5 ms·

> and we are not even close to achieving insect-level intelligence. Aren't we close to this? Most insects only have a few million neurons in their central nerv

> and we are not even close to achieving insect-level intelligence. Aren't we close to this? Most insects only have a few million neurons in their central nervous system, so we can model their intelligence in real time at least. Maybe we still lack the tools for training such networks into useful configurations?

tiborsaas 7y ago
We are and in a sense we know how they work. It's called swarm intelligence which does not even require neural nets to begin with. OP probably just wanted to downplay the current state of AI.
- macleginn 7y ago
  We still cannot convincingly model behaviour of even simplest individual organisms whose neural circuitry we know in minute detail.
  - tiborsaas 7y ago
    What do you mean by "model behavior"? We have AI systems that can learn walking, running and other behavior with just trial and error, I would call that simple behavior. Now here's a more advanced example to teach a virutal character how to flex in the gym: https://www.youtube.com/watch?v=kie4wjB1MCw https://www.youtube.com/watch?v=kie4wjB1MCw That's a bit more advanced than simple walking. Here's a deployed AI to a real robot "crab": https://www.youtube.com/watch?v=UMSNBLAfC7o https://www.youtube.com/watch?v=UMSNBLAfC7o How about virtual characters learning to cooperate? https://www.youtube.com/watch?v=LmYKfU5O_NA https://www.youtube.com/watch?v=LmYKfU5O_NA
    - misterman0 7y ago
      "We have AI systems that can learn walking, running and other [...]" In one of your examples, which are all of narrow AI, we see a mechanical crab powered by ML that has become specialized in walking with a broken limb, which is not even close to what we need if we aim for AGI. For AGI we don't need agents that mimic simple behavior. In my opinion, _mimicking_ behavior will not lead to AGI. What _will_ lead to AGI? No one knows.
      - kowdermeister 7y ago
        macleginn's complaint was that we haven't even modelled simple behavior and I brought these narrow AI examples as a counter argument since they demonstrate that we can, even complex ones. Domain specific? Yeah, bummer. Nowhere I have stated this is the clear path to AGI and you are right, we are missing key building blocks. But I feel like there's too much skepticism agains this field while the advancements are not appreciated enough. I don't know either what will lead there, but I see more and more examples of different networks being combined to achieve more than they are capable of individually.
        iguy 7y ago
        > macleginn's complaint was that we haven't even modelled simple behavior No, the complaint was about modelling the behavior of simple organisms. Certainly we can model some of their behaviors, many of which are highly stereotyped. But the real fly (say) doesn't only walk/fly/scratch/etc, it also decides when to do all of these things. It has ways to decide what search pattern to fly given confusing scents of food nearby. It has ways to judge the fitness of a potential mate, and ways to try to fool potential mates. Our simulations of these things are, I think, really terrible.
        tiborsaas 7y ago
        I linked to modeled organisms, I always feel the HN crowd expects academic level of precision and discussions, but that kills regular discussions I would have at dinner tables with friends, I wish it would be a more casual place. Yes, I meant "behavior of simple organisms" :) Since everything here is loosely defined I feel it's totally pointless to discuss AI, but it's still an intriguing topic. If you look at those insects, they tend to follow brownian motion in 3D, get food and get confused by light, we can get an accurate model of them and more [0]. The key word here is to model, not replication. Simulations are just that, simulations. Given current examples of what's already possible if someone wanted to, could model a detailed 3D environment with physics, scents and food for our little AI fly. [0] https://www.techradar.com/news/ai-fly-by-artificial-intelligence-is-mapping-the-brains-of-flies https://www.techradar.com/news/ai-fly-by-artificial-intellig... Is that a terrible attempt?
dade_ 7y ago
Once we know how a neuron works, ask again. I am not sure how this detail keeps getting glossed over.
- wetpaws 7y ago
  You don't need planes to flap the wings in order to fly.
  - mattnewton 7y ago
    But you do need to understand that they generate lift, and be able to mathematically describe something that generates lift. The Wright brothers wrote to the Smithsonian in 1899 and got back, among other things, workable equations for lift and drag. I think people think back propagation is the metaphorical lift equation here and we just need a “manufacturing” advancement (ie, more compute and techniques for using it). We’re close to that (I personally feel like with poor evidence) but definitely not there yet (as evidenced by nobody publishing this). We cannot describe what is happening with modern architectures as fully as a lift equation predicts fixed wing flight, and so it is largely an intuition + trial and error, which is a slow unreliable way to make progress.
    - deleted 7y ago
      [deleted]
    - testvox 7y ago
      Yeah but we didn't need to fully understand how animal wings actually work, we just needed to understand what they do (generate lift). Similarly I don't understand the focus in this conversation on fully understanding the protein interactions that make neurons work. We just need to understand what neurons do. And I thought what they do is actually pretty simple due to the "all or nothing" principle. https://en.wikipedia.org/wiki/All-or-none_law https://en.wikipedia.org/wiki/All-or-none_law
      - mattnewton 7y ago
est31 7y ago
Yes, if you assume the technical model of each neuron only having one scalar output bfloat16, then we could simulate insect brains right now. But the technical neuron model of sum of inputs plus sigmoid activation function is only an approximation. Neurons communicate with each other with a multitude of neurotransmitters and receptors [1]. As a cell, each neuron is a complex organism of its own that undergoes transcriptomic and metabolic changes. We aren't even close to simulating all protein interactions in a single cell yet, let alone in millions of them. Of course you could say that full protein simulation of an entire brain is not neccessary if we can build an accurate enough technical model of a single neuron. In fact, already now we have to apply a model of how we believe proteins behave as "properly" simulating interactions of two proteins (or one with itself) with lattice QCD approaches is beyond our computational capabilities. For protein interaction we have pretty good models already. But finding a model of all types of neurons in insect brains is right now an open, unsolved challenge. [1] https://en.wikipedia.org/wiki/Neurotransmitter#List_of_neurotransmitters,_peptides,_and_gaseous_signaling_molecules https://en.wikipedia.org/wiki/Neurotransmitter#List_of_neuro...
- whatshisface 7y ago
  Lattice QCD is used for sub-nuclear simulations, proteins are studied with much more tractable methods based on regular quantum mechanics.
  - est31 7y ago
    Yes, that's my point: you don't need to simulate a protein with that tool because we have good enough models of higher level structures like atoms. And similarly we might find models for neurons that allow us to avoid full emulation all protein interactions. We figured out how atoms work before we figured out how nuclei work, but with neurons it's the opposite: we know/can figure out how the the parts (proteins) of the machine work but not how the entire machine works.
- wyldfire 7y ago
  > we could simulate insect brains right now AFAICT this suggests that we have the computational power but wouldn't it also be a significant challenge to create an accurate model for the brain simulation?

tiborsaas 7y ago

We are and in a sense we know how they work. It's called swarm intelligence which does not even require neural nets to begin with. OP probably just wanted to downplay the current state of AI.

macleginn 7y ago
We still cannot convincingly model behaviour of even simplest individual organisms whose neural circuitry we know in minute detail.
- tiborsaas 7y ago
  What do you mean by "model behavior"? We have AI systems that can learn walking, running and other behavior with just trial and error, I would call that simple behavior. Now here's a more advanced example to teach a virutal character how to flex in the gym: https://www.youtube.com/watch?v=kie4wjB1MCw https://www.youtube.com/watch?v=kie4wjB1MCw That's a bit more advanced than simple walking. Here's a deployed AI to a real robot "crab": https://www.youtube.com/watch?v=UMSNBLAfC7o https://www.youtube.com/watch?v=UMSNBLAfC7o How about virtual characters learning to cooperate? https://www.youtube.com/watch?v=LmYKfU5O_NA https://www.youtube.com/watch?v=LmYKfU5O_NA
  - misterman0 7y ago
    "We have AI systems that can learn walking, running and other [...]" In one of your examples, which are all of narrow AI, we see a mechanical crab powered by ML that has become specialized in walking with a broken limb, which is not even close to what we need if we aim for AGI. For AGI we don't need agents that mimic simple behavior. In my opinion, _mimicking_ behavior will not lead to AGI. What _will_ lead to AGI? No one knows.
    - kowdermeister 7y ago
      macleginn's complaint was that we haven't even modelled simple behavior and I brought these narrow AI examples as a counter argument since they demonstrate that we can, even complex ones. Domain specific? Yeah, bummer. Nowhere I have stated this is the clear path to AGI and you are right, we are missing key building blocks. But I feel like there's too much skepticism agains this field while the advancements are not appreciated enough. I don't know either what will lead there, but I see more and more examples of different networks being combined to achieve more than they are capable of individually.
      - iguy 7y ago
        > macleginn's complaint was that we haven't even modelled simple behavior No, the complaint was about modelling the behavior of simple organisms. Certainly we can model some of their behaviors, many of which are highly stereotyped. But the real fly (say) doesn't only walk/fly/scratch/etc, it also decides when to do all of these things. It has ways to decide what search pattern to fly given confusing scents of food nearby. It has ways to judge the fitness of a potential mate, and ways to try to fool potential mates. Our simulations of these things are, I think, really terrible.
        tiborsaas 7y ago
        I linked to modeled organisms, I always feel the HN crowd expects academic level of precision and discussions, but that kills regular discussions I would have at dinner tables with friends, I wish it would be a more casual place. Yes, I meant "behavior of simple organisms" :) Since everything here is loosely defined I feel it's totally pointless to discuss AI, but it's still an intriguing topic. If you look at those insects, they tend to follow brownian motion in 3D, get food and get confused by light, we can get an accurate model of them and more [0]. The key word here is to model, not replication. Simulations are just that, simulations. Given current examples of what's already possible if someone wanted to, could model a detailed 3D environment with physics, scents and food for our little AI fly. [0] https://www.techradar.com/news/ai-fly-by-artificial-intelligence-is-mapping-the-brains-of-flies https://www.techradar.com/news/ai-fly-by-artificial-intellig... Is that a terrible attempt?

dade_ 7y ago

Once we know how a neuron works, ask again. I am not sure how this detail keeps getting glossed over.

wetpaws 7y ago
You don't need planes to flap the wings in order to fly.
- mattnewton 7y ago
  But you do need to understand that they generate lift, and be able to mathematically describe something that generates lift. The Wright brothers wrote to the Smithsonian in 1899 and got back, among other things, workable equations for lift and drag. I think people think back propagation is the metaphorical lift equation here and we just need a “manufacturing” advancement (ie, more compute and techniques for using it). We’re close to that (I personally feel like with poor evidence) but definitely not there yet (as evidenced by nobody publishing this). We cannot describe what is happening with modern architectures as fully as a lift equation predicts fixed wing flight, and so it is largely an intuition + trial and error, which is a slow unreliable way to make progress.
  - deleted 7y ago
    [deleted]
  - testvox 7y ago
    Yeah but we didn't need to fully understand how animal wings actually work, we just needed to understand what they do (generate lift). Similarly I don't understand the focus in this conversation on fully understanding the protein interactions that make neurons work. We just need to understand what neurons do. And I thought what they do is actually pretty simple due to the "all or nothing" principle. https://en.wikipedia.org/wiki/All-or-none_law https://en.wikipedia.org/wiki/All-or-none_law
    - mattnewton 7y ago

est31 7y ago

Yes, if you assume the technical model of each neuron only having one scalar output bfloat16, then we could simulate insect brains right now. But the technical neuron model of sum of inputs plus sigmoid activation function is only an approximation. Neurons communicate with each other with a multitude of neurotransmitters and receptors [1]. As a cell, each neuron is a complex organism of its own that undergoes transcriptomic and metabolic changes. We aren't even close to simulating all protein interactions in a single cell yet, let alone in millions of them. Of course you could say that full protein simulation of an entire brain is not neccessary if we can build an accurate enough technical model of a single neuron. In fact, already now we have to apply a model of how we believe proteins behave as "properly" simulating interactions of two proteins (or one with itself) with lattice QCD approaches is beyond our computational capabilities. For protein interaction we have pretty good models already. But finding a model of all types of neurons in insect brains is right now an open, unsolved challenge. [1] https://en.wikipedia.org/wiki/Neurotransmitter#List_of_neurotransmitters,_peptides,_and_gaseous_signaling_molecules https://en.wikipedia.org/wiki/Neurotransmitter#List_of_neuro...

whatshisface 7y ago
Lattice QCD is used for sub-nuclear simulations, proteins are studied with much more tractable methods based on regular quantum mechanics.
- est31 7y ago
  Yes, that's my point: you don't need to simulate a protein with that tool because we have good enough models of higher level structures like atoms. And similarly we might find models for neurons that allow us to avoid full emulation all protein interactions. We figured out how atoms work before we figured out how nuclei work, but with neurons it's the opposite: we know/can figure out how the the parts (proteins) of the machine work but not how the entire machine works.
wyldfire 7y ago
> we could simulate insect brains right now AFAICT this suggests that we have the computational power but wouldn't it also be a significant challenge to create an accurate model for the brain simulation?