6 ms·

Towards Machine Intelligence by IBM Research

111 points by laudney 11y ago

laudney 11y ago
There exists a theory of a single general-purpose learning algorithm which could explain the principles of its operation. This theory assumes that the brain has some initial rough architecture, a small library of simple innate circuits which are prewired at birth and proposes that all significant mental algorithms can be learned. Given current understanding and observations, this paper reviews and lists the ingredients of such an algorithm from both architectural and functional perspectives.
- bcook 11y ago
  You should probably put that in quotes, since those words are not your own.
andreyk 11y ago
Do people generally read these things before upvoting? Legitimately curious. It lists some possible goals to achieve more general human-like intelligence beyond the fancy function approximation we get with deep supervised learning, as stated in the abstract for both architectural and functional perspectives. In general I find the language fairly wishi-washy and the writing often awkward, but it is a nice summary of relevant thoughts and concepts. Beyond the abstract, here is a bit of summary and my thoughts. For architectural aspects, it lists: 1) Unsupervised - Agrees with LeCun, Bengio, etc. But not sure it's fair to conclude this yet, maybe it should be reinforcement? our brains are prewired to do some things 2) Compositional - basically hierarchical, aka deep. Seems reasonable. 3) Sparse and Distributed - again plausible and empirically seen in deep learning. One reason ReLu neurons are nice is that they lead to sparser distributed representations. 4) Objectiveless - a metaphysical statement having to do with the Chinese room argument? This seems to mean not optimizing an objective function with gradient descent, and instead "Clearly, the learning algorithm should have a goal, which might be defined very broadly such as the theory of curiosity, creativity and beauty described by J. Schmidhuber". Seems vague and not clear. 5) Scalable - Again not the best choice of words, it seems to argue for parallelism as well as a "hierarchical structure allowing for separate parallel local and global updates of synapses, scalability and unsupervised learning at the lower levels with more goal-oriented finne-tuning in higher regions. " I am disappointed no discussion of memristors or neuromorphic computing was here. For function aspects, it lists: 1) Compression - sure, pattern matching is in a sense compression so this seems fairly obvious. 2) Prediction - "Whereas the smoothness prior may be considered as a type of spatial coherence, the assumption that the world is mostly predictable corresponds to temporal or more generally spatiotemporal coherence. This is probably the most important ingredient of a general-purpose learning procedure." Again, reasonable enough. 3) Understanding - basically equivalent to predicting? 4) Sensorimotor - not clear? Similar to human eye movement? 5) Spatiotemporal Invariance - "one needs to inject additional contex" having constant concepts of things? 6) Context update/pattern completion - "The last functional component postulated by this paper is a continuous (in the- ory) loop between bottom-up predictions and top-down context." Constant cycling between prediction and word state update, pretty clear.
- deleted 11y ago
  [deleted]
- mindcrime 11y ago
  Do people generally read these things before upvoting? Legitimately curious. I don't. And that's because HN doesn't have separate "upvote" and "save" features... upvoting is saving (or, saving is upvoting, however you want to look at it). So I save (upvote) anything that meets the bar of "the title is interesting enough for me to think I might want to read this eventually".
  - andreyk 11y ago
    A save feature would be nice perhaps. Although maybe the philosophy of HN is precisely "upvote if something seems interesting enough to read" rather than (or in addition to) "upvote if you have read this and think it's good".
- bcook 11y ago
  I usually upvote the comments section of a post, rather than the linked URL's information.
- graycat 11y ago
  Thanks for the summary. Net, I was afraid of some such. I.e., IMHO, it won't even start to work. Here's my first-cut architecture: The core of the intelligence is some concepts and relationships between them. The relationships are based on historical input data from experience since birth. The concepts are close to nouns in language. The relationships are close but not as close to verbs in language. The relationships are closely related to what we regard as causes. The relationships can be moderated or qualified by, right, adverbs. And, sure, adjectives can modify the nouns. Then, start like a baby does, with just nouns and then some simple verbs with sentences of just two words. As for say, kitty cats, early on the input is not in words or language at all -- actual language as a way to get input data, say, fairly directly to concepts with nouns, is later. For rational and deductive reasoning, that is just a special case where the causal relationships are taken much more seriously than usual. For vision, to do at all well, need to do at least fairly well with concepts -- that is, vision needs to identify objects for which the intelligence already has, or at least is in the process of learning, concepts and, say, recognizing a 3D object from just its 2D image. I'm busy with my startup now, but later maybe I'll try to code up what I just described. Here I'd be using electronic logic; how biologic logic does such things, e.g., search the collection of 3D internal visualizations of concepts that are 3D objects, I don't have a clue.
fizixer 11y ago
Cites Schmidhuber besides Hinton and friends, so that is good. But where is mention of Marcus Hutter and his AIXI formalism? Any review of machine intelligence is incomplete without that.
- TheOtherHobbes 11y ago
  I was unfamiliar with AIXI, so that was some interesting Google. Also, this: "Since AIXI is incomputable, it assigns zero probability to its own existence."
  - Houshalter 11y ago
    AIXI has many known problems. The interesting thing is that it is kind of like a mathematically pure and elegant model of machine learning based AI. So any problems with AIXI might apply to our current approaches to AI. The biggest issue is that AIXI doesn't believe it exists in the universe it is observing. It thinks it's playing some kind of video game. From this it doesn't believe that it can truly die, it doesn't believe it can affect it's own brain in any way, and it doesn't care about anything other than maximising some "score" that the "game" provides it.
    - eli_gottlieb 11y ago
      Also, AIXI has only one layer of hierarchy to its model, and has no latent parameters for its causal structures (Turing machines), nor any allowance for compactness or uncountability of learned spaces. It's a really incredibly bare-bones, skeletal model of a learning agent that makes very rigid, unrealistic modeling assumptions and then adds on computational infeasibility.
      - Houshalter 11y ago
        I don't know what you mean by hierarchy, latent parameters, or compactness. It models with Turing machine which are very general and can simulate other Turing machine that are unboundedly huge. The biggest issue is that an agent can't model the universe as merely a set of inputs, outputs, and score. But there may not be a better way, it's quite a difficult problem.
mindcrime 11y ago
From reading the introduction, it sounds like the author is covering similar ground as the book The Master Algorithm[1] by Pedro Domingos[2]. If you find this interesting, you may find his book interesting as well. [1]: https://en.wikipedia.org/wiki/The_Master_Algorithm https://en.wikipedia.org/wiki/The_Master_Algorithm [2]: http://homes.cs.washington.edu/~pedrod/ http://homes.cs.washington.edu/~pedrod/
grondilu 11y ago
I'm too lazy to read this but not enough not to throw my two cents. Non-human mammals are amazing considering how many of them are incredibly capable very early. I'm thinking mostly of large prey for whom the ability to walk and run is crucial. Basically they need to be able to do many things very quickly. It's incredible to see how fast so many new-born grow in so many species. Also, I've searched for the word "play" in this article and found no occurrence. To me how young mammals play and more importantly what drives them to do so is the core mystery behind the development of the mammalian brain. I suspect that once this is cracked, a big part of the work will be done.
KasianFranks 11y ago
Favorite quote: "An intelligent algorithm (strong AI [66], among other names) should be able to reveal hidden knowledge which might not even be discoverable to humans."
- jrcii 11y ago
  I am confident that knowledge exists which is not only undiscoverable by humans, but also beyond our intellectual capacity and impossible to grasp.
tvural 11y ago
I think the main idea can be summarized pretty simply. The most important next step towards general intelligence is creating a learning algorithm that can solve a sufficiently general class of problems without much tweaking by humans, and it couldn't hurt to list out the properties such an algorithm would have to have.
bra-ket 11y ago
how very unsurprising. my advice to the author and the rest of the field would be to read a bit more on learning and memory in humans. AI starts with I.
kasev 11y ago
Most of this ideas have been pioneered and implemented by Jeff Hawkins and his team at Numenta. See his book "On Inteligence" or the open source project at numenta.org.

111 points by laudney 11y ago

There exists a theory of a single general-purpose learning algorithm which could explain the principles of its operation. This theory assumes that the brain has some initial rough architecture, a small library of simple innate circuits which are prewired at birth and proposes that all significant mental algorithms can be learned. Given current understanding and observations, this paper reviews and lists the ingredients of such an algorithm from both architectural and functional perspectives.

bcook 11y ago
You should probably put that in quotes, since those words are not your own.

andreyk 11y ago

Do people generally read these things before upvoting? Legitimately curious. It lists some possible goals to achieve more general human-like intelligence beyond the fancy function approximation we get with deep supervised learning, as stated in the abstract for both architectural and functional perspectives. In general I find the language fairly wishi-washy and the writing often awkward, but it is a nice summary of relevant thoughts and concepts. Beyond the abstract, here is a bit of summary and my thoughts. For architectural aspects, it lists: 1) Unsupervised - Agrees with LeCun, Bengio, etc. But not sure it's fair to conclude this yet, maybe it should be reinforcement? our brains are prewired to do some things 2) Compositional - basically hierarchical, aka deep. Seems reasonable. 3) Sparse and Distributed - again plausible and empirically seen in deep learning. One reason ReLu neurons are nice is that they lead to sparser distributed representations. 4) Objectiveless - a metaphysical statement having to do with the Chinese room argument? This seems to mean not optimizing an objective function with gradient descent, and instead "Clearly, the learning algorithm should have a goal, which might be defined very broadly such as the theory of curiosity, creativity and beauty described by J. Schmidhuber". Seems vague and not clear. 5) Scalable - Again not the best choice of words, it seems to argue for parallelism as well as a "hierarchical structure allowing for separate parallel local and global updates of synapses, scalability and unsupervised learning at the lower levels with more goal-oriented finne-tuning in higher regions. " I am disappointed no discussion of memristors or neuromorphic computing was here. For function aspects, it lists: 1) Compression - sure, pattern matching is in a sense compression so this seems fairly obvious. 2) Prediction - "Whereas the smoothness prior may be considered as a type of spatial coherence, the assumption that the world is mostly predictable corresponds to temporal or more generally spatiotemporal coherence. This is probably the most important ingredient of a general-purpose learning procedure." Again, reasonable enough. 3) Understanding - basically equivalent to predicting? 4) Sensorimotor - not clear? Similar to human eye movement? 5) Spatiotemporal Invariance - "one needs to inject additional contex" having constant concepts of things? 6) Context update/pattern completion - "The last functional component postulated by this paper is a continuous (in the- ory) loop between bottom-up predictions and top-down context." Constant cycling between prediction and word state update, pretty clear.

deleted 11y ago
[deleted]
mindcrime 11y ago
Do people generally read these things before upvoting? Legitimately curious. I don't. And that's because HN doesn't have separate "upvote" and "save" features... upvoting is saving (or, saving is upvoting, however you want to look at it). So I save (upvote) anything that meets the bar of "the title is interesting enough for me to think I might want to read this eventually".
- andreyk 11y ago
  A save feature would be nice perhaps. Although maybe the philosophy of HN is precisely "upvote if something seems interesting enough to read" rather than (or in addition to) "upvote if you have read this and think it's good".
bcook 11y ago
I usually upvote the comments section of a post, rather than the linked URL's information.
graycat 11y ago
Thanks for the summary. Net, I was afraid of some such. I.e., IMHO, it won't even start to work. Here's my first-cut architecture: The core of the intelligence is some concepts and relationships between them. The relationships are based on historical input data from experience since birth. The concepts are close to nouns in language. The relationships are close but not as close to verbs in language. The relationships are closely related to what we regard as causes. The relationships can be moderated or qualified by, right, adverbs. And, sure, adjectives can modify the nouns. Then, start like a baby does, with just nouns and then some simple verbs with sentences of just two words. As for say, kitty cats, early on the input is not in words or language at all -- actual language as a way to get input data, say, fairly directly to concepts with nouns, is later. For rational and deductive reasoning, that is just a special case where the causal relationships are taken much more seriously than usual. For vision, to do at all well, need to do at least fairly well with concepts -- that is, vision needs to identify objects for which the intelligence already has, or at least is in the process of learning, concepts and, say, recognizing a 3D object from just its 2D image. I'm busy with my startup now, but later maybe I'll try to code up what I just described. Here I'd be using electronic logic; how biologic logic does such things, e.g., search the collection of 3D internal visualizations of concepts that are 3D objects, I don't have a clue.

fizixer 11y ago

Cites Schmidhuber besides Hinton and friends, so that is good. But where is mention of Marcus Hutter and his AIXI formalism? Any review of machine intelligence is incomplete without that.

TheOtherHobbes 11y ago
I was unfamiliar with AIXI, so that was some interesting Google. Also, this: "Since AIXI is incomputable, it assigns zero probability to its own existence."
- Houshalter 11y ago
  AIXI has many known problems. The interesting thing is that it is kind of like a mathematically pure and elegant model of machine learning based AI. So any problems with AIXI might apply to our current approaches to AI. The biggest issue is that AIXI doesn't believe it exists in the universe it is observing. It thinks it's playing some kind of video game. From this it doesn't believe that it can truly die, it doesn't believe it can affect it's own brain in any way, and it doesn't care about anything other than maximising some "score" that the "game" provides it.
  - eli_gottlieb 11y ago
    Also, AIXI has only one layer of hierarchy to its model, and has no latent parameters for its causal structures (Turing machines), nor any allowance for compactness or uncountability of learned spaces. It's a really incredibly bare-bones, skeletal model of a learning agent that makes very rigid, unrealistic modeling assumptions and then adds on computational infeasibility.
    - Houshalter 11y ago
      I don't know what you mean by hierarchy, latent parameters, or compactness. It models with Turing machine which are very general and can simulate other Turing machine that are unboundedly huge. The biggest issue is that an agent can't model the universe as merely a set of inputs, outputs, and score. But there may not be a better way, it's quite a difficult problem.

mindcrime 11y ago

From reading the introduction, it sounds like the author is covering similar ground as the book The Master Algorithm[1] by Pedro Domingos[2]. If you find this interesting, you may find his book interesting as well. [1]: https://en.wikipedia.org/wiki/The_Master_Algorithm https://en.wikipedia.org/wiki/The_Master_Algorithm [2]: http://homes.cs.washington.edu/~pedrod/ http://homes.cs.washington.edu/~pedrod/

grondilu 11y ago

I'm too lazy to read this but not enough not to throw my two cents. Non-human mammals are amazing considering how many of them are incredibly capable very early. I'm thinking mostly of large prey for whom the ability to walk and run is crucial. Basically they need to be able to do many things very quickly. It's incredible to see how fast so many new-born grow in so many species. Also, I've searched for the word "play" in this article and found no occurrence. To me how young mammals play and more importantly what drives them to do so is the core mystery behind the development of the mammalian brain. I suspect that once this is cracked, a big part of the work will be done.

KasianFranks 11y ago

Favorite quote: "An intelligent algorithm (strong AI [66], among other names) should be able to reveal hidden knowledge which might not even be discoverable to humans."

jrcii 11y ago
I am confident that knowledge exists which is not only undiscoverable by humans, but also beyond our intellectual capacity and impossible to grasp.

tvural 11y ago

I think the main idea can be summarized pretty simply. The most important next step towards general intelligence is creating a learning algorithm that can solve a sufficiently general class of problems without much tweaking by humans, and it couldn't hurt to list out the properties such an algorithm would have to have.

bra-ket 11y ago

how very unsurprising. my advice to the author and the rest of the field would be to read a bit more on learning and memory in humans. AI starts with I.

kasev 11y ago

Most of this ideas have been pioneered and implemented by Jeff Hawkins and his team at Numenta. See his book "On Inteligence" or the open source project at numenta.org.