ChatGPT seems to know programming languages like Python and C very well. There's far less training data for Mathematica on the internet, and it shows. For instance: "Make a song in mathematica" yields

Here is a simple example of how you can generate a song in Mathematica: (* Define a simple melody as a list of MIDI notes *) melody = {60, 62, 64, 65, 67, 69, 71, 72}; (* Convert the melody to sound using the built-in sound synthesis *) sound = Sound[SoundNote[#, 1/8] & /@ melody]; (* Play the sound *) Play[sound]

Wrong in more ways than one. "Make a song in python" yields correct python code, but it's pretty ugly to look at and the tune it produces is even worse: just a single C note repeated 12 times.

It is interesting to hear the voices of more experienced individuals on the history and direction of AI since I was not alive when diverse opinions regarding symbolic and statistical approaches to recreating human intelligence were still alive and thriving. Instead, I have come up in a unipolar world in which the empiricists have clearly won, delivering engineering marvels which seem lightyears away from anything that logic-based approaches have demonstrated.

However, it does seem like learning from data using our current formulations can only take us so far. We can train a self-driving car's neural networks on centuries of real driving recordings and simulated driving scenarios, and such a system still makes mistakes that would baffle a five-year-old. We can train a language model on close to every piece of text that has been digitized and uploaded to the internet, and that language model still does not seem to have captured many basic concepts required to hint at human-level intelligence. It is clear to many that there are some fundamental architectural deficiencies present in current-generation approaches to AI, and I excited to see what advancements will be achieved through 'hybrid' statistical + logical approaches in the coming years. I suspect that fundamental structural modifications to current neural networks and training strategies will be required to get to something equivalent to human-level general usefulness, but perhaps strapping an NLP logic engine and knowledge base to ChatGPT is good enough and strong AI is right around the corner. The immediacy with which that hypothesis can be tested is very appealing at least.

After playing with this (see below), I have lost hope in ChatGPT as a path toward strong AI. The input mechanism is too primitive, and the memory is too short. Furthermore, the neural network seems to have been fine-tuned and prompted on the "conversations of people who only completely agree with each other" dataset.

Anyway, this discussion reminds me of a 2020 paper "The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence" by Gary Marcus

Abstract

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute.

In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.

Me playing with WA and ChatGPT follows:

This article has a nice sense of humor. And we can all be glad that A.I. isn't better than us at math yet. The original programmers are aware of the issue:

ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers. Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.

Someone or some article also claimed that ChatGPT could write Haiku poems, so I asked for a few and found that ChatGPT could reliably create seemingly reasonable examples. However, I also noticed something odd.

In every poem the 季語 (kigo, season word) came on the final line, and was quite obnoxiously the season itself. This shows a lack of creativity, since most Haiku poets have studied lists of kigo, and would try and avoid such bluntness. Saying "Spring" or "Autumn" is essentially wasting a word, when you already start out with so few!

The other issue was that ChatGPT couldn't get anything nearly like 切れ字 (Kireji, "cutting word"). Perhaps this isn't to the fault of the programmers behind the show. Given the description available on wikipedia, it seems even humans have difficulty understanding or agreeing what is meant.

It would be interesting to see if a pre-trained model could ever write a good Haiku poem. I guess a basic question is whether the notions of kigo and kireji can be inferred from pre-existing samples. I'd like to think there's a bit of deductive theory behind it, but who knows what we'll find. The technology is already quite skilled at conversation, even if it does seem to blather at times.

Congrats, to OpenAI and Wolfram | Alpha, for developing interesting products with different strengths! Looking forward to seeing how "source of truth" issue is solved, and not just in math.

I agree with S.Wolfram's article and think it is prescient. However, it seems that the combination of Wolfram Language and ChatGPT is not easy.

For example, yesterday I asked ChatGPT "What is the second highest mountain in Japan? and the answer was "Mount Okuhira". When I asked him, "Where is Mt. Okuhira?" he replied, "It is northwest of Sapporo in Hokkaido. Actually, there is no such mountain.

In the illustration of ChatGPT by S. Wolfram, the main component is a language model neural net. As his paper shows, ChatGPT uses this language model neural net for statistical inference. This language model, like most humans, are not good at mathematical inference, as is to be expected from the vast amount of documents. Can this shortcoming be basically eliminated?

As the figure shows, we can expect reinforcement learning to help, but will mathematical thinking ever have the upper hand in this vast amount of human knowledge? As far as the recent world situation is concerned, we cannot expect much.

Therefore, it seems that what Wolfram Language should do is to develop its own. For the time being, as I mentioned in my article, ChatGPT will be an unexpected inspiration.