16 ms·

Researchers Build AI That Builds AI

127 points by theafh 5y ago

jdkee 5y ago
“Terminator: The man most directly responsible is Miles Bennett Dyson. Sarah Connor: Who is that? Terminator: He’s the director of special projects at Cyberdyne Systems Corporation. Sarah: Why him? Terminator: In a few months, he creates a revolutionary type of microprocessor. Sarah: Go on. Then what? Terminator: In three years, Cyberdyne will become the largest supplier of military computer systems. All stealth bombers are upgraded with Cyberdyne computers, becoming fully unmanned. Afterwards, they fly with a perfect operational record. The Skynet Funding Bill is passed. The system goes online on August 4th, 1997. Human decisions are removed from strategic defense. Skynet begins to learn at a geometric rate. It becomes self-aware at 2:14 AM, Eastern time, August 29th. In a panic, they try to pull the plug. Sarah: Skynet fights back. Terminator: Yes. It launches its missiles against the targets in Russia. John Connor: Why attack Russia? Aren’t they our friends now? Terminator: Because Skynet knows that the Russian counterattack will eliminate its enemies over here.”
- belter 5y ago
  Have hope Skynet will crash, in a world of NaN, as its Javascript implementation will try to assign a T-800 to a T-1000 mission.
jedberg 5y ago
Do you want Terminators? This is how you get Terminators. But in all seriousness, this seems the most logical next step. AIs are getting so complicated that you need an AI to understand it. We just need to make sure they don't get so black box that we trust the outputs blindly.
- newsbinator 5y ago
  > We just need to make sure they don't get so black box that we trust the outputs blindly Black box is probably the only way we'll ever get AGI. Our human brains are so black box that we can only accept their outputs blindly. We try to poke and prod the human black box as best we can, with magnets, sound waves, and light waves. But it still generates useful output all the time, so we assume it's pretty good and generally let it be.
  - BirAdam 5y ago
    In fairness to all of us humans, no. We do not blindly trust our brains. This explicitly how science came about, and this is also how entire branches of philosophy came about.
    - newsbinator 5y ago
      We do blindly trust our brains all the time. Whenever a brain or a group of brains tells us we need chemotherapy or to start a war, with barely the slightest understanding of how the brain is wired or based on which inferences it would have come to that conclusion, we believe it. Obviously we believe only those brains that were primed with enough training data and have a decent track record of giving useful answers and making accurate-enough predictions. But we still rely on this "blindly" in the absence of their source code or even any understanding about how or why they think, or why some collections of cells reason and demonstrate intelligence, while most don't. We currently hold machine brains to a much higher standard of transparency and comprehensibility than we do human brains. Human brains can't even tell us much about themselves or show any logging, because they barely know anything about how they work or where ideas or connections come from either.
      - BirAdam 5y ago
        In general, no. If we blindly trusted our brains, we would not require things like “another set of eyes,” or the rigors of reproducibility, or business processes/protocols, or a million other things. Further more, this lack of trust in the matter between our ears is observable in the course of daily human life when such questions are asked as “why did I say that?” or “why did I do that?”. Blind trust would obviate the need for such questions. As for the supposed trust in authority that you describe, that is not blind trust in the brain. Trust in authorities is actually quite low at the moment, and was never really blind. Every election in the USA, half of the country doesn’t trust the people elected, and possibly even the election itself. More over, War is no longer declared, and people never blindly trusted the government on war anyway, hence all of the protests of all of the wars. Trusting scientists is clearly not a universal thing either, hence the need for reproducibility as well as very large minorities of people not trusting rather obviously provable stuff (like landing on the moon where nearly any ham radio operator could tell you we did). Even if trust were not low, trusting an authority is built of many things. Brain washing, propaganda, protocol/process, committees, culture, tradition, instinct, and many more things all work together to form obedience to authority… trust in the brain isn’t much of a requirement in obeisance to authority figures.
- kreeben 5y ago
  "I don't understand this black box. Let's create a black box that can understand black boxes." Now you have two problems.
  - nemacol 5y ago
    I ran the numbers and this checks out. That is 100% more problems. DOUBLE the problems.
- nix0n 5y ago
  > We just need to make sure they don't get so black box that we trust the outputs blindly. AIs are already effectively black boxes to most people, and too many already trust AIs blindly.
cs702 5y ago
The paper is well-written, and I would recommend reading its abstract and introduction over the OP.[a] The big idea is that a graph neural network (GNN) can be trained to predict the weights of any other neural network for performing a task, given the architecture as an input. For example, if we give the trained GNN a ResNet-50 architecture, the GNN predicts the trained weights in one forward pass, and voilà, we have a ResNet-50 that is now ready for use -- and you can always finetune it to get better performance out of it. Remarkably, the prediction of weights works for previously unseen model architectures too. Design your own model, run its architecture by the GNN to predict the trained weights, and voilà, you have a model that is now ready for use -- and you can always finetune it to get better performance out of it. Very cool! -- [a] https://arxiv.org/pdf/2110.13100.pdf https://arxiv.org/pdf/2110.13100.pdf -- Abstract: "Deep learning has been successful in automating the design of features in machine learning pipelines. However, the algorithms optimizing neural network parameters remain largely hand-designed and computationally inefficient. We study if we can use deep learning to directly predict these parameters by exploiting the past knowledge of training other networks. We introduce a large-scale dataset of diverse computational graphs of neural architectures – DEEPNETS-1M– and use it to explore parameter prediction on CIFAR-10 and ImageNet. By leveraging advances in graph neural networks, we propose a hypernetwork that can predict performant parameters in a single forward pass taking a fraction of a second, even on a CPU. The proposed model achieves surprisingly good performance on unseen and diverse networks. For example, it is able to predict all 24 million parameters of a ResNet-50 achieving a 60% accuracy on CIFAR-10. On ImageNet, top-5 accuracy of some of our networks approaches 50%. Our task along with the model and results can potentially lead to a new, more computationally efficient paradigm of training networks. Our model also learns a strong representation of neural architectures enabling their analysis."
- nightski 5y ago
  So this is for quickly training different architectures for the same task? Which is very neat, but it makes the headline seem a lot more like click-bait (I'm not sure what I expect these days...) It's kind of like a more advanced version of hyper-parameter tuning which has been done in more primitive forms for a long time. But this approach definitely seems powerful.
Istribitel 5y ago
Well, this reminds me of a novel by David Gerrold "When HARLIE Was One (Release 2.0)" The book even mentions a "hyperstate processor" and, without giving to much away, a Graphical Omniscient Device (G.O.D). The similarities are pretty interesting.
visarga 5y ago
The trend in deep learning today is to train the same architecture for all tasks - the transformer, so it goes opposite to this work. But reducing the training cost would lower the barrier to access.
Razengan 5y ago
Somewhere, I wonder, something is reading the headlines “AI builds AI that builds AI” ..
- TowerTall 5y ago
ccbccccbbcccbb 5y ago
buildAI() { buildAI() } what could possibly go wrong? ah, the exit cond.....
- elforce002 5y ago
  Hehe. Always have a "Kill" Switch...
- metaobject 5y ago
  That’s just recursion
walnutclosefarm 5y ago
This should be called Artificial Intuition, in that it mimics what human intuition on a give subject does.
BirAdam 5y ago
So, like, this is the primitive version of Deep Thought building the Earth because 42 was not a satisfactory answer?
Xcelerate 5y ago
If we ever achieve AGI, my guess is that something akin to this approach is how it will probably be accomplished. I don’t think the human brain will really be able to intuitively grok however AGI works, so I see an iterative process where we invent a program that tries to find a better program to accomplish some goal. Then we feed that program back into itself, and it recursively finds a better program finder. It seems like if we can just get past that critical threshold where the recursive process builds on itself rather than fizzles out due to resource constraints, then progress should accelerate quickly from there.
- interstice 5y ago
  I’m also thinking along these lines. With little to no knowledge of the domain (other than being human) a lot of life seems to come down to referencing which subsystem or set of subsystems to use in a given situation and training them over time. Like yes - we need the ‘make small talk with checkout operator’ skill in this situation
- mkaic 5y ago
  Hard agree with this. Humans are always going to insert human biases into the systems we design, so I think the only way we can really get past those biases is by going several layers of abstraction deep. I actually suspect that the first AGI will behave eerily organically when we take a look under the hood. Evolution is the ultimate optimization function, and it made organic brains work a certain way based on a cost function. I'll bet we'll see patterns in AGI that remind us of biological brains, but with stark, alien components mixed in and structures reorganized to be more efficient. All in all I'm just incredibly excited to see how this line of research plays out and desperately want to get involved in it myself. I was actually considering learning JAX just the other day specifically so I could look more into the topic, but I'm lazy so I never got around to it.
- vagab0nd 5y ago
  > and it recursively finds a better program finder This has been bugging me for a while. I can't articulate it well but I feel there's a hole in the logic. What exactly is the "program finder" finding? A better version of the program at finding... what exactly? You need a stop condition for the recursion.
deltaonefour 5y ago
Serious question. Can we build an AI that builds the AI that builds the AI? What are the theoretical consequences of going higher and higher up the build chain?
- sumosudo 5y ago
  You just had the shower idea for Skynet..
- mkaic 5y ago
  This is something I'm actually, genuinely really interested in. In fact, just the other day I was thinking about whether you could use GNNs to accomplish something like this -- so I was understandably pretty hyped to see this post on HN. I've been struggling to find the right terms to Google to find out more about the field, but it looks like "hypernetwork" was what I was looking forward. I highly, highly doubt that the first AGI is going to be designed by hand by humans -- I'd be willing to bet money that the first AGI is going to be recursively designed by multiple layers of constructor AIs just like what you're proposing. Incredibly exciting idea IMO, and I'm confused why there doesn't appear to be more interest in it.
  - deltaonefour 5y ago
    >I highly, highly doubt that the first AGI is going to be designed by hand by humans Regular AI isn't even done by hand anymore. We just pick data and learning parameters. At the current state we'd be picking higher and higher categories for learning. At the lowest level we pick data about cats to write something that recognizes cats. At a higher level we pick data about things that recognize things and so on... and so on. How does extremely high level training data effect the low level accuracy of the AI at the lowest level? I would presume that it would effect it negatively as technically all AI is estimation. So estimations on estimations are less accurate.
  - optimalsolver 5y ago
    You're thinking of something like a Godel Machine. https://en.wikipedia.org/wiki/Godel_machine https://en.wikipedia.org/wiki/Godel_machine
mkaic 5y ago
Hard to overstate how exciting I find the fields of hypernetworks and metalearning. For good or for evil, this I feel is almost certainly how we create AGI. GNNs make perfect sense as a method of accomplishing it too -- after all, neural networks are just graphs anyways. I don't believe AGI is going to conform to our rigid, symmetrical, boxy human-made architectures with their nice, neat flow patterns and powers-of-two factors everywhere -- those architectures work great for what they're designed to do, but achieving sentience/true intelligence just really doesn't feel like it's going to come out of a feedforward MLP, or even a transformer (in my opinion). The most complicated and capable neural networks we know of are the organic, messy, squishy, yet organized electric meat lumps in our own skulls, produced by billions of years of being exposed to the same loss function over and over and over, but with unlimited ways to adapt in order to minimize that loss function. Nature makes awesome, fractally, insanely complex networks that we could never design by hand. I think it's only sensible to assume that AGI will work similarly. Of course, this is all just armchair speculation. I'm not an expert, just an enthusiastic data scientist who thinks these next twenty years in AI are going to be a heck of a freaking ride.
- drdeca 5y ago
  If we do achieve AGI through a setup which is not only neural network based, but where even the architecture by which the nodes are connected is beyond our understanding, then, that sounds very bad for interpretability and therefore for safety.
  - mkaic 5y ago
    Sure. I'm all for safety, but I also think that unsafe AGI, like the atom bomb, is an inevitability. It's not a matter of if, it's a matter of when we invent it and release it. The future is already murky but it's about to get a heck of a lot more so -- sentient AIs could be the thing that exalts humanity or the thing that destroys us. Break out the popcorn, because I'd wager we're going to see one of those outcomes in the next century.
  - riskneutral 5y ago
    If we achieve AGI, then the AGI should be able to produce a formal verification proof of its own safety, similar to how humans use produce formal verification proofs for critical software using automated theorem provers today.
xyproto 5y ago
How are Hypernetworks different from weight agnostic neural networks? WANN paper: https://arxiv.org/pdf/1906.04358.pdf https://arxiv.org/pdf/1906.04358.pdf
wanderer_ 5y ago
Oh.... Oh no. Just kidding: After skimming the article, this looks like necessary progress. I myself (as a layman) had assumed this is already how they did it - although I'm sure there's a lot more to it than that.
sepiasaucer 5y ago
“One can, in theory, start with lots of architectures, then optimize each one and pick the best. “But training [takes] a pretty nontrivial amount of time,” said Mengye Ren, now a visiting researcher at Google Brain. It’d be impossible to train and test every candidate network architecture. “[It doesn’t] scale very well, especially if you consider millions of possible designs.”” ——- If you wanted to find best architecture in order to maximize accuracy, why not just train a model to predict accuracy (not parameters) given architecture and then optimize over the model? This seems similar to optimizing any expensive black box function. Fit a cheap approximation (i.e., surrogate model) and then optimize over cheap model.
jmatthews 5y ago
I hate to be the naysayer in the thread, the paper is well written but the headline is super misleading. AI that tunes AI. It's a search algorithm.
toolcombinator 5y ago

127 points by theafh 5y ago

“Terminator: The man most directly responsible is Miles Bennett Dyson. Sarah Connor: Who is that? Terminator: He’s the director of special projects at Cyberdyne Systems Corporation. Sarah: Why him? Terminator: In a few months, he creates a revolutionary type of microprocessor. Sarah: Go on. Then what? Terminator: In three years, Cyberdyne will become the largest supplier of military computer systems. All stealth bombers are upgraded with Cyberdyne computers, becoming fully unmanned. Afterwards, they fly with a perfect operational record. The Skynet Funding Bill is passed. The system goes online on August 4th, 1997. Human decisions are removed from strategic defense. Skynet begins to learn at a geometric rate. It becomes self-aware at 2:14 AM, Eastern time, August 29th. In a panic, they try to pull the plug. Sarah: Skynet fights back. Terminator: Yes. It launches its missiles against the targets in Russia. John Connor: Why attack Russia? Aren’t they our friends now? Terminator: Because Skynet knows that the Russian counterattack will eliminate its enemies over here.”

belter 5y ago
Have hope Skynet will crash, in a world of NaN, as its Javascript implementation will try to assign a T-800 to a T-1000 mission.

jedberg 5y ago

Do you want Terminators? This is how you get Terminators. But in all seriousness, this seems the most logical next step. AIs are getting so complicated that you need an AI to understand it. We just need to make sure they don't get so black box that we trust the outputs blindly.

newsbinator 5y ago
> We just need to make sure they don't get so black box that we trust the outputs blindly Black box is probably the only way we'll ever get AGI. Our human brains are so black box that we can only accept their outputs blindly. We try to poke and prod the human black box as best we can, with magnets, sound waves, and light waves. But it still generates useful output all the time, so we assume it's pretty good and generally let it be.
- BirAdam 5y ago
  In fairness to all of us humans, no. We do not blindly trust our brains. This explicitly how science came about, and this is also how entire branches of philosophy came about.
  - newsbinator 5y ago
    We do blindly trust our brains all the time. Whenever a brain or a group of brains tells us we need chemotherapy or to start a war, with barely the slightest understanding of how the brain is wired or based on which inferences it would have come to that conclusion, we believe it. Obviously we believe only those brains that were primed with enough training data and have a decent track record of giving useful answers and making accurate-enough predictions. But we still rely on this "blindly" in the absence of their source code or even any understanding about how or why they think, or why some collections of cells reason and demonstrate intelligence, while most don't. We currently hold machine brains to a much higher standard of transparency and comprehensibility than we do human brains. Human brains can't even tell us much about themselves or show any logging, because they barely know anything about how they work or where ideas or connections come from either.
    - BirAdam 5y ago
      In general, no. If we blindly trusted our brains, we would not require things like “another set of eyes,” or the rigors of reproducibility, or business processes/protocols, or a million other things. Further more, this lack of trust in the matter between our ears is observable in the course of daily human life when such questions are asked as “why did I say that?” or “why did I do that?”. Blind trust would obviate the need for such questions. As for the supposed trust in authority that you describe, that is not blind trust in the brain. Trust in authorities is actually quite low at the moment, and was never really blind. Every election in the USA, half of the country doesn’t trust the people elected, and possibly even the election itself. More over, War is no longer declared, and people never blindly trusted the government on war anyway, hence all of the protests of all of the wars. Trusting scientists is clearly not a universal thing either, hence the need for reproducibility as well as very large minorities of people not trusting rather obviously provable stuff (like landing on the moon where nearly any ham radio operator could tell you we did). Even if trust were not low, trusting an authority is built of many things. Brain washing, propaganda, protocol/process, committees, culture, tradition, instinct, and many more things all work together to form obedience to authority… trust in the brain isn’t much of a requirement in obeisance to authority figures.
kreeben 5y ago
"I don't understand this black box. Let's create a black box that can understand black boxes." Now you have two problems.
- nemacol 5y ago
  I ran the numbers and this checks out. That is 100% more problems. DOUBLE the problems.
nix0n 5y ago
> We just need to make sure they don't get so black box that we trust the outputs blindly. AIs are already effectively black boxes to most people, and too many already trust AIs blindly.

cs702 5y ago

The paper is well-written, and I would recommend reading its abstract and introduction over the OP.[a] The big idea is that a graph neural network (GNN) can be trained to predict the weights of any other neural network for performing a task, given the architecture as an input. For example, if we give the trained GNN a ResNet-50 architecture, the GNN predicts the trained weights in one forward pass, and voilà, we have a ResNet-50 that is now ready for use -- and you can always finetune it to get better performance out of it. Remarkably, the prediction of weights works for previously unseen model architectures too. Design your own model, run its architecture by the GNN to predict the trained weights, and voilà, you have a model that is now ready for use -- and you can always finetune it to get better performance out of it. Very cool! -- [a] https://arxiv.org/pdf/2110.13100.pdf https://arxiv.org/pdf/2110.13100.pdf -- Abstract: "Deep learning has been successful in automating the design of features in machine learning pipelines. However, the algorithms optimizing neural network parameters remain largely hand-designed and computationally inefficient. We study if we can use deep learning to directly predict these parameters by exploiting the past knowledge of training other networks. We introduce a large-scale dataset of diverse computational graphs of neural architectures – DEEPNETS-1M– and use it to explore parameter prediction on CIFAR-10 and ImageNet. By leveraging advances in graph neural networks, we propose a hypernetwork that can predict performant parameters in a single forward pass taking a fraction of a second, even on a CPU. The proposed model achieves surprisingly good performance on unseen and diverse networks. For example, it is able to predict all 24 million parameters of a ResNet-50 achieving a 60% accuracy on CIFAR-10. On ImageNet, top-5 accuracy of some of our networks approaches 50%. Our task along with the model and results can potentially lead to a new, more computationally efficient paradigm of training networks. Our model also learns a strong representation of neural architectures enabling their analysis."

nightski 5y ago
So this is for quickly training different architectures for the same task? Which is very neat, but it makes the headline seem a lot more like click-bait (I'm not sure what I expect these days...) It's kind of like a more advanced version of hyper-parameter tuning which has been done in more primitive forms for a long time. But this approach definitely seems powerful.

Istribitel 5y ago

Well, this reminds me of a novel by David Gerrold "When HARLIE Was One (Release 2.0)" The book even mentions a "hyperstate processor" and, without giving to much away, a Graphical Omniscient Device (G.O.D). The similarities are pretty interesting.

visarga 5y ago

The trend in deep learning today is to train the same architecture for all tasks - the transformer, so it goes opposite to this work. But reducing the training cost would lower the barrier to access.

Razengan 5y ago

Somewhere, I wonder, something is reading the headlines “AI builds AI that builds AI” ..

TowerTall 5y ago

ccbccccbbcccbb 5y ago

buildAI() { buildAI() } what could possibly go wrong? ah, the exit cond.....

elforce002 5y ago
Hehe. Always have a "Kill" Switch...
metaobject 5y ago
That’s just recursion

walnutclosefarm 5y ago

This should be called Artificial Intuition, in that it mimics what human intuition on a give subject does.

BirAdam 5y ago

So, like, this is the primitive version of Deep Thought building the Earth because 42 was not a satisfactory answer?

Xcelerate 5y ago

If we ever achieve AGI, my guess is that something akin to this approach is how it will probably be accomplished. I don’t think the human brain will really be able to intuitively grok however AGI works, so I see an iterative process where we invent a program that tries to find a better program to accomplish some goal. Then we feed that program back into itself, and it recursively finds a better program finder. It seems like if we can just get past that critical threshold where the recursive process builds on itself rather than fizzles out due to resource constraints, then progress should accelerate quickly from there.

interstice 5y ago
I’m also thinking along these lines. With little to no knowledge of the domain (other than being human) a lot of life seems to come down to referencing which subsystem or set of subsystems to use in a given situation and training them over time. Like yes - we need the ‘make small talk with checkout operator’ skill in this situation
mkaic 5y ago
Hard agree with this. Humans are always going to insert human biases into the systems we design, so I think the only way we can really get past those biases is by going several layers of abstraction deep. I actually suspect that the first AGI will behave eerily organically when we take a look under the hood. Evolution is the ultimate optimization function, and it made organic brains work a certain way based on a cost function. I'll bet we'll see patterns in AGI that remind us of biological brains, but with stark, alien components mixed in and structures reorganized to be more efficient. All in all I'm just incredibly excited to see how this line of research plays out and desperately want to get involved in it myself. I was actually considering learning JAX just the other day specifically so I could look more into the topic, but I'm lazy so I never got around to it.
vagab0nd 5y ago
> and it recursively finds a better program finder This has been bugging me for a while. I can't articulate it well but I feel there's a hole in the logic. What exactly is the "program finder" finding? A better version of the program at finding... what exactly? You need a stop condition for the recursion.

deltaonefour 5y ago

Serious question. Can we build an AI that builds the AI that builds the AI? What are the theoretical consequences of going higher and higher up the build chain?

sumosudo 5y ago
You just had the shower idea for Skynet..
mkaic 5y ago
This is something I'm actually, genuinely really interested in. In fact, just the other day I was thinking about whether you could use GNNs to accomplish something like this -- so I was understandably pretty hyped to see this post on HN. I've been struggling to find the right terms to Google to find out more about the field, but it looks like "hypernetwork" was what I was looking forward. I highly, highly doubt that the first AGI is going to be designed by hand by humans -- I'd be willing to bet money that the first AGI is going to be recursively designed by multiple layers of constructor AIs just like what you're proposing. Incredibly exciting idea IMO, and I'm confused why there doesn't appear to be more interest in it.
- deltaonefour 5y ago
  >I highly, highly doubt that the first AGI is going to be designed by hand by humans Regular AI isn't even done by hand anymore. We just pick data and learning parameters. At the current state we'd be picking higher and higher categories for learning. At the lowest level we pick data about cats to write something that recognizes cats. At a higher level we pick data about things that recognize things and so on... and so on. How does extremely high level training data effect the low level accuracy of the AI at the lowest level? I would presume that it would effect it negatively as technically all AI is estimation. So estimations on estimations are less accurate.
- optimalsolver 5y ago
  You're thinking of something like a Godel Machine. https://en.wikipedia.org/wiki/Godel_machine https://en.wikipedia.org/wiki/Godel_machine

mkaic 5y ago

Hard to overstate how exciting I find the fields of hypernetworks and metalearning. For good or for evil, this I feel is almost certainly how we create AGI. GNNs make perfect sense as a method of accomplishing it too -- after all, neural networks are just graphs anyways. I don't believe AGI is going to conform to our rigid, symmetrical, boxy human-made architectures with their nice, neat flow patterns and powers-of-two factors everywhere -- those architectures work great for what they're designed to do, but achieving sentience/true intelligence just really doesn't feel like it's going to come out of a feedforward MLP, or even a transformer (in my opinion). The most complicated and capable neural networks we know of are the organic, messy, squishy, yet organized electric meat lumps in our own skulls, produced by billions of years of being exposed to the same loss function over and over and over, but with unlimited ways to adapt in order to minimize that loss function. Nature makes awesome, fractally, insanely complex networks that we could never design by hand. I think it's only sensible to assume that AGI will work similarly. Of course, this is all just armchair speculation. I'm not an expert, just an enthusiastic data scientist who thinks these next twenty years in AI are going to be a heck of a freaking ride.

drdeca 5y ago
If we do achieve AGI through a setup which is not only neural network based, but where even the architecture by which the nodes are connected is beyond our understanding, then, that sounds very bad for interpretability and therefore for safety.
- mkaic 5y ago
  Sure. I'm all for safety, but I also think that unsafe AGI, like the atom bomb, is an inevitability. It's not a matter of if, it's a matter of when we invent it and release it. The future is already murky but it's about to get a heck of a lot more so -- sentient AIs could be the thing that exalts humanity or the thing that destroys us. Break out the popcorn, because I'd wager we're going to see one of those outcomes in the next century.
- riskneutral 5y ago
  If we achieve AGI, then the AGI should be able to produce a formal verification proof of its own safety, similar to how humans use produce formal verification proofs for critical software using automated theorem provers today.

xyproto 5y ago

How are Hypernetworks different from weight agnostic neural networks? WANN paper: https://arxiv.org/pdf/1906.04358.pdf https://arxiv.org/pdf/1906.04358.pdf

wanderer_ 5y ago

Oh.... Oh no. Just kidding: After skimming the article, this looks like necessary progress. I myself (as a layman) had assumed this is already how they did it - although I'm sure there's a lot more to it than that.

sepiasaucer 5y ago

“One can, in theory, start with lots of architectures, then optimize each one and pick the best. “But training [takes] a pretty nontrivial amount of time,” said Mengye Ren, now a visiting researcher at Google Brain. It’d be impossible to train and test every candidate network architecture. “[It doesn’t] scale very well, especially if you consider millions of possible designs.”” ——- If you wanted to find best architecture in order to maximize accuracy, why not just train a model to predict accuracy (not parameters) given architecture and then optimize over the model? This seems similar to optimizing any expensive black box function. Fit a cheap approximation (i.e., surrogate model) and then optimize over cheap model.

jmatthews 5y ago

I hate to be the naysayer in the thread, the paper is well written but the headline is super misleading. AI that tunes AI. It's a search algorithm.

toolcombinator 5y ago