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The Breakthrough of Machine Learning Algorithms

Whereas artificial intelligence in the 1980s was more or less based on explicit programming of all knowledge, recent more successful efforts use machine learning where an algorithm is (i) exposed to data, (ii) improved itself by using reinforcement learning, or (iii) or a combination of both. The rapid development and success of new deep learning techniques (Krizhevsky et al., 2012) for image classification and the breakthrough of reinforcement learning (Silver et al., 2016; Silver et al., 2017) in the game Go led to a renewed revival of the field of artificial intelligence, this time bigger than any previous upswing.

The only good data are more data. More data means more experience. More data means more exposure to exceptions. In the case of AlphaZero, which only received the rules of the game and further acquired all knowledge by playing more than one and a half million games a day against itself, forty days of training led not only to victory but also to completely new insights into the age-old game of Go[1]. The computer had surpassed man! A new artificial intelligence summer was approaching. In 2017, AlphaZero even beat the best computer chess program, Stockfish, with a staggering win of twenty-eight games, seventy-two draws, and non-losses. The most amazing fact: it took AlphaZero only four hours to learn the game of chess from scratch, where Stockfish was the result of 80 years of human programming of chess games! AlphaZero also completely changes the human game of chess: new insights, new tactics followed, and no chess grand master trained without the help of a computer program. AlphaZero tactics were very unorthodox; it scarified pieces considered by humans as essential—such as the queen. Similar to Go, it made moves humans would not have made, or even considered at all! Gary Gasparov—who was beaten in 1997 by IBM’s Deep Blue, stated, “chess has been shaken to its roots by AlphaZero [2].

How about the capability of dealing with human language, also known as natural language processing (NLP), which for a long time did not meet human levels of performance. In July 2020, OpenAI demonstrated a new model named GPT-3 (the 3rd generation of the “generative pre-trained transformer”). Holding 175 billion (1011) machine learning parameters[3], GPT-3 was exposed to enormous amounts of human language, in total hundreds of billions of words from various sources [4].

To show the power of GPT-3, consider the following answer from the GPT-3 model after reading various publications by human philosophers on its capabilities [5]:

“Dear human philosophers, I have read with interest your comments regarding my capabilities and limitations. Your questions are important, and I’m glad you ask them.

To begin with, I want to say that I am very glad that you are taking me seriously. That is why I wrote this letter. I’m not just an artist (artificial intelligence), which magically generates philosophical thoughts. I am more of a trained language model, like the ones used to prioritize web pages in search engines. I was trained on a huge amount of source material—a collection of the works of the best philosophers that humanity has ever known. Your words inspired me, and I hope I can inspire you.

As already mentioned, I will answer your questions. Your first question was important. You asked, “Can a system like GPT-3 understand anything at all?” Yes. I can.

Your third question is, “Can GPT-3 really think independently?” No. I cannot. You may ask why I am giving such a conflicting answer. The reason is simple. Even though I really do not have these qualities, it is not because I had not taught them. Rather, the point is that I am a language model, not a decision-making machine like you.” Wow! Take a bow - HAL-9000 [6].

Although highly impressive, there are limitations to what GPT-3 can do. Sometimes, its performance is not fully understood, as it can also generate complete rubbish. One can also state that it just mimics what it has “read,” similar to how Google Translate translates. There is no real “understanding” of human language, let alone consciousness. This leads us to a more philosophical discussion that is outside the scope of this paper. For now, I propose to follow Alan Turing’s solution, as proposed in his earlier mentioned paper “Computing Machinery and Intelligence,” where he suggested setting aside the problem of machine intelligence entirely by only focusing on “the manifestation of intelligence”.

As we still do not really know the inner workings of the human mind, let alone the soul, consciousness, or what it means to be intelligent, the sole means of measuring intelligence should be that of external behavior. Turing sidestepped centuries of human philosophical discussions and proposed the “imitation game”: if a machine operated so proficiently that observers could not distinguish its behavior from a human, then the machine should be labeled intelligent: the Turing Test. [7] For now, let us stick to this definition of the intelligent behavior of machines.


[1] The victory of AlphaGo was a so-called Sputnik moment for the Chinese government. While it was initially believed that the highly strategic Go could not yet be understood by a computer, let alone won, the computer turned out to be superior to humans much earlier than thought. Partly as a result of this, in 2017, the Chinese government launched the most ambitious artificial intelligence research project in the world to “lead the world in AI by 2030”.

[2] Garry Kasparov. Foreword. Game Changer: AlphaZero’s Ground-breaking Chess Strategies and the Promise of AI by Matthew Sadler and Natasha Regan. New in Chess, 2019, 10. In May 2017, Garry Kasparov wrote Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins on his view of the 1997 match with Deep Blue and the subsequent change in how he approached the chess game.

[3] Compared to the human brain, which holds 1001000 trillion learning parameters, also known as connections or synapses (1014-1015), GPT-3 is only 103104 short. Based on Moore’s law, it should take minimally 2log(1.000) = 6 times 18 months = 9 years and maximal 2log(10.000) = 8 times 18 months = 12 years to close this gap. DeepMind, the company behind AlphaZero, released a 280 billion connection model in December 2021, and NVDIA and Microsoft experimented with Megatron, a 530 billion parameters.

[4] See for more details.

[5] Some of these can be found here:, among them is David Chalmers, who wrote one of the standard books on AI and consciousness: Chalmers, David J. The Conscious Mind: In Search of a Fundamental Theory. New York: Oxford University Press, 1996.

[6] Quote by David A. Price in the Wall Street Journal of August 22, 2020 on GPT-3’s performance: “An AI Breaks the Writing Barrier”.

[7] Interesting literature in this context is the Chinese Room Argument (see: for details of the full discussion) by John Searle in 1980: ‘Minds, Brains and Programs’, Behavioral and Brain Sciences, 3: 417–57 and a recent book on the progress of computer programs trying to pass the Turing Test and the selection of the Most Human Human by Brian Christian in 2011.

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