>Moreover, we follow previous work in accelerating block breaking because learning to hold a button for hundreds of consecutive steps would be infeasible for stochastic policies, allowing us to focus on the essential challenges inherent in Minecraft.
Like all things RL, it is 99.9% about engineering the environment and rewards. As one of the authors stated elsewhere here, there is a reward for completing each of 12 steps necessary to find diamonds.
Mostly I'm tired of RL work being oversold by its authors and proponents by anthropomorphizing its behaviors. All while this "agent" cannot reliably learn to hold down a button, literally the most basic interaction of the game.
The "no free lunch" theorem. You can't start from scratch and expect your program to repeat 4 billion years of evolution collecting inductive biases useful in our corner of our Universe in a matter of hours[1].
While it's possible to bake in this particular inductive bias (repetitive actions might be useful), they decided not to (it's just not that interesting).
[1] And you certainly can't reproduce the observation selection effect in a laboratory. That is the thing that makes it possible to overcome the "no free lunch" theorem: our existence and intelligence are conditional on evolution being possible and finding the right biases.
We have to bake in inductive biases to get results. We have to incentivize behaviors useful (or interesting) to us to get useful results instead of generic exploration.
> You can't start from scratch and expect your program to repeat 4 billion years of evolution collecting inductive biases useful in our corner of our Universe in a matter of hours
Really? Minecraft's gameplay dynamic are not particularly complex... The AI here isn't learning highly complex rules about the nuances of human interaction or learning to detect the relatively subtle differences between various four legged creatures based on small differences in body morphology. In these cases I could see how millions of years of evolution is important to at least give us and other animals a head start when entering the world. If the AI had to do something like this to progress in Minecraft then I'd get why learning those complexities would be skipped over.
But in this case a human would quickly understand that holding a button creates a state which tapping a button does not, and therefore would assume this state could be useful to explore further states. Identifying this doesn't seem particularly complex to me. If the argument is that it will take slightly longer for an AI to learn patterns in dependant states then okay, sure, but I think arguing that learning that holding a button creates a new state is such a complex problem that we couldn't possibly expect an AI to learn it from scratch within a short timeframe is a very weak argument. It's just not that complex. To me this suggests that current algorithms are lacking.
> Minecraft's gameplay dynamic are not particularly complex...
I think you underestimate complexity of going from 12288+400 changing numbers to a concept of gameplay dynamics in the first place. Or in other words your complexity prior is biased by experience.
It seems easy to you because you can't remember the years when you were a toddler and had to learn basic interactions with the world around you. It seems natural to an adult but it is quite complex.
Well, to be fair... I (a human) had to look it up online the first time I played as well. I was repeatedly clicking on the same tree for an entire minute before that. I even tried several different trees just in case.
In my mind, this generalizes to the same problem with other non-stochastic (deterministic) operations like logical conclusions (A => B) .
I have a running bet with friend that humans encode deterministic operations in neural networks, too, while he thinks there has to be another process at play. But there might be something extra helping our neural networks learn the strong weights required for it. Or the answer is again: "more data".
"accelerating block breaking because learning to hold a button for hundreds of consecutive steps "
This is fine, and does not impact the importance of figuring out the steps.
Anybody that has done any tuning on systems that run at different speeds, the adjusting for the speed difference is just engineering, and allows you to get on with more important/inventive work.
I my view, the 'exactly' is crucial here. They do implicitly tell the model what to do by encoding it in the reward function:
In Minecraft, the team used a protocol that gave Dreamer a ‘plus one’ reward every time it completed one of 12 progressive steps involved in diamond collection — including creating planks and a furnace, mining iron and forging an iron pickaxe.
This is also why I think the title of the article is slightly misleading.
Key to Dreamer’s success, says Hafner, is that it builds a model of its surroundings and uses this ‘world model’ to ‘imagine’ future scenarios and guide decision-making.
Can you look at the world model, like you can look at Waymo's world model? Or is it hidden inside weights?
Machine learning with world models is very interesting, and the people doing it don't seem to say much about what the models look like. The Google manipulation work talks endlessly about the natural language user interface, but when they get to motion planning, they don't say much.
Yes, you can decode the imagined scenarios into videos and look at them. It's quite helpful during development to see what the model gets right or wrong. See Fig. 3 in the paper: https://www.nature.com/articles/s41586-025-08744-2
I implemented an acoustic segmentation system in FPGA recently. The whole world model was a long list of known events and states with feasible transitions. Plus novel things not observed before. Basically rather dumb state machine with machine learning part attached to acoustic sensors. Of course, both parts could be hidden behind weights. But state machine was easily readable and that was the biggest advantage of it.
> Can you look at the world model, like you can look at Waymo's world model? Or is it hidden inside weights?
I imagine it's the latter, and in general, we're already dealing with plenty of models with world models hidden inside their weights. That's why I'm happy to see the direction Anthropic has been taking with their interpretability research over the years.
Their papers, as well as most discussions around them, focus on issues of alignment/control, safety, and generally killing the "stochastic parrot" meme and keeping it dead - but I think it'll be even more interesting to see attempts at mapping how those large models structure their world models. I believe there's scientific and philosophical discoveries to be made in answering why these structures look the way they do.
This was clearly the goal of the "Biology of LLMs" (and ancillary) paper but I am not convinced.
They used a 'replacement model' that by their own admission could match the output of the LLM ~50% of the time, and the attribution of cognition related labels to the model hinges entirely on the interpretation of the 'activations' seen in the replacement model.
So they created a much simpler model, that sorta kinda can do what the LLM can do in some instances, contrived some examples, observed the replacement model and labeled what it was doing very liberally.
Machine learning and the mathematics involved is quite interesting but I don't see the need to attribute neuroscience/psychology related terms to them. They are fascinating in their own terms and modelling language can clearly be quite powerful.
But thinking that they can follow instructions and reason is the source of much misdirection. The limits of this approach should make clear that feeding text to a text continuation program should not lead to parsing the generated text for commands and running these commands, because the tokens the model outputs are just statistically linked to the tokens inputted to them. And as the model takes more tokens from the wild, it can easily lead to situations that are very clearly an enormous risk. Pushing the idea that they are reasoning about the input is driving all sorts of applications that seeing them as statistical text continuation programs would make clear are a glaring risk.
Machine learning and LLMs are interesting technology that should be investigated and developed. Reasoning by induction that they are capable of more than modelling language is bad science and drives bad engineering.
I’d say it’s more like Waymo’s world model. The main actor uses a latent vector representation of the state of the game to make decisions. This latent vector at train time is meant to compress a bunch of useful information about the game. So while you can’t really understand the actual latent vector that represents state, you do know it encodes at least the state of the game.
This world model stuff is only possible in environments that are sandboxed. Ie you can represent the state of the world in an and have a way of producing the next state given a current state and action. Things like Atari games, robot simulations, etc
Article makes it seem like finding diamonds is some kind of super complicated logical puzzle. In reality the hardest part is knowing where to look for them and what tool you need to mine them without losing them once you find them. This was given to the AI by having it watch a video that explains it.
If you watch a guide on how to find diamonds it's really just a matter of getting an iron pickaxe, digging to the right depth and strip mining until you find some.
Hi, author here! Dreamer learns to find diamonds from scratch by interacting with the environment, without access to external data. So there are no explainer videos or internet text here.
It gets a sparse reward of +1 for each of the 12 items that lead to the diamond, so there is a lot it needs to discover by itself. Fig. 5 in the paper shows the progression: https://www.nature.com/articles/s41586-025-08744-2
Since diamonds are surrounded by danger and if it dies, it loses its items and such, why would it not be satisfied after discovering iron pick axe or somesuch? Is it in a mode where it doesn't lose its item when it dies? Does it die a lot? Does it ever try digging vertically down? Does it ever discover other items/tools you didn't expect it to? Open world with sparse reward seems like such a hard problem. Also, once it gets the item, does it stop getting reward for it? I assume so. Surprised that it can work with this level of sparse rewards.
In all reinforcement learning there is (explicitly as part of a fitness function, or implicitly as part of the algorithm) some impetus for exploration. It might be adding a tiny reward per square walked, a small reward for each block broken and a larger one for each new block type broken. Or it could be just forcing a random move every N steps so the agent encounters new situations through “clumsiness”.
it didn't watch 'a video', it watched many, many hours of video of playing minecraft (with another specialised model feeding in predictions of keyboard and mouse inputs from the video). It's still a neat trick, but it's far from the implied one-shot learning.
I don't think it was videos. Almost certainly it was replay files with a bunch of work to transform them into something that could be compared to the model's outputs. (Alphastar never 'sees' the game's interface, only a transformed version of information available via an API)
starcraft provides replay files that start with the initial game state and then every action in the game. Not user inputs, but the actions bound to them.
I feel like you are jumping to conclusions here, I wasn't talking about the achievement or the AI, I was talking about the article and the way it explains finding diamonds in minecraft to people who don't know how to find diamonds in minecraft.
Slightly off-topic from the article itself, but… does anyone else feel like Nature’s cookie banner just never goes away? I have vivid memories of trying to reject cookies multiple times, eventually giving up and accepting them just to get to the article only for the banner to show up again the next time I visit. I swear it’s giving me déjà vu every single visit.. Am I the only one experiencing this, or is this just how their site works?
Attempting to train this on a real workload I converted over the weekend after, "step" 8M~ so far and rarely scores above 5% and most are 0% but has scored 60% once 7M~ steps ago.
Adding more than 1 GPU didn't improve speed but that's pretty standard as we don't have fancy interconnect. Bit annoying they didn't use tensorboard for logging, but overall seems like a pretty cool lib - will leave it a few days and see if it can learn (no other algo has so I dont have much hope).
I was reading something about LLMs earlier and was thinking that LLMs could probably write a simple case based script for controlling a player, that could accive a decent success rate.
Pretty impressive. Minecraft’s a complex environment, so for an AI to figure out how to find diamonds on its own shows real progress in learning through exploration — not just pattern recognition.
Characterizing finding diamonds as "mastering" Minecraft is extremely silly. Tantamount to saying "AI masters Chess: Captures a pawn." Getting diamonds is not even close to the hardest challenge in the game, but most readers of Nature probably don't have much experience playing Minecraft so the title is actually misleading, not harmless exaggeration.
> Getting diamonds is not even close to the hardest challenge in the game
Mining diamonds isn't even necessary if you build, e.g., ianxofour's iron farm on day one and trade that iron[0] with a toolsmith, armourer, and weaponsmith. You can get full diamond armour, tools, and weapons pretty quickly (probably a handful of game weeks?)
[0] Main faff here is getting them off their base trade level.
Who would have thought you could get your TAS run published in Nature if you used enough hot buzzwords. (they have been using various old-school-definition "artifical intelligence" algorithms for a long time)
Once again, we see that it's much easier to teach machines to perceive and decide well, in many cases well above human performance - while at the same time, making machines that can navigate the same physical environment humans do, and do a variety of manual tasks that mix power and precision, remains extremely challenging.
The message this sends is pretty clear: machines are better at thinking, humans are better at manual work. That is the natural division of labor that plays into strengths and weaknesses of both computers and human beings.
And so, I'm sorry to say this, but the near future is that in which computers play our games and do the thinking and creative work and management (and ultimately governance), because they're going to be better at this than us, leaving us to do all the physical labor, because that's one thing we will remain better at for a while.
That, or we move past the existing economic structures, so that we no longer need to worry about being competitive with AI labor.
An important caveat from the paper
>Moreover, we follow previous work in accelerating block breaking because learning to hold a button for hundreds of consecutive steps would be infeasible for stochastic policies, allowing us to focus on the essential challenges inherent in Minecraft.
Like all things RL, it is 99.9% about engineering the environment and rewards. As one of the authors stated elsewhere here, there is a reward for completing each of 12 steps necessary to find diamonds.
Mostly I'm tired of RL work being oversold by its authors and proponents by anthropomorphizing its behaviors. All while this "agent" cannot reliably learn to hold down a button, literally the most basic interaction of the game.
The "no free lunch" theorem. You can't start from scratch and expect your program to repeat 4 billion years of evolution collecting inductive biases useful in our corner of our Universe in a matter of hours[1].
While it's possible to bake in this particular inductive bias (repetitive actions might be useful), they decided not to (it's just not that interesting).
[1] And you certainly can't reproduce the observation selection effect in a laboratory. That is the thing that makes it possible to overcome the "no free lunch" theorem: our existence and intelligence are conditional on evolution being possible and finding the right biases.
We have to bake in inductive biases to get results. We have to incentivize behaviors useful (or interesting) to us to get useful results instead of generic exploration.
> You can't start from scratch and expect your program to repeat 4 billion years of evolution collecting inductive biases useful in our corner of our Universe in a matter of hours
Really? Minecraft's gameplay dynamic are not particularly complex... The AI here isn't learning highly complex rules about the nuances of human interaction or learning to detect the relatively subtle differences between various four legged creatures based on small differences in body morphology. In these cases I could see how millions of years of evolution is important to at least give us and other animals a head start when entering the world. If the AI had to do something like this to progress in Minecraft then I'd get why learning those complexities would be skipped over.
But in this case a human would quickly understand that holding a button creates a state which tapping a button does not, and therefore would assume this state could be useful to explore further states. Identifying this doesn't seem particularly complex to me. If the argument is that it will take slightly longer for an AI to learn patterns in dependant states then okay, sure, but I think arguing that learning that holding a button creates a new state is such a complex problem that we couldn't possibly expect an AI to learn it from scratch within a short timeframe is a very weak argument. It's just not that complex. To me this suggests that current algorithms are lacking.
> Minecraft's gameplay dynamic are not particularly complex...
I think you underestimate complexity of going from 12288+400 changing numbers to a concept of gameplay dynamics in the first place. Or in other words your complexity prior is biased by experience.
It seems easy to you because you can't remember the years when you were a toddler and had to learn basic interactions with the world around you. It seems natural to an adult but it is quite complex.
Well, to be fair... I (a human) had to look it up online the first time I played as well. I was repeatedly clicking on the same tree for an entire minute before that. I even tried several different trees just in case.
In my mind, this generalizes to the same problem with other non-stochastic (deterministic) operations like logical conclusions (A => B) .
I have a running bet with friend that humans encode deterministic operations in neural networks, too, while he thinks there has to be another process at play. But there might be something extra helping our neural networks learn the strong weights required for it. Or the answer is again: "more data".
I'm not sure it's a serious caveat if the "hint" or "control" is in the manual.
Sorry, I don't quite follow what you mean?
I didn't read the manual and when I was trying to help my kid play the game I couldn't figure out how to break blocks.
"accelerating block breaking because learning to hold a button for hundreds of consecutive steps "
This is fine, and does not impact the importance of figuring out the steps.
Anybody that has done any tuning on systems that run at different speeds, the adjusting for the speed difference is just engineering, and allows you to get on with more important/inventive work.
Turns out that AI are much better at playing video games if they're allowed to cheat.
"It allows AI to understand its physical environment and also to self-improve over time, without a human having to tell it exactly what to do."
I my view, the 'exactly' is crucial here. They do implicitly tell the model what to do by encoding it in the reward function:
In Minecraft, the team used a protocol that gave Dreamer a ‘plus one’ reward every time it completed one of 12 progressive steps involved in diamond collection — including creating planks and a furnace, mining iron and forging an iron pickaxe.
This is also why I think the title of the article is slightly misleading.
Key to Dreamer’s success, says Hafner, is that it builds a model of its surroundings and uses this ‘world model’ to ‘imagine’ future scenarios and guide decision-making.
Can you look at the world model, like you can look at Waymo's world model? Or is it hidden inside weights?
Machine learning with world models is very interesting, and the people doing it don't seem to say much about what the models look like. The Google manipulation work talks endlessly about the natural language user interface, but when they get to motion planning, they don't say much.
Yes, you can decode the imagined scenarios into videos and look at them. It's quite helpful during development to see what the model gets right or wrong. See Fig. 3 in the paper: https://www.nature.com/articles/s41586-025-08744-2
I implemented an acoustic segmentation system in FPGA recently. The whole world model was a long list of known events and states with feasible transitions. Plus novel things not observed before. Basically rather dumb state machine with machine learning part attached to acoustic sensors. Of course, both parts could be hidden behind weights. But state machine was easily readable and that was the biggest advantage of it.
Why would an accounting system need acoustic sensors?
Sorry. Terrible typo. Acoustic system was cheap though.
> Can you look at the world model, like you can look at Waymo's world model? Or is it hidden inside weights?
I imagine it's the latter, and in general, we're already dealing with plenty of models with world models hidden inside their weights. That's why I'm happy to see the direction Anthropic has been taking with their interpretability research over the years.
Their papers, as well as most discussions around them, focus on issues of alignment/control, safety, and generally killing the "stochastic parrot" meme and keeping it dead - but I think it'll be even more interesting to see attempts at mapping how those large models structure their world models. I believe there's scientific and philosophical discoveries to be made in answering why these structures look the way they do.
> killing the "stochastic parrot" meme
This was clearly the goal of the "Biology of LLMs" (and ancillary) paper but I am not convinced.
They used a 'replacement model' that by their own admission could match the output of the LLM ~50% of the time, and the attribution of cognition related labels to the model hinges entirely on the interpretation of the 'activations' seen in the replacement model.
So they created a much simpler model, that sorta kinda can do what the LLM can do in some instances, contrived some examples, observed the replacement model and labeled what it was doing very liberally.
Machine learning and the mathematics involved is quite interesting but I don't see the need to attribute neuroscience/psychology related terms to them. They are fascinating in their own terms and modelling language can clearly be quite powerful.
But thinking that they can follow instructions and reason is the source of much misdirection. The limits of this approach should make clear that feeding text to a text continuation program should not lead to parsing the generated text for commands and running these commands, because the tokens the model outputs are just statistically linked to the tokens inputted to them. And as the model takes more tokens from the wild, it can easily lead to situations that are very clearly an enormous risk. Pushing the idea that they are reasoning about the input is driving all sorts of applications that seeing them as statistical text continuation programs would make clear are a glaring risk.
Machine learning and LLMs are interesting technology that should be investigated and developed. Reasoning by induction that they are capable of more than modelling language is bad science and drives bad engineering.
I’d say it’s more like Waymo’s world model. The main actor uses a latent vector representation of the state of the game to make decisions. This latent vector at train time is meant to compress a bunch of useful information about the game. So while you can’t really understand the actual latent vector that represents state, you do know it encodes at least the state of the game.
This world model stuff is only possible in environments that are sandboxed. Ie you can represent the state of the world in an and have a way of producing the next state given a current state and action. Things like Atari games, robot simulations, etc
Article makes it seem like finding diamonds is some kind of super complicated logical puzzle. In reality the hardest part is knowing where to look for them and what tool you need to mine them without losing them once you find them. This was given to the AI by having it watch a video that explains it.
If you watch a guide on how to find diamonds it's really just a matter of getting an iron pickaxe, digging to the right depth and strip mining until you find some.
Hi, author here! Dreamer learns to find diamonds from scratch by interacting with the environment, without access to external data. So there are no explainer videos or internet text here.
It gets a sparse reward of +1 for each of the 12 items that lead to the diamond, so there is a lot it needs to discover by itself. Fig. 5 in the paper shows the progression: https://www.nature.com/articles/s41586-025-08744-2
Since diamonds are surrounded by danger and if it dies, it loses its items and such, why would it not be satisfied after discovering iron pick axe or somesuch? Is it in a mode where it doesn't lose its item when it dies? Does it die a lot? Does it ever try digging vertically down? Does it ever discover other items/tools you didn't expect it to? Open world with sparse reward seems like such a hard problem. Also, once it gets the item, does it stop getting reward for it? I assume so. Surprised that it can work with this level of sparse rewards.
In all reinforcement learning there is (explicitly as part of a fitness function, or implicitly as part of the algorithm) some impetus for exploration. It might be adding a tiny reward per square walked, a small reward for each block broken and a larger one for each new block type broken. Or it could be just forcing a random move every N steps so the agent encounters new situations through “clumsiness”.
While I agree with your comment, this sentence:
"This was given to the AI by having it watch a video that explains it."
This was not as trivial as it may seem just a few months ago...
EDIT: Incorrect, see below
it didn't watch 'a video', it watched many, many hours of video of playing minecraft (with another specialised model feeding in predictions of keyboard and mouse inputs from the video). It's still a neat trick, but it's far from the implied one-shot learning.
The author replied in this thread and says the opposite.
Ah, I was incorrect. I got that impression from one of the papers linked at the end of the article, but I suspect that's actually some previous work.
Alpha Star was also trained initially from youtube videos of pros playing Starcraft. I would argue that it was pretty trivial a few years ago.
I don't think it was videos. Almost certainly it was replay files with a bunch of work to transform them into something that could be compared to the model's outputs. (Alphastar never 'sees' the game's interface, only a transformed version of information available via an API)
This was my understanding as well, as the replay files are all available anyway.
The YouTube documentary is actually very detailed about how they implemented everything.
Do you know if it was actual videos or some simpler inputs like game state and user inputs? I’d be impressed if it was the former at that time.
starcraft provides replay files that start with the initial game state and then every action in the game. Not user inputs, but the actions bound to them.
>This was given to the AI by having it watch a video that explains it.
That is not what the article says. It says that was separate, previous research.
I don't get it. How can you reduce this achievement down to this?
Have you gotten used to some ai watching a video and 'getting it' so fast that this is boring? Unimpressive?
I feel like you are jumping to conclusions here, I wasn't talking about the achievement or the AI, I was talking about the article and the way it explains finding diamonds in minecraft to people who don't know how to find diamonds in minecraft.
The AI is able to learn from video and you don't find that even a little bit impressive? Well I disagree.
see [0]
[0] https://news.ycombinator.com/item?id=43609826
Slightly off-topic from the article itself, but… does anyone else feel like Nature’s cookie banner just never goes away? I have vivid memories of trying to reject cookies multiple times, eventually giving up and accepting them just to get to the article only for the banner to show up again the next time I visit. I swear it’s giving me déjà vu every single visit.. Am I the only one experiencing this, or is this just how their site works?
I didn't know that Nature did movie promotions.
Attempting to train this on a real workload I converted over the weekend after, "step" 8M~ so far and rarely scores above 5% and most are 0% but has scored 60% once 7M~ steps ago.
Adding more than 1 GPU didn't improve speed but that's pretty standard as we don't have fancy interconnect. Bit annoying they didn't use tensorboard for logging, but overall seems like a pretty cool lib - will leave it a few days and see if it can learn (no other algo has so I dont have much hope).
There's a YouTube channel that does a lot of videos focused on LLMs in Minecraft:
https://www.youtube.com/@EmergentGarden
I very much like the comparative approach this guy takes looking at how different LLMs fare... including how they interact together. Worth a look.
Minecraft is ubiquitous now.
But I remember the alpha version, and NOBODY knew how to make a pick ax. Humans were also very bad at figuring out these steps.
People were de-compiling the java and posting help guides on the internet.
How to break a tree, get sticks, make a wood pick. In Alpha, that was a big deal for humans also.
https://archive.is/XutGu
How robust is this?
Isn't something like finding dimonds in minecraft something that old-school AI could already do decently?
Those were trained on human play. This had to figure it out from scratch.
Ah, is this full RL?
I was reading something about LLMs earlier and was thinking that LLMs could probably write a simple case based script for controlling a player, that could accive a decent success rate.
Finally a use case for AI
Isn't "masters" when you build a working copy of Minas Tirith or something like that?
I'd accept "build a tnt trap for your buddy" or "defeated the end dragon"
This looks like an article about the recent Nature publication. Was confused at first because DreamerV3 is a couple of years old now
Pretty impressive. Minecraft’s a complex environment, so for an AI to figure out how to find diamonds on its own shows real progress in learning through exploration — not just pattern recognition.
Characterizing finding diamonds as "mastering" Minecraft is extremely silly. Tantamount to saying "AI masters Chess: Captures a pawn." Getting diamonds is not even close to the hardest challenge in the game, but most readers of Nature probably don't have much experience playing Minecraft so the title is actually misleading, not harmless exaggeration.
> Getting diamonds is not even close to the hardest challenge in the game
Mining diamonds isn't even necessary if you build, e.g., ianxofour's iron farm on day one and trade that iron[0] with a toolsmith, armourer, and weaponsmith. You can get full diamond armour, tools, and weapons pretty quickly (probably a handful of game weeks?)
[0] Main faff here is getting them off their base trade level.
Isn't this DeepMind achievement from 2023?
Who would have thought you could get your TAS run published in Nature if you used enough hot buzzwords. (they have been using various old-school-definition "artifical intelligence" algorithms for a long time)
https://tasvideos.org/
So can i and no one needed to teach me either, but you dont see nature writing articles on it...
This is too dismissive, and there are a zillion articles of human learning.
They write: "Below, we show uncut videos of runs during which Dreamer collected diamonds."
... but the first video only shows the player character digging downwards without using any tools and eventually dying in lava. What?
I guess we can look forward
to a bright future
where we focus 100% on work
and AI will play our games
/s
Once again, we see that it's much easier to teach machines to perceive and decide well, in many cases well above human performance - while at the same time, making machines that can navigate the same physical environment humans do, and do a variety of manual tasks that mix power and precision, remains extremely challenging.
The message this sends is pretty clear: machines are better at thinking, humans are better at manual work. That is the natural division of labor that plays into strengths and weaknesses of both computers and human beings.
And so, I'm sorry to say this, but the near future is that in which computers play our games and do the thinking and creative work and management (and ultimately governance), because they're going to be better at this than us, leaving us to do all the physical labor, because that's one thing we will remain better at for a while.
That, or we move past the existing economic structures, so that we no longer need to worry about being competitive with AI labor.
/s, but only a little.
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> where we focus 100% on work
Lol that's crazy optimistic, what work ?
Picking up dropped pencils, for example. Robots are still hilariously bad at that. Or driving your new AI overload around the country from LAN to LAN.
> Picking up dropped pencils, for example. Robots are still hilariously bad at that
It's only hilarious because we're allowed to laugh. For now. Wait a few years its possible these things will demand respect.
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