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Jeff Hawkins Is Finally Ready to Explain His Brain Research

491 points by tysone 8y ago

andbberger 8y ago
I never really know what to make of Hawkin's research, but I'll always be grateful to him for his contributions to starting the Redwood Center at Berkeley.
mirceal 8y ago
After reading “On intelligence” and being disappointed by people eagerly associating ML with AI, I think this guy and his team may be onto something. It may not be much, but I welcome any attempt/perspective that is different from the “mainstream” approaches.
mrep 8y ago
This will be exciting. His book "on intelligence" was my favorite book on how the brain works nuerally and how that relates to computers. Much better than Ray kurzweils books in my opinion.
- Agebor 8y ago
  I can recommend The Mind is Flat https://www.amazon.com/Mind-Flat-Illusion-Mental-Improvised-ebook/dp/B077Y95D6V https://www.amazon.com/Mind-Flat-Illusion-Mental-Improvised-... which is more high level, but still seems compatible with this theory.
bobosha 8y ago
I have been following Jeff's work since 2005, only time will tell if his work will bear fruition. But science will be immensely grateful to Jeff for igniting the passions of several AI researchers during the deepest and darkest of AI winters (mid-2000), I was one of them and pursued research at MIT.
- rvense 8y ago
  It was so exhilarating, that promise of maybe knowing ourselves. I remember watching the lectures on Youtube and getting On Intelligence from the library at another university department. I was a student of linguistics when I discovered it, having started a move from theoretical linguistics to a masters in more computational stuff just as machine learning was beginning to become more widely used. Hawkins was one of a few things that combined to make me so completely disenchanted with the entire field of computational linguistics, especially as it was practiced at my university, that I just left and became a regular software developer. I decided I'd rather have to center things in CSS than throw progressively fancier maths at bag-of-words models and poorly annotated corpora and pretending it had anything to do with thinking machines. (I don't regret this move at all since another major factor was I didn't want to go do a PhD somewhere far away; I do somewhat regret going into computational stuff in the first place, though - before that I wanted to become one of those Indiana Jones linguists who go to the mountains and write grammars of languages only spoken by a handful of 80-year-olds... that would have been really fun)
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mushishi 8y ago
This seems to be article that gives more information: A Theory of How Columns in the Neocortex Enable Learning the Structure of the World: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5661005/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5661005/ Excerpt: A simple thought experiment may be useful to understand our model. Imagine you reach your hand into a black box and try to determine what object is in the box, say a coffee cup. Using only one finger it is unlikely you could identify the object with a single touch. However, after making one contact with the cup, you move your finger and touch another location, and then another. After a few touches, you identify the object as a coffee cup. Recognizing the cup requires more than just the tactile sensation from the finger, the brain must also integrate knowledge of how the finger is moving, and hence where it is relative to the cup. Once you recognize the cup, each additional movement of the finger generates a prediction of where the finger will be on the cup after the movement, and what the finger will feel when it arrives at the new location. This is the first problem we wanted to address, how a small sensory array (e.g., the tip of a finger) can learn a predictive model of three dimensional objects by integrating sensation and movement-derived location information.
- Cixelyn 8y ago
  I think the paper that the NYT article is referencing is actually this one, published yesterday: A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex https://www.biorxiv.org/content/early/2018/10/13/442418 https://www.biorxiv.org/content/early/2018/10/13/442418 Jeff Hawkins is scheduled to give a talk at the Human Brain Project Open Day[1] in Maastricht, NL about this paper. The one linked in the parent is from last year, related to the mentioned story of him running his finger across the coffee cup. [1]: https://www.hbpopendaysummit-2018.org/programme/keynote-speakers https://www.hbpopendaysummit-2018.org/programme/keynote-spea...
  - mirceal 8y ago
    thanks. direct link to pdf: https://www.biorxiv.org/content/biorxiv/early/2018/10/13/442418.full.pdf https://www.biorxiv.org/content/biorxiv/early/2018/10/13/442...
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kev009 8y ago
I do like this all encompassing approach and was a fan of his book "On Intelligence" In computer hardware and software terms: we don't necessarily need to clone an x86 CPU, glue and I/O chips, and run an actual copy of firmware, bootloader, and Windows. If we understood just one of these, how the x86-like CPU works, and could implement a functional simulator that works similarly enough that would be extraordinarily useful. ANNs are a primordial stab at this. But this article is incredibly light on detail. I hope he does something like a follow up book to explain what he has learned in the time since.
georgewfraser 8y ago
The premise of this research, that the cortex is broken up into discontinuous “columns”, is only partly true. If you record several neurons that are lined up one on top of another in different layers of the cortex, they will usually respond to the same stimulus or activity: for example, responding to touch on the same finger. But the word “column” implies discontinuous modules. You can actually observe this in sensory cortex of mice and rats, where each whisker has a 1-1 relationship with a “column” of neurons that primarily respond to that whisker. This observation is what originally led neuroscientists to propose the concept of the column as a fundamental unit of organization of the cortex. But it turned out that lots of other brain regions don’t have this kind of discontinuous organization. Neuroscientists still use the term “cortical column” but they’re really just referring to the fact that vertically aligned neurons seem to be closely connected. When the Numenta people talk about “cortical columns” they seem to be describing the original idea of cortical columns that didn’t pan out. It’s really weird.
- momerath 8y ago
  I don't know where you get this idea; they talk a lot about the connections between columns and how they reinforce each other.
- hyperion2010 8y ago
  Not to mention that the blanket assertion that 'columnar' structure is similar across cortex is simply not accurate. Humans seem to have ~180 distinct cortical regions [0], and when we start digging into the actual cytoarchitecture they all start to look different. Yes it is possible to try to shove them all into the '6 layer' model, but if we weren't imposing an extremely strong prior for 6 onto these cell populations we would think that they were all radically different, not uniformly the same as many computationalists and theorists would like to believe. 0. Glasser and Van Essen https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990127/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990127/
  - samstave 8y ago
    knowing practically nothing of this, and more specifically the cytoarchitectural makeup of various cortical regions, I am curious with the following questions: (I dont have a mature lexicon for formulating my questions on this subject - so please take an ELI5 perspective on my questions) 1. Does the stimuli for which a region is responsive to dictate the cytoarchitecture found in that region? Or conversely, does the cytoarchitecture dictate the response-nature of that region? 2. Can we find commonality in cytoarchitecture for regions which respond to a certain stimuli across species? (Is 'touch' always handled by the same cytoarchitecture of brains regardless of mice/men/mammal/reptile/etc? 3. re: 2. - Is 'touch' always handled by the same cytoarchitecture in all Humans? What, if any, differences in cytoarchitecture may be found in language handling regions for people who speak Japanese vs French? Or deaf vs hearing? 4. Is the mass of a a particular region along with its cytoarchitecture corollary to ones given intelligence Lots of questions....
mindgam3 8y ago
“When the brain builds a model of the world, everything has a location relative to everything else,” Mr. Hawkins said. “That is how it understands everything.” This is an exciting idea to me. I don’t fully understand it, but it resonates on some intuitive level. Makes me think of spatial audio, echolocation, our sense of balance, all these forms of intelligence that rely on understanding space. Random trivia- I interviewed with Numenta back in 2007. Got an offer, was excited by the vision, but I didn’t see how they would have any kind of product within the next 5 years. 11 years later, they’re maybe about to launch something. Much respect for them pushing through on this wildly ambitious idea. I’m rooting for them.
- devoply 8y ago
  How is black related to loud?
  - tlb 8y ago
    In my mind, black is straight down, while loud is in front of me a little above eye level, rotated clockwise. You can map any set of concepts into an N-dimensional space if you have a similarity metric between them. If some concepts are anchored in space, they define the overall layout. It could be that loud is related to large, which is related to tall, which is naturally anchored above eye level.
  - tuespetre 8y ago
    - Black bears - Helicopters - Heavy metal - Clanky furnaces - Stompy boots Just a few.
- hprotagonist 8y ago
  >Makes me think of spatial audio, echolocation, our sense of balance, all these forms of intelligence that rely on understanding space. These are all brainstem functions. No cortex required! Binaural hearing is basically sorted out by the time you hit thalamus, and most of the heavy lifting is done in the inferior colliculus, medial superior olive and dorsal cochlear nucleus.
divan 8y ago
His book "On intelligence" is already extremely good on explaining the basic principles of how neocortex works. I still find this book one of the most influential reads ever, and have been following Numenta work ever since. It's been a bit frustrating that after 15 years they still didn't make headlines with something more practical and groundbreaking, so I'm a bit excited to hear what will be unveiled at this conference tomorrow.
- mrec 8y ago
  Aside from anything else, you've gotta love a book on something as poorly-understood as "intelligence" that comes with an appendix of "Testable Predictions". His theories may be right or wrong, but he's doing science right.
- y-c-o-m-b 8y ago
  I LOVE this book. I wish they had a place on school bookshelves when it first came out as the concepts are extremely easy to follow. He provides some very simple real world examples to explain the principles and it's so damn interesting I found it hard to put the book down when I finished a chapter.
- minkzilla 8y ago
  I am very interested in the book. Considering that other comments have been saying that his ideas are not widely seen as worth considering by the academic community I want to compare it with the ideas that hold more merit in the eyes of academics. Could anyone point me to a good book about some of the more widely accepted theories ?
Kozoxy 8y ago
Having a "formula" for the brain, even if incomplete, will better allow those in power to understand how to more fully enslave the rest of society. No matter how intellectually amusing it is to figure out the human brain, I think we should curb our enthusiasm about the novel technical aspects and really think out, and stop, the dystopian implications of mastering the human brain.
nihil75 8y ago
I'm no expert but Jeff Hawkins' theories seem a bit dated to me. The concept described in this article is the same one in his book "On Intelligence", which was written long ago. Before we knew the impact of neural network arrangement and architecture on its performance. Examples like that paper that described a task-specific region of the brain that handles face recognition is not supported by his model.
nabla9 8y ago
Their paper from few days ago is here: https://www.biorxiv.org/content/biorxiv/early/2018/10/13/442418.full.pdf https://www.biorxiv.org/content/biorxiv/early/2018/10/13/442... Rest of them and the code can be found in https://numenta.com/ https://numenta.com/ The ideas represented and level of detail in their theory is such that the most likely response from everyone else is just 'Nice theory' and a shrug. Hawkins and Co. have been working on this for years. Numenta sends frequent newsletters and try to keep people interested, but there is little concrete there except some interesting ideas.
WhitneyLand 8y ago
“You do not have to emulate the entire brain,” he said. “But you do have to understand how the brain works and emulate the important parts.” Nonsense. What % parts of a bird were necessary to understand for the first successful aircraft? It may be very helpful all the way to unnecessary, no one knows that yet.
- blhack 8y ago
  I'm not sure I'm following your point. What percentage of parts of a bird were necessary for the first successful flight? I don't know, less than 1%? Maybe less than 0.1%? But you do have to understand [generally] how a bird works to [generally] emulate its wings [and fly].
  - fermienrico 8y ago
    For every biologically inspired invention, there are plenty more that were invented by understanding principles of engineering and science. Evolution has given us some incredible stuff, but there could exist simpler, more elegant solutions. A barn door will give you lift just not as efficient as a NACA airfoil shape. Evolutionary mimicry has its place in engineering, no doubt. But arrogantly stating that we must emulate biological systems is blatantly and demonstrably false. Otherwise, we wouldn't have nuclear power, superconductivity, lightbulb, sewing machines and computers.
    - greglindahl 8y ago
      Did the original source actually arrogantly state that? I suspect it had some nuance to it.
  - deleted 8y ago
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- tragomaskhalos 8y ago
  I think you are disagreeing with the rather absolute nature of Hawkins' statement while accepting that he may be on to something. My take is: we know that the brain works. OTOH the current ML-oriented focus in AI is apparently promising but leads to some surprising and disturbing anomalies - thinking in particular of how an image can be minutely "gamed" to fool a classifier into completely misidentifying it. So to me (as an outside amateur admittedly) it seems like we are missing some essential meta-information about how cognition really works, and that the known precedent of the brain is the obvious place to look.
gfodor 8y ago
To prove that the brain runs a single learning algorithm would actually be a bigger breakthrough than going from there to determining the algorithm itself. What a profound thing it would be to learn that the mechanics of the brain are fundamentally simple but applied on a massive scale of data, time, and parallelism. I applaud the effort of trying to uncover this model -- embracing the idea that The Brain is comprehensible despite A Brain being impenetrably complex is a liberating concept.
- memory_grep 8y ago
  Surely there has to be a single learning algorithm for most of the skills a modern human learns. There has been no time to evolve specialized algorithms for things like chess, programming, riding a bike, etc., and yet we can easily learn these skills.
  - tuespetre 8y ago
    They’re all just optimizable functions, right?
  - nickpsecurity 8y ago
    "There has been no time to evolve specialized algorithms for things like chess, programming, riding a bike, etc., and yet we can easily learn these skills." Not necessarily. We got a mix of innate head-start and training from parents/others for a really, really, long time before we could do that. The brains learning style and speed even changes over time going from rapid learning in intuitive way to learning slower and with more stability in a combo of self- and others-driven way. At some point, our mind is pretty well-formed where our own thoughts and explorations are driving much of our behavior. And somewhere between the kid and adult parts we start easily learning new skills. There's probably a mix of general, specialized, and stuff in between algorithms. It changes over time, too, instead of being a static, pre-trained model.
  - sdenton4 8y ago
    You could conceivably have different algorithms at different layers - e.g., early inputs directly from sensors vs deeper representation layers. You could also easily have different algorithms for different senses, evolved at various historical stages...
teabee89 8y ago
I’ve been following Numenta’s work since 2010 and today I’m happy to see this at the top of HN. Hopefully the deep learning community longing for AGI, will finally take a look at Numenta’s work. I also would like to shout out to Matt who’s been involved in the open source work as well as the amazing HTMSchool YouTube videos which I highly recommend for anyone interested to learn about the underlying concepts: https://www.youtube.com/playlist?list=PL3yXMgtrZmDqhsFQzwUC9V8MeeVOQ7eZ9 https://www.youtube.com/playlist?list=PL3yXMgtrZmDqhsFQzwUC9...
- nupic 8y ago
  Thank you!
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Confusion 8y ago
before the world can build artificial intelligence, it must explain human intelligence so it can create machines that genuinely work like the brain Why must intelligent machines work like the human brain? It’s just an accidental result of evolution and doesn’t define what ‘intelligence’ is?
- rvense 8y ago
  We have no definition of intelligence that doesn't refer to human behaviour. In part this is a curse on ML/AI, since so many of the terms that are used to define the subject and goals of the field are pre-scientific, even religious in origin - consciousness and will are two other examples.
visarga 8y ago
I don't buy the "one algorithm" idea. I think small changes from region to region can generate completely different dynamics - for example different types of neuron cells, different use of neurotransmitters, or different responses in time. It might look the same under a microscope but hyper-parameters differ between regions. In other words it is a parametrised family of algorithms, not just one. What would interest me more than the one algorithm is to find out the loss functions that steer our brain into fast learning after birth. We have prior knowledge about the world encoded in the structure of the brain and our loss functions (what the brain optimises for). The brain has many such loss functions - it can't possibly do all it does with just one like neural networks, and they are genetically evolved. These priors could be readily transferred to AI models.
- bra-ket 8y ago
  There are no ‘loss functions’, our brain doesn’t optimize in machine learning sense
  - visarga 8y ago
    I think that if it learns, it must have loss functions. We just don't know how they are implemented. If we did, we could replicate the brain priors. They are probably distributed, unlike ML loss functions which distill everything to a single number.
    - bra-ket 8y ago
      well if it learns by remembering sets of features and matching new stimuli against this "database" than we can talk about distance functions in associative memory, and the problem of optimization reduces to a problem of matching (as well as generalizing, i.e constructing abstract representations from simpler ones, as in "chunking"). brain priors would be the distributed representations of stimulus-response pairs stored in memory
alimw 8y ago
The idea that much (all?) of human thought, need, fear etc. is associated with (identifiable with?) sensation having definite location in the body, is one that should be familiar to anyone who has practised meditation as taught to Vipassana students or basic relaxation as taught to method actors. And perhaps someone will be able to correct me on this, but didn't Kant treat spatial awareness as a priori and presumably rather fundamental?
- cosmic_ape 8y ago
  In linguistics there is also work that attempts to understand the formation of abstractions from sensor-motoric concepts. Here's one example: http://fas-philosophy.rutgers.edu/goldman/Spring%202008%20Seminar/Gallese%20&%20Lakoff%20%28The%20brain%27s%20concepts%29.pdf http://fas-philosophy.rutgers.edu/goldman/Spring%202008%20Se...
westurner 8y ago
Cortical column: https://en.wikipedia.org/wiki/Cortical_column https://en.wikipedia.org/wiki/Cortical_column > In the neocortex 6 layers can be recognized although many regions lack one or more layers, fewer layers are present in the archipallium and the paleopallium. What this means in terms of optimal artificial neural network architecture and parameters will be interesting to learn about; in regards to logic, reasoning, and inference. According to "Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function" https://www.frontiersin.org/articles/10.3389/fncom.2017.00048/full https://www.frontiersin.org/articles/10.3389/fncom.2017.0004... , the human brain appears to be [at most] 11-dimensional (11D); in terms of algebraic topology https://en.wikipedia.org/wiki/Algebraic_topology https://en.wikipedia.org/wiki/Algebraic_topology Relatedly, "Study shows how memories ripple through the brain" https://www.ninds.nih.gov/News-Events/News-and-Press-Releases/Press-Releases/Study-shows-how-memories-ripple-through-brain https://www.ninds.nih.gov/News-Events/News-and-Press-Release... > The [NeuroGrid] team was also surprised to find that the ripples in the association neocortex and hippocampus occurred at the same time, suggesting the two regions were communicating as the rats slept. Because the association neocortex is thought to be a storage location for memories, the researchers theorized that this neural dialogue could help the brain retain information.
- westurner 8y ago
  Re: Topological graph theory [1], is it possible to embed a graph on a space filling curve [2] (such as a Hilbert R-tree [3])? [1] https://en.wikipedia.org/wiki/Topological_graph_theory https://en.wikipedia.org/wiki/Topological_graph_theory [2] https://en.wikipedia.org/wiki/Space-filling_curve https://en.wikipedia.org/wiki/Space-filling_curve [3] https://en.wikipedia.org/wiki/Hilbert_R-tree https://en.wikipedia.org/wiki/Hilbert_R-tree [4] https://github.com/bup/bup https://github.com/bup/bup (git packfiles)
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stupidcar 8y ago
It’s weird how this headline and article makes it sound like Hawkins is being coy about his theories, or preferring to work in secret than explain them. But On Intelligence was published years ago, and contained a section on testable predictions. Likewise, Numenta has published papers, and made source code available. So it doesn’t seem to me that the problem is that Hawkins has been uncommunicative, it’s that the wider academic and research community just hasn’t been interested. Perhaps that’s simply because his theories don’t have merit, but it’s also hard to shake the feeling that’s it more because his career, and therefore his theories, hasn’t fit the standard academic mould. It seems to me that, if you want to be an influential theorist, you’re expected to study at a prestigious university, work with an older, respected researcher while getting your PhD, then become a professor, setup a research lab, mentor some PhD students of your own, then finally maybe spin off your research into some startups, or take a high-paying research position at a big company. Hawkins hasn’t followed this path, so he has no network of peers and former students to evangelise his work and carry it forward. He’s done research and published it, like the scientific ideal says you should, but he’s missing the structure necessary to make it land in the research world. It’s like a sort-of reverse cargo cult. He’s done the work of constructing a real airstrip and working planes, but he can’t persuade anybody to fly, because they’ve never seen or heard of air travel before.
- CogDisco 8y ago
  Do you feel it's likely that he's created something that works and rather than using or elaborating upon his work, and thus bettering their own career, everyone has thought, "Wait a second, isn't this the Palm Pilot guy?" and discarded the idea? Instead of just cutting out the middle bit of getting a PhD, becoming a professor, setting up a research lab, and mentoring students for decades, and leaping straight to "make a startup from an idea that works", every single person decided to go the hard road because it seems proper? Your cargo cult idea works - he's arranged all the trappings of being an influential theorist - funding and research labs and media coverage - except perhaps for the one thing that lets him fly: a worthwhile idea.
- buboard 8y ago
  It's not always like that. If his ideas worked, or somehow led to better understanding , his models would be adopted (like how neuroscientists are now diving into deep-learning). But he hasn't for decades, and at some point people just stop listening.

491 points by tysone 8y ago

I never really know what to make of Hawkin's research, but I'll always be grateful to him for his contributions to starting the Redwood Center at Berkeley.

mirceal 8y ago

After reading “On intelligence” and being disappointed by people eagerly associating ML with AI, I think this guy and his team may be onto something. It may not be much, but I welcome any attempt/perspective that is different from the “mainstream” approaches.

mrep 8y ago

This will be exciting. His book "on intelligence" was my favorite book on how the brain works nuerally and how that relates to computers. Much better than Ray kurzweils books in my opinion.

Agebor 8y ago
I can recommend The Mind is Flat https://www.amazon.com/Mind-Flat-Illusion-Mental-Improvised-ebook/dp/B077Y95D6V https://www.amazon.com/Mind-Flat-Illusion-Mental-Improvised-... which is more high level, but still seems compatible with this theory.

bobosha 8y ago

I have been following Jeff's work since 2005, only time will tell if his work will bear fruition. But science will be immensely grateful to Jeff for igniting the passions of several AI researchers during the deepest and darkest of AI winters (mid-2000), I was one of them and pursued research at MIT.

rvense 8y ago
It was so exhilarating, that promise of maybe knowing ourselves. I remember watching the lectures on Youtube and getting On Intelligence from the library at another university department. I was a student of linguistics when I discovered it, having started a move from theoretical linguistics to a masters in more computational stuff just as machine learning was beginning to become more widely used. Hawkins was one of a few things that combined to make me so completely disenchanted with the entire field of computational linguistics, especially as it was practiced at my university, that I just left and became a regular software developer. I decided I'd rather have to center things in CSS than throw progressively fancier maths at bag-of-words models and poorly annotated corpora and pretending it had anything to do with thinking machines. (I don't regret this move at all since another major factor was I didn't want to go do a PhD somewhere far away; I do somewhat regret going into computational stuff in the first place, though - before that I wanted to become one of those Indiana Jones linguists who go to the mountains and write grammars of languages only spoken by a handful of 80-year-olds... that would have been really fun)

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mushishi 8y ago

This seems to be article that gives more information: A Theory of How Columns in the Neocortex Enable Learning the Structure of the World: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5661005/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5661005/ Excerpt: A simple thought experiment may be useful to understand our model. Imagine you reach your hand into a black box and try to determine what object is in the box, say a coffee cup. Using only one finger it is unlikely you could identify the object with a single touch. However, after making one contact with the cup, you move your finger and touch another location, and then another. After a few touches, you identify the object as a coffee cup. Recognizing the cup requires more than just the tactile sensation from the finger, the brain must also integrate knowledge of how the finger is moving, and hence where it is relative to the cup. Once you recognize the cup, each additional movement of the finger generates a prediction of where the finger will be on the cup after the movement, and what the finger will feel when it arrives at the new location. This is the first problem we wanted to address, how a small sensory array (e.g., the tip of a finger) can learn a predictive model of three dimensional objects by integrating sensation and movement-derived location information.

Cixelyn 8y ago
I think the paper that the NYT article is referencing is actually this one, published yesterday: A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex https://www.biorxiv.org/content/early/2018/10/13/442418 https://www.biorxiv.org/content/early/2018/10/13/442418 Jeff Hawkins is scheduled to give a talk at the Human Brain Project Open Day[1] in Maastricht, NL about this paper. The one linked in the parent is from last year, related to the mentioned story of him running his finger across the coffee cup. [1]: https://www.hbpopendaysummit-2018.org/programme/keynote-speakers https://www.hbpopendaysummit-2018.org/programme/keynote-spea...
- mirceal 8y ago
  thanks. direct link to pdf: https://www.biorxiv.org/content/biorxiv/early/2018/10/13/442418.full.pdf https://www.biorxiv.org/content/biorxiv/early/2018/10/13/442...
- 8y ago

kev009 8y ago

I do like this all encompassing approach and was a fan of his book "On Intelligence" In computer hardware and software terms: we don't necessarily need to clone an x86 CPU, glue and I/O chips, and run an actual copy of firmware, bootloader, and Windows. If we understood just one of these, how the x86-like CPU works, and could implement a functional simulator that works similarly enough that would be extraordinarily useful. ANNs are a primordial stab at this. But this article is incredibly light on detail. I hope he does something like a follow up book to explain what he has learned in the time since.

georgewfraser 8y ago

The premise of this research, that the cortex is broken up into discontinuous “columns”, is only partly true. If you record several neurons that are lined up one on top of another in different layers of the cortex, they will usually respond to the same stimulus or activity: for example, responding to touch on the same finger. But the word “column” implies discontinuous modules. You can actually observe this in sensory cortex of mice and rats, where each whisker has a 1-1 relationship with a “column” of neurons that primarily respond to that whisker. This observation is what originally led neuroscientists to propose the concept of the column as a fundamental unit of organization of the cortex. But it turned out that lots of other brain regions don’t have this kind of discontinuous organization. Neuroscientists still use the term “cortical column” but they’re really just referring to the fact that vertically aligned neurons seem to be closely connected. When the Numenta people talk about “cortical columns” they seem to be describing the original idea of cortical columns that didn’t pan out. It’s really weird.

momerath 8y ago
I don't know where you get this idea; they talk a lot about the connections between columns and how they reinforce each other.
hyperion2010 8y ago
Not to mention that the blanket assertion that 'columnar' structure is similar across cortex is simply not accurate. Humans seem to have ~180 distinct cortical regions [0], and when we start digging into the actual cytoarchitecture they all start to look different. Yes it is possible to try to shove them all into the '6 layer' model, but if we weren't imposing an extremely strong prior for 6 onto these cell populations we would think that they were all radically different, not uniformly the same as many computationalists and theorists would like to believe. 0. Glasser and Van Essen https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990127/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990127/
- samstave 8y ago
  knowing practically nothing of this, and more specifically the cytoarchitectural makeup of various cortical regions, I am curious with the following questions: (I dont have a mature lexicon for formulating my questions on this subject - so please take an ELI5 perspective on my questions) 1. Does the stimuli for which a region is responsive to dictate the cytoarchitecture found in that region? Or conversely, does the cytoarchitecture dictate the response-nature of that region? 2. Can we find commonality in cytoarchitecture for regions which respond to a certain stimuli across species? (Is 'touch' always handled by the same cytoarchitecture of brains regardless of mice/men/mammal/reptile/etc? 3. re: 2. - Is 'touch' always handled by the same cytoarchitecture in all Humans? What, if any, differences in cytoarchitecture may be found in language handling regions for people who speak Japanese vs French? Or deaf vs hearing? 4. Is the mass of a a particular region along with its cytoarchitecture corollary to ones given intelligence Lots of questions....

mindgam3 8y ago

“When the brain builds a model of the world, everything has a location relative to everything else,” Mr. Hawkins said. “That is how it understands everything.” This is an exciting idea to me. I don’t fully understand it, but it resonates on some intuitive level. Makes me think of spatial audio, echolocation, our sense of balance, all these forms of intelligence that rely on understanding space. Random trivia- I interviewed with Numenta back in 2007. Got an offer, was excited by the vision, but I didn’t see how they would have any kind of product within the next 5 years. 11 years later, they’re maybe about to launch something. Much respect for them pushing through on this wildly ambitious idea. I’m rooting for them.

devoply 8y ago
How is black related to loud?
- tlb 8y ago
  In my mind, black is straight down, while loud is in front of me a little above eye level, rotated clockwise. You can map any set of concepts into an N-dimensional space if you have a similarity metric between them. If some concepts are anchored in space, they define the overall layout. It could be that loud is related to large, which is related to tall, which is naturally anchored above eye level.
- tuespetre 8y ago
  - Black bears - Helicopters - Heavy metal - Clanky furnaces - Stompy boots Just a few.
hprotagonist 8y ago
>Makes me think of spatial audio, echolocation, our sense of balance, all these forms of intelligence that rely on understanding space. These are all brainstem functions. No cortex required! Binaural hearing is basically sorted out by the time you hit thalamus, and most of the heavy lifting is done in the inferior colliculus, medial superior olive and dorsal cochlear nucleus.

divan 8y ago

His book "On intelligence" is already extremely good on explaining the basic principles of how neocortex works. I still find this book one of the most influential reads ever, and have been following Numenta work ever since. It's been a bit frustrating that after 15 years they still didn't make headlines with something more practical and groundbreaking, so I'm a bit excited to hear what will be unveiled at this conference tomorrow.

mrec 8y ago
Aside from anything else, you've gotta love a book on something as poorly-understood as "intelligence" that comes with an appendix of "Testable Predictions". His theories may be right or wrong, but he's doing science right.
y-c-o-m-b 8y ago
I LOVE this book. I wish they had a place on school bookshelves when it first came out as the concepts are extremely easy to follow. He provides some very simple real world examples to explain the principles and it's so damn interesting I found it hard to put the book down when I finished a chapter.
minkzilla 8y ago
I am very interested in the book. Considering that other comments have been saying that his ideas are not widely seen as worth considering by the academic community I want to compare it with the ideas that hold more merit in the eyes of academics. Could anyone point me to a good book about some of the more widely accepted theories ?

Kozoxy 8y ago

Having a "formula" for the brain, even if incomplete, will better allow those in power to understand how to more fully enslave the rest of society. No matter how intellectually amusing it is to figure out the human brain, I think we should curb our enthusiasm about the novel technical aspects and really think out, and stop, the dystopian implications of mastering the human brain.

nihil75 8y ago

I'm no expert but Jeff Hawkins' theories seem a bit dated to me. The concept described in this article is the same one in his book "On Intelligence", which was written long ago. Before we knew the impact of neural network arrangement and architecture on its performance. Examples like that paper that described a task-specific region of the brain that handles face recognition is not supported by his model.

nabla9 8y ago

Their paper from few days ago is here: https://www.biorxiv.org/content/biorxiv/early/2018/10/13/442418.full.pdf https://www.biorxiv.org/content/biorxiv/early/2018/10/13/442... Rest of them and the code can be found in https://numenta.com/ https://numenta.com/ The ideas represented and level of detail in their theory is such that the most likely response from everyone else is just 'Nice theory' and a shrug. Hawkins and Co. have been working on this for years. Numenta sends frequent newsletters and try to keep people interested, but there is little concrete there except some interesting ideas.

WhitneyLand 8y ago

“You do not have to emulate the entire brain,” he said. “But you do have to understand how the brain works and emulate the important parts.” Nonsense. What % parts of a bird were necessary to understand for the first successful aircraft? It may be very helpful all the way to unnecessary, no one knows that yet.

blhack 8y ago
I'm not sure I'm following your point. What percentage of parts of a bird were necessary for the first successful flight? I don't know, less than 1%? Maybe less than 0.1%? But you do have to understand [generally] how a bird works to [generally] emulate its wings [and fly].
- fermienrico 8y ago
  For every biologically inspired invention, there are plenty more that were invented by understanding principles of engineering and science. Evolution has given us some incredible stuff, but there could exist simpler, more elegant solutions. A barn door will give you lift just not as efficient as a NACA airfoil shape. Evolutionary mimicry has its place in engineering, no doubt. But arrogantly stating that we must emulate biological systems is blatantly and demonstrably false. Otherwise, we wouldn't have nuclear power, superconductivity, lightbulb, sewing machines and computers.
  - greglindahl 8y ago
    Did the original source actually arrogantly state that? I suspect it had some nuance to it.
- deleted 8y ago
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tragomaskhalos 8y ago
I think you are disagreeing with the rather absolute nature of Hawkins' statement while accepting that he may be on to something. My take is: we know that the brain works. OTOH the current ML-oriented focus in AI is apparently promising but leads to some surprising and disturbing anomalies - thinking in particular of how an image can be minutely "gamed" to fool a classifier into completely misidentifying it. So to me (as an outside amateur admittedly) it seems like we are missing some essential meta-information about how cognition really works, and that the known precedent of the brain is the obvious place to look.

gfodor 8y ago

To prove that the brain runs a single learning algorithm would actually be a bigger breakthrough than going from there to determining the algorithm itself. What a profound thing it would be to learn that the mechanics of the brain are fundamentally simple but applied on a massive scale of data, time, and parallelism. I applaud the effort of trying to uncover this model -- embracing the idea that The Brain is comprehensible despite A Brain being impenetrably complex is a liberating concept.

memory_grep 8y ago
Surely there has to be a single learning algorithm for most of the skills a modern human learns. There has been no time to evolve specialized algorithms for things like chess, programming, riding a bike, etc., and yet we can easily learn these skills.
- tuespetre 8y ago
  They’re all just optimizable functions, right?
- nickpsecurity 8y ago
  "There has been no time to evolve specialized algorithms for things like chess, programming, riding a bike, etc., and yet we can easily learn these skills." Not necessarily. We got a mix of innate head-start and training from parents/others for a really, really, long time before we could do that. The brains learning style and speed even changes over time going from rapid learning in intuitive way to learning slower and with more stability in a combo of self- and others-driven way. At some point, our mind is pretty well-formed where our own thoughts and explorations are driving much of our behavior. And somewhere between the kid and adult parts we start easily learning new skills. There's probably a mix of general, specialized, and stuff in between algorithms. It changes over time, too, instead of being a static, pre-trained model.
- sdenton4 8y ago
  You could conceivably have different algorithms at different layers - e.g., early inputs directly from sensors vs deeper representation layers. You could also easily have different algorithms for different senses, evolved at various historical stages...

teabee89 8y ago

I’ve been following Numenta’s work since 2010 and today I’m happy to see this at the top of HN. Hopefully the deep learning community longing for AGI, will finally take a look at Numenta’s work. I also would like to shout out to Matt who’s been involved in the open source work as well as the amazing HTMSchool YouTube videos which I highly recommend for anyone interested to learn about the underlying concepts: https://www.youtube.com/playlist?list=PL3yXMgtrZmDqhsFQzwUC9V8MeeVOQ7eZ9 https://www.youtube.com/playlist?list=PL3yXMgtrZmDqhsFQzwUC9...

nupic 8y ago
Thank you!

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Confusion 8y ago

before the world can build artificial intelligence, it must explain human intelligence so it can create machines that genuinely work like the brain Why must intelligent machines work like the human brain? It’s just an accidental result of evolution and doesn’t define what ‘intelligence’ is?

rvense 8y ago
We have no definition of intelligence that doesn't refer to human behaviour. In part this is a curse on ML/AI, since so many of the terms that are used to define the subject and goals of the field are pre-scientific, even religious in origin - consciousness and will are two other examples.

visarga 8y ago

I don't buy the "one algorithm" idea. I think small changes from region to region can generate completely different dynamics - for example different types of neuron cells, different use of neurotransmitters, or different responses in time. It might look the same under a microscope but hyper-parameters differ between regions. In other words it is a parametrised family of algorithms, not just one. What would interest me more than the one algorithm is to find out the loss functions that steer our brain into fast learning after birth. We have prior knowledge about the world encoded in the structure of the brain and our loss functions (what the brain optimises for). The brain has many such loss functions - it can't possibly do all it does with just one like neural networks, and they are genetically evolved. These priors could be readily transferred to AI models.

bra-ket 8y ago
There are no ‘loss functions’, our brain doesn’t optimize in machine learning sense
- visarga 8y ago
  I think that if it learns, it must have loss functions. We just don't know how they are implemented. If we did, we could replicate the brain priors. They are probably distributed, unlike ML loss functions which distill everything to a single number.
  - bra-ket 8y ago
    well if it learns by remembering sets of features and matching new stimuli against this "database" than we can talk about distance functions in associative memory, and the problem of optimization reduces to a problem of matching (as well as generalizing, i.e constructing abstract representations from simpler ones, as in "chunking"). brain priors would be the distributed representations of stimulus-response pairs stored in memory

alimw 8y ago

The idea that much (all?) of human thought, need, fear etc. is associated with (identifiable with?) sensation having definite location in the body, is one that should be familiar to anyone who has practised meditation as taught to Vipassana students or basic relaxation as taught to method actors. And perhaps someone will be able to correct me on this, but didn't Kant treat spatial awareness as a priori and presumably rather fundamental?

cosmic_ape 8y ago
In linguistics there is also work that attempts to understand the formation of abstractions from sensor-motoric concepts. Here's one example: http://fas-philosophy.rutgers.edu/goldman/Spring%202008%20Seminar/Gallese%20&%20Lakoff%20%28The%20brain%27s%20concepts%29.pdf http://fas-philosophy.rutgers.edu/goldman/Spring%202008%20Se...

westurner 8y ago

Cortical column: https://en.wikipedia.org/wiki/Cortical_column https://en.wikipedia.org/wiki/Cortical_column > In the neocortex 6 layers can be recognized although many regions lack one or more layers, fewer layers are present in the archipallium and the paleopallium. What this means in terms of optimal artificial neural network architecture and parameters will be interesting to learn about; in regards to logic, reasoning, and inference. According to "Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function" https://www.frontiersin.org/articles/10.3389/fncom.2017.00048/full https://www.frontiersin.org/articles/10.3389/fncom.2017.0004... , the human brain appears to be [at most] 11-dimensional (11D); in terms of algebraic topology https://en.wikipedia.org/wiki/Algebraic_topology https://en.wikipedia.org/wiki/Algebraic_topology Relatedly, "Study shows how memories ripple through the brain" https://www.ninds.nih.gov/News-Events/News-and-Press-Releases/Press-Releases/Study-shows-how-memories-ripple-through-brain https://www.ninds.nih.gov/News-Events/News-and-Press-Release... > The [NeuroGrid] team was also surprised to find that the ripples in the association neocortex and hippocampus occurred at the same time, suggesting the two regions were communicating as the rats slept. Because the association neocortex is thought to be a storage location for memories, the researchers theorized that this neural dialogue could help the brain retain information.

westurner 8y ago
Re: Topological graph theory [1], is it possible to embed a graph on a space filling curve [2] (such as a Hilbert R-tree [3])? [1] https://en.wikipedia.org/wiki/Topological_graph_theory https://en.wikipedia.org/wiki/Topological_graph_theory [2] https://en.wikipedia.org/wiki/Space-filling_curve https://en.wikipedia.org/wiki/Space-filling_curve [3] https://en.wikipedia.org/wiki/Hilbert_R-tree https://en.wikipedia.org/wiki/Hilbert_R-tree [4] https://github.com/bup/bup https://github.com/bup/bup (git packfiles)

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stupidcar 8y ago

It’s weird how this headline and article makes it sound like Hawkins is being coy about his theories, or preferring to work in secret than explain them. But On Intelligence was published years ago, and contained a section on testable predictions. Likewise, Numenta has published papers, and made source code available. So it doesn’t seem to me that the problem is that Hawkins has been uncommunicative, it’s that the wider academic and research community just hasn’t been interested. Perhaps that’s simply because his theories don’t have merit, but it’s also hard to shake the feeling that’s it more because his career, and therefore his theories, hasn’t fit the standard academic mould. It seems to me that, if you want to be an influential theorist, you’re expected to study at a prestigious university, work with an older, respected researcher while getting your PhD, then become a professor, setup a research lab, mentor some PhD students of your own, then finally maybe spin off your research into some startups, or take a high-paying research position at a big company. Hawkins hasn’t followed this path, so he has no network of peers and former students to evangelise his work and carry it forward. He’s done research and published it, like the scientific ideal says you should, but he’s missing the structure necessary to make it land in the research world. It’s like a sort-of reverse cargo cult. He’s done the work of constructing a real airstrip and working planes, but he can’t persuade anybody to fly, because they’ve never seen or heard of air travel before.

CogDisco 8y ago
Do you feel it's likely that he's created something that works and rather than using or elaborating upon his work, and thus bettering their own career, everyone has thought, "Wait a second, isn't this the Palm Pilot guy?" and discarded the idea? Instead of just cutting out the middle bit of getting a PhD, becoming a professor, setting up a research lab, and mentoring students for decades, and leaping straight to "make a startup from an idea that works", every single person decided to go the hard road because it seems proper? Your cargo cult idea works - he's arranged all the trappings of being an influential theorist - funding and research labs and media coverage - except perhaps for the one thing that lets him fly: a worthwhile idea.
buboard 8y ago
It's not always like that. If his ideas worked, or somehow led to better understanding , his models would be adopted (like how neuroscientists are now diving into deep-learning). But he hasn't for decades, and at some point people just stop listening.