Past results are no guarantee of future performance.

That field is called Explainable AI and the answer is because that costs money and the only reason AI is being used is to cut costs

It’s not always as simple as measuring an observable system or simulating the parameters the best you can. Lots of parameters + lots of variables = we have a good idea how it should go, we can get close, but don’t actually know. That’s part of why emergent behavior and chaos theory are so difficult, even in theoretically closed systems.

Seems no, to me: a human lawyer wouldn’t, for instance, make up case law that doesn’t exist. AI has done that one already. If it had even the most basic understanding of what the law is and does, it would have known not to do that.

LLMs don’t have an understanding of anything, but that doesn’t mean all AI in perpetuity is incapable of having an understanding of eg. what the law is. Edit: oh and also, it’s not like human lawyers are incapable of mistakenly “inventing” case law just by remembering something wrong.

As to whether human intelligence is statistics, well… our brains are neural networks, and ultimately neural networks – whether made from meat or otherwise – are “just statistics.” So in a way I guess our intelligence is “just statistics”, but honestly the whole question is sort of missing the point; the problem with AI (which right now really means LLMs) isn’t the fact that they’re “just statistics”, and whether you think human intelligence is or isn’t “just statistics” doesn’t really tell you anything about why our brains perform better than LLMs

There’s a wide range of “explainability” in machine learning - the most “explainable” model is a decision tree, which basically splits things into categories by looking at the data and making (training) an entropy-minimizing flowchart. Those are very easy for humans to follow, but they don’t have the accuracy of, say, a Random Forest Classifier, which is exactly the same thing done 100 times with different subsets.

One flowchart is easy to look at and understand, 100 of them is 100 times harder. Neural nets are another 100 times harder, usually. The reasoning can be done by hand by humans (maybe) but there’s no regulations forcing you to do it, so why would you?

But the actions taken by the model in the virtual environments can always be described as discrete steps.

That’s technically correct, but practically useless information. Neural networks are stochastic by design, and while Turing machines are technically deterministic, most operating systems’ random number generators will try to introduce noise from the environment (current time, input devices data, temperature readings, etc). So because of that randomness, those discrete steps you’d have to walk through would require knowing intimate details of the environment that the PC was in at precisely the time it ran, which isn’t stored. And even if it was or you used a deterministic psuedo-random number generator, you’d still essentially be stuck reverse engineering the world’s worse spaghetti code written entirely in huge matrix multiplications, code that we already know can’t possibly be optimal anyway.

If a software needs guaranteed optimality, then a neural network (or any stochastic algorithm) is simply the wrong tool for the job. No need to shove a square peg in a round hole.

Also I can’t speak for AI devs, in fact I’ve only taken an applied neural networks course myself, but I can tell you that computer architecture was like a prerequisite of a prerequisite of a prerequisite of that course.

It’s true that each individual building block is easy to understand. It’s easy to trace each step.

The problem is that the model is like a 100 million line program with no descriptive variable names or any comments. All lines are heavily intertwined with each other. Changing a single line slightly can completely change the outcome of the program.

Imagine the worst code you’ve ever read and multiply it by a million.

It’s practically impossible to use traditional reverse engineering techniques to make sense of the AI models. There are some techniques to get a better understanding of how the models work, but it’s difficult to get a full understanding.

I’d have to check my bookmarks when I get home for a link, but I recently read a paper linked to this that floored me. It was research on visualisation of AI models and involved subject matter experts using an AI model as a tool in their field. Some of the conclusions the models made were wrong, and the goal of the study was to see how good various ways of visualising the models were — the logic being that better visualisations = easier for subject matter experts to spot flaws in the model’s conclusions instead of having to blindly trust it.

What they actually found was that the visualisations made the experts less likely to catch errors made by the models. This surprised the researchers, and caused them to have to re-evaluate their entire research goal. On reflection, they concluded that what seemed to be happening was that the better the model appeared to explain itself through interactive visualisations, the more likely the experts were to blindly trust the model.

I found this fascinating because my field is currently biochemistry, but I’m doing more bioinformatics and data infrastructure stuff as time goes on, and I feel like my research direction is leading me towards the explainable/interpretable AI sphere. I think I broadly agree with your last sentence, but what I find cool is that some of the “libraries” we are yet to build are more of the human variety i.e. humans figuring out how to understand and use AI tools. It’s why I enjoy coming at AI from the science angle, because many scientists alreadyuse machine learning tools without any care or understanding of how they work (and have done for years), whereas a lot of stuff branded AI nowadays seems like a solution in search of a problem.

Wow that is sick

The explanation is not that simple. Some model configurations work well. Others don’t. Not all continuous and differentiable models cut it.

It’s not given a model can generalize the problem so well. It can just memorize the training data, but completely fail on any new data it hasn’t seen.

What makes a model be able to see a picture of a cat it has never seen before, and respond with “ah yes, that’s a cat”? What kind of “cat-like” features has it managed to generalize? Why does these features work well?

When I ask ChatGPT to translate a script from Java to Python, how is it able to interpret the instruction and execute it? What features has it managed to generalize to be able to perform this task?

Just saying “why wouldn’t it work” isn’t a valid explanation.