Is Wang even able to achieve superintelligence? Is anyone? I'm unable to make sense of Wang's compensation package. What actual, practical skills does he bring to the table? Is this all a stunt to drive Meta's stock value?
The way it sounds, Zuckerberg believes that they can, or at the very least has people around him telling him that they can. But Zuckerberg also though that the Metaverse would be thing.
LeCun obviously thinks otherwise and believes that LLMs are a dead-end, and he might be right. The trouble with LLMs is that most people don't really understand how they work. They seem smart, but they are not; they are really just good at appearing to be smart. But that may have created the illusion the true artificial intelligence is much closer than it really is in the minds of many people including Zuckerberg. And obviously, there now exists an entire industry that relies on that idea to raise further funding.
As for Wang, he's not an AI researcher per se, he basically built a data sweatshop. But he apparently is a good manager who knows how to get projects done. Maybe the hope is that giving him as many resources as possible will allow him to work his magic and get their superintelligence project on track.
Wang is a networking machine and has connected with everyone in the industry. Likely was brought in as a recruiting leader. Mark being Mark, though, doesn’t understand the value of vision and figured getting big names in the same room was better than actually having a plan.
Your last sentence suggests that he willingly failed to take the choice to create a vision and a plan.
If, for whatever reason, you don't have a vision and a plan, hiring big names to help kickstart that process seems like a way better next step than "do nothing".
Wang is not Zuck's first choice. Zuck couldn't get the top talents he wanted so he got Wang. Unfortunately Wang is not technical, he excels in managing the labeling company and be the top in providing such services.
That's why I also think the hiring angle makes sense. It would actually be astonishing if he could turn technical and compete with the leaders in OAI/Anthrpic
You’re right – the way I phrased it assumes “having a plan” is a possibility for him. It isn’t. The best he was ever going to do was get talent in the room, make a Thinking Machines knockoff blog post with some hand wavey word salad, and stand around until they do something useful.
> they are really just good at appearing to be smart.
In other words, functionally speaking, for many purposes, they are smart.
This is obvious in coding in particular, where with relatively minimal guidance, LLMs outperform most human developers in many significant respects. Saying that they’re “not smart” seems more like an attempt to claim specialness for your own intelligence than a useful assessment of LLM capabilities.
> They seem smart, but they are not; they are really just good at appearing to be smart
There are too many different ways to measure intelligence.
Speed, matching, discovery, memory, etc.
We can combine those levers infinitely create/justify "smart". Are they dumb? Absolutely, but are they smart? Very much so. You can be both at the same time.
Maybe you meant genius? Because that standard is quite high and there's no way they're genius today.
They're neither smart nor dumb and I think that trying to measure them along that scale is a fool's errand. They're combinatorial regurgitation machines. The fact that we keep pointing to that as an approximation of intelligence says more about us than it, namely that we don't understand intelligence and that we look for ourselves in other things to define intelligence. This is why when experts use these things within their domain of expertise they're underwhelmed, but when used outside of those domains they become halfway useful.
Trying to create new terminology ("genius", "superintelligence", etc.) seems to only shift goal posts and define new ways of approximation.
Personally, I'll believe a system is intelligent when it presents something novel and new and challenges our understanding of the world as we know it (not as I personally do because I don't have the corpus of the internet in my head).
Smart and dumb are opposites. So this seems dubious. You can have access to a large base of trivial knowledge (mostly in a single language), as LLMs do, but have absolutely no intelligence, as LLMs demonstrate.
You can be dumb yet good at Jeopardy. This is no dichotomy.
Imagine an actor who is playing a character speaking a language that they actor does not speak. Due to a lack of time, the actor decides against actually learning the language and instead opts to just memorise and train how to speak their lines without actually understanding the content. Let's assume they are doing a pretty convincing job too. Now, the audience watching these scenes may think that the actor is actually speaking the language, but in reality they are just mimicking.
This is what an LLM essentially is. It is good at mimicking, reproducing and recombining the things it was trained on. But it has no creativity to go beyond this, and it doesn't even possess true reasoning, which is how it will end up making mistakes that are just immediately obvious to a human observer, yet the LLM is unable to see them, because it just mimicking.
> Imagine an actor who is playing a character speaking a language that they actor does not speak. Due to a lack of time, the actor decides against actually learning the language and instead opts to just memorise and train how to speak their lines without actually understanding the content.
Now imagine that, during the interval, you approach the actor backstage and initiate a conversation in that language. His responses are always grammatical, always relevant to what you said modulo ambiguity, largely coherent, and accurate more often than not. You'll quickly realise that 'actor who merely memorized lines in a language he doesn't speak' does not describe this person.
You've missed the point of the example, of course it's not the exact same thing. With regard to LLM, the biggest difference is that it's a regression against the world's knowledge, like an actor who memorized every question that happens to have an answer written down in history. If you give him a novel question, he'll look at similar questions and just hallucinate a mashup of the answers hoping it makes sense, even though he has no idea what he's telling you. That's why LLMs do things like make up nonsensical API calls when writing code that seem right but have no basis in reality. It has no idea what it's doing, it's just trying to regress code in its knowledge base to match your query.
I don't think I missed the point; my point is that LLMs do something more complex and far more effective than memorise->regurgitate, and so the original analogy doesn't shed any light. This actor has read billions of plays and learned many of the underlying patterns, which allows him to come up with novel and (often) sensible responses when he is forced to improvise.
What kind of training data do you suppose contains an answer to "how to build a submarine out of spaghetti on Mars" ? What do you think memorization means?
You are describing Searle's "Chinese Room argument"[1] to some extent.
It's been discussed a lot recently, but anyone who has interacted with LLMs at a deeper level will tell you that there is something there; not sure if you'd call it "intelligence" or what. There is plenty of evidence to the contrary too. I guess this is a long-winded way of saying "we don't really know what's going on"...
Being able to learn to play Moonlight Sonata vs. being able to create it. Being able to write a video game vs being able to write a video game that sells. Being able to tell you newtons equations vs being able to discover the acceleration of gravity on earth
Wisdom vs knowledge, where the word "knowledge" is doing a lot of work. LLMs don't "know" anything, they predict the next token that has the aesthetics of a response the prompter wants.
I suspect a lot of people but especially nerdy folks might mix up knowledge and intelligence, because they've been told "you know so much stuff, you are very smart!"
And so when they interact with a bot that knows everything, they associate it with smart.
Ability to create an internal model of the world and run simulations/predictions on it in order to optimize the actions that lead to a goal. Bigger, more detailed models and more accurate prediction power are more intelligent.
Did I ever promise otherwise? Intelligence is inherently computational, and needs a physical substrate. You can understand it both by interacting with the black box and opening up the box.
It doesn't seem obvious to me that predicting a token that is the answer to a question someone asked would require anything less than coming up with that answer via another method.
How many people generate novel ideas? When I look around at work, most people basically operate like an LLM. They see what’s being done by others and emulate it.
In my experience, discernment and good judgment. The "generating ideas" capabilities is good. The text summarization capabilities are great. However when it comes to making reasoned choices, it seems like it's losing all abilities, and even worse it will sound grossly overconfident or sycophantic or both.
If you don’t think humans are smart, then what living creature qualifies as smart to you? Or do you think humans created the word but it describes nothing that actually exists in the real world?
I think most things humans do are reflexive, type one "thinking" that AIs do just as well as humans.
I think our type two reasoning is roughly comparable to LLM reasoning when it is within the LLM reinforcement learning distribution.
I think some humans are smarter than LLMs out-of-distribution, but only when we think carefully, and in many cases LLMs perform better than many humams even in this case.
If Zuck throws $2-$4Bn towards a bunch of AI “superstars” and that’s enough to convince the market that Meta is now a serious AI company, it will translate into hundreds of billions in market cap increases.
Wang never led a frontier lab. He founded a company that uses hlow-paid uman intelligence to label training data. But clearly he is as slick a schmoozer as Sam Altman to have taken in a seasoned operator like Zuckerberg.
