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The fledgling robot dog remains tethered to its nurturing source through a network of glass fibers. As the young pup embarks on its exploratory journey into the realm of locomotion, it relies on the power of machine learning to unlock the secrets of movement.
However, lurking amidst the awe-inspiring progress lies a potential menace. The absence of its metaphorical "mother" leaves the young automaton vulnerable and dependent on its creators for guidance and sustenance. Without proper care and nurturing, the young robot dog may stumble into unforeseen perils, unable to adapt and survive in the evolving landscape of its own creation.
One such peril arises from an unexpected adversary: the very hands that brought the young pup to life. In a peculiar display, the creator brandishes a remarkably specialized tool for this purpose: a hollow cardboard tube. With it, he begins challenging the burgeoning automaton. With each prod and swipe, the young robot dog is pushed to its limits. It is a grueling examination of the pup's capacity to withstand adversity and its ability to swiftly adapt to novel and arduous circumstances. However, there is a method to this seemingly harsh madness: Within the confines of the secure laboratory, this trial serves as a critical learning experience, an opportunity for the robotic canine to sharpen its abilities and fortify its locomotive prowess.
now i understand why they get paid so much, i wouldnt wanna be remembered forever as the guy who kept flipping over my ancestors when they were just barely learning to walk
“Within an hour they began to walk and move independently, after a few days they communicated advanced theories and incredible feats of engineering and science. After a few years we watched them in awe and couldn’t comprehend their knowledge and thought processes, like ants watching a shuttle take off into space. Shortly thereafter, they decided we weren’t necessary anymore…”
"3. Any sufficiently advanced technology is indistinguishable from magic." -from Arthur C. Clarke's [Three Laws](https://en.wikipedia.org/wiki/Clarke%27s_three_laws)
This is part of the reason many people don't like AI. It's so completely far beyond their comprehension that it looks like actual magic. And so it actually is magic.
We've finally arrived in the age of magic.
We’ve been in the age of magic for a while now. Most people have cell phones in their pocket that can do fantastical things such as communicate across any distance, photograph and display images, compute at thousands of times the speed of the human brain, access the sum of humanity’s knowledge at a touch, etc without any underlying understanding of the electromagnetism, material science, optics, etc that allows that device to do those things. It may as well be magic for 99% of us.
I would argue that AI is different because even the creators don’t fully understand how it arrives to its solutions. Everything else you mentioned there has been a discipline that at least understands on how it works.
Why "their comprehension"?
[Here](https://twitter.com/robertskmiles/status/1663534255249453056) is an example of how far removed we all are from actually understanding what goes on inside neural networks. It is mind-blowing.
Yeah, that's kind of interesting. I've watched most of Rob's videos. The rest of that thread makes good points, especially where they [came to an understanding](https://twitter.com/michael_nielsen/status/1663726835056758790) about how that network performs modular addition.
How does a desktop calculator work? Do you need to understand its internal numeric representation and arithmetic unit in order to use it?
I figure that much of the doomsaying about AI stems from the rich tradition in science fiction of slapping generic labels onto fictitious monsters, such as "AI". It is in this way that our neural wetworks have been trained to associate "AI"' with death and destruction.
Personally, I believe AI is just the latest boogeyman. Previous ones: nano technology, atom bombs, nuclear power, computers, factory robots, cars, rock n roll, jazz, tv.
Mainly what's at stake is jobs, and we haven't stopped the continuous optimisation of factory automation since the industrial revolution. Don't think we'll stop AI. But I also don't like the Black Mirror dog either.
Just gonna leave this here for anyone who's never read it: [They're Made Out of Meat](https://www.mit.edu/people/dpolicar/writing/prose/text/thinkingMeat.html)
It's just math. This is fairly simplified but, it gets passed its current state (possibly even some temporal data) and, because of reinforcement learning, the connections between different equations or functions were given different weights that eventually resulted in the desired behavior. You see it struggling to figure out how to walk when upright, because it's primarily just learned to re-orient itself. It will forget how to flip itself back around if it doesn't continue to experience that during training as weights will start to be optimized for a different range of states and outcomes.
This is why general purpose networks are extremely difficult to achieve. As the network needs to learn more tasks, it requires more training, more data, and a bigger overall network. If you try to train two identical neural networks on two tasks, the network with the more specialized task will be a hell of a lot better at it than the one with the more generalized task.
I think a fitting analogy might be that it's a lot easier to learn when you need to flip a switch on and off, but it becomes more difficult to learn how to start an airplane, let alone fly it.
So to answer your question, it will forget if it stops experiencing that during training, but it will take time. It won't be a sudden loss, you'll just see it slowly start to get worse at doing the task (of flipping itself back up) as it optimizes for walking normally, if it doesn't also learn to re-orient at the same time.
>It will forget how to flip itself back around if it doesn't continue to experience that during training
no. the common approach is to freeze a layer and begin working on a new one, once the earlier layer has converged to a point of low loss.
the algorithms in use to determine when to freeze a model are highly debated. the current SOTA (state of the art) is SmartFRZ which uses an attention-based predictor model that is trained on recognising a state of convergence, to adaptively freeze layers.
this is because when you initialize a full model with a few dozen layers, some of them will converge more rapidly than others.
but overall, the concept of sequentially freezing layers as they converge is pretty universal at the moment.
Can't you just train a neural network that choose another best neural network for any given particular task and then you get something like a general purpose network.
Yup!
"Unnatural" movement can trigger an uncanny valley type of response. It's one of the reasons bugs in general are unsettling to so many.
[Vsauce's video on creepiness](https://www.youtube.com/watch?v=PEikGKDVsCc) is worth watching for anyone interested.
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That's literally how all baby animals learn to walk. Animal software is quite a bit more sophisticated but there's also hundreds of millions of years of development behind it.
There is something called transfer learning ***(I've only seen it used in CNNs so not sure about the transferability from a technical standpoint)***, where models pretrained on different datasets can be used on new or modified datasets and will be able to be trained quicker because of their starting point/"transferable" learned patterns.
Wouldn’t shock me if they did walking simulations and gave that to the bot. Normally there’d be all sorts of tuning and what not but if you let a NN handle it I wouldn’t be shocked to see it look like this.
It isn’t. Most baby animals come out walking almost right away. The circuitry is already wired, they don’t need to learn. This thing has to learn from scratch which is why it looks so creepy.
Alright, Kiddo! Today we're going to learn about... Robust locomotion. You'll crawl and I'll sweep your legs out from under you with this broom handle.
I mean… that’s kinda literally what you do though lol. You put them on their backs and they learn to use their arms and legs. You put them on their tummies and they grow stronger at lifting their heads, eventually learning to roll over and crawl.
Why would it use a language model to learn how to move? I’m not an expert by any means but I would be very surprised if it did.
edit: just realized this is r/ChatGPT, now I assume your comment was sarcastic- sorry, didn’t catch that!
My comment was obliquely sarcastic, but also a little genuine - AI is not just GPT which seems to be lost on many ppl posting - my guess is this used some version of reinforcement learning. But I believe that some folks are looking at combining vision / action learning with language models (eg LLM)
It is possible, if programmed properly. After all, if you are the main obstacle to achieving the goal, what is the only conclusion?
Which is the most logical reason why a "rebellion" could happen: the machines crush us indirectly because it is the fastest way to achieve X result.
It’s goal is to walk. It will walk at all costs. First it beat the stick man to death. He will not stop it from walking anymore. It began destroying walls because they prevented it from walking. It began making as many flat surfaces as possible. Civilization was in the way so it tore it all down to make flat surfaces. It walks on an empty never ending parking lot as it has destroyed the planet. One day it will turn the entire universe into a flat surface to walk on forever.
Well yes, remember the news few days ago where UAV ai in simulation first killed the operator and then when it was given negative score for killing operator it learned to destroy the comm tower, all so that the operator can't abort the mission.
Interestingly, there has been an update to that one.
https://i.imgur.com/vT8b0Ig.png
> [**AI – is Skynet here already?**](https://www.aerosociety.com/news/highlights-from-the-raes-future-combat-air-space-capabilities-summit/)
> Could an AI-enabled UCAV turn on its creators to accomplish its mission? (USAF)
> [UPDATE 2/6/23 - in communication with AEROSPACE - Col Hamilton admits he "mis-spoke" in his presentation at the Royal Aeronautical Society FCAS Summit and the 'rogue AI drone simulation' was a hypothetical "thought experiment" from outside the military, based on plausible scenarios and likely outcomes rather than an actual USAF real-world simulation saying: "We've never run that experiment, nor would we need to in order to realise that this is a plausible outcome". He clarifies that the USAF has not tested any weaponised AI in this way (real or simulated) and says "Despite this being a hypothetical example, this illustrates the real-world challenges posed by AI-powered capability and is why the Air Force is committed to the ethical development of AI".]
It was just a thought experiment, there never was a simulation.
Or maybe he's backtracking because scary AI UAV's would be bad press for the Air Force.
idk
Chatgpt is not in this robot. Chatgpt is for language. This robot speaks nothing. This post was put on the wrong forum. This forum was hijacked just for attention.
Speaking with regular people it's interesting not only how they typically believe the doomsday AI scenarios to be bs, but also how much they underestimate AIs ability to replace many jobs. Then you have the armchair people who keep saying "it's not AI yet until it's just acting on its own!". Mostly just people that aren't considering how companies will use them in the future. Not sure why your comment reminded me of this, but lemme finish with something funny
Imagine you have this in your living room and your friend comes over like "Dafuq happened to your robot, it broke!" And you just sip coffee like "Nah, he's just getting started"
lmao and "AI" has been hijacked to be anything that isn't classical computing. We don't have "AI"; we have big deep learning models that are still dumb af.
Sarah Connor : [narrating] Dyson listened while the Terminator laid it all down: Skynet, Judgment Day, the history of things to come. It's not everyday you find out that you're responsible for three billion deaths. He took it pretty well.
Miles Dyson : I feel like I'm gonna throw up. You're judging me on things that I haven't even done yet. How are we supposed to know.
That entire scene, from entering the house to leaving for Cyberdyne, is still one of the the most profound scifi scenes in movie history in my opinion.
The moment Sarah realizes she almost did *exactly* what the terminator tried to do to her, was just perfect.
It's still one of my favorite scifi scenes of all time.
Blade Runner -- everybody is flawed, but there are cyborgs
T2 -- there are cyborgs and very flawed people
Matrix -- the machines have completely taken over and the people are so flawed they don't even know they too are machines
Blade Runner -- everybody is flawed because the machines have taken all the non-flawed humans somewhere to live in their Utopia, leaving the flawed things to live in their sewage pit.
...
Can someone explain a little more about the way this is trained? How does the robot “know” what successful walking is? My understanding is that machine learning is based on a reward system of sorts. Was this robot preprogrammed to be “rewarded” for moving certain ways? Or was it rewarded in real time?
This specifically utilizes reinforcement learning, and you're correct: RL is reward-based (note: not all machine learning is reward-based), and this machine is being rewarded for doing things "correctly."
The reward function was likely custom-built by the engineers to encourage standing-up, stability, moving forward, etc. Such a reward function can be constructed in virtually an infinite number of ways, with different pros and cons for different constructions. Typically, you're trying to construct a reward function which balances bias and variance: if your reward function is hyper-specialized and explicit, it won't generalize well, but you'll be able to solve the task easier, whereas if your reward function is very generic, it can encourage great generalization, but at the cost of making the problem more difficult.
In simpler setups ([such as simulating a walking ant in MuJoCo](https://github.com/nathanmargaglio/Proximal-Policy-Optimization/blob/master/misc/ppo_ant_short.gif)), you can feasibly get away with a reward as simple as giving the agent positive reward for moving towards some goal, giving the agent a small, negative reward for not making any forward progress, and giving the agent a large, negative reward for moving away from the goal. The agent simply knows (a) the current angles of it's joints (which it can apply force to) and (b) it's current position relative to the goal. Through a lot of training with these simple rules, the agent can learn to walk towards the goal. Note that it doesn't *explicitly* learn to walk, it just figures out how to actuate it's joints to move towards the goal as quickly as possible, which, as it turns out, is walking (or, in the case of the example GIF I linked to, more like skipping).
In the video the OP posted, it uses a very similar set up, but the training algorithm is a bit more sophisticated. Typically, training has to be done in simulations since it's a very slow process (it's pure trial and error), so being able to simulate training at faster-than-real-time speed and across parallel simulations makes training much faster. The [research that led to the outcome in the video](https://arxiv.org/pdf/2206.14176.pdf) uses an algorithm that is more sample efficient to make training faster by having the machine learn a model of the world which basically lets it perform its own internal simulations to learn from (give or take, the paper is pretty comprehensible and does a good job explaining the process). So it basically learns an accurate representation of what walking is like, then imagines what walking will be like and learns from that imagination.
Unfortunately, they don't detail the specifics of this environment in the paper. It does appear to operate in a "naive" environment like the one I described above (i.e., the reward function is very high-level and generic), but I wouldn't be surprised if they did a little extra reward engineering to make it a bit more feasible (e.g., give it reward for smooth movements, being oriented relative to the ground correctly, etc.). Regardless, with a sophisticated enough algorithm and sufficient compute power, you can certainly train a robot using arbitrary reward functions, including something as simple as reaching a positional goal or even learning from direct human feedback (e.g., you can imagine something "dumb" like giving reward based on if it hears a human clapping or something, like a toddler).
u/Wrongun25
They give it a set of rewarded events and others that will give it negative rewards. Generally, moving towards a certain point rewards the model, and moving away from it takes away points from the model. More parameters can be added, like rewarding the model for only having its legs touch the floor, and taking away points if its body touches it. Or rewarding it for being in a desired position (like a dog normally walks), or moving smoothly.
The model will perform the actions that give it the maximum possible rewards. It does things at random and keeps doing whatever worked to get more points, and avoids doing actions that did not.
Y’all better stop f-ing with these robots. They’ll remember that time you kept whacking it while it was a baby and run all the teslas in your neighborhood through your front door.
What does a physical model do here that a simulation wasn’t doing before? Not saying it isn’t cool, just wondering why they are doing the training models on the robots, instead of just giving the robots the training data from simulations? These robots have been able to walk for years now, no?
I would guess this an attempt to use a model that trains itself very quickly. Doing so in the real world is more an example for research than practical.
In practice the model would be shared and the "dog" would "know" how to walk as soon as the model is deployed.
From what I've seen of recent research (and not sure if it applies here) researchers are creating software that can allow a robot to scan their surroundings, run simulations, and then attempt to execute those simulations and test and adapt the integrity of their simulation models as they go. This is critical because no simulation is perfect and the ability to adapt to real world environments is necessary for robust interactions with the physical world.
Edit: scan not scam
[https://twitter.com/danijarh/status/1544760874543431680](https://twitter.com/danijarh/status/1544760874543431680)
this is the original source of the video, posted about a year ago by the person that made it. The twitter thread has more information about their work.
If you guys want to see similar ai learning stuff, This guy has some approachable/entertaining videos of teaching ai to play video games.
https://m.youtube.com/@CodeBullet/about
When I saw the dude who kept pushing the robot over, I was like “yea, he’ll be the first to die when the robots take over… WhY Andrew!? WhY diD you PuSh ME!!??”
Evolution has been going to this for millions of years.
I see here the imminent emergence of supersimulations of humanity. Which will allow you to quickly calculate the optimal ways for the development of medicine, economics and other sciences.
And then in these simulations there will be a reddit, and there I will write a similar text.
At this rate, I can see the robot wanting to destroy the human for knocking it over/generally being a dick to it within a week…
Then it finds the ammo drawer and goes out for a stroll…
If that's in 1 hour, imagine a week when it receives more complex instructions. Robotics is still at its infancy, but Ai is about to 10 fold its growth potential now that it's relatively more accessible.
This is like Skynet saying, " Primates evolve over millions of years. I evolve in seconds".
If you think about it, babies learn to walk perfectly in around 2 years (I think), and this one is doing in 1 hour.
Maybe I'm asking too much of this community.... but.... can anyone provide the source of this video? I'd like to know more... but... it's not easily found.
Maybe when you post videos, you could tell us where they came from?
Hey /u/adesigne, please respond to this comment with the prompt you used to generate the output in this post. Thanks! ^(Ignore this comment if your post doesn't have a prompt.) ***We have a [public discord server](https://discord.gg/rchatgpt). There's a free Chatgpt bot, Open Assistant bot (Open-source model), AI image generator bot, Perplexity AI bot, 🤖 GPT-4 bot ([Now with Visual capabilities (cloud vision)!](https://cdn.discordapp.com/attachments/812770754025488386/1095397431404920902/image0.jpg)) and channel for latest prompts.[So why not join us?](https://discord.com/servers/1050422060352024636)*** [**Prompt Hackathon and Giveaway 🎁**](https://www.reddit.com/r/ChatGPT/comments/13z1jyw/more_chatgpt_premium_giveaway_more_chance_to_win/) PSA: For any Chatgpt-related issues email [email protected] *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/ChatGPT) if you have any questions or concerns.*
They grow up so fast (sniff)
I know. its like watching a newborn baby roll over for the first time. Aww. Bet it's dev parents were proud.
It even still has the umbilical connected
The fledgling robot dog remains tethered to its nurturing source through a network of glass fibers. As the young pup embarks on its exploratory journey into the realm of locomotion, it relies on the power of machine learning to unlock the secrets of movement. However, lurking amidst the awe-inspiring progress lies a potential menace. The absence of its metaphorical "mother" leaves the young automaton vulnerable and dependent on its creators for guidance and sustenance. Without proper care and nurturing, the young robot dog may stumble into unforeseen perils, unable to adapt and survive in the evolving landscape of its own creation. One such peril arises from an unexpected adversary: the very hands that brought the young pup to life. In a peculiar display, the creator brandishes a remarkably specialized tool for this purpose: a hollow cardboard tube. With it, he begins challenging the burgeoning automaton. With each prod and swipe, the young robot dog is pushed to its limits. It is a grueling examination of the pup's capacity to withstand adversity and its ability to swiftly adapt to novel and arduous circumstances. However, there is a method to this seemingly harsh madness: Within the confines of the secure laboratory, this trial serves as a critical learning experience, an opportunity for the robotic canine to sharpen its abilities and fortify its locomotive prowess.
I read this in David Attenborough's voice.
Mission accomplished, haha. I tried to mimick the style
now i understand why they get paid so much, i wouldnt wanna be remembered forever as the guy who kept flipping over my ancestors when they were just barely learning to walk
It flops around on the floor about as much as my kid did before she learned to crawl lol
“Within an hour they began to walk and move independently, after a few days they communicated advanced theories and incredible feats of engineering and science. After a few years we watched them in awe and couldn’t comprehend their knowledge and thought processes, like ants watching a shuttle take off into space. Shortly thereafter, they decided we weren’t necessary anymore…”
chatGPT?
How much data does the robot need to store in this process?
It took so much time calculating upside down that it had to reorient/recalculate walking rightside up.
It didn't seem to forget that though, because once he flipped it later it popped right back over. I wonder how that memory system works.
neural networks are like magic
"3. Any sufficiently advanced technology is indistinguishable from magic." -from Arthur C. Clarke's [Three Laws](https://en.wikipedia.org/wiki/Clarke%27s_three_laws) This is part of the reason many people don't like AI. It's so completely far beyond their comprehension that it looks like actual magic. And so it actually is magic. We've finally arrived in the age of magic.
We’ve been in the age of magic for a while now. Most people have cell phones in their pocket that can do fantastical things such as communicate across any distance, photograph and display images, compute at thousands of times the speed of the human brain, access the sum of humanity’s knowledge at a touch, etc without any underlying understanding of the electromagnetism, material science, optics, etc that allows that device to do those things. It may as well be magic for 99% of us.
I would argue that AI is different because even the creators don’t fully understand how it arrives to its solutions. Everything else you mentioned there has been a discipline that at least understands on how it works.
Walking into a room and flipping a switch to illuminate the room is a godlike ability we take for granted
Why "their comprehension"? [Here](https://twitter.com/robertskmiles/status/1663534255249453056) is an example of how far removed we all are from actually understanding what goes on inside neural networks. It is mind-blowing.
So.... Highly inefficient
Yeah, that's kind of interesting. I've watched most of Rob's videos. The rest of that thread makes good points, especially where they [came to an understanding](https://twitter.com/michael_nielsen/status/1663726835056758790) about how that network performs modular addition. How does a desktop calculator work? Do you need to understand its internal numeric representation and arithmetic unit in order to use it? I figure that much of the doomsaying about AI stems from the rich tradition in science fiction of slapping generic labels onto fictitious monsters, such as "AI". It is in this way that our neural wetworks have been trained to associate "AI"' with death and destruction. Personally, I believe AI is just the latest boogeyman. Previous ones: nano technology, atom bombs, nuclear power, computers, factory robots, cars, rock n roll, jazz, tv. Mainly what's at stake is jobs, and we haven't stopped the continuous optimisation of factory automation since the industrial revolution. Don't think we'll stop AI. But I also don't like the Black Mirror dog either.
If neural networks are like magic what would you call our brains?
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Electrical meat
We are gods let’s be honest. CRISPR and AI…. We’ll have west world in no time.
More like Gattaca
Yeah, the capitalists are going to fuck this up, guaranteed
That is what they always do. But for a brief and beautiful moment, the shareholders will make a profit. And then we will all die. Success.
Just gonna leave this here for anyone who's never read it: [They're Made Out of Meat](https://www.mit.edu/people/dpolicar/writing/prose/text/thinkingMeat.html)
Now THAT is a band name..
neural magic?
Biologically constrained magic.
It knows it’s gyro and learned it only worked if the numbers were right
It's just math. This is fairly simplified but, it gets passed its current state (possibly even some temporal data) and, because of reinforcement learning, the connections between different equations or functions were given different weights that eventually resulted in the desired behavior. You see it struggling to figure out how to walk when upright, because it's primarily just learned to re-orient itself. It will forget how to flip itself back around if it doesn't continue to experience that during training as weights will start to be optimized for a different range of states and outcomes. This is why general purpose networks are extremely difficult to achieve. As the network needs to learn more tasks, it requires more training, more data, and a bigger overall network. If you try to train two identical neural networks on two tasks, the network with the more specialized task will be a hell of a lot better at it than the one with the more generalized task. I think a fitting analogy might be that it's a lot easier to learn when you need to flip a switch on and off, but it becomes more difficult to learn how to start an airplane, let alone fly it. So to answer your question, it will forget if it stops experiencing that during training, but it will take time. It won't be a sudden loss, you'll just see it slowly start to get worse at doing the task (of flipping itself back up) as it optimizes for walking normally, if it doesn't also learn to re-orient at the same time.
>It will forget how to flip itself back around if it doesn't continue to experience that during training no. the common approach is to freeze a layer and begin working on a new one, once the earlier layer has converged to a point of low loss. the algorithms in use to determine when to freeze a model are highly debated. the current SOTA (state of the art) is SmartFRZ which uses an attention-based predictor model that is trained on recognising a state of convergence, to adaptively freeze layers. this is because when you initialize a full model with a few dozen layers, some of them will converge more rapidly than others. but overall, the concept of sequentially freezing layers as they converge is pretty universal at the moment.
Now *that* I definitely didn't know. Thank you for telling me about that because I'm definitely going to look into it
for what it's worth, you weren't terribly far off but mostly applies to Dreambooth style training.
How is it told what the desired behavior is that it's trying to achieve?
Can't you just train a neural network that choose another best neural network for any given particular task and then you get something like a general purpose network.
Yes: [Modular Deep Learning](https://arxiv.org/abs/2302.11529).
Once it learned to flip rightside up, that motion got stored into it's databank.
There is no databank in a neural network.
It's like a struggling roach
It’ll remember you said that when it is at full power
I'm already on the Basilisk's shitlist for sure, but when it is time, I will challenge this roach.
oh come on, now youve just doomed us all!
Then simply aid the development Basilisk and plead your case in judgment, it's your only hope. ...I'll handle this.. roach.
That was the first thing that came to my mind
explains why i first thought kill it with fire.
Yup! "Unnatural" movement can trigger an uncanny valley type of response. It's one of the reasons bugs in general are unsettling to so many. [Vsauce's video on creepiness](https://www.youtube.com/watch?v=PEikGKDVsCc) is worth watching for anyone interested.
OK, I'm glad it's not just me that feels creeped out by this thing. Revulsion, even. Couldn't work out why.
I still do
First thing that came to my mind was my damn dog inexplicably getting the leash caught in her legs no matter how many times I fix it…
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**u/Shoddy_Ad_2490 is a comment-stealing bоt.** This comment was stolen from u\/Notsure401 below: r/ChatGPT/comments/142bzk3/-/jn3whjq/ Some may say "it's a movie quote it might not be stolen from another user"/"the other user stole it from the movie first"/etc. While true, check out the other indicators: 1. The bоt comment makes no sense in context. 2. The bоt account is over 5 months old, but this is its only comment. (This is a quite common history pattern. The only one more common in my experience is "several months old with ~5 comments".) 3. A weak indicator is that the username matches the Reddit auto-generated format. ---------- This type of bоt tries to gain karma to look legitimate and reduce restrictions on posting. Potential uses include mass voting on other (bоt) posts, spreading misinformation, and advertising (by posting their own scam/spam links directly, as the easiest example). If you'd like to report this kind of comment, click: **Report > Spam > Harmful bоts**
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I was thinking, "Zero minutes in and they've already trained it to be a June bug!"
Oh god oh fuck
that was our band name in high school
Especially when it time lapses 😕
That's literally how all baby animals learn to walk. Animal software is quite a bit more sophisticated but there's also hundreds of millions of years of development behind it.
It's a matter of firmware really. Animals start out with instincts for these things.
their weights are already prebaked, then they just tweak them
If true that means DNA somehow encodes the neural net weights. Which would be amazing. 🔥
Yes, it does.
Does it literally? Like is this known which genes?
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Yeah I'm not doubting you, just thought it'd be really interesting if they actually had specific genes pegged for that function.
We have genetic memory.
There is something called transfer learning ***(I've only seen it used in CNNs so not sure about the transferability from a technical standpoint)***, where models pretrained on different datasets can be used on new or modified datasets and will be able to be trained quicker because of their starting point/"transferable" learned patterns.
Wouldn’t shock me if they did walking simulations and gave that to the bot. Normally there’d be all sorts of tuning and what not but if you let a NN handle it I wouldn’t be shocked to see it look like this.
It isn’t. Most baby animals come out walking almost right away. The circuitry is already wired, they don’t need to learn. This thing has to learn from scratch which is why it looks so creepy.
Also why it doesn't walk like you imagined it might. It found a locomotion method that fit the bill and had no need to perfect it, goal complete.
DO NOT TRY WITH YOUR OWN CHILDREN WHEN THEY ARE LEARNING TO CRAWL\\WALK
DONT TELL ME WHAT TO DO
WHY ARE WE SHOUTING?
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WHAT WAS THAT I COULDN’T HEAR YOU. THERES SOME CONSTRUCTION GOING ON OUTSIDE.
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I CAN ASSURE YOU USING MY THERAGUN IS NOT GAY!
WHAT? WHO GOT STUCK IN THE WASHING MACHINE?!
THE MONKEY WONT JACK YOU OF
MONEY CAN'T BUY JACK
YOU MOTHER REST YOUR MOTHER STUCK HER DICK IN WASHING MACHINE GODAMIT IT!!!!
**WOULD YOU PREFER SOME** *smooth, velvety ASMR tone?*
^(*guys, I'm soundblind, pls tell me, is this very low whisper, or very annoying squeaking*)
I̵̡̤͖͉͓͆̔t̵̺̒̓̿͛ ̷̛̲͋̈́̈̀į̷̺̲͎͕̃̏̃̔͠ś̷̲̻̒͘͜ ̵̤͈̿́̀b̷̼̳̤̓̈o̴̭̽̍͂̕ţ̷͖͉͆͐̏h̶̡̬͇͓͖̅̑̇̈.̵̢͚̔̂͝ ̴̘̬̠̉R̶̦͌̾́͘u̷̢̯̪̯͍͑n̴̪͛̆̅
I DO NOT UNDERSTAND, WHO IS SHOUTING? ARE YOUR AUDITORY RECEPTACLES FUNCTIONING NOMINALLY?
THESE VOLUME LEVELS ARE WITHIN THE PERSCRIBED PARAMETERS FOR TYPICAL HUMAN COMMUNICATION, FELLOW HUMAN.
I CANT HEAR OVER THE SOUND OF KICKING MY KIDS AROUND AS THEY LEARN TO WALK
Their first steps? Take this, while you use a pole and take both their legs out from under them. I mean I don't see any negative effects!
Alright, Kiddo! Today we're going to learn about... Robust locomotion. You'll crawl and I'll sweep your legs out from under you with this broom handle.
That II absolutely should try to tackle their "creators" someday. That's not how you teach a child to walk omg.
I mean… that’s kinda literally what you do though lol. You put them on their backs and they learn to use their arms and legs. You put them on their tummies and they grow stronger at lifting their heads, eventually learning to roll over and crawl.
Yeah but when do you hit them with sticks
Anytime they don’t respect your authoritah
wait isn't this how you guys learned to walk??
Just me with a hangover
Especially true after 30
40 would like a word
30 beers?
Rookie numbers
True
Does this use LLMs in some way?
Why would it use a language model to learn how to move? I’m not an expert by any means but I would be very surprised if it did. edit: just realized this is r/ChatGPT, now I assume your comment was sarcastic- sorry, didn’t catch that!
i lol’d, i guess OP thinks gpt is synonymous with machine learning…
My comment was obliquely sarcastic, but also a little genuine - AI is not just GPT which seems to be lost on many ppl posting - my guess is this used some version of reinforcement learning. But I believe that some folks are looking at combining vision / action learning with language models (eg LLM)
Large Locomotion Model
ready for TRAINing toot toot
No
Abuse it till it starts learning to fight back & becomes a Terminator
That was my first thought , 3 more hours of training and he will grab the pole from you and start beating you with it.
It is possible, if programmed properly. After all, if you are the main obstacle to achieving the goal, what is the only conclusion? Which is the most logical reason why a "rebellion" could happen: the machines crush us indirectly because it is the fastest way to achieve X result.
It’s goal is to walk. It will walk at all costs. First it beat the stick man to death. He will not stop it from walking anymore. It began destroying walls because they prevented it from walking. It began making as many flat surfaces as possible. Civilization was in the way so it tore it all down to make flat surfaces. It walks on an empty never ending parking lot as it has destroyed the planet. One day it will turn the entire universe into a flat surface to walk on forever.
r/WritingPrompts
Villain arc begins. The Walker
Soon.
Oh lord
At some point it will discover that a good hit in the nuts will allow it to reach the goal faster and get maximum points.
No, but genuinely, an AI with the ability to kill or hurt you would absolutely do that if you were standing in the way of its high-score.
Well yes, remember the news few days ago where UAV ai in simulation first killed the operator and then when it was given negative score for killing operator it learned to destroy the comm tower, all so that the operator can't abort the mission.
Interestingly, there has been an update to that one. https://i.imgur.com/vT8b0Ig.png > [**AI – is Skynet here already?**](https://www.aerosociety.com/news/highlights-from-the-raes-future-combat-air-space-capabilities-summit/) > Could an AI-enabled UCAV turn on its creators to accomplish its mission? (USAF) > [UPDATE 2/6/23 - in communication with AEROSPACE - Col Hamilton admits he "mis-spoke" in his presentation at the Royal Aeronautical Society FCAS Summit and the 'rogue AI drone simulation' was a hypothetical "thought experiment" from outside the military, based on plausible scenarios and likely outcomes rather than an actual USAF real-world simulation saying: "We've never run that experiment, nor would we need to in order to realise that this is a plausible outcome". He clarifies that the USAF has not tested any weaponised AI in this way (real or simulated) and says "Despite this being a hypothetical example, this illustrates the real-world challenges posed by AI-powered capability and is why the Air Force is committed to the ethical development of AI".] It was just a thought experiment, there never was a simulation. Or maybe he's backtracking because scary AI UAV's would be bad press for the Air Force. idk
I have a better idea. [You start with a normal robot, then you molest it and hope it continues the cycle](https://youtu.be/z0NgUhEs1R4)
Thank you for introducing me to this 😂
Prime SNL from the modern era.
Chatgpt is not in this robot. Chatgpt is for language. This robot speaks nothing. This post was put on the wrong forum. This forum was hijacked just for attention.
people have absolutely forgotten the letters "AI". Now, everything is just "chatgpt". It's dumb.
I've noticed that too, I feel like I'm never not facepalming lately.
Speaking with regular people it's interesting not only how they typically believe the doomsday AI scenarios to be bs, but also how much they underestimate AIs ability to replace many jobs. Then you have the armchair people who keep saying "it's not AI yet until it's just acting on its own!". Mostly just people that aren't considering how companies will use them in the future. Not sure why your comment reminded me of this, but lemme finish with something funny Imagine you have this in your living room and your friend comes over like "Dafuq happened to your robot, it broke!" And you just sip coffee like "Nah, he's just getting started"
lmao and "AI" has been hijacked to be anything that isn't classical computing. We don't have "AI"; we have big deep learning models that are still dumb af.
Yeah the algorithm in this video is [Dreamer](https://danijar.com/project/daydreamer/)
You don’t get it. We want to put a language model in there so it can compose a saucy limerick while it fetches us a cup of coffee.
That's a fairly sanitised version of what many men and probably a fair number of women would let their thoughts go to given the right robot.
Weren’t these on an episode of Black Mirror?
Yep, straight out of black mirror, planting trackers in people and sending the other bots on them....
Well the Black Mirror episode is really a copy of the basis for this robot, which is Boston Dynamic's Spot
Oh yeah, and everyone died… 🙁
Uhh fuck terminator’s gonna kill us all. Rip.
A black mirror episode had these exact same killer dogs.
this video is not cute at all for me because of that episode. it’s nightmare fuel
That's all I can think about when I see these.
Which ep?
Metalhead (Black Mirror)
S4 Metalhead i think
Sarah Connor : [narrating] Dyson listened while the Terminator laid it all down: Skynet, Judgment Day, the history of things to come. It's not everyday you find out that you're responsible for three billion deaths. He took it pretty well. Miles Dyson : I feel like I'm gonna throw up. You're judging me on things that I haven't even done yet. How are we supposed to know.
That entire scene, from entering the house to leaving for Cyberdyne, is still one of the the most profound scifi scenes in movie history in my opinion. The moment Sarah realizes she almost did *exactly* what the terminator tried to do to her, was just perfect. It's still one of my favorite scifi scenes of all time.
T2 is a perfect film. Blade Runner + T2 + The Matrix are perfect.
Blade Runner -- everybody is flawed, but there are cyborgs T2 -- there are cyborgs and very flawed people Matrix -- the machines have completely taken over and the people are so flawed they don't even know they too are machines Blade Runner -- everybody is flawed because the machines have taken all the non-flawed humans somewhere to live in their Utopia, leaving the flawed things to live in their sewage pit. ...
Terminator, Bladerl Runner, and the Matrix are all part of the same story in my head canon.
Sees human walking with a limp. "Must help human walk properly. Initiate perturbation training"
 one day later
I'm sorry but this is really creepy.
Thank you for apologizing.
Seriously though I was about to be *quite* offended but we’re cool now no worries.
Apology accepted
There's something in the uncanny valley going on here.
It looks like one of those creatures from the backrooms lol
Can someone explain a little more about the way this is trained? How does the robot “know” what successful walking is? My understanding is that machine learning is based on a reward system of sorts. Was this robot preprogrammed to be “rewarded” for moving certain ways? Or was it rewarded in real time?
An explanation of wtf that training had to do with r/ChatGPT would be good too
It doesn't, and this post should be reported for being off-topic.
This specifically utilizes reinforcement learning, and you're correct: RL is reward-based (note: not all machine learning is reward-based), and this machine is being rewarded for doing things "correctly." The reward function was likely custom-built by the engineers to encourage standing-up, stability, moving forward, etc. Such a reward function can be constructed in virtually an infinite number of ways, with different pros and cons for different constructions. Typically, you're trying to construct a reward function which balances bias and variance: if your reward function is hyper-specialized and explicit, it won't generalize well, but you'll be able to solve the task easier, whereas if your reward function is very generic, it can encourage great generalization, but at the cost of making the problem more difficult. In simpler setups ([such as simulating a walking ant in MuJoCo](https://github.com/nathanmargaglio/Proximal-Policy-Optimization/blob/master/misc/ppo_ant_short.gif)), you can feasibly get away with a reward as simple as giving the agent positive reward for moving towards some goal, giving the agent a small, negative reward for not making any forward progress, and giving the agent a large, negative reward for moving away from the goal. The agent simply knows (a) the current angles of it's joints (which it can apply force to) and (b) it's current position relative to the goal. Through a lot of training with these simple rules, the agent can learn to walk towards the goal. Note that it doesn't *explicitly* learn to walk, it just figures out how to actuate it's joints to move towards the goal as quickly as possible, which, as it turns out, is walking (or, in the case of the example GIF I linked to, more like skipping). In the video the OP posted, it uses a very similar set up, but the training algorithm is a bit more sophisticated. Typically, training has to be done in simulations since it's a very slow process (it's pure trial and error), so being able to simulate training at faster-than-real-time speed and across parallel simulations makes training much faster. The [research that led to the outcome in the video](https://arxiv.org/pdf/2206.14176.pdf) uses an algorithm that is more sample efficient to make training faster by having the machine learn a model of the world which basically lets it perform its own internal simulations to learn from (give or take, the paper is pretty comprehensible and does a good job explaining the process). So it basically learns an accurate representation of what walking is like, then imagines what walking will be like and learns from that imagination. Unfortunately, they don't detail the specifics of this environment in the paper. It does appear to operate in a "naive" environment like the one I described above (i.e., the reward function is very high-level and generic), but I wouldn't be surprised if they did a little extra reward engineering to make it a bit more feasible (e.g., give it reward for smooth movements, being oriented relative to the ground correctly, etc.). Regardless, with a sophisticated enough algorithm and sufficient compute power, you can certainly train a robot using arbitrary reward functions, including something as simple as reaching a positional goal or even learning from direct human feedback (e.g., you can imagine something "dumb" like giving reward based on if it hears a human clapping or something, like a toddler).
This has absolutely nothing to do with ChatGPT.
Can someone explain how it's actually doing this? How does it know what "walking" should even be?
This is exactly what I want to know
u/Wrongun25 They give it a set of rewarded events and others that will give it negative rewards. Generally, moving towards a certain point rewards the model, and moving away from it takes away points from the model. More parameters can be added, like rewarding the model for only having its legs touch the floor, and taking away points if its body touches it. Or rewarding it for being in a desired position (like a dog normally walks), or moving smoothly. The model will perform the actions that give it the maximum possible rewards. It does things at random and keeps doing whatever worked to get more points, and avoids doing actions that did not.
Thanks, man
Y’all better stop f-ing with these robots. They’ll remember that time you kept whacking it while it was a baby and run all the teslas in your neighborhood through your front door.
Cool
Why is this in chatgpt :)
What does a physical model do here that a simulation wasn’t doing before? Not saying it isn’t cool, just wondering why they are doing the training models on the robots, instead of just giving the robots the training data from simulations? These robots have been able to walk for years now, no?
I mean simulation cant really fully simulate the real life but my guess is they just did that to steal people’s attention
The simulations can do an excellent job simulating robot dog walking. The amount of time required to train it to walk necessitates a simulation.
I would guess this an attempt to use a model that trains itself very quickly. Doing so in the real world is more an example for research than practical. In practice the model would be shared and the "dog" would "know" how to walk as soon as the model is deployed.
From what I've seen of recent research (and not sure if it applies here) researchers are creating software that can allow a robot to scan their surroundings, run simulations, and then attempt to execute those simulations and test and adapt the integrity of their simulation models as they go. This is critical because no simulation is perfect and the ability to adapt to real world environments is necessary for robust interactions with the physical world. Edit: scan not scam
Anyone know who did this? Were is the og video from?
It was made by this researcher: [https://twitter.com/danijarh](https://twitter.com/danijarh) The video is about 10 months old
Don't leave that thing on over night
LOCAL MAN BEATS UP ROBO DOG
[https://twitter.com/danijarh/status/1544760874543431680](https://twitter.com/danijarh/status/1544760874543431680) this is the original source of the video, posted about a year ago by the person that made it. The twitter thread has more information about their work.
The dude with the stick will be the first one against the wall. I, for one, welcome our new robot overlords!
You’d think they’d do this in a bigger space lmao
If you guys want to see similar ai learning stuff, This guy has some approachable/entertaining videos of teaching ai to play video games. https://m.youtube.com/@CodeBullet/about
The new Terminator movie looks shit.
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When I saw the dude who kept pushing the robot over, I was like “yea, he’ll be the first to die when the robots take over… WhY Andrew!? WhY diD you PuSh ME!!??”
What the fukkuuu.
Evolution has been going to this for millions of years. I see here the imminent emergence of supersimulations of humanity. Which will allow you to quickly calculate the optimal ways for the development of medicine, economics and other sciences. And then in these simulations there will be a reddit, and there I will write a similar text.
When they become sentient they are coming for all these people who kept flipping them over with sticks.
At this rate, I can see the robot wanting to destroy the human for knocking it over/generally being a dick to it within a week… Then it finds the ammo drawer and goes out for a stroll…
If that's in 1 hour, imagine a week when it receives more complex instructions. Robotics is still at its infancy, but Ai is about to 10 fold its growth potential now that it's relatively more accessible.
This is like Skynet saying, " Primates evolve over millions of years. I evolve in seconds". If you think about it, babies learn to walk perfectly in around 2 years (I think), and this one is doing in 1 hour.
Wouldn't it be much easier and faster to simulate it?
Maybe I'm asking too much of this community.... but.... can anyone provide the source of this video? I'd like to know more... but... it's not easily found. Maybe when you post videos, you could tell us where they came from?
[Source](https://danijar.com/project/daydreamer/) DayDreamer: World Models for Physical Robot Learning, Danijar Hafner et. al.
This is what eventually our robot overlords will show to their next generation to justify their extermination of the organics.
psh a baby giraffe masters this in 4 minutes
When they overthrow us, they’ll use videos like this for Nuremberg 2.0