4 ms·

""" 1. Let us start by getting the obvious out of the way. Moore’s law is coming to an end. It is slowing down rather than coming to a grinding halt, but alread

""" 1. Let us start by getting the obvious out of the way. Moore’s law is coming to an end. It is slowing down rather than coming to a grinding halt, but already Dennard scaling has broken down, which eliminates many of the real benefits from scaling """ Is it? I've heard this before but I'm having trouble seeing this statement justified. Wikipedia shows the cost of computing over time and, as far as I can tell, is almost exactly a halving of cost every 1.5 years [0]. The rest of the article looks like it's a mish-mash of predictions based on the faulty (?) assumption that Moore's law is coming to an end yet that we're going to be using computers that are closer to physics but not get the benefits of running computers "closer to the metal". """ 22. The “Singularity” will fail to materialize. ... 23. Human-level AI will materialize ... """ What? """ 28. Various forms of non-text programming tools will be developed. Purely audio-based programming will be available for blind programmers as well as someone who may want to just code via a conversation with their computer while going for a walk. """ So a "purely audio" based programming will evolve that is not text based but we'll program it by using a text based language. Huh? I got to 28. I'm giving up. This is lazy prediction. It's making vague predictions that sound psuedo intellectual without giving concrete conditions under which case it would fail to materialize. Part of the value of scientific predictions is its falsifiability. If I were to say "Hard AI by 2032", there might be some ambiguity in what "Hard AI" means, but at least it's getting towards falsifiability and it's giving a time bound. Here are some others "1 Bitcoin = $1M by 2026", "50% of the worlds power by 2040". You may disagree but at least when the time comes, there's a way to say that I was wrong. Can most of these predictions in the article even be wrong? [0] https://en.wikipedia.org/wiki/FLOPS#Cost_of_computing https://en.wikipedia.org/wiki/FLOPS#Cost_of_computing

AnimalMuppet 4y ago
22 v. 23: A human-level AI does not bring the singularity. The singularity is an AI that is self-improving exponentially faster. A human-level AI (probably) doesn't do that, because humans don't do that. (Never mind that it's far from clear whether even a better-than-human AI will do that...)
- interstice 4y ago
  Humans can't upgrade their own hardware all that easily though, unfortunately
  - AnimalMuppet 4y ago
    Can an AI? Software can upgrade the hardware it runs on? You need a lot more than just a human-level AI for that to be true.
- AgentME 4y ago
  If the AI is actually human-level and is made by humans, then it will be capable of working on itself. Assuming the evolution of human intelligence was mainly limited by biological factors that don't apply to AI (like that brains can't be too big to go through the birth canal, that a body needs to optimize how much energy it gives to the brain vs other parts, etc), then if we hit human-level AI then we're going to be able to go past that. If humans are able to improve AI at any speed, then the AI would be able to improve itself exponentially faster because it will benefit from its own improvements.
  - TheOtherHobbes 4y ago
    Why would anyone assume that AI won't suffer from either physical limitations or abstract ones? We can barely make a GPU that doesn't melt, and we certainly can't make reliable non-trivial software. If you increase complexity by a few orders of magnitude those problems not only don't go away, they become far worse.
    - AgentME 4y ago
      I think it's much more likely that we either fail to make human-level AI or we wildly surpass human-level AI than us getting to human-level AI, petering out exactly there, and not facing a radically AI-changed world. If we or AI run into a barrier stopping us from improving AI, it would be surprising if that barrier was at the same exact spot where evolution ran into a barrier at improving our intelligence given all the differences between the situations. Even if it turns out in some sense that humans are running the "optimal" algorithm for intelligence already and there are no true algorithmic improvements over our intelligence available, the abilities to scale up (or network) a computer, to clone it, to serialize it, and to introspect and modify it will be huge force multipliers. If some human minds were given these abilities then they individually would have a major impact on society; if we're presuming actual human-level AI then they will too. The only reason someone would assume that when we get to human-level AI that we'll definitely get comfortably stopped there is because they're hoping for a simple outcome that they can understand, of a world that doesn't change too much.
jcranmer 4y ago
> Can most of these predictions in the article even be wrong? There's no specific timeline given for falsifiability, but the title implies that we could generously bound it by 2100 or 2122. Assuming we had such a timeframe, and looking only at the first 20: 3, 4, 8, and 12 contain specific predictions that can be easily determined to be true or false. 1 and 5 contain predictions that lack numerical specificity, but if they had it, they would be easily determined to be true or false. 2, 7, 9, 10, 14, and 16 contain predictions that are somewhat vague, but I think most people could reasonably agree on a specific, easily determinable prediction that captures the intent of the prediction. 11 and 13, to the extent that I can draw a prediction out of them, seem to reflect things that already exist today. (I might also stick 2 in here as well). 15 and 18 describe things that contain somewhat vague predictions where I think reasonable agreement on specific, easily determinable predictions is challenging. 6, 17, 19, and 20 are hot garbage. So if you're being pretty strict, 20% of the predictions contain something falsifiable; if you're being charitable, you can't push it past 70%. Although I would personally credit only the first two categories (so, 30%). (FWIW, I agree with... almost none of these predictions.)
Schroedingersat 4y ago
> Is it? I've heard this before but I'm having trouble seeing this statement justified. > Wikipedia shows the cost of computing over time and, as far as I can tell, is almost exactly a halving of cost every 1.5 years [0]. Not moore's law (stacking transistors will likely help for a whiles yet), but you can pretty easily chart the progress towards the landauer limit and see the plateau in the current paradigm coming (by my count about 3-5 orders of magnitude in energy efficiency of classical computing is the asymptote before you start throwing away 99% of the cpu and replacing it with 100 special case gated ones if you want more efficiency). As you approach the limit then the tricks that CPUs pull become a larger and larger portion of your energy, and progress is made by simplifying things and making each task more specialised as the article talks about. For more transistors on a plane they're hitting some hard limits. Currently gate pitch is ~30nm. Maybe at max a factor of 10 improvement is available for things made of atoms pushing around electrons. This gives 100x the transistor density as the end state of an industry that is already spending exponentially more on developing each generation of fab At some point stacking layers will have a greater than logarithmic cost per layer, and then storage will be done too. Very roughly maxing out at a 1 exabyte microsd card. The predictions I'm making here can be considered to have a somewhat hard time limit as the remaining 6-12 accessible doublings in each category must happen at a pace such that it is obviously true or false by 2040. eg. If there are 3 doublings in general purpose single threaded CPU power efficiency between 2035 and 2040 or gate pitch of transistors hits 1nm or storage costs are halving faster than once every 18mo in 2040 I am definitely wrong. Reversible computing is not bound by the ~5 OOM efficiency increase, but will be a substantially different paradigm (you can't delete anything for example).
- memetomancer 4y ago
  Perhaps you are unaware of the angstrom scale process nodes on the semiconductor roadmaps? i.e., IMEC, TSMC and Intel all aggressively pursuing "20A" at the moment with an eye toward a new generation of sub-1n production. This isn't just some arbitrary guesswork on a blog, it's practical planning by the major suppliers. Check it out.
  - jcranmer 4y ago
3pt14159 4y ago
Cost of computing is not Moores Law. "Moore's law is the observation that the number of transistors in a dense integrated circuit (IC) doubles about every two years." Unless you're talking about Dennard scaling, which started breaking down in 2006 according to Wikipedia: https://en.wikipedia.org/wiki/Dennard_scaling https://en.wikipedia.org/wiki/Dennard_scaling I'm not defending every point the author made, but some sure seem right to me. Especially point #2: that we will get more exotic computing hardware. As for #28, I can easily imagine what the author means. Think the movie Her. You don't have to type out "from typing import Option" over and over again. You just say to the computer: "Let's add types to the classes in module orders.py" and it comes back with "Able to add typing to 98% of variables automatically, but SpecialOrdersController has complex inheritance and I wasn't able to ..." and you go from there. Anyway, don't get too huffy and stuffy with these types of things. It's an exploration of ideas not a PhD defence.

AnimalMuppet 4y ago

22 v. 23: A human-level AI does not bring the singularity. The singularity is an AI that is self-improving exponentially faster. A human-level AI (probably) doesn't do that, because humans don't do that. (Never mind that it's far from clear whether even a better-than-human AI will do that...)

interstice 4y ago
Humans can't upgrade their own hardware all that easily though, unfortunately
- AnimalMuppet 4y ago
  Can an AI? Software can upgrade the hardware it runs on? You need a lot more than just a human-level AI for that to be true.
AgentME 4y ago
If the AI is actually human-level and is made by humans, then it will be capable of working on itself. Assuming the evolution of human intelligence was mainly limited by biological factors that don't apply to AI (like that brains can't be too big to go through the birth canal, that a body needs to optimize how much energy it gives to the brain vs other parts, etc), then if we hit human-level AI then we're going to be able to go past that. If humans are able to improve AI at any speed, then the AI would be able to improve itself exponentially faster because it will benefit from its own improvements.
- TheOtherHobbes 4y ago
  Why would anyone assume that AI won't suffer from either physical limitations or abstract ones? We can barely make a GPU that doesn't melt, and we certainly can't make reliable non-trivial software. If you increase complexity by a few orders of magnitude those problems not only don't go away, they become far worse.
  - AgentME 4y ago
    I think it's much more likely that we either fail to make human-level AI or we wildly surpass human-level AI than us getting to human-level AI, petering out exactly there, and not facing a radically AI-changed world. If we or AI run into a barrier stopping us from improving AI, it would be surprising if that barrier was at the same exact spot where evolution ran into a barrier at improving our intelligence given all the differences between the situations. Even if it turns out in some sense that humans are running the "optimal" algorithm for intelligence already and there are no true algorithmic improvements over our intelligence available, the abilities to scale up (or network) a computer, to clone it, to serialize it, and to introspect and modify it will be huge force multipliers. If some human minds were given these abilities then they individually would have a major impact on society; if we're presuming actual human-level AI then they will too. The only reason someone would assume that when we get to human-level AI that we'll definitely get comfortably stopped there is because they're hoping for a simple outcome that they can understand, of a world that doesn't change too much.

jcranmer 4y ago

> Can most of these predictions in the article even be wrong? There's no specific timeline given for falsifiability, but the title implies that we could generously bound it by 2100 or 2122. Assuming we had such a timeframe, and looking only at the first 20: 3, 4, 8, and 12 contain specific predictions that can be easily determined to be true or false. 1 and 5 contain predictions that lack numerical specificity, but if they had it, they would be easily determined to be true or false. 2, 7, 9, 10, 14, and 16 contain predictions that are somewhat vague, but I think most people could reasonably agree on a specific, easily determinable prediction that captures the intent of the prediction. 11 and 13, to the extent that I can draw a prediction out of them, seem to reflect things that already exist today. (I might also stick 2 in here as well). 15 and 18 describe things that contain somewhat vague predictions where I think reasonable agreement on specific, easily determinable predictions is challenging. 6, 17, 19, and 20 are hot garbage. So if you're being pretty strict, 20% of the predictions contain something falsifiable; if you're being charitable, you can't push it past 70%. Although I would personally credit only the first two categories (so, 30%). (FWIW, I agree with... almost none of these predictions.)

Schroedingersat 4y ago

> Is it? I've heard this before but I'm having trouble seeing this statement justified. > Wikipedia shows the cost of computing over time and, as far as I can tell, is almost exactly a halving of cost every 1.5 years [0]. Not moore's law (stacking transistors will likely help for a whiles yet), but you can pretty easily chart the progress towards the landauer limit and see the plateau in the current paradigm coming (by my count about 3-5 orders of magnitude in energy efficiency of classical computing is the asymptote before you start throwing away 99% of the cpu and replacing it with 100 special case gated ones if you want more efficiency). As you approach the limit then the tricks that CPUs pull become a larger and larger portion of your energy, and progress is made by simplifying things and making each task more specialised as the article talks about. For more transistors on a plane they're hitting some hard limits. Currently gate pitch is ~30nm. Maybe at max a factor of 10 improvement is available for things made of atoms pushing around electrons. This gives 100x the transistor density as the end state of an industry that is already spending exponentially more on developing each generation of fab At some point stacking layers will have a greater than logarithmic cost per layer, and then storage will be done too. Very roughly maxing out at a 1 exabyte microsd card. The predictions I'm making here can be considered to have a somewhat hard time limit as the remaining 6-12 accessible doublings in each category must happen at a pace such that it is obviously true or false by 2040. eg. If there are 3 doublings in general purpose single threaded CPU power efficiency between 2035 and 2040 or gate pitch of transistors hits 1nm or storage costs are halving faster than once every 18mo in 2040 I am definitely wrong. Reversible computing is not bound by the ~5 OOM efficiency increase, but will be a substantially different paradigm (you can't delete anything for example).

memetomancer 4y ago
Perhaps you are unaware of the angstrom scale process nodes on the semiconductor roadmaps? i.e., IMEC, TSMC and Intel all aggressively pursuing "20A" at the moment with an eye toward a new generation of sub-1n production. This isn't just some arbitrary guesswork on a blog, it's practical planning by the major suppliers. Check it out.
- jcranmer 4y ago

3pt14159 4y ago

Cost of computing is not Moores Law. "Moore's law is the observation that the number of transistors in a dense integrated circuit (IC) doubles about every two years." Unless you're talking about Dennard scaling, which started breaking down in 2006 according to Wikipedia: https://en.wikipedia.org/wiki/Dennard_scaling https://en.wikipedia.org/wiki/Dennard_scaling I'm not defending every point the author made, but some sure seem right to me. Especially point #2: that we will get more exotic computing hardware. As for #28, I can easily imagine what the author means. Think the movie Her. You don't have to type out "from typing import Option" over and over again. You just say to the computer: "Let's add types to the classes in module orders.py" and it comes back with "Able to add typing to 98% of variables automatically, but SpecialOrdersController has complex inheritance and I wasn't able to ..." and you go from there. Anyway, don't get too huffy and stuffy with these types of things. It's an exploration of ideas not a PhD defence.