Yes. My PhD was on applied ML. My current day job is at a center for AI. There are many people dimensions smarter than me -- but AI is all I deal with every day.

The reason for the nebulous concept is that intelligence is hard to define. Thus, in the past it was often defined by relating it back to human intelligence, e.g. "automating tasks that would require human intelligence to solve" and even the Turing Test.
But there are harder definitions of intelligence, such as Francois Chollet's paper.

It is definitely NOT correct to say << [AI] is just using some search algorithm with heuristics to make the search more "intelligent">>.
AI covers way more and goes far beyond search.

I guess I would be concerned with claiming that evolutionary algorithms are AI, because that’s not how most people understand the current AI. And right now pretty much everything is being advertised as AI for marketing purposes, without being able to distinguish one solution from another. But you’ve made a good point.

Is this something you've studied at a university? I only took a few AI/ML classes at uni so I'm not going to pretend to be an expert.

In any case "field of study that deals with replicating intelligence" isn't exactly a rigorous definition. Really that description speaks to how broad and nebulous it is.

seems a bit premature since foundation models have only been around for 3-5 years

How does this translate to the other Alpha* AIs? I’m thinking of AlphaCraft (star craft) which has too many move options to model it as a tree.

The real bitter lesson is how Standford got so many authors cited for introducing nothing but a less descriptive name than "Large models"

Thanks! Will read about it!

>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".

No. Just no.

AI is the vision / effort / field of study that deals with replicating (human) intelligence.

Isn't that the standard way research papers are written: you only compare your solution to methods that are worse that yours? ;)

I think the point of the metaphor was Amazon stealing product ideas from third party vendors on their site and undercutting them. They know what sells better than anyone and can then just produce it.

If Google or OpenAI offers people the opportunity to finetune their foundation models they will know when something valuable comes out of it and simply replicate it then. There is close to zero institutional cost for them to do so.

That's a reason why I think all these startups that want to build business models around ChatGPT are insane: if you do it and it actually turns out to work OpenAI will just steal your lunch and you have no way of stopping that.

If you see it on slides, it is AI. If you see it in Python, it is ML.

Why do some tokenizers assign negative floats to each token? For instance, I am looking at this json file, and the tokens start about 1/3 of the way down the page. Each one is part of a two-element list with the structure "[<token>, negative decimal number with 15 digits of accuracy]"

That depends on what you mean. I don't think any of the LLMs use it, but it has some citations and follow-up literature.

No. I would argue, for instance, that a particle filter is probably considered an AI technique, but it is not an ML technique. Similarly, some optimization algorithms such as genetic algorithms kind of fall into that category. I more attribute ML to the whole "using some form of gradient descent to tune data that may not have a physical or statistical interpretation" thing that we do with ANNs because it is really the training process that differentiates it from GOFAI and others.

Does anyone have working example code for the Supervised Clustering algorithms (SPAM, SRIDHCR, and SCEC) by Eick et al.? I haven’t been able to find any online.

the one thing that could blow all this up is requirements for explainability; which could push the industry into low cost (but maybe low performance) methods like neurosymbolic computing whose predictions are much more understandable and explainable

I can see something to do with self driving cars (or LegalTech, or HealthTech) that results in a terrible prediction with real consequences. This would then drive the public backlash against unexplainable models, and maybe laws against them too.

Lastly this would then make deep learning models and LLMs less attractive if they fall under new regulatory regimes.

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Company is essentially a grow house. Think growing weed in containers (but that's not it). So they want to integrate AI in this whole thing. My understanding is that we will way too few data points to train some sort of model. In my mind we could probably use some statistical analysis (if I pee on that plant, 2 weeks later it's grown 10% than the unpeed one)

Does that make sense? How best to go about this? Thanks!

What's the 'old' joke? If you work in R it's statistical learning. If you're working in Python, it's machine learning. And it's AI if you're working in Powerpoint.

Eh, idk, using the term "machine learning" gets a lot of attention now too, to the point that it's kind of a buzzword even though it is a legitimate field of study. I think its more that the average non-technical person hears "AI" and "machine learning" and assumes they're the same thing.

I don't see too much of a need to have a dedicated group for paper submission of a specific conference, which lasts three months or so. If you have any specific questions, you are most welcome to ask here. People are looking to network with others, but during the conference.

Machine learning is still a subset of AI. That being said I think its clear that ML is the most popular subset of AI at the moment, and so when the average person hears of AI they likely think of ML as well, to the point that some people may just use the two terms interchangeably.

Edit:
OK I had a snarky comment here, but instead I'd like to suggest that the business models are fundamentally different: Amazon sells products that they (mostly) don't produce, and offers a platform for third-party vendors. In contrast to something like OpenAI, they're an aggregator and an intermediary.

There are people that use an automata theory approach to natural language processing.

These people’s gripe with ML is that it has a major computational constraint.

From this perspective you could say there are products that wouldn't make sense for Amazon to bother with. How's that working out.