How AI and humans think?
How AI and humans think?
From the mechanism of AI, we may reflect on how humans think. New AIs, such as ChatGPT, are GPT (Generative Pre-trained Transformer). What are transformers?
Transformer is a relatively new concept since 2017. Before the concept of transformer, AI process languages one word after another. With transformer, AI processes many words simultaneously. Suppose we analyse sentences like: I eat a sweet apple. With transformer, AI will not only calculate the statistical correlation between sweet and apple, which are adjacent, but also the correlation between eat and apple, eat and sweet, eat and I. In essence, AI calculates the correlation matrices of many words. AI are trained with the massive amount of language and other digital material. The calculations of correlation matrices, especially for long texts, are extremely computationally intensive. That is why computational power from GPU is in great demand.
The current AI information processing is very similar to how humans process information. There is a Chinese idiom called 一目十行 (to read ten lines at a single glance). We don’t read word by word. Instead, we scan many lines simultaneously.
AI understand texts from probability relations, with massive amount of earlier training on real texts. AI don’t understand texts by grammatical analysis of individual sentences. This is also how we learn. We learn to speak and write from talking to many people and reading many books, not from classroom teaching of grammatical structures, paragraph structures, and …
Earlier AI, such as Deep Blue from IBM, were based on logic deduction. Newer AI, such as AlphaGo, are based on statistical inference. How do they differ from each other? Deep Blue beats the best human chess player. AlphaGo beats the best human go player. The size of the chess board is 8*8 squares. The size of go board is 19*19 intersections. Suppose we use Deep Blue to play go. It will be overwhelmed by the sheer number of possible moves. AlphaGo does not exhaust all possible moves. Instead, it will assess the situation long before a game ends. It makes assessment based on learning. Suppose a similar situation occurred before and one side eventually won. AlphaGo will assume the situation is advantageous to one side. AlphaZero, a version of AlphaGo, was trained with 21 million games played against itself in three days.
On complex issues, possibilities based on logic deduction can be overwhelming. For example, the game go has roughly 10^170 possible legal moves. This will exhaust any supercomputer. Statistical inference is more manageable. You don’t need to examine all possibilities. You only need to perform better statistical inference than the best human go player. This is relatively easy. After all, a supercomputer consumes megawatts of energy, and a human brain only consumes about 20 watts of energy. Human brain is tasked with many jobs in manage internal physiology and compete for external resources. Human brain is not optimized for game playing. AlphaGo, on the other hand, is optimized for the single task of playing go.
AI built on statistical inference can be less computationally intensive than those built on logic deduction, especially on complex issues. As a result, these new AI can be applied to much broader areas. But statistical inferences may differ from logic and facts. How do AI handle this problem? I asked Gemini. Gemini uses DeepSeek as an example. It stated that on many issues, DeepSeek has to abandon logic and facts to align its stance with the authority. Gemini singles out DeepSeek, an AI from the opponent’s camp. What about other AIs, including Gemini itself?
From the mechanism of AI, I gain a deeper understanding how humans process information. We may rely on logic. But more often, we rely more on statistical inference, which is simpler. On important issues, the interest of the ruling elite may diverge from logic and facts. Very often, the ruling elites would devote great amount of resources to induce ideas and opinions in their favor. As a result, authoritative opinions often diverge from logic and facts. Usually, logic is not attacked upfront. Instead, layers after layers of complexity are added to the original questions, making people difficult to understand. As a result, the public have to rely on statistical inference from opinions of the experts, who are certified and paid by the ruling institutions.
Take the theory of relativity as an example. The original problem of Lorentz transformation was very simple. It was logically inconsistent. Instead of discussing the original problem, which is simple, experts have created ever more complex scenarios to confuse people. More importantly, only those supporting the theory of relativity can get their papers published and become experts. Over time, all experts support relativity. Those with different opinions, unable to get academic posts, are reduced to amateurs. Few people go over the details of the relativity theory. Yet most people are convinced that relativity theory is correct. Humans process information, especially difficult information, not from their own investigation, but from statistical inference. If most authorities agree on something, you naturally conclude it is correct.
A more recent example is the research on climate change. Authorities have long claimed that the science of climate change has settled. If so, why do governments keep pouring money into the research on climate change in a massive scale? By funding more people who will toe the official line, the statistical inference on climate catastrophe will get stronger over time. Instead of a consensus of 80%, it will climb to 90% or even 95%. With increasing consensus from scientific community, more people will be certain that climate catastrophe is imminent. A small number of people seeking truth will be increasingly labeled as “a fringe minority with unacceptable ideas”.
