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This is a video of a talk from Gerald Sussman in 2011 at the Strange Loop conference. Abstract below. Though we have been building and programming computing ma

This is a video of a talk from Gerald Sussman in 2011 at the Strange Loop conference. Abstract below. Though we have been building and programming computing machines for about 60 years and have learned a great deal about composition and abstraction, we have just begun to scratch the surface. A mammalian neuron takes about ten milliseconds to respond to a stimulus. A driver can respond to a visual stimulus in a few hundred milliseconds, and decide an action, such as making a turn. So the computational depth of this behavior is only a few tens of steps. We don't know how to make such a machine, and we wouldn't know how to program it. The human genome -- the information required to build a human from a single, undifferentiated eukariotic cell -- is about 1GB. The instructions to build a mammal are written in very dense code, and the program is extremely flexible. Only small patches to the human genome are required to build a cow or a dog rather than a human. Bigger patches result in a frog or a snake. We don't have any idea how to make a description of such a complex machine that is both dense and flexible. New design principles and new linguistic support are needed. I will address this issue and show some ideas that can perhaps get us to the next phase of engineering design. Gerald Sussman Massachusetts Institute of Technology

danybittel 3y ago
And yet the computer is so much better in many areas than a human. It so weird to compare the two. Why would you even want that? We have already plenty of brains on earth. Who sais, that once a computer works like a brain.. it doesn't also come with it's downsides? There are lot's of problems in compute.. making it more brain like, more linguistic isn't going to solve that. I believe the whole notion of "a computer as an assistant", is doing more damage than good.
- moffkalast 3y ago
  Well an assistant is traditionally the one who eventually replaces the master, so that notion is not conceptually wrong. Do people still think there will be any humans left alive in a few hundred years? We've gone as far as we could as biological constructs, switching to a more adaptable architecture is just the next step forward. Our bodies are overfitted for living on this one specific planet which is greatly limiting the real goal of life: to expand to new areas and replicate.
  - kweingar 3y ago
    > Do people still think there will be any humans left alive in a few hundred years? I will go on the record and predict there will be some human beings alive in a few hundred years.
    - moffkalast 3y ago
      Alright there will always be some that stubbornly cling on, but I would bet that most entities that could be considered intelligent beings won't be human by that point, just out of practicality.
      - imtringued 3y ago
        Stupid flesh and blood humans with their finite needs! Let's build cyborgs with infinite information processing capability so their needs can never be saturated!
    - TeMPOraL 3y ago
      Question is, would they be alive by choice, and would they want to continue being alive?
      - kweingar 3y ago
        Are you and I alive by choice? I don’t know what this means. I will go further and boldly predict that some of these future people will like living.
        TeMPOraL 3y ago
        Ok, I'll spell out what I meant. One of the possible scenarios where, centuries after GAI was created, there would still be humans (recognizable to us as such) alive, is the same as the reason why horses and pigs and cows are alive today - because they're bred as inputs to some industrial process.
- pfarrell 3y ago
  My takeaway is that Sussman is looking at his field after a lifetime of contribution. He's looking for signposts on where we can go next. He's not valuing one over the other, but looking at the brain and saying, "Here's something that exists that's operating on problems in a way we can't begin to approach using current software/hardware techniques". He sees some hints available to us in the noise reduction things he talks about at the end. In that way, the talk is more him challenging the audience to question our approaches and implied constraints rather than him trying to present us with an answer. It's one of my favorite videos (the way too loud HI at the beginning, notwithstanding).
unstuck3958 3y ago
> So the computational depth of this behavior is only a few tens of steps. One of the primary "criticisms" of pre-LLM machine learning was that it is inefficient compared to, say, a human, because it requires a plethora of examples to classify something just to match the performance of a human who has only seen a few examples. That argument no longer holds the same value since the discovery of few-shot learning features of LLMs (though they still have a long way to go). Which makes me think: maybe the reason behind human brain's apparent "efficiency" is the complexity that hides beneath the billions of neurons we have. Disclaimer: I am an amateur and probably have only a litte idea of what I'm talking about.
- imtringued 3y ago
  Sorry but there is no need for a whole brain to outperform ANNs in learning speed. https://bigthink.com/neuropsych/brain-cells-chip-play-pong/ https://bigthink.com/neuropsych/brain-cells-chip-play-pong/
deleted 3y ago
[deleted]
MichaelZuo 3y ago
F1 drivers can instinctively respond to a minor bump on a curve and make a decision in probably ~100 milliseconds, so the lower bound is just 10 computational steps. A more complex decision such as swerving to avoid a wheel that just came off the car in front, probably takes around ~500 milliseconds, or 50 computational steps. Considering that modern computers need billions of computational steps to start a calculator app and draw it on the desktop, we have a long way to go. Though there has been regression on this point, a 1990 NeXTStation Color could do the same with ~50 million cycles on a 1120×832 display. (on 12 MB of RAM and 1.5 MB of VRAM.)
- TeMPOraL 3y ago
  I think the trick is, our computational steps are massively parallel. Take the visual system. By the time you notice - consciously - the pothole you're about to drive into, it's already processed into high-level concepts. Our visual senses aren't camera feeds - the image we are aware of isn't raw input from the eyes, but rather it's generated out of some combination of raw inputs, low-level hacks to hide limited FOV and discontinuities caused by saccades, and higher-level interpretations based on how we feel, what seems to fit the context based on our prior experiences, etc. I think it makes sense to assume that this process has many extra off-ramps that can trigger high-level reactions while bypassing conscious awareness. I.e. imagine that half-way through the post-processing stack, the relevant parts of the brain are somewhat convinced you're looking at a fast-moving object about to hit you in the face. There's a point at which it makes sense for the brain to make your body start a dodging move, even if it may be a misprediction, rather than waste a few dozen milliseconds to make sure, at the risk of getting a ball (or a rock, or a fist) up your nose.
  - marcosdumay 3y ago
    > I think the trick is, our computational steps are massively parallel. Yes, one trick is that. Our computers have to serialize everything. We don't know how to build or program massively parallel computers with distributed memory. But the GP already touched on that point.
    - 3y ago
lostmsu 3y ago
> A driver can respond to a visual stimulus in a few hundred milliseconds, and decide an action, such as making a turn. So the computational depth of this behavior is only a few tens of steps. We don't know how to make such a machine, and we wouldn't know how to program it. IMHO, that makes no sense. Assuming we have a really large state machine with all many possible situations in a state of driving on a straight highway with a few vehicles with more or less constant relative velocity, determining if we need to make a turn is definitely doable in a few computation steps.
- leroy-is-here 3y ago
  We attempted the symbolic route of AI programming and it didn’t work because it is explosive in computation time and exhaustive to model in code. We can’t program our AI “models” now because they are based on chains of probabilities. Therefore they can run in real time. They don’t have any real logic that we specifically programmed, only what the model was able to infer and then encode. Perhaps there should be a marriage between these two attempts of AI.
  - marcosdumay 3y ago
    > it is explosive in computation time Hum... The numerical route for AI necessarily has the same complexity in computation time. Those two have equivalent semantics. If you can get a result with one, you can get the same result with the other, it can just differ on how easy it is to program. By the way, symbolic AI can learn too, you don't need to model things in code.
    - leroy-is-here 3y ago
      I was talking about attempts such as expert systems. We attempted to symbolically encode things as sums of their parts in plain terms, e.g. a bicycle is two wheels, an axle, a chain, some peddles, etc. but in more detail than that. The problem is the level of detail required for general knowledge: firstly it is hard to codify, secondly as it grows computation becomes exponentially more expensive. We as humans have the intelligence to simply see for fractions of a second and then just know — we can know the answers to math problems and how a person is feeling. The symbolic AI programming was an attempt to codify what symbols we are seeing but they are so diverse and nuanced it became apparent this was an ineffective scientific route.
karmakaze 3y ago
Not really a fair comparison--250 ms is reaction time. A deliberate conscious response would be much longer. "Reaction times can reflect habits rather than computations"[0]. [0] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582865/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582865/
bionhoward 3y ago
Seems like fractals are the difference maker here because if you don’t use a fractal you have to program the whole thing whereas if you use fractals (embryology) then your code can be massively recursive. Alternative splicing and epigenetics (holy shit, I can’t believe the iPhone autocorrect doesn’t know the word “epigenetics” in ‘23) also multiply the flexibility of the genome. Also, phosphorylation: proteins are not Boolean switches, they have arbitrary numbers of activation states. Ternary logic is already way more dense than Boolean so when you go even further like into 6-ary, 9-ary logics you’re a lot more free to program stuff. Also the network motifs are a lot simpler than first appear. The metabolic map looks like spaghetti on the wall but each point is composed of reusable blocks like feedforward and feedback etc (see Uri Alon’s book and “Wetware” book and Synthetic Biology: A primer for more detail) I’m biased as a molecular biologist but genetics and systems biology are the most incredible thing. Natural nanotech
- MisterTea 3y ago
  > Natural nanotech When I was a kid in the 80's there were a number of gross/gore/horror trading cards for kids - Garbage Pail Kids being the prime example. One of the sets I had was set in some far flung future, I can barely remember the series but part of the theme were living, biological machines. The only card I can clearly remember featured a flying prisoner transport where the prisoners were seated in the ships cavernous stomach and "if upset during transport could accidentally digest the prisoners." (Honorable mention to HL2 featuring "synthtech" - the drop ship, strider and gun ships were living biological machines.) This stuck in my head as the idea of designing machines which grow like plants or raised like animals was an incredibly amazing and interesting concept. But as I got older and educated the idea them seemed as far flung as the cards themselves. Though reading you post makes me think, maybe they aren't so far flung after all. Though I still fell that a creature which grows steel or titanium skeletons is still quite firmly in the realm of sci-fi.
alcover 3y ago
> The human genome [...] is about 1GB I don't get how this is impressive or relevant on its own. Sure, it sounds small in face of the final result : a full-grown human. But this 1GB program is born out of and expressed into the full OS that is the physical world.

danybittel 3y ago

And yet the computer is so much better in many areas than a human. It so weird to compare the two. Why would you even want that? We have already plenty of brains on earth. Who sais, that once a computer works like a brain.. it doesn't also come with it's downsides? There are lot's of problems in compute.. making it more brain like, more linguistic isn't going to solve that. I believe the whole notion of "a computer as an assistant", is doing more damage than good.

moffkalast 3y ago
Well an assistant is traditionally the one who eventually replaces the master, so that notion is not conceptually wrong. Do people still think there will be any humans left alive in a few hundred years? We've gone as far as we could as biological constructs, switching to a more adaptable architecture is just the next step forward. Our bodies are overfitted for living on this one specific planet which is greatly limiting the real goal of life: to expand to new areas and replicate.
- kweingar 3y ago
  > Do people still think there will be any humans left alive in a few hundred years? I will go on the record and predict there will be some human beings alive in a few hundred years.
  - moffkalast 3y ago
    Alright there will always be some that stubbornly cling on, but I would bet that most entities that could be considered intelligent beings won't be human by that point, just out of practicality.
    - imtringued 3y ago
      Stupid flesh and blood humans with their finite needs! Let's build cyborgs with infinite information processing capability so their needs can never be saturated!
  - TeMPOraL 3y ago
    Question is, would they be alive by choice, and would they want to continue being alive?
    - kweingar 3y ago
      Are you and I alive by choice? I don’t know what this means. I will go further and boldly predict that some of these future people will like living.
      - TeMPOraL 3y ago
        Ok, I'll spell out what I meant. One of the possible scenarios where, centuries after GAI was created, there would still be humans (recognizable to us as such) alive, is the same as the reason why horses and pigs and cows are alive today - because they're bred as inputs to some industrial process.
pfarrell 3y ago
My takeaway is that Sussman is looking at his field after a lifetime of contribution. He's looking for signposts on where we can go next. He's not valuing one over the other, but looking at the brain and saying, "Here's something that exists that's operating on problems in a way we can't begin to approach using current software/hardware techniques". He sees some hints available to us in the noise reduction things he talks about at the end. In that way, the talk is more him challenging the audience to question our approaches and implied constraints rather than him trying to present us with an answer. It's one of my favorite videos (the way too loud HI at the beginning, notwithstanding).

unstuck3958 3y ago

> So the computational depth of this behavior is only a few tens of steps. One of the primary "criticisms" of pre-LLM machine learning was that it is inefficient compared to, say, a human, because it requires a plethora of examples to classify something just to match the performance of a human who has only seen a few examples. That argument no longer holds the same value since the discovery of few-shot learning features of LLMs (though they still have a long way to go). Which makes me think: maybe the reason behind human brain's apparent "efficiency" is the complexity that hides beneath the billions of neurons we have. Disclaimer: I am an amateur and probably have only a litte idea of what I'm talking about.

imtringued 3y ago
Sorry but there is no need for a whole brain to outperform ANNs in learning speed. https://bigthink.com/neuropsych/brain-cells-chip-play-pong/ https://bigthink.com/neuropsych/brain-cells-chip-play-pong/

deleted 3y ago

[deleted]

MichaelZuo 3y ago

F1 drivers can instinctively respond to a minor bump on a curve and make a decision in probably ~100 milliseconds, so the lower bound is just 10 computational steps. A more complex decision such as swerving to avoid a wheel that just came off the car in front, probably takes around ~500 milliseconds, or 50 computational steps. Considering that modern computers need billions of computational steps to start a calculator app and draw it on the desktop, we have a long way to go. Though there has been regression on this point, a 1990 NeXTStation Color could do the same with ~50 million cycles on a 1120×832 display. (on 12 MB of RAM and 1.5 MB of VRAM.)

TeMPOraL 3y ago
I think the trick is, our computational steps are massively parallel. Take the visual system. By the time you notice - consciously - the pothole you're about to drive into, it's already processed into high-level concepts. Our visual senses aren't camera feeds - the image we are aware of isn't raw input from the eyes, but rather it's generated out of some combination of raw inputs, low-level hacks to hide limited FOV and discontinuities caused by saccades, and higher-level interpretations based on how we feel, what seems to fit the context based on our prior experiences, etc. I think it makes sense to assume that this process has many extra off-ramps that can trigger high-level reactions while bypassing conscious awareness. I.e. imagine that half-way through the post-processing stack, the relevant parts of the brain are somewhat convinced you're looking at a fast-moving object about to hit you in the face. There's a point at which it makes sense for the brain to make your body start a dodging move, even if it may be a misprediction, rather than waste a few dozen milliseconds to make sure, at the risk of getting a ball (or a rock, or a fist) up your nose.
- marcosdumay 3y ago
  > I think the trick is, our computational steps are massively parallel. Yes, one trick is that. Our computers have to serialize everything. We don't know how to build or program massively parallel computers with distributed memory. But the GP already touched on that point.
  - 3y ago

lostmsu 3y ago

> A driver can respond to a visual stimulus in a few hundred milliseconds, and decide an action, such as making a turn. So the computational depth of this behavior is only a few tens of steps. We don't know how to make such a machine, and we wouldn't know how to program it. IMHO, that makes no sense. Assuming we have a really large state machine with all many possible situations in a state of driving on a straight highway with a few vehicles with more or less constant relative velocity, determining if we need to make a turn is definitely doable in a few computation steps.

leroy-is-here 3y ago
We attempted the symbolic route of AI programming and it didn’t work because it is explosive in computation time and exhaustive to model in code. We can’t program our AI “models” now because they are based on chains of probabilities. Therefore they can run in real time. They don’t have any real logic that we specifically programmed, only what the model was able to infer and then encode. Perhaps there should be a marriage between these two attempts of AI.
- marcosdumay 3y ago
  > it is explosive in computation time Hum... The numerical route for AI necessarily has the same complexity in computation time. Those two have equivalent semantics. If you can get a result with one, you can get the same result with the other, it can just differ on how easy it is to program. By the way, symbolic AI can learn too, you don't need to model things in code.
  - leroy-is-here 3y ago
    I was talking about attempts such as expert systems. We attempted to symbolically encode things as sums of their parts in plain terms, e.g. a bicycle is two wheels, an axle, a chain, some peddles, etc. but in more detail than that. The problem is the level of detail required for general knowledge: firstly it is hard to codify, secondly as it grows computation becomes exponentially more expensive. We as humans have the intelligence to simply see for fractions of a second and then just know — we can know the answers to math problems and how a person is feeling. The symbolic AI programming was an attempt to codify what symbols we are seeing but they are so diverse and nuanced it became apparent this was an ineffective scientific route.

karmakaze 3y ago

Not really a fair comparison--250 ms is reaction time. A deliberate conscious response would be much longer. "Reaction times can reflect habits rather than computations"[0]. [0] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582865/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582865/

bionhoward 3y ago

Seems like fractals are the difference maker here because if you don’t use a fractal you have to program the whole thing whereas if you use fractals (embryology) then your code can be massively recursive. Alternative splicing and epigenetics (holy shit, I can’t believe the iPhone autocorrect doesn’t know the word “epigenetics” in ‘23) also multiply the flexibility of the genome. Also, phosphorylation: proteins are not Boolean switches, they have arbitrary numbers of activation states. Ternary logic is already way more dense than Boolean so when you go even further like into 6-ary, 9-ary logics you’re a lot more free to program stuff. Also the network motifs are a lot simpler than first appear. The metabolic map looks like spaghetti on the wall but each point is composed of reusable blocks like feedforward and feedback etc (see Uri Alon’s book and “Wetware” book and Synthetic Biology: A primer for more detail) I’m biased as a molecular biologist but genetics and systems biology are the most incredible thing. Natural nanotech

MisterTea 3y ago
> Natural nanotech When I was a kid in the 80's there were a number of gross/gore/horror trading cards for kids - Garbage Pail Kids being the prime example. One of the sets I had was set in some far flung future, I can barely remember the series but part of the theme were living, biological machines. The only card I can clearly remember featured a flying prisoner transport where the prisoners were seated in the ships cavernous stomach and "if upset during transport could accidentally digest the prisoners." (Honorable mention to HL2 featuring "synthtech" - the drop ship, strider and gun ships were living biological machines.) This stuck in my head as the idea of designing machines which grow like plants or raised like animals was an incredibly amazing and interesting concept. But as I got older and educated the idea them seemed as far flung as the cards themselves. Though reading you post makes me think, maybe they aren't so far flung after all. Though I still fell that a creature which grows steel or titanium skeletons is still quite firmly in the realm of sci-fi.

alcover 3y ago

> The human genome [...] is about 1GB I don't get how this is impressive or relevant on its own. Sure, it sounds small in face of the final result : a full-grown human. But this 1GB program is born out of and expressed into the full OS that is the physical world.