[throw_nbvc1234]

This sounds like a problem that could be solved around the corner with a caveat.

Games generally are solvable for AI because they have feedback loops and a clear success or failure criteria. If the "picking up a Joystick" part is the limiting factor, sure. But why would we want robots to use an interface (especially a modern controller) heavily optimized for human hands; that seems like the definition of a horseless carriage.

I'm sure if you compared a monkey and a dolphins performance using a joystick you'd get results that aren't really correlated with their intelligence. I would guess that if you gave robots an R2D2 like port to jack into and play a game, that problem could be solved relatively quickly.

[xnickb]

Just like OpenAI early on promised us an AGI and showed us how it "solved" Dota 2.

They also claimed it "learned" to play by playing itself only however it was clear that most of the advanced techniques were borrowed from existing AI and by observing humans.

No surprise they gave up on that project completely and I doubt they'll ever engage in anything like that again.

Money better spent on different marketing platforms.

[jsheard]

It also wasn't even remotely close to learning Dota 2 proper. They ran a massively simplified version of the game where the AI and humans alternated between playing one of two pre-defined team compositions, meaning >90% of the games characters and >99.999999% of the possible compositions and matchups weren't even on the table, plus other standard mechanics were also changed or disabled altogether for the sake of the AI team.

Saying you've solved Dota after stripping out nearly all of its complexity is like saying you've solved Chess, but on a version where the back row is all Bishops.

[xnickb]

Exactly. What I find surprising in this story though is not the OpenAI. It's investors not seeing through these blatant.. lets call them exaggerations of the reality and still trusting the company with their money. I know I wouldn't have. But then again, maybe that's why I'm poor.

[ryandrake]

In their hearts, startup investors are like Agent Mulder: they Want To Believe. Especially after they’ve already invested a little. They are willing to overlook obvious exaggerations up to and including fraud, because the alternative is admitting their judgment is not sound.

Look at how long Theranos went on! Miraculous product. Attractive young founder with all the right pedigree, credentials, and contacts, dressed in black trurtlenecks. Hell, she even talked like Steve Jobs! Investors never had a chance.

[jdross]

They already have 400 million daily users and a billion people using the product, with billions of consumer subscription revenue, faster than any company ever. They are also aggregating R&D talent at a density never before seen in Silicon Valley

That is what investors see. You seem to treat this as a purity contest where you define purity

[zaphar]

Also apparently still not making a profit.

[xnickb]

I'm speaking about past events. Perhaps I didn't make it clear enough

[rowanG077]

I agree that restricting the hero pool is a huge simplification. But they did play full 5v5 standard dota with just a restricted hero pool of 17 heroes and no illusions/control units according to theverge (https://www.theverge.com/2019/4/13/18309459/openai-five-dota...). It destroyed the professionals.

As an ex dota player, I don't think this is that far off from having full on, all heroes dota. Certainly not as far of as you are making it sound.

And dota is one of the most complex games, I expect for example that an AI would instantly solve CS since aim is such a large part of the game.

[mistercheph]

Another issue with the approach is that the model had direct access to game data, that is simply an unfair competitive advantage in dota, and it is obvious why that advantage would be unfair in CS.

It is certainly possible, but i won't be impressed by anything "playing CS" that isn't running a vision model on a display and moving a mouse, because that is the game. The game is not abstractly reacting to enemy positions and relocating the cursor, it's looking at a screen, seeing where the baddy is and then using this interface (the mouse) to get the cursor there as quickly as possible.

It would be like letting an AI plot its position on the field and what action its taking during a football match and then saying "Look, The AI would have scored dozens of times in this simulation, it is the greatest soccer player in the world!" No, sorry, the game actually requires you to locomote, abstractly describing your position may be fun but it's not the game

[rowanG077]

Did you read the paper? It had access to the dota 2 bot API, which is some gamestate but very far from all gamestate. It also had artifially limited reaction to something like 220ms, worse then professional gamers.

But then again, that is precisely the point. A chess bot also has access to gigabytes of perfect working memory. I don't see people complaining about that. It's perfectly valid to judge the best an AI can do vs the best a human can do. It's not really fair to take away exactly what a computer is good at from an AI and then say: "Look but the AI is now worse". Else you would also have to do it the other way around. How well could a human play dota if it only had access to the bot API. I don't think they would do well at all.

[mistercheph]

It's fine if the computer has access to gigabytes of working memory, it can use all of the "natural" advantages that it has to play the game, that's perfectly fair, but there is no comparison to make when you give models bespoke machine interfaces to play games whose core mechanics revolve around perception and physical coordination, it may be impressive, but they are playing a different game, something akin to HvH Counter Strike.

And you can try to play some game where you create disadvantages to try to balance out all of the advantages of the machine interface, but again, hard to reason about the edge cases, and easy to create a misleading headline like "artificially limited reaction time worse than professional gamers" while in practice being able to react to information much more quickly than a human player because of its exclusive interface to game state. All of that is fair and well, and doesn't take anything away from the very cool achievements of google et al., but when you change the core mechanics of the game to accommodate a uniquely challenged player, you're playing a different game! Chess is ~mostly not about physically moving the pieces on the board, but Counter Strike is about little more than that! (And dota is somewhere in between.)

[lukeschlather]

> But then again, that is precisely the point. A chess bot also has access to gigabytes of perfect working memory. I don't see people complaining about that.

There are ~86 billion neurons in the human brain. If we assume each neuron stores a single bit a human also has access to gigabytes of working memory. If we assume each synapse is a bit that's terabytes. Petabytes is not unreasonable assuming 1kb of storage per synapse. (And more than 1kb is also not unreasonable.)

The whole point of the exercise is figuring out how much memory compares to a human brain.

[Jensson]

> It destroyed the professionals.

Only the first time, later when it played better players it always lost. Players learned the faults of the AI after some time in game and the AI had very bad late game so they always won later.

[rowanG077]

Not on the last iteration.

[scotty79]

It was 6 years ago. I'm sure now there'd be no contest now if OpenAI dedicated resources to it, which it won't because it's busy with solving entirety of human language before others eat their lunch.

[spektral23]

Funnily enough, even dota2 has grown much more complex than it was 6 years ago, so it's a harder problem to solve today than it was back then

[xnickb]

What do you base your certainty on? Were there any significant enough breakthroughs in the AGI?

[scotty79]

ARC-AGI, while imagined as super hard for AI, was beaten enough that they had to come up with ARC-AGI-2.

[hbsbsbsndk]

"AI tend to be brittle and optimized for specific tasks, so we made a new specific task and then someone optimized for it" isn't some kind of gotcha. Once ARC puzzles became a benchmark they ceased to be meaningful WRT "AGI".

[fennecfoxy]

To be fair humans have had quite a few million years across a growing population to gather all of the knowledge that we have.

As we're learning with LLMs, the dataset is what matters - and what's awesome is that you can see that in us, as well! I've read that our evolution is comparatively slow to the rate of knowledge accumulation in the information age - and that what this means is that you can essentially take a caveman, raise them in our modern environment and they'll be just as intelligent as the average human today.

But the core of our intelligence is logic/problem solving. We just have to solve higher order problems today, like figuring out how to make that chart in excel do the thing you want, but in days past it was figuring out how to keep the fire lit when it's raining. When you look at it, we've possessed the very core of that problem solving ability for quite a while now. I think that is the key to why we are human, and our close ancestors monkeys are...still just monkeys.

It's that problem solving ability that we need to figure out how to produce within ML models, then we'll be cooking with gas!

[mellosouls]

The point isn't about learning video games its about learning tasks unrelated to its specific competency generally.

[jappgar]

A human would learn it faster, and could immediately teach other humans.

AI clearly isn't at human level and it's OK to admit it.

[jandrese]

> But why would we want robots to use an interface (especially a modern controller) heavily optimized for human hands; that seems like the definition of a horseless carriage.

Elon's response to this is that if we want these androids to replace human jobs then the lowest friction alternative is for the android to be able to do anything a human can do in a human amount of space. A specialized machine is faster and more efficient, but comes with engineering and integration costs that create a barrier to entry. Elon learned this lesson the hard way when he was building out the gigafactories and ended up having to hire a lot of people to do the work while they sorted out the issues with the robots. To someone like Elon a payroll is an ever growing parasite on a companies bottom line, far better if the entire thing is automated.

[johnb231]

No, the joystick part is really not the limiting factor. They’ve already done this with a direct software interface. Physical interface is a new challenge. But overall you are missing the point.