4 ms·

They just "solved" protein folding late last year. How can you say things are slowing down? Do you honestly expect life-changing discoveries every other week?

0xB31B1B 5y ago
Protein folding is a well modeled math problem. The alpha fold solution is extremely good at pruning (aka guessing) folding chain structure possibilities. I am Impressed and this is a difficult problem but this is extremely different from AGI as this is a well scoped easily modelable problem that is basically a chain of 26 inputs types of links of arbitrary length. I am not trying to take away that the protein folding is incredible but AGI is extremely different. AGI is literally having a model that can both do alpha fold and self driving cars, as well as the ability to generate novel models to solve new well scopes problems. RL can do 0 to 1, the 1 to n (generalizeability) is the extremely difficult part.
- ackbar03 5y ago
  I may be really only speaking for myself here but I have very sincere doubts that anyone who has done any moderately serious work on "AI" and wrangled with the nitty gritty details of it all is really having any huge expectations for AGI. I mean if it comes during my lifetime, hurrah! But personally I'm not gonna sit around waiting for it or depend on it for anything. That being said, is the current AI tech as we have it useless? Of course not. Things like protein folding and alphago are still huge leaps forward in tech, it'd be kind of silly to treat AGI as the only thing worth achieving
- tinco 5y ago
  It is entirely not necessary for an AGI to be able to drive a car. Frankly, after seeing AlphaZero and AlphaFold I'm surprised they didn't declare AGI right there and then. People assume that when AGI happens, computers can suddenly outsmart humans in every way and solve every problem imaginable. The reality is just that it could in theory given enough time and resources. It is like quantum computing. In theory it can instantly factor and break our nice cryptographic primes. In reality the largest number it factored is 21.
  - croon 5y ago
    > It is entirely not necessary for an AGI to be able to drive a car. "Artificial general intelligence (AGI) is the hypothetical[1] ability of an intelligent agent to understand or learn any intellectual task that a human being can." What definition are you using?
    - tinco 5y ago
      The same, that they understand and can learn how to drive a car doesn't mean they would actually be able to do it in the real world. You can read a book on how to hit a ball with a baseball bat, you can even practice and get good at it, but that still doesn't mean you would actually be able to hit a ball thrown by a professional pitcher.
      - croon 5y ago
        The interface to the car is a solved problem. > You can read a book on how to hit a ball with a baseball bat, you can even practice and get good at it, but that still doesn't mean you would actually be able to hit a ball thrown by a professional pitcher. If I hade incredibly fast reflexes and actuators I could.
        tinco 5y ago
        Similarly, DeepMind's software might be able to drive a car, would it have a similar neuron count, connectivity, perception systems and training you received. Or maybe it couldn't, because the software is not as efficient as the organisation of your brain is. Or because there's hardcoded routines evolved in your brain that it lacks. What I'm saying is that just that because an AGI can't drive a car, it doesn't mean it's an AGI. For the same reason there's loads of people out there that are generally intelligent that can't drive cars for all sorts of physical reasons.
        croon 5y ago
        > Similarly, DeepMind's software might be able to drive a car, would it have a similar neuron count, connectivity, perception systems and training you received. Admittedly I'm a layman in this area, but could it? AFAICT it would only work on trained set data and whatever generalizations can be made on that and not infer unseen scenarios like humans do readily. > What I'm saying is that just that because an AGI can't drive a car, it doesn't mean it's an AGI. I understood what you meant from your first post, I'm simply disagreeing on account of the very definition of AGI. You can't have an ameoba level AGI and still call it (a limited) AGI. Either it can understand/learn any human task, or it can't. The definition is made for a reason. Watering it down for any specific generation of AI serves no benefit.

0xB31B1B 5y ago

Protein folding is a well modeled math problem. The alpha fold solution is extremely good at pruning (aka guessing) folding chain structure possibilities. I am Impressed and this is a difficult problem but this is extremely different from AGI as this is a well scoped easily modelable problem that is basically a chain of 26 inputs types of links of arbitrary length. I am not trying to take away that the protein folding is incredible but AGI is extremely different. AGI is literally having a model that can both do alpha fold and self driving cars, as well as the ability to generate novel models to solve new well scopes problems. RL can do 0 to 1, the 1 to n (generalizeability) is the extremely difficult part.

ackbar03 5y ago
I may be really only speaking for myself here but I have very sincere doubts that anyone who has done any moderately serious work on "AI" and wrangled with the nitty gritty details of it all is really having any huge expectations for AGI. I mean if it comes during my lifetime, hurrah! But personally I'm not gonna sit around waiting for it or depend on it for anything. That being said, is the current AI tech as we have it useless? Of course not. Things like protein folding and alphago are still huge leaps forward in tech, it'd be kind of silly to treat AGI as the only thing worth achieving
tinco 5y ago
It is entirely not necessary for an AGI to be able to drive a car. Frankly, after seeing AlphaZero and AlphaFold I'm surprised they didn't declare AGI right there and then. People assume that when AGI happens, computers can suddenly outsmart humans in every way and solve every problem imaginable. The reality is just that it could in theory given enough time and resources. It is like quantum computing. In theory it can instantly factor and break our nice cryptographic primes. In reality the largest number it factored is 21.
- croon 5y ago
  > It is entirely not necessary for an AGI to be able to drive a car. "Artificial general intelligence (AGI) is the hypothetical[1] ability of an intelligent agent to understand or learn any intellectual task that a human being can." What definition are you using?
  - tinco 5y ago
    The same, that they understand and can learn how to drive a car doesn't mean they would actually be able to do it in the real world. You can read a book on how to hit a ball with a baseball bat, you can even practice and get good at it, but that still doesn't mean you would actually be able to hit a ball thrown by a professional pitcher.
    - croon 5y ago
      The interface to the car is a solved problem. > You can read a book on how to hit a ball with a baseball bat, you can even practice and get good at it, but that still doesn't mean you would actually be able to hit a ball thrown by a professional pitcher. If I hade incredibly fast reflexes and actuators I could.
      - tinco 5y ago
        Similarly, DeepMind's software might be able to drive a car, would it have a similar neuron count, connectivity, perception systems and training you received. Or maybe it couldn't, because the software is not as efficient as the organisation of your brain is. Or because there's hardcoded routines evolved in your brain that it lacks. What I'm saying is that just that because an AGI can't drive a car, it doesn't mean it's an AGI. For the same reason there's loads of people out there that are generally intelligent that can't drive cars for all sorts of physical reasons.
        croon 5y ago
        > Similarly, DeepMind's software might be able to drive a car, would it have a similar neuron count, connectivity, perception systems and training you received. Admittedly I'm a layman in this area, but could it? AFAICT it would only work on trained set data and whatever generalizations can be made on that and not infer unseen scenarios like humans do readily. > What I'm saying is that just that because an AGI can't drive a car, it doesn't mean it's an AGI. I understood what you meant from your first post, I'm simply disagreeing on account of the very definition of AGI. You can't have an ameoba level AGI and still call it (a limited) AGI. Either it can understand/learn any human task, or it can't. The definition is made for a reason. Watering it down for any specific generation of AI serves no benefit.