You have this survey about ML for Combinatorial Optimization from Bengio, Lodi, and Provost.

OpenAI's paper about a robot hand learning to solve a rubik's cube

Also check-out anything mentioning neurosymbolic AI, which aims to combine neural network learning with logic-based reasoning.

Gary Marcus wrote an extensive note on the subject that I recommend as well

Slam is pure Mechatronics which I don't consider ml.

ML is AI, AI is not necessarily ML.

All birds are dinosaurs, not all dinosaurs are birds.

Ok thank you. I kind of hate the mob mentality of this site though. It discourages learning and experimenting.

Already happening unfortunately

I was just asking more of a question tbh than pretending to know it. It’s why I said correct me if I am wrong.

IDK, I guess I ran into the Reeeeeditors. Mob mentality is what drives the website and blind dislike. I just don’t worry about it and enjoy my life outside this site.

Counter point: markets that are small and specialised and require tons of domain knowledge. E.g. training the model on israeli law in hebrew.

Generally speaking it is more a concept. “Machine Learning” is the more rigorously defined subject. Machine learning specifically means iteratively modifying a model using training data, then applying that model to real world data. AI is a bit more of a nebulous concept, but generally speaking it's just using some search algorithm with heuristics to make the search more "intelligent".

I agree.

I still resist though and do not say that "I have developed an AI that does xyz" but say that "I developed a predictive ML model that does xyz".

I think marketers have really influenced the definitions. Calling a linear regression house price estimator "AI" seems like a stretch unless you're trying to get venture capitalists excited. But today, most business people probably will.

Is the Amazon product recommender system "AI"? Is it ML?

Slam exactly fits the definition of ml.

The more data you give it, the better the map gets, and the better we expect localisation to be.

It has no generalisation at all, but it is learning something very specific about a particular environment.

Hi,

I need to build a prediction model using a classification model and pattern recognition model cascaded.

Classification model will have two classes 0 and 1. This model will give a series of 0s and 1s, which will then be fed to pattern recognition model. Please, Let me know if this sounds feasible.

I would proceed with caution because smaller models are generally not that easy to finetune. In fact, the whole point of a larger model is that it not only contains a lot of information, but that it is fairly easy to adapt to new tasks because it has plenty of "space" to restructure itself. A smaller model trying to restructure itself is more likely to diverge or not be able to adapt to the task at all.

It would be more viable in that case to run the larger model layer by layer, finetune it, and then distill onto a smaller one. That way you use the maximum potential of a larger model to adapt to a different task, and you distill it into whatever you need.

That’s literally just the original bitter lesson.

The compute is cheap but the data may not be easily accessible.

I think the whole concept of AI is bizarre to be honest. What are people? We’re chemical and electrical connections responding in a closed system. Consciousness is just an illusory result of various stimuli coming together.

The only difference between a human brain and a computer algorithm is the complexity/amount of reaction. If an algorithm can learn and respond, what’s the difference?

Humanity’s obsession with ideas like the soul etc color the whole conversation around AI way too much.

That is to say I don’t know the answer to OP’s question ☠️

I think one counter-argument is that Andrew Ng has said that there are profitable opportunities that Google knows about but doesn't go after simply because they're too small to matter to Google (or Microsoft or any megacorp), even though those opportunities are large enough to support a "normal size" business.

From this view, it makes sense to "outsource" the fine-tuning to businesses that are buying the foundational models because why bother with a project that would "only" add a few million/year in revenue?

Additionally, if the fine-tuning data is very domain-specific or proprietary (e.g. your company's customer service chat logs for example) then the foundational model providers might literally not be able to do it.

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Having said all this, I certainly expect a small industry of fine-tuning consultants/tooling/etc to grow over the coming years

It's an NLP conference.

This is because making AI's without using ML is much more harder and expensive. And now everyone can make neural networks with less than 50 lines of code, so we tend to ignore AI's which aren't made using ML because they are over shadowed heavily by functionality and efficiency of ML ones.

/r/confidentlyincorrect/

AI has basically become a buzzword that means "this thing is capable of achieving what it does because it's powered by ML", and in this context especially, ML has become synonymous with deep learning.

What is ACL?

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No but if you use a decoder model (autoregressive) you can generate more tokens for the same task, depending on its difficulty. Chain-of-thought makes use of this trick.

you get downvoted, but you are right. There is nothing intelligent about an accurate regression model. It is the application of that regression model to a certain task that we anthropomorphize to "intelligence".