Live service doesn’t need to be shit.

There could have been games where there was just a brilliant idea for a game that keeps having engaging content on an ongoing basis with passionate devs.

But live service so an exec could check a box for their quarterly shareholder call was always going to be DOA.

More “can fool the average idiot.”

‘Passing’ isn’t fooling a single participant, but the majority of them beyond statistical chance.

The problem with the experiment is that there exists a set of instructions for which the ability to complete them necessitates understanding due to conditional dependence on the state in each iteration.

In which case, only agents that can actually understand the state in the Chinese would be able to successfully continue.

So it’s a great experiment for the solipsism of understanding as it relates to following pure functional operations, but not functions that have state changing side effects where future results depend on understanding the current state.

There’s a pretty significant body of evidence by now that transformers can in fact ‘understand’ in this sense, from interpretability research around neural network features in SAE work, linear representations of world models starting with the Othello-GPT work, and the Skill-Mix work where GPT-4 and later models are beyond reasonable statistical chance at the level of complexity for being able to combine different skills without understanding them.

If the models were just Markov chains (where prior state doesn’t impact current operation), the Chinese room is very applicable. But pretty much by definition transformer self-attention violates the Markov property.

TL;DR: It’s a very obsolete thought experiment whose continued misapplication flies in the face of empirical evidence at least since around early 2023.

Used Google and social media as well, and allegedly sometimes even listened to rock and roll.

True deviant, that one.

Which is typical of tech that hasn’t yet hit the sweet spot for a tipping point.

Look at how many palm pilots or handheld note taking mobile devices existed (and how many cycles) before the iPhone.

Yes and no. It really depends on the model.

The newest Claude Sonnet I’d probably guess will come in above average compared to the humans available for a program like this in making learning fun and personally digestible for each student.

The newest Gemini models could literally cost kids their lives.

The gap between what the public is aware of (and even what many employees at labs, including the frontier ones) and the reality of just how far things have come in the last year is wild.

In many cases yes (though I’ve been in good ones when playing off and on, usually the smaller the more there’s actual group activities).

But they are essential to be a part of for blueprints and trading, which are very core parts of the game.

You’ll almost always end up doing missions with other people other than when you intentionally want to do certain tasks solo.

A lot of the game is built around guilds and player to player interactions.

PvP sucks and it’s almost all PvE content vs Destiny though.

Let there be this kind of light in these dark times.

Oh nice, another Gary Marcus “AI hitting a wall post.”

Like his “Deep Learning Is Hitting a Wall” post on March 10th, 2022.

Indeed, not much has changed in the world of deep learning between spring 2022 and now.

No new model releases.

No leaps beyond what was expected.

\s

Gary Marcus is like a reverse Cassandra.

Consistently wrong, and yet regularly listened to, amplified, and believed.

Base model =/= Corpo fine tune

I’m a seasoned dev and I was at a launch event when an edge case failure reared its head.

In less than a half an hour after pulling out my laptop to fix it myself, I’d used Cursor + Claude 3.5 Sonnet to:

1. Automatically add logging statements to help identify where the issue was occurring
2. Told it the issue once identified and had it update with a fix
3. Had it remove the logging statements, and pushed the update

I never typed a single line of code and never left the chat box.

My job is increasingly becoming Henry Ford drawing the ‘X’ and not sitting on the assembly line, and I’m all for it.

And this would only have been possible in just the last few months.

We’re already well past the scaffolding stage. That’s old news.

Developing has never been easier or more plain old fun, and it’s getting better literally by the week.

Edit: I agree about junior devs not blindly trusting them though. They don’t yet know where to draw the X.

Actually, they are hiding the full CoT sequence outside of the demos.

What you are seeing there is a summary, but because the actual process is hidden it’s not possible to see what actually transpired.

People are very not happy about this aspect of the situation.

It also means that model context (which in research has been shown to be much more influential than previously thought) is now in part hidden with exclusive access and control by OAI.

There’s a lot of things to be focused on in that image, and “hur dur the stochastic model can’t count letters in this cherry picked example” is the least among them.

Yep:

https://openai.com/index/learning-to-reason-with-llms/

First interactive section. Make sure to click “show chain of thought.”

The cipher one is particularly interesting, as it’s intentionally difficult for the model.

The tokenizer is famously bad at two letter counts, which is why previous models can’t count the number of rs in strawberry.

So the cipher depends on two letter pairs, and you can see how it screws up the tokenization around the xx at the end of the last word, and gradually corrects course.

Will help clarify how it’s going about solving something like the example I posted earlier behind the scenes.

You should really look at the full CoT traces on the demos.

I think you think you know more than you actually know.

I’d recommend everyone saying “it can’t understand anything and can’t think” to look at this example:

https://x.com/flowersslop/status/1834349905692824017

Try to solve it after seeing only the first image before you open the second and see o1’s response.

Let me know if you got it before seeing the actual answer.

They got off to a great start with the PS5, but as their lead grew over their only real direct competitor, they became a good example of the problems with monopolies all over again.

This is straight up back to PS3 launch all over again, as if they learned nothing.

Right on the tail end of a horribly mismanaged PSVR 2 launch.

We still barely have any current gen only games, and a $700 price point is insane for such a small library to actually make use of it.

Meanwhile, here’s an excerpt of a response from Claude Opus on me tasking it to evaluate intertextuality between the Gospel of Matthew and Thomas from the perspective of entropy reduction with redactional efforts due to human difficulty at randomness (this doesn’t exist in scholarship outside of a single Reddit comment I made years ago in /r/AcademicBiblical lacking specific details) on page 300 of a chat about completely different topics:

Yeah, sure, humans would be so much better at this level of analysis within around 30 seconds. (It’s also worth noting that Claude 3 Opus doesn’t have the full context of the Gospel of Thomas accessible to it, so it needs to try to reason through entropic differences primarily based on records relating to intertextual overlaps that have been widely discussed in consensus literature and are thus accessible).

This is pretty much every study right now as things accelerate. Even just six months can be a dramatic difference in capabilities.

For example, Meta’s 3-405B has one of the leading situational awarenesses of current models, but isn’t present at all to the same degree in 2-70B or even 3-70B.

Self destructive addiction even happens to corporations.