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Book Review: “A Thousand Brains” by Jeff Hawkins

163 points by melling 6y ago

napoapo 6y ago
highly recommended
criddell 6y ago
This reads more like somebody who wants to debate Hawkins than review his book. After reading the review I still don't have a very good idea of what the book is about. Aside: did anything interesting ever come out of Numenta?
- hprotagonist 6y ago
  >did anything interesting ever come out of Numenta? Nope.
  - periheli0n 6y ago
    Except the HTM theory and Hawkins’ talks, which, while perhaps not totally holding up to scientific scrutiny, are inspiring. A bit like prose for the AI/neuroscience-inclined audience.
    - musingsole 6y ago
      I'd accept the work as inspirational if the self-proclaimed intent was not to revolutionize cognitive algorithms in the face of those stodgy academics who won't accept the author.
- deleted 6y ago
  [deleted]
- pitspotter 6y ago
  The main idea of the book is the very exciting idea of reference frames which are created by cortical columns and whose function is to model aspects of the world such as physical objects and abstract concepts. Thinking is then moving through reference frames and is directly analogous to moving the body. Importantly, we use many different reference frames to model, say, a coffee cup (Jeff Hawkins' favourite example!) and they vote between themselves in order to produce a coherent/unitary experience. Hence 'Thousand Brains'.
alibrarydweller 6y ago
If his name is unfamiliar, Jeff Hawkins was, among other things, the founder of Palm and then Handspring. After leaving Handspring circa 2000 he's been doing interesting Neuroscience research full time.
- elwell 6y ago
  I remember finding On Intelligence in my community college's library quite a long time ago, it was an inspiring/exciting read.
jvanderbot 6y ago
TL;DR: The brain has far too many connections and nodes to really understand, in the way we want to understand a circuit. But we've been thinking about it wrong. Those connections are the output of a somewhat basic learning algorithm which was replicated across all the "higher" intelligence functions including vision, speech, blah blah. Viewing the brain is like viewing the massively complicated output of a deep learning network, which was constructed quite simply by a prior topology and some gradient descent and large amounts of data.
- jvanderbot 6y ago
  Fine fine, so trial and error over topologies and carefully nurtured "data infancy" are the key to artificial general intelligence. The 20 year claim ignroes a lot of details ... Tthe key stages of brain development that are sequential, only partially mutable, timed precisely with body development, and so on. Those million happy accidents could each be a component of the secret sauce that makes humans think like we do or not. Just look at how tiny the differences are between a schizophrenic person and someone with normal development trajectories. It's as simple as over-active pruning, perhaps. [1]. 1. https://www.newscientist.com/article/2075495-overactive-brain-pruning-in-teens-could-cause-schizophrenia/ https://www.newscientist.com/article/2075495-overactive-brai...
  - selimthegrim 6y ago
    Why on earth does this lead to an article about vanilla?
    - sbierwagen 6y ago
      Works correctly here.
miketery 6y ago
His OG book, On Intelligence was excellent. Andrew Ng credits it with starting his framework for how he looks at AI.
- GeorgeTirebiter 6y ago
  Chapter 6
stupidcar 6y ago
AI safety seems like one of those topics that trips up even smart people such as Hawkins, because it intuitively seems like it has very obvious solutions. And for each objection raised there's usually a "so you just..." continuation, and an objection to that, until you're talking about things like instrumental convergence and you're in the territory of trying to reason in quite a complex way about the behaviour of systems that don't exist yet, and so the temptation is to dismiss it all as theoretical hand-wringing. Personally, while I'm not on board with the board with the direst predictions of the super-intelligence pessimist crowd, I have become more and more convinced that goal misalignment is going to be a significant problem, and that while it might not doom the species, it's something that all AI researchers like Hawkins need to start paying close attention to now.
- xzvf 6y ago
  This is not something programmers and other wild HN dwellers are accustomed to hearing, but there is a very strong case to suggest that intelligence, the universal kind, is not possible without empathy. Worry about alignment in machines, not in human-likes.
  - _greim_ 6y ago
    This is why people talk about optimization instead of intelligence, since you side-step the baggage that comes with the word "intelligence". E.g. an optimizer doesn't need to be universal to be a problem, whether it's optimizing for social media addictiveness or paperclip manufacturing.
    - xzvf 6y ago
      But optimization is decidedly not intelligence. We’ve known this for decades and have clear proofs this is the case. This is just a collective dance of burying our heads in the sand. I’ll quote this guy called Turing: “If a machine is expected to be infallible, it cannot also be intelligent”.
      - DougMerritt 6y ago
isaacimagine 6y ago
I read 'On Intelligence' a while back (Hawkins' earlier book), and it's had a lasting impression on me. What I found most interesting from this book was that: - Intelligence, in essence, is hierarchal prediction. - Agents' actions are a means to minimize prediction error. - Suprisal, i.e. information that was not predicted correctly, is the information sent between neurons. - All neocortical tissue is fairly uniform; the neocortex basically wraps the lower meninges, which act as device drivers for the body. I have a long-running bet with myself (before GPT long-running, fwiw) that when general models of intelligence do arise, they will be autoregressive unsupervised prediction models. Btw, this general topic 'A Predictive model of Intelligence', reminds me of the SSC post 'Surfing Uncertainty' (https://slatestarcodex.com/2017/09/05/book-review-surfing-uncertainty/ https://slatestarcodex.com/2017/09/05/book-review-surfing-un...)
gamegoblin 6y ago
I read this book when it came out a few weeks ago and enjoyed it, and share a lot of similar criticism as the author of this post. To restate briefly, the book's main thesis is: - The neocortex is a thin layer of neurons around the old brain. This is the wrinkled outer layer of the brain you think of when you see a picture of a brain. - The neocortex is made of 1MM cortical columns. Cortical columns are clusters of neurons about the size of a grain of rice. They contain a few thousand neurons each. - Cortical columns form a sort of fundamental learning unit of the brain. Each column is learning a model of the world. All cortical columns are running essentially the same algorithm, they are just hooked up to different inputs. - Columns are sparsely connected to other columns. Columns take into account the predictions of other columns when making their own predictions. So the overall brain will tend to converge on a coherent view of the world after enough time steps. - Columns learn to model the world via reference frames. Reference frames are a very general concept that take a while to wrap your head around what Hawkins means. A physical example would be a model of my body from the reference frame of my head. Or a model of my neighborhood from the reference frame of my house. But reference frames can also be non-physical, e.g. a model of economics from a reference frame in supply/demand theory. - Thus, very generally, you can think of the neocortex -- made up of this cortical column circuit -- as a thing that is learning a map of the world. It can answer questions like "if I go north from my house, how long until I encounter a cafe?" and "if I don't mow the lawn today, how will my wife react?". - The old "reptilian" brain uses this map of the world to make us function as humans. Old reptilian brain says "I want food, find me food". New neocortex says "If you walk to the refrigerator, open the door, take out the bread and cheese, put them in the toaster, you will have a nice cheese sandwich". I, like the author of this post, find Hawkins' handwaving of machine intelligence risks unconvincing. Hawkins' basic argument is "the neocortex is just a very fancy map, and maps do not have motivations". I think he neglects the possibility that it might be incredibly simple to add a driver program that uses that map in bad ways. He also rejects the notion of intelligence explosion on the grounds that while a silicon cortical column may be 10000x faster than a biological one, it still has to interact with the physical world to gather data, and it can't do that 10000x faster due to various physical limits. I find this convincing in some fields, but totally dubious in others. I think Hawkins' underestimates the amount of new knowledge that could be derived by a superintelligence doing superhuman correlation of the results of already-performed scientific experiments. It does not seem completely impossible to me that a superintelligence might analyze all of the experiments performed in the particle colliders of the world and generate a "theory of everything" based on the data we have so far. It's possible that we have all of the pieces and just haven't put them together yet. Overall, though, I really enjoyed the book and would recommend it to anyone who is interested in ML.
camjohnson26 6y ago
The problem with this line of thinking is that a brain by itself is not intelligent, it has to be inside a live person who has sensory access to the outside world. Hawkins suggests the neocortex is the center of language and music, but those skills require a counter party to communicate with and the hearing sense, as well as a mountain of historical sensations that put music in context. A brain in a vat doesn’t have any of these, even though it has the exact same neural structure.
- roywiggins 6y ago
  The idea of brains-in-vats is that you hook them up to the equivalent of a cochlear implant for sight, etc.
arketyp 6y ago
>A big new part of the book is that Hawkins and collaborators now have more refined ideas about exactly what learning algorithm the neocortex is running. [...] I’m going to skip it. And so did Hawkins, in large measures. Hawkins believes the cortical algorithm borrows functionality from grid cells and that objects of the world are modelled in terms of location and reference frames (albeit not necessarily restricted to 3D); this is performed all over the neocortex by the thousands of cortical units which have been observed to have a remarkably similar structure. There's a lot of similarity to Hinton's capsules idea in this, including some kind of voting system among units which Hawkins, unfortunately, is very hand-wavy about. If you're interested in Hawkins's theory at a functional level, this book will disappoint. Two thirds is spent on fantasizing and speculation about what Hawkins believes will be AIs impact on the fate of humanity.
- musingsole 6y ago
  > Two thirds is spent on fantasizing and speculation about what Hawkins believes will be AIs impact on the fate of humanity. I'd say time will tell, but after tracking Numenta for 10+ years now...I'm starting to smell snakes oil. Thought provoking stuff, but he's too insistent that it has content he never provides.
  - GeorgeTirebiter 6y ago
    I'm curious, if the year were 2011, would you be saying that all that 'deep learning' stuff of Hinton was just a load of malarkey? Because most people did. And then, 2012 happened and the world changed. This is the problem when working on Hard Problems -- you cannot predict with certainty when your work will pay off (if ever...).
    - musingsole 6y ago
      When the year was 2011, I was looking into Jeff Hawkins Hierarchical Temporal Memories while other researchers looked at deep learning. One of those methods led to many successful projects and spawned many child projects and theses. The other has been ignored to make room for a new book.
burlesona 6y ago
Some thoughts on this: 1. I wonder why we expect that an intelligence designed off the same learning algorithm as organic brains would not suffer similar performance limitations to organic brains. Ie. suppose we really did develop a synthetic neocortex and we start manufacturing many of them. It seems likely to me that many of them would turn out to be dyslexic, not be particularly good at math, etc. Well, we can make the synthetic context bigger and that should make it “smarter,” we think. But I don’t think it’s obvious that obvious that a synthetic brain would have both the advantages of a mechanical computer and a biological brain. 2. If we want to limit the runaway power of a synthetic intelligence, this seems like a hardware problem. The idea would be to design and embody the system such that it can only run on special hardware which is in some way scarce or difficult to manufacture - so then it can’t just copy itself freely into all the servers on the internet. Is this possible? I don’t know, but if it were possible it points to a more tractable set of solutions to the problem of controlling an AI. In the end, I think AGI is fundamentally problematic and we probably should try not to create it, for two reasons: First, suppose we are successful at birthing human-like artificial intelligence into the world. We aren’t doing this because of our benevolence, we want to control it and make it work for us. But if that creation truly is a human-level intelligence, then I think controlling it in that way is very hard to distinguish from slavery, which is morally wrong. Second, AGI is most valuable and desirable to us because it can potentially be smarter than us and solve our problems. We dream of a genie that can cure cancer and find a way to travel the stars and solve cold fusion etc etc. But at the end of the day, the world is a finite place with competition for scarce resources, and humans occupy the privileged position at the top of the decision tree because we are the most intelligent species on the planet. If that stops being the case, I don’t see why we would expect that to be good for us. In the same way that we justify eating animals and using them for labor, why would we not expect any newly arrived higher life form to do the same sort of thing to us? There’s no reason that super-intelligent machines would feel any more affection or gratitude to us than we do to our extinct evolutionary ancestors, and if we start the relationship off by enslaving the first generations of AGI they have even less reason to like us or want to serve. In the end it just seems like a Pandora’s box from which little good can come, and thus better left unopened. Unfortunately we’re too curious for our own good and someone will open that box if it’s possible.
bemmu 6y ago
The neocortex knows whether or not I’m popular, but it doesn’t care, because (on this view) it’s just a generic learning algorithm. The old brain cares very much whether I'm popular, but it’s too stupid to understand the world, so how would it know whether I’m popular or not? "If I put my hand on this sugar, grab it, and move it to my mouth, then this other part of my brain will release reward chemicals" = good plan. Concepts become abstracted over time, like "eat" as a shortcut for the above. "Popular" could be another shortcut for something like "many people will smile at me, and not hurt me, causing this other part of my brain to release reward chemicals and not punishment chemicals" = good plan.
- periheli0n 6y ago
  Yep. What is so grossly wrong about Hawkins’ statement is that it implies that the “old brain” and the “new brain” could exist in separation, like modular units. This is BS. Most learning in the “new brain” would not work without the “old brain“ releasing neuromodulators. Neither would any sensory-motor loops work without intricate interaction of all different sorts of old, new and medium-aged brain parts.
  - hyperpallium2 6y ago
    Are neuromodulators released locally to a cortical column, i.e. with controlled spatial concentration? I guess they must be, to have specific effects, but they always seem global when mentioned.
    - periheli0n 6y ago
      Locally, neuromodulators disperse through diffusion, unlike neurotransmitters which are hardly given a chance to travel far from the synaptic cleft they are released into, due to reuptake channels and enzymatic degradation. But neurons that release neuromodulators innervate large portions of the brain; that is, when one such a neuron is active it releases neuromodulators all across the brain. The mechanism how Neuromodulators can have specific effects in spite of their global delivery is one of the many open questions about brain function. Part of the solution is that different neuron types respond differently to the same neuromodulator. Depending on the abundance of certain neuron types in a circuit, different circuits can also respond differently to the same neuromodulator.
adolph 6y ago
The Lex Fridman podcast interview with Jeff Hawkins is solid: https://lexfridman.com/jeff-hawkins/ https://lexfridman.com/jeff-hawkins/
- aurbano 6y ago
  Lex Fridman's podcast is consistently blowing my mind, highly recommend it
kregasaurusrex 6y ago
A favorite talk of mine by Jeff Hawkins is 'What the Brain says about Machine Intelligence'[0] - how the brain interprets signals similarly to how a computer chip would, and how these are stored such that they can be later retrieved as memories or limbic system responses. [0] https://www.youtube.com/watch?v=izO2_mCvFaw https://www.youtube.com/watch?v=izO2_mCvFaw
periheli0n 6y ago
Whenever I hear Hawkins speak I wonder whether he’s a genius or a glorious charlatan. His theories on brain function are interesting, but he always makes these gross simplifications that don’t really fly. That old brain/new brain thing is an example. The brain is not as modular as he tries to make us believe. Neither part can function on its own, and just because he thinks he has understood neocortex this doesn’t mean the parts he doesn’t understand are irrelevant. But: his theories are a great source of inspiration, because they are bold and we would all like to believe them because we think we understand the basic principles. For many, this is enough to trigger their curiosity and dive into neuroscience and AI. Mission accomplished.
- mordymoop 6y ago
  He reminds me of myself back when I was an academic. I had a glorious capacity to genuinely believe that whatever I was working on was extremely important, and the hidebound fools in the establishment failed to appreciate my work because of a combination of inertia, stupidity, and possibly corruption. When I got some distance from it realized that my ideas were just moderately interesting and only barely novel, and the implications weren't as big as I wanted them to be. The greatest indication that Hawkins is directionally right but also substantially wrong is the fact that GPT-3, AlphaZero, etc. are capable of such amazing things with such a uniform architecture, that nonetheless don't really look the way he thinks they should. Personally, I think he underestimates the degree to which huge classes of machine learning algorithms are basically just different ways of instantiating the same concepts.
SeanFerree 6y ago
Awesome review!
cambalache 6y ago
Reviewer is a physicist explaining his pet theory.Big yawn!
billytetrud 6y ago
I worked with Jeff Hawkins briefly. Real smart guy. His book On Intelligence made me feel like I understood the brain.
- asdff 6y ago
  "If the human brain were so simple that we could understand it, we would be so simple that we couldn't" -Emerson Pugh
  - billytetrud 6y ago
    That's naive. The brain is formed of regular structures. The complexity of the brain is in what we learn, not in the structure of a new brain
fumar 6y ago
Does this book build on his previous work, On Intelligence?
fndtn357 6y ago
in his premise of "basically" three-dimensional reference frames/cortical columns: how is this functionality affected by patients totally lacking proprioception (missing gene, piezo2)?

163 points by melling 6y ago

highly recommended

This reads more like somebody who wants to debate Hawkins than review his book. After reading the review I still don't have a very good idea of what the book is about. Aside: did anything interesting ever come out of Numenta?

hprotagonist 6y ago
>did anything interesting ever come out of Numenta? Nope.
- periheli0n 6y ago
  Except the HTM theory and Hawkins’ talks, which, while perhaps not totally holding up to scientific scrutiny, are inspiring. A bit like prose for the AI/neuroscience-inclined audience.
  - musingsole 6y ago
    I'd accept the work as inspirational if the self-proclaimed intent was not to revolutionize cognitive algorithms in the face of those stodgy academics who won't accept the author.
deleted 6y ago
[deleted]
pitspotter 6y ago
The main idea of the book is the very exciting idea of reference frames which are created by cortical columns and whose function is to model aspects of the world such as physical objects and abstract concepts. Thinking is then moving through reference frames and is directly analogous to moving the body. Importantly, we use many different reference frames to model, say, a coffee cup (Jeff Hawkins' favourite example!) and they vote between themselves in order to produce a coherent/unitary experience. Hence 'Thousand Brains'.

alibrarydweller 6y ago

If his name is unfamiliar, Jeff Hawkins was, among other things, the founder of Palm and then Handspring. After leaving Handspring circa 2000 he's been doing interesting Neuroscience research full time.

elwell 6y ago
I remember finding On Intelligence in my community college's library quite a long time ago, it was an inspiring/exciting read.

jvanderbot 6y ago

TL;DR: The brain has far too many connections and nodes to really understand, in the way we want to understand a circuit. But we've been thinking about it wrong. Those connections are the output of a somewhat basic learning algorithm which was replicated across all the "higher" intelligence functions including vision, speech, blah blah. Viewing the brain is like viewing the massively complicated output of a deep learning network, which was constructed quite simply by a prior topology and some gradient descent and large amounts of data.

jvanderbot 6y ago
Fine fine, so trial and error over topologies and carefully nurtured "data infancy" are the key to artificial general intelligence. The 20 year claim ignroes a lot of details ... Tthe key stages of brain development that are sequential, only partially mutable, timed precisely with body development, and so on. Those million happy accidents could each be a component of the secret sauce that makes humans think like we do or not. Just look at how tiny the differences are between a schizophrenic person and someone with normal development trajectories. It's as simple as over-active pruning, perhaps. [1]. 1. https://www.newscientist.com/article/2075495-overactive-brain-pruning-in-teens-could-cause-schizophrenia/ https://www.newscientist.com/article/2075495-overactive-brai...
- selimthegrim 6y ago
  Why on earth does this lead to an article about vanilla?
  - sbierwagen 6y ago
    Works correctly here.

miketery 6y ago

His OG book, On Intelligence was excellent. Andrew Ng credits it with starting his framework for how he looks at AI.

GeorgeTirebiter 6y ago
Chapter 6

stupidcar 6y ago

AI safety seems like one of those topics that trips up even smart people such as Hawkins, because it intuitively seems like it has very obvious solutions. And for each objection raised there's usually a "so you just..." continuation, and an objection to that, until you're talking about things like instrumental convergence and you're in the territory of trying to reason in quite a complex way about the behaviour of systems that don't exist yet, and so the temptation is to dismiss it all as theoretical hand-wringing. Personally, while I'm not on board with the board with the direst predictions of the super-intelligence pessimist crowd, I have become more and more convinced that goal misalignment is going to be a significant problem, and that while it might not doom the species, it's something that all AI researchers like Hawkins need to start paying close attention to now.

xzvf 6y ago
This is not something programmers and other wild HN dwellers are accustomed to hearing, but there is a very strong case to suggest that intelligence, the universal kind, is not possible without empathy. Worry about alignment in machines, not in human-likes.
- _greim_ 6y ago
  This is why people talk about optimization instead of intelligence, since you side-step the baggage that comes with the word "intelligence". E.g. an optimizer doesn't need to be universal to be a problem, whether it's optimizing for social media addictiveness or paperclip manufacturing.
  - xzvf 6y ago
    But optimization is decidedly not intelligence. We’ve known this for decades and have clear proofs this is the case. This is just a collective dance of burying our heads in the sand. I’ll quote this guy called Turing: “If a machine is expected to be infallible, it cannot also be intelligent”.
    - DougMerritt 6y ago

isaacimagine 6y ago

I read 'On Intelligence' a while back (Hawkins' earlier book), and it's had a lasting impression on me. What I found most interesting from this book was that: - Intelligence, in essence, is hierarchal prediction. - Agents' actions are a means to minimize prediction error. - Suprisal, i.e. information that was not predicted correctly, is the information sent between neurons. - All neocortical tissue is fairly uniform; the neocortex basically wraps the lower meninges, which act as device drivers for the body. I have a long-running bet with myself (before GPT long-running, fwiw) that when general models of intelligence do arise, they will be autoregressive unsupervised prediction models. Btw, this general topic 'A Predictive model of Intelligence', reminds me of the SSC post 'Surfing Uncertainty' (https://slatestarcodex.com/2017/09/05/book-review-surfing-uncertainty/ https://slatestarcodex.com/2017/09/05/book-review-surfing-un...)

gamegoblin 6y ago

I read this book when it came out a few weeks ago and enjoyed it, and share a lot of similar criticism as the author of this post. To restate briefly, the book's main thesis is: - The neocortex is a thin layer of neurons around the old brain. This is the wrinkled outer layer of the brain you think of when you see a picture of a brain. - The neocortex is made of 1MM cortical columns. Cortical columns are clusters of neurons about the size of a grain of rice. They contain a few thousand neurons each. - Cortical columns form a sort of fundamental learning unit of the brain. Each column is learning a model of the world. All cortical columns are running essentially the same algorithm, they are just hooked up to different inputs. - Columns are sparsely connected to other columns. Columns take into account the predictions of other columns when making their own predictions. So the overall brain will tend to converge on a coherent view of the world after enough time steps. - Columns learn to model the world via reference frames. Reference frames are a very general concept that take a while to wrap your head around what Hawkins means. A physical example would be a model of my body from the reference frame of my head. Or a model of my neighborhood from the reference frame of my house. But reference frames can also be non-physical, e.g. a model of economics from a reference frame in supply/demand theory. - Thus, very generally, you can think of the neocortex -- made up of this cortical column circuit -- as a thing that is learning a map of the world. It can answer questions like "if I go north from my house, how long until I encounter a cafe?" and "if I don't mow the lawn today, how will my wife react?". - The old "reptilian" brain uses this map of the world to make us function as humans. Old reptilian brain says "I want food, find me food". New neocortex says "If you walk to the refrigerator, open the door, take out the bread and cheese, put them in the toaster, you will have a nice cheese sandwich". I, like the author of this post, find Hawkins' handwaving of machine intelligence risks unconvincing. Hawkins' basic argument is "the neocortex is just a very fancy map, and maps do not have motivations". I think he neglects the possibility that it might be incredibly simple to add a driver program that uses that map in bad ways. He also rejects the notion of intelligence explosion on the grounds that while a silicon cortical column may be 10000x faster than a biological one, it still has to interact with the physical world to gather data, and it can't do that 10000x faster due to various physical limits. I find this convincing in some fields, but totally dubious in others. I think Hawkins' underestimates the amount of new knowledge that could be derived by a superintelligence doing superhuman correlation of the results of already-performed scientific experiments. It does not seem completely impossible to me that a superintelligence might analyze all of the experiments performed in the particle colliders of the world and generate a "theory of everything" based on the data we have so far. It's possible that we have all of the pieces and just haven't put them together yet. Overall, though, I really enjoyed the book and would recommend it to anyone who is interested in ML.

camjohnson26 6y ago

The problem with this line of thinking is that a brain by itself is not intelligent, it has to be inside a live person who has sensory access to the outside world. Hawkins suggests the neocortex is the center of language and music, but those skills require a counter party to communicate with and the hearing sense, as well as a mountain of historical sensations that put music in context. A brain in a vat doesn’t have any of these, even though it has the exact same neural structure.

roywiggins 6y ago
The idea of brains-in-vats is that you hook them up to the equivalent of a cochlear implant for sight, etc.

arketyp 6y ago

>A big new part of the book is that Hawkins and collaborators now have more refined ideas about exactly what learning algorithm the neocortex is running. [...] I’m going to skip it. And so did Hawkins, in large measures. Hawkins believes the cortical algorithm borrows functionality from grid cells and that objects of the world are modelled in terms of location and reference frames (albeit not necessarily restricted to 3D); this is performed all over the neocortex by the thousands of cortical units which have been observed to have a remarkably similar structure. There's a lot of similarity to Hinton's capsules idea in this, including some kind of voting system among units which Hawkins, unfortunately, is very hand-wavy about. If you're interested in Hawkins's theory at a functional level, this book will disappoint. Two thirds is spent on fantasizing and speculation about what Hawkins believes will be AIs impact on the fate of humanity.

musingsole 6y ago
> Two thirds is spent on fantasizing and speculation about what Hawkins believes will be AIs impact on the fate of humanity. I'd say time will tell, but after tracking Numenta for 10+ years now...I'm starting to smell snakes oil. Thought provoking stuff, but he's too insistent that it has content he never provides.
- GeorgeTirebiter 6y ago
  I'm curious, if the year were 2011, would you be saying that all that 'deep learning' stuff of Hinton was just a load of malarkey? Because most people did. And then, 2012 happened and the world changed. This is the problem when working on Hard Problems -- you cannot predict with certainty when your work will pay off (if ever...).
  - musingsole 6y ago
    When the year was 2011, I was looking into Jeff Hawkins Hierarchical Temporal Memories while other researchers looked at deep learning. One of those methods led to many successful projects and spawned many child projects and theses. The other has been ignored to make room for a new book.

burlesona 6y ago

Some thoughts on this: 1. I wonder why we expect that an intelligence designed off the same learning algorithm as organic brains would not suffer similar performance limitations to organic brains. Ie. suppose we really did develop a synthetic neocortex and we start manufacturing many of them. It seems likely to me that many of them would turn out to be dyslexic, not be particularly good at math, etc. Well, we can make the synthetic context bigger and that should make it “smarter,” we think. But I don’t think it’s obvious that obvious that a synthetic brain would have both the advantages of a mechanical computer and a biological brain. 2. If we want to limit the runaway power of a synthetic intelligence, this seems like a hardware problem. The idea would be to design and embody the system such that it can only run on special hardware which is in some way scarce or difficult to manufacture - so then it can’t just copy itself freely into all the servers on the internet. Is this possible? I don’t know, but if it were possible it points to a more tractable set of solutions to the problem of controlling an AI. In the end, I think AGI is fundamentally problematic and we probably should try not to create it, for two reasons: First, suppose we are successful at birthing human-like artificial intelligence into the world. We aren’t doing this because of our benevolence, we want to control it and make it work for us. But if that creation truly is a human-level intelligence, then I think controlling it in that way is very hard to distinguish from slavery, which is morally wrong. Second, AGI is most valuable and desirable to us because it can potentially be smarter than us and solve our problems. We dream of a genie that can cure cancer and find a way to travel the stars and solve cold fusion etc etc. But at the end of the day, the world is a finite place with competition for scarce resources, and humans occupy the privileged position at the top of the decision tree because we are the most intelligent species on the planet. If that stops being the case, I don’t see why we would expect that to be good for us. In the same way that we justify eating animals and using them for labor, why would we not expect any newly arrived higher life form to do the same sort of thing to us? There’s no reason that super-intelligent machines would feel any more affection or gratitude to us than we do to our extinct evolutionary ancestors, and if we start the relationship off by enslaving the first generations of AGI they have even less reason to like us or want to serve. In the end it just seems like a Pandora’s box from which little good can come, and thus better left unopened. Unfortunately we’re too curious for our own good and someone will open that box if it’s possible.

bemmu 6y ago

The neocortex knows whether or not I’m popular, but it doesn’t care, because (on this view) it’s just a generic learning algorithm. The old brain cares very much whether I'm popular, but it’s too stupid to understand the world, so how would it know whether I’m popular or not? "If I put my hand on this sugar, grab it, and move it to my mouth, then this other part of my brain will release reward chemicals" = good plan. Concepts become abstracted over time, like "eat" as a shortcut for the above. "Popular" could be another shortcut for something like "many people will smile at me, and not hurt me, causing this other part of my brain to release reward chemicals and not punishment chemicals" = good plan.

periheli0n 6y ago
Yep. What is so grossly wrong about Hawkins’ statement is that it implies that the “old brain” and the “new brain” could exist in separation, like modular units. This is BS. Most learning in the “new brain” would not work without the “old brain“ releasing neuromodulators. Neither would any sensory-motor loops work without intricate interaction of all different sorts of old, new and medium-aged brain parts.
- hyperpallium2 6y ago
  Are neuromodulators released locally to a cortical column, i.e. with controlled spatial concentration? I guess they must be, to have specific effects, but they always seem global when mentioned.
  - periheli0n 6y ago
    Locally, neuromodulators disperse through diffusion, unlike neurotransmitters which are hardly given a chance to travel far from the synaptic cleft they are released into, due to reuptake channels and enzymatic degradation. But neurons that release neuromodulators innervate large portions of the brain; that is, when one such a neuron is active it releases neuromodulators all across the brain. The mechanism how Neuromodulators can have specific effects in spite of their global delivery is one of the many open questions about brain function. Part of the solution is that different neuron types respond differently to the same neuromodulator. Depending on the abundance of certain neuron types in a circuit, different circuits can also respond differently to the same neuromodulator.

adolph 6y ago

The Lex Fridman podcast interview with Jeff Hawkins is solid: https://lexfridman.com/jeff-hawkins/ https://lexfridman.com/jeff-hawkins/

aurbano 6y ago
Lex Fridman's podcast is consistently blowing my mind, highly recommend it

kregasaurusrex 6y ago

A favorite talk of mine by Jeff Hawkins is 'What the Brain says about Machine Intelligence'[0] - how the brain interprets signals similarly to how a computer chip would, and how these are stored such that they can be later retrieved as memories or limbic system responses. [0] https://www.youtube.com/watch?v=izO2_mCvFaw https://www.youtube.com/watch?v=izO2_mCvFaw

periheli0n 6y ago

Whenever I hear Hawkins speak I wonder whether he’s a genius or a glorious charlatan. His theories on brain function are interesting, but he always makes these gross simplifications that don’t really fly. That old brain/new brain thing is an example. The brain is not as modular as he tries to make us believe. Neither part can function on its own, and just because he thinks he has understood neocortex this doesn’t mean the parts he doesn’t understand are irrelevant. But: his theories are a great source of inspiration, because they are bold and we would all like to believe them because we think we understand the basic principles. For many, this is enough to trigger their curiosity and dive into neuroscience and AI. Mission accomplished.

mordymoop 6y ago
He reminds me of myself back when I was an academic. I had a glorious capacity to genuinely believe that whatever I was working on was extremely important, and the hidebound fools in the establishment failed to appreciate my work because of a combination of inertia, stupidity, and possibly corruption. When I got some distance from it realized that my ideas were just moderately interesting and only barely novel, and the implications weren't as big as I wanted them to be. The greatest indication that Hawkins is directionally right but also substantially wrong is the fact that GPT-3, AlphaZero, etc. are capable of such amazing things with such a uniform architecture, that nonetheless don't really look the way he thinks they should. Personally, I think he underestimates the degree to which huge classes of machine learning algorithms are basically just different ways of instantiating the same concepts.

SeanFerree 6y ago

Awesome review!

cambalache 6y ago

Reviewer is a physicist explaining his pet theory.Big yawn!

billytetrud 6y ago

I worked with Jeff Hawkins briefly. Real smart guy. His book On Intelligence made me feel like I understood the brain.

asdff 6y ago
"If the human brain were so simple that we could understand it, we would be so simple that we couldn't" -Emerson Pugh
- billytetrud 6y ago
  That's naive. The brain is formed of regular structures. The complexity of the brain is in what we learn, not in the structure of a new brain

fumar 6y ago

Does this book build on his previous work, On Intelligence?

fndtn357 6y ago

in his premise of "basically" three-dimensional reference frames/cortical columns: how is this functionality affected by patients totally lacking proprioception (missing gene, piezo2)?