was the first step in giving computers reasoning ability, this because as the field of computing evolved, a number of researchers noticed that binary numbers, 1 and 0, in conjunction with Boolean logic, TRUE and FALSE, could be used to analyze electrical switching circuits. This is referred to as combinational logic, in other words, logic ‘gates’ that output a resultant based on their inputs. There are a variety of different types of gates: AND, OR, XOR, NOT, etc, and as the connections between different gates became more complex, led to the design of electronic computers. Combinational logic is the first layer in automaton theory, in other words, the study of abstract and self-operating machines. As computing evolved, additional layers began to be established, with the next one being, finite state machines. These machines essentially blackbox sets of logic gates, and use logic between the black-boxes to trigger more complex events. For an illustrative example of a type of state machine, think of an oven that has three states: off, heating and idle. In state diagrams we can illustrate state transitions and the values that will trigger them, for example, the on and off button presses of the oven, the oven being too hot, the oven being too cold, etc. The next layer in automaton theory is pushdown automaton, in other words, machines with memory which was pioneered by many individuals such as William Eccles and Frank William Jordan, who invented the first circuits capable of memory, flip-flops, and John von Neumann who abstracted the relationship of memory in a computing system. Finally, the last layer of automaton theory and the class of machines we use today is, Turing Machines. Before continuing I want to point out that this was an extremely simplistic overview of a subset of automaton theory, and to definitely research with other sources for a more in-depth overview. The final layer of automaton theory was based on a mathematical model of computation that Alan Turing proposed in 1936, dubbed the Universal Turing Machine. Once again like those before him, Turing broke down logic into a mathematical form, in this case, translating it to machine that reasons through abstract symbol manipulation, much like the symbolic reasoning done in our minds. As stated earlier, early computing devices were hard-coded to solve problems, Turing’s belief with his universal computer was instead of deciding what a computer should do when you build it, design a computer in such a way that it can compute anything that is computable, so long as it is given the right instructions. This concept is the basis of modern computing. At this point in the 1930s, with the field of modern computing officially born and rapidly evolving, the concept of artificial beings and intelligence based on computing technology began permeating across mainstream society of that time. The first popular display of this was Elektro, the nickname of a humanoid robot built by the Westinghouse Electric Corporation and shown at the 1939 New York World Fair: Ladies and Gentlemen, I’ll be very glad to tell my story. I am a smart fellow as I have a very fine brain. Elektro wowed many and one can say is the basis of how mainstream society thinks of a computing based artificial intelligence, as evident by the various movies, TV shows, books and other entertainment media portraying the concept. As a side note, Westinghouse’s Elektro draws many parallels to modern-day Hanson Robotics Sophia. They are not truly intelligent but are more of a way for mainstream society to get a glimpse of future technology, in other words, they’re imitating intelligence. Going back to Alan Turing in the 1950s, he pondered this dilemma of true versus imitated intelligence in section 1 of his paper, Computing Machinery and Intelligence, titled, The Imitation Game. In this paper he lays the foundations for what we now refer to as the, Turing Test, the first serious proposal in the philosophy of a computing based artificial intelligence. The Turing Test essentially states, if a machine acts as intelligently as a human being then it is as intelligent as a human being. An example often thrown around is the online chatroom, in which if we are talking to an AI bot but aren’t told this until after, and believed during the conversation that it was a human, then the bot passes the Turing Test and is deemed intelligent. Around the same time as Turing’s proposal, another titan of the field of computing, the father of the information age, Claude Shannon, published the basis of information theory, in his landmark paper, A Mathematical Theory of Communication, in 1948. Information theory is the backbone of all digital systems today and a very complex topic, in layman’s terms and in relation to computing, Shannon’s theory states, all information in the entire universe could be represented in binary. This has profound implications for artificial intelligence, meaning we could break down human logic and more so the human brain and replicate its processes with computing technology. This fact was demonstrated a few years later in 1955 by what is dubbed as the first artificial intelligence program, called, Logic Theorist. A program able to prove 38 of the first 52 theorems in Principia Mathematica, a three-volume work on the foundations of mathematics. This program was written by Alan Newell, Herbert Simon and Cliff Shaw, who like philosophers and mathematicians before them also believed human thought could be broken down, with them stating, “the mind can be viewed as a device operating on bits of information according to formal rules”. That being they realized that a machine that can manipulate numbers could also manipulate symbols and that symbol manipulation is the essence of human thought. As a fun side note, Herbert Simon stated, ‘a system composed of matter can have the properties of mind’, a throwback to alchemy of the Middle Ages, in which matter was attempted to be converted to mind. Also, during this time-period, in 1951, Marvin Minsky, one of the founding fathers of the field of artificial intelligence, built the first machine incorporating a neural net, the stochastic neural analog reinforcement calculator, SNARC, for short. As you can see, at this point in the mid-1900s, with computers becoming more capable every year, increasing research into abstracting and human logic and behavior, development of the first neural net and various other innovations – the field of modern computing based artificial intelligence was being born! We’ll cover the official birth of AI leading to present-day in the next video in this AI series, however, that doesn’t mean you have to wait to learn more! If you want to learn more about artificial intelligence and neural networks, and I mean really learn how they work, from their foundational building blocks, perceptrons, to more advanced architectures, then is the place for you to go! My primary goal with this channel is to inspire and educate about the various technologies and innovations that are changing the world, but to do so on a higher-level requires going a step beyond these videos and actually learning the mathematics and science beyond the concepts I discuss. Brilliant does this by making math and science learning exciting and cultivates curiosity by showing the interconnectedness between a variety of different topics. To support Singularity Prosperity and learn more about Brilliant, go to and sign up for free! Additionally, the first 200 people that go to that link will get 20% off their annual premium subscription! At this point the video has come to a conclusion, I’d like to thank you for taking the time to watch it! If you enjoyed it, consider supporting me on Patreon to keep this channel growing, and if you have any topic suggestions, please leave them in the comments below! Consider subscribing for more content, follow my Medium publication for accompanying blogs and like my Facebook page for more bite-sized chunks of content. This has been Ankur, you’ve been watching Singularity Prosperity, and I’ll see you again soon! [Music]

Comments 60

  • Become a YouTube member for many exclusive perks from early previews, bonus content, shoutouts and more! – AND – Join our Discord server for much better community discussions! – ALSO – This video was made possible by Brilliant. Be one of the first 200 people to sign up with this link and get 20% off your premium subscription with Brilliant!

  • Amazing content as always

  • As always, love the video. Keep it up.

  • Awesome as always! Thank you

  • what movie is that robot from @ 9:00 ?

  • "Looking inside the brain for consciousness is like looking inside a radio for the announcer." Nassim Haramein

  • Top quality content right here.

  • 666th viewer

  • Please make a video about Ray Kurzweil and his prediction.

  • Wah! I feel stupid now.

  • Isn't it funny that us as human beings Try to mimic what the brain does so that we can make AI. That should be a clear indication that God the universal architect is real. While we sit here and try to figure all of this out people have the audacity to say that life was a spontaneous coincidence

  • Perfect sound. Thank you for fixing it. 👍👍

    Great content, as always…

  • This is the only channel which has the best content and covers everything from the ground. And he has always done his proper research before making any videos. Hats off for the dedication and quality content.

  • A computer that can prove a theorem and a computer that can pose a theorem would be very different things – if the latter can ever actually be produced, that is. 😛

  • Don't forget to smash the likes guys.

  • I love to see other people who like AI, I have some AI demo's on my channel, let me know if you'd like to see tutorials.

  • hi, this video should be called as "how people try to create artificial intelligence", which has not yet been created

  • Inb4 100K and 1Mil subscribers!

  • Common misconception that alchemy is about the transmutation of lead into gold. You touched upon this, but the real foundation of alchemy is spiritual in basis. Its about working through the animistic side of humanity and expanding your consciousness in a way that breaks free of the physical world and purifies your soul into so called gold. In a ironic twist, because of human nature, greed took over, and changed the foundation from a spirit based one into a physical and greed based one, trying to maximize a person's physical wealth on this planet, which is the exact opposite goal of alchemy in the first place

  • Hi, I just stumbled upon your video and wanted to say that the information that you condensed and explained was really awesome! If you wanted to make your video even better though, I'd recommend working on vocal inflection a bit more – your voice can come across as a bit monotone at times. But all and all, great research and great content!

  • Excellent video as ever. I just wanna say about the part at 7:10 for viewers not into automata theory.
    I wouldn't include Combinational Logic into the hierarchy but i would add Linear Bound Automatons between Pushdown Automatons and Turing Machines in accordance with the Chomsky hierarchy of formal grammars.
    So in the context of digital logic there are Combinational circuits (having only logic elements (logic gates)) and Sequential circuits (having logic and memory elements (logic gates and flip-flops)).
    In context of Automata theory,which is closely related to Formal language theory:
    Type 0 – "Unrestricted" grammars produce "Recursively enumerable" languages recognized by Turing machines
    Type 1 – "Context-sensitive" grammars produce "Context-sensitive" languages recognized by Linear bound Automatons
    Type 2 – "Context-free" grammars produce "Context-free" languages recognized by Pushdown Automatons
    Type 3 – "Regular" grammars produce "Regular" languages recognized by Finite State Automata.
    (not defined originally: between types 0 and 1 a grammar which produces "Recursive" languages which are recognized by Turing machines that always halt)
    Again still a very simplified version but I think it's much better to connect it with formal languages than digital logic 😀

    P.S. hierarchy types are all proper subsets (i.e. all regulars are CF,all CF are CS,all CS are R,all R are RE…but the reverse is not true) with type 3 having least expressive power and type 0 having the most.
    P.P.S. Sequential circuits are equivalent to Finite State Automata

  • OMG is this channel going to blow up!

  • Keep up the good work

  • You will succeed Ankur keep it is starting to appreciate your content.. i bet within 9 months this channel is going to have at least 300k subscribers… just don't stop..

  • Your editing is solid man! Were you self taught? Do you use Premiere?

  • I found out about your channel because my electrical engineering professor showed us one of your videos during lecture. I must say, you have pretty amazing content and I hope you keep doing what you're doing!

  • Wish i could have a way to give you money easy. I will do it, like a button or a click

  • Thank you Singularity Prosperity for being what YouTube needs more of, an educational and entertaining channel! Keep up the amazing work and thank you for inspiring and motivating me in my own YouTube journey!

  • Gottfried Wilhelm Leibniz. Here is how you pronounce his surname –

  • I think i just discovered the most underrated channel on youtube.

  • i found a comment by you on some video…
    i can tell that more quality content will be coming…

  • at the beggining, elektro the robot sounds likes electro the rabot

  • Thanks for adding my music 🙂

  • I wonder when we are goin to get a new video I been looking at old vids lately

  • I've seen 2 of your videos, and I got one complaint: you're speaking too fast, slow down a little 🙂

  • So. thank you.

  • You provide a most useful format here to introduce child brains like mine to a complex reality. Thank you.

  • i saw your comment on vsauce and subbed , your channel is interesting

  • Pascal came up with the first working mechanical calculator. Leonardo Da Vinci drew the first mechanical calculator.

  • these videos basically stress that A.I. comes out of symbolic logic. This is correct; but, A.I. and their researchers are blinded by the symbolic logic and wonder why they can't get A.I that understands context.

    They think because they proved theorems from Whitehead and Bertrand Russel's "Principia" that they are able to do mathematics; but, this misses the point of the difference between calculators and mathematicians who can conceive of new mathematical theories.

    I should be keeping this to myself – but A.I. researchers are working from from a polished higher level programming language(already created mathematics), and not understanding the nature and origins of mathematics. Jacob Bronowski solved all this in his Origins of Knowledge and Imagination."

  • hey, love your videos. I’ve been watching them for a while now. please forgive me for the unsolicited pointers, but… although you have improved your enunciation, I think there’s still room for improvement. Slow down a bit, speak louder and more clearly, and try to add more intonation just so it doesn’t sound too flat. The subjects you cover are super cool and they pick the interest of people from all around the world. In my case, English is not my first language and although it’s the only language I’ve used for the past 15 years after moving to England, when watching your videos I struggle to understand and stay focused, and I often find myself going back seconds, sometimes minutes. I know, captions would help, but I would rather keep my eyes on the animations and everything else you put on the screen. 😉

  • You should make a video on cybernetics

  • Your Leibniz pronunciation is wrong.

  • What is that video clip on the left @9:38?

    Right = Westworld

  • Cool to see you used my instrumental. I feel honored being part of this video!!

  • That Golem doe

  • "Such a machine should produce arrangements of words as to give an appropriately meaningful answer to whatever is said in its presence."

    Holy crap, that's the Turing Test.

  • What is that video clip on the left @9:38?

    Right = Westworld

  • Keep it up, need more content!

  • I think therefore I am. Sounds legit.

  • Amazing! thanks a lot! I'm eager to watch more videos like this.

  • Hitted liked and subs….. Great vids keep it up.

  • very good

  • I can't be the only one thinking Computer Science IS AI after seeing this video. (Note I didn't say AI is a subfield of CS. I actually mean CS, in it's entirety, culminates into AI)

  • Out of ordinary. excellence. quick.

  • I had no idea that the concept of artificial computing/intelligence went as far as Descartes

  • The website is all Quiz, I didn't know where to start 😒

  • I'll Be Back!!!

  • AI is the larger set here, and all other related terms are its subsets. For instance, machine learning is a way to achieve artificial intelligence. Deep learning is a way to achieve machine learning.To enable you to understand the history of AI, this guide has been structured in the following sections:

    Artificial Intelligence: A Notion (pre-1950s)

    Artificial Intelligence: The Realms of Reality (1950 – 1960)

    The First Summer of AI (1956 – 1973)

    The First Winter of AI(1974 – 1980)

    The Second Summer of AI (1981- 1987)

    The Status Quo (1993 – 2011)

    The Renaissance (Since 2011)

    AI – Today and Beyond

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