Thanks for the suggestion, I’ll take a read!

Any of the papers in the AlphaZero family do this: they combine tree search with learned policy and value functions.

Hia!! As others have suggested, please use transfer learning.

Genetic algorithms are a type of evolutionary algorithm, which are themselves a part of AI. Have a look at the wiki page.

I think I can see your point though. The term AI is used quite differently in research than in the popular meaning. We sometimes joke that the cultural definition of AI is "everything that can't yet be done with a computer" :)

This is a bit of a running joke in the field. Chess was AI, until we solved it, then it wasn't. Asking a computer random questions and getting an answer Star Trek style was AI until Google then it was just 'searching the internet'. The list goes on...

Aside from a general similarity of goals, do you really think the paper you linked makes this one non-novel? I have trouble seeing that. As far as I can tell, there's absolutely nothing comparable to tensor product representations or NECST in your link.

> And integrating good-old-fashioned-ai > (GOFAI) with more modern ML is becoming an area of increasing research interest.

Any papers you recommend on this topic?

No.

AI is about problems. ML is a solution to these problems.

AI is basically decision making. Giving information, how does a machine learn from its environment, take decisions, without human oversight. How does a machine adapt itself with more experience.

ML is just a way to create models.

For example the SLAM algorithm is an important algorithm in AI, because it allows robots to map their environment. However, this is not ML at all.

Another example of AI is knowledge graphs, like the earliest chess engines. A perfect chess AI can be made without any machine learning at all.

It's important to keep making the distinction.

Yes true! Most models will eventually saturate and perhaps and even become worse. I guess it's our job then to just make the algorithms better :). A great example of this is the new Large Langauge Models (LLM), which are trained on billions if not trillions of tokens, and still keep getting better :)

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oh i mixed thermometer with thermostat.. yeah then i agree thermostat can be AI..

I view ML as a way to create an accurate model, and AI as a way to have a model take decisions in a complex environment without humans in the loop.

There is a large overlap, but you can easily have one without the other.

yeah I can see the point behind ML definition.. i guess i was trying to say you don’t always get better with more data. the performance might saturate at some point or the new data you add could be garbage.. so i found it a bit odd to tie definition of ML to the quantity of data.. the definition you linked talks about experience.. i’m not sure how it’s defined.

Everything is new in its current form :) AI, however, goes back a long way... perhaps Turing would be a reasonable starting point, though, with him writing about COMPUTING MACHINERY AND INTELLIGENCE back in 1950.

edit: gramma.

Sort of. The promise of differentiable programming is to be able to implement discrete algorithms in ways that are transparent to gradient descent, but it's really only the numerical values of the inputs that are transparent to gradient descent, not the structure itself. The key idea here is the use of so-called TPRs (tensor product representations) to encode not just values but structure as well in a continuous way, so that one has an entire continuous deformation from the representation of one discrete structure to another. (Obviously, this deformation has to pass through intermediate states that are not directly interpretable as a single discrete structure, but the article argues that even these can represent valid states in some situations.)

A thermostat responds to the environment. It turns on the heating when it gets too cold.

and the ML definition is just repeating the formal definition by Mitchell:

> A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

https://towardsdatascience.com/what-is-machine-learning-and-types-of-machine-learning-andrews-machine-learning-part-1-9cd9755bc647#:~:text=Tom%20Mitchell%20provides%20a%20more,simple%20example%20to%20understand%20better%20.

I think Russell and Norvig is a good place to start if you want to read more. The AI defintion is a taken from their textbook which is one of the most cited references I've ever seen. I do agree however that the first defintion has a problem. Namely with what 'intellegently' means.

The second defintion is just the textbook defintion of ML. Hard to argue with that one. It's taken from Tom Mitchell. Formally “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” (Machine Learning, Tom Mitchell, McGraw Hill, 1997).

I'd be curious to know what your thoughts on a good defintion for AI would be? This is an actively debated topic, and so far no one really has a great defintion (that I know of).

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Try influence functions: https://arxiv.org/abs/1703.04730

Data Shapely is one option but can be computationally expensive. If you’re looking for practical code to try running on real data, here are some tutorials to find the least useful data:

https://docs.cleanlab.ai/stable/tutorials/image.html

https://docs.cleanlab.ai/stable/tutorials/outliers.html

as well as the MOST useful data to label next (or collect an extra label for):

https://github.com/cleanlab/examples/blob/master/active_learning_multiannotator/active_learning.ipynb

There are many, many examples of AI that have nothing to do with machine learning.

In fact, I'd wager that the vast majority of currently running AI code out there is not machine learning AI code at all but instead more rudimentary algorithms which could still be classified as a form of AI.

Almost all chess and gaming code for instance use various flavors of AI algorithms save for the most sophisticated coming from groups like deepmind.

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This concept already exists so there are plenty of resources (papers, etc) online to learn from.

However, current code generation models are huge and hefty, and take a lot of time & resources to build using our current 2023 technology. So it probably isn't a great idea to build a large code-gen language model from scratch.

However, to do a school project about Large Language Models (LLMs), which includes finetuning a pretrained model as well as doing a small model from scratch as a demonstration, would be cool!

There are still many search-based advances/breakthroughs coming out that utilize ML but GOFAI as well, eg. Cicero AI for Diplomacy

i currently have some Logix/Symbolic AI course. So, yes