Lots of talk about “neuro-symbolic” AI, which just sounds like the connectionists finally conceding that the symbolic approach is necessary, but trying desperately to cling on to their dignity by putting a “neural” in front.

It’s a discussion we’ve been having since the 70s but where is the progress in symbolic AI algorithms? Are we still building the semantic web? Can we solve the framing problem?

Is this just more hype to fuel the bubble?

Is this Neuro-Symbolic AI in room with us? Or is it just theoretical, as of yet?

Just one more AI revolution and we'll have AGI, trust me guys. This one is gonna be like 100x better at... well, AI stuff obviously.

We already had symbolic software, it was simply called software before AI arrived, now it should probably be called "classical software" or "deterministic software".

If they claim their model runs faster, then why not. Let's hope it can prevent AI driven energy overconsumption.

I'm just going to focus on the 2 words "AI could" in that title and ignore everything after that

New AI PR campaign just dropped.

So algorithms then?

LLMs have some interesting properties and certainly can do a good job sifting through large amounts of raw data. They are however a very brute force approach compared to say a network routing protocol. Sooner or later people will start to realise (again) that engineering is about trade offs and you need to work out what your constraints are and stop trying to solve every problem with massive amounts of multiplication.

Soooo, programming?

I've been wondering, if you could combine LLMs with a logic programming language like Prolog. The latter is actually able to reason through things, you "just" have to express them in Prolog facts and rules.

Well, from doing a quick online search, I'm most certainly not the first person to think of this, which does not surprise me at all...

Unlike familiar large language models (LLMs) such as ChatGPT and Gemini, the team focuses on AI systems used in robotics.

Misleading title, then