10 ms·

My path to OpenAI

233 points by craigkerstiens 11y ago

refrigerator 11y ago
Would be interested to hear about his role at Open AI - I would have thought you'd need at least a PhD in Statistics/CS to be able to contribute to AI research, no?
- wasd 11y ago
  He's written a post about how he defined CTO role at Stripe (http://blog.gregbrockman.com/figuring-out-the-cto-role-at-stripe http://blog.gregbrockman.com/figuring-out-the-cto-role-at-st...). I imagine he'll approaching the role similarly.
- eachro 11y ago
  I imagine a guy as talented as him(published papers on combinatorics in undergrad) would have no trouble learning all the things necessary to contribute to AI research.
- j2kun 11y ago
  I'm sure a big part of it is who you know.
- gdb 11y ago
  In the earliest days, I was doing everything required to make sure everyone else could focus on research: recruiting, operations, spinning up the cluster, taking out the garbage; really whatever needed to be done. There's a surprising amount of traditional engineering work needed to make AI systems happen. Over the past month or two, I've focused largely on launching OpenAI Gym: https://twitter.com/gdb/status/726099677716713472 https://twitter.com/gdb/status/726099677716713472. That being said, I'm planning on working on a research project next. As Ilya likes to say, deep learning is a shallow field — it's actually relatively easy to pick up and start making contributions.
  - throwaway6497 11y ago
    Greg, I don't know you. It is pretty incredible reading the blog post and what you have signed up to do. Kudos to Sama for the vision, and all the amazing people you were able to get together for the shared mission. Making the org non-profit is the right model for this. It is all the more surprising given that the whole VC model is to make profit. I am curious to see what you guys come up with. All the best.
    - throwaway6497 11y ago
      This comment at best should be neutral. Why the down voting. Just curious.
grey-area 11y ago
This is an illuminating definition of deep learning from the end of the article: The goal of supervised deep learning is to solve almost any problem of the form “map X to Y”. X can include images, speech, or text, and Y can include categories or even sentences. Interesting to think about the implications when such systems become commonplace.
- tlrobinson 11y ago
  I know what they mean, but you could say the goal of any program is to "map X to Y" where X are the inputs and Y are the desired outputs :)
  - grey-area 11y ago
    It's a bit more precise than that because of the examples given, but also the interesting bit is that it does it with only general guidance from the programmer, on a set of x which is not previously known. Up to now algorithms have encoded domain knowledge, but that's about to change if deep learning lives up to its promise.
  - zardo 11y ago
    That's kind of the point. If you can solve the general case, you can solve all the instances too.
  - chubot 11y ago
    One thing I've heard talked about is to use deep learning to re-learn arbitrary programs from their inputs and outputs... Then you can throw away the program and just run the neural network. I'm sort of a skeptic, so I think that would just make the programs massively less efficient (usually), more incorrect, and harder to maintain, but I guess it's a research direction. Maybe programmers need to be put out of a job :)
    - alphydan 11y ago
      > arbitrary programs It would be really fun to see a web app built on those principles! User clicks on /login/ link -> neural network {"hum ... I wonder what she's after ... let's send her a login form"} -> response. Trained on millions of MechanicalTurkers ... optimizing the function "which user can you keep engaged the longest". :)
kovek 11y ago
Commenting about the footnote. It seems like significant credit is given to the backpropagation method. I see backpropagation as a core mechanism in how neural networks learn. Are there other ideas present (or that could be explored) for a different core mechanism for learning? The backpropagation algorithm makes sense, but I still feel like there are other core mechanisms through which natural intelligence has developped. These natural mechanism might not be applicable to artificial intelligence, but I think we could learn from them.
- jimfleming 11y ago
  The core learning method in biological neurons is believed to be something like STDP (Spike-Timing-Dependent Plasticity). Basically, the arrival time of a spike at the post-synaptic end of a neuron is compared to the arrival time of a spike at the pre-synaptic end. The sign and scale of the difference of arrival times will cause a change in the respective synaptic strengths. It has similarities to backprop, and depends on backpropagated signals but backprop is simpler, faster and can exploit knowledge about the loss function (as far as we know STDP cannot). The downside (?) to backprop is that it cannot exploit temporal information like STDP but several recurrent models have found ways around that.
- Houshalter 11y ago
  There is a theory by Hinton that the brain uses some variation of backprop for learning, and that it explains STDP. However backprop is difficult to use on recurrent signals, as opposed to feed forward ones. I have been thinking about methods to train RNNs in real time. As opposed to saving every time step and iterating over it later. It's a much more difficult problem because you lose the nice properties of backproping error signals over long time spans, and instead have to save any information that might be relevant in the future. One idea to do that is autoencoders. They can be done recurrently, compressing the information from previous time steps into a single vector. But they don't work very well, or at least no one has figured out how to do it well. There's also HTM which is a totally different system inspired by models of the neocortex, but it also isn't as good as LSTM yet.
- deepnet 11y ago
  Stochastic Gradient Descent is a core mechanism for generalising from many examples in (non-biological) Neural Nets.
studentrob 11y ago
Best of luck. Do you have a hypothesis? More experienced ML folks don't seem to be on board [1] [2]. I haven't seen anyone posit how to build a machine that can set its own goals. I'm sure you'll build and learn something cool nonetheless Another organization that tried to build AI was called Thinking Machines [3]. They were good at parallel programming. A few of them would later form "Ab Initio Software" [4]. Their motto is "from first principles," which Elon likes to say. Socrates did too. My first job out of college trained us on that software. It's easy to use even if you're not a programmer, which was impressive for parallel software in 2004. I still get job emails for having it on my resume. The company is private and does not share its training resources. Boston mentality, I guess. Despite being so closed, they appeared to be quite successful in 2004 and have managed to stick around. [1] http://hunch.net/?p=3692542 http://hunch.net/?p=3692542 [2] https://www.facebook.com/yann.lecun/posts/10153442884182143?__mref=message_bubble https://www.facebook.com/yann.lecun/posts/10153442884182143?... [3] https://www.technologyreview.com/s/406781/thinking-machines/ https://www.technologyreview.com/s/406781/thinking-machines/ [4] http://www.abinitio.com/ http://www.abinitio.com/
- joe_the_user 11y ago
  The question of how to create general AI is fascinating but I don't think OpenAI is actually aiming for anything like a leap to full general intelligence at the moment. Rather, it seems like their goal is to follow the same incremental path mainstream AI is following but without having to hide it's methods. "OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return." "In the short term, we're building on recent advances in AI research and working towards the next set of breakthroughs." -- Which is to say, ordinary progress, not extraordinary progress. https://openai.com/about/ https://openai.com/about/
  - studentrob 11y ago
    Preface: I wish OpenAI the best of luck. That said, the quoted verbiage is vague and different from what Altman and Musk said in interviews [0]. > Altman: The organization is trying to develop a human positive AI > Musk: There’s two schools of thought — do you want many AIs, or a small number of AIs? We think probably many is good. And to the degree that you can tie it to an extension of individual human will, that is also good. OpenAI's about page is also different from what was written in the AI open letters signed by Musk and presumably other backers of OpenAI last year, > Artificial Intelligence (AI) technology has reached a point where the deployment of such systems is — practically if not legally — feasible within years, not decades, and the stakes are high: autonomous weapons have been described as the third revolution in warfare, after gunpowder and nuclear arms. [1] > The key question for humanity today is whether to start a global AI arms race or to prevent it from starting ... We therefore believe that a military AI arms race would not be beneficial for humanity [1] Then, in another letter, > We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do It sounds like Musk either thinks we are approaching AI, or he is misusing the term AI. The AI letter signees and backers of OpenAI may benefit from more discussion with experienced AI researchers such as those I linked above. What Altman and Musk said about the founding goals of OpenAI is not grounded in reality. It's great to have more funding in this field. [0] https://backchannel.com/how-elon-musk-and-y-combinator-plan-to-stop-computers-from-taking-over-17e0e27dd02a#.g33qtn9ni https://backchannel.com/how-elon-musk-and-y-combinator-plan-... [1] http://futureoflife.org/open-letter-autonomous-weapons/ http://futureoflife.org/open-letter-autonomous-weapons/ [2] http://futureoflife.org/ai-open-letter/ http://futureoflife.org/ai-open-letter/
studentrob 11y ago
> Before I finalized my decision to leave, Patrick asked me to go talk to Sam Altman. He said Sam had a good outsider’s perspective, had seen lots of people in similar circumstances, and would probably have a good recommendation on what I should do. > Within five minutes of talking to Sam, he told me I was definitely ready to leave. He said to let him know if he could be helpful in figuring out my next thing. Wow. Probably not what Patrick had in mind.
- pc 11y ago
  Actually, it was. (Structurally speaking.) Greg and I had had plenty of conversations about what he was looking for and I asked Sam to share his honest assessment with him. It's no good for Stripe to have a CTO who wants to be something else :-).
  - studentrob 11y ago
    That's not movie making material =(
infyr 11y ago
"Very few people today would have the audacity to explicitly try building human-level AI." Hmmm, there are a considerable number of people/groups who have this audacity... Have for decades... and they have been explicitly trying with much incremental success. One thing such people speak about is ridding the space of outlandish buzz words/promotions that mask the true nature of how things function. This 'hype' creates barriers to contribution, learning, and progress. Furthermore, the difficult efforts have been overshadowed by statistically mapped input/output flow models currently being called "A.I". There is no mystery w.r.t how deep learning/etc work I.M.O. You have inputs 'X'. You take a known solution space 'Y' (supervised learning) or you create an arbitrary one (unsupervised learning) 'Z'. You break apart the input space and map it to nodes in a graph. You break apart the output space and map it to nodes in a graph. Input flows are decomposed into minimal component parts and recomposed into higher orders of correlation. This is then compared (via flow restricted weighting) to increasing orders of the output space. How does this magical 'piece apart and and piece back together' process work during active flows? It works based on guided encoding of 'importance' weights on the partial information represented by individual nodes in the flow graph network. Thus why under/over fitting can occur if you have too many/too little nodes. How are the weights codified? By encoding the partial derivative (partial contribution) a node has w.r.t to the accuracy of the solution ... Error function (Desired - Actual). Curve fitting. It's essentially distributed brute force statistical gradient descent which is why you have to beat on it, tune it, anneal it, and cram hoards of data through for it for it to yield anything of value. "throw enough dirt and it will stick" Frankly, there is nothing to understand ... NNs/etc are distributed optimizers guided by partial objective information. The resultant network/weights are a spaghetti jumble of 'whatever gets the right output out the other end'. You're throwing a bunch of 'agents' at a solution space and having them gradually combine their results to a final solution. This was previously known as constraint optimization before it got the silicon valley treatment of buzzwords : Distributed constraint optimization... http://mirlab.org/jang/matlab/toolbox/machineLearning/image/gdDemo01.png http://mirlab.org/jang/matlab/toolbox/machineLearning/image/... This is not A.I. I don't feel anyone who has a grain of integrity ever thought it was. It's very slimmed down version of cortical Algorithms with lots of missing pieces at best. Strong A.I is being developed far from such thinking and is a totally different animal. People who work in this space are necessarily guarded and un open as there is a lack of appreciation, value, and funding for their 'audacious' efforts. Of course, once more solid systems are developed, I'm sure you'll hear from them again in the form of 'blackbox' capability presentations. Currently, the spotlight and money are being thrown at PHDs and names as no one has a clear understanding of what they're looking for. Namely because no one wants to spend the time/money on defining that. People are moreso interested in getting products/results out the door. ".... lets get the best minds, throw them in a room, throw money at them and hopefully a solution will come about" Seems very similar to distributed brute forcing of a problem space with a made up objective function...."throw enough dirt and it will stick" Most of the time should be spent on defining what were after ... The method is : https://en.wikipedia.org/wiki/Philosophy_of_science https://en.wikipedia.org/wiki/Philosophy_of_science not cramming mathematical formulas and PHDs into white-papers. There are a lack of generalist being brought into these A.I labs and efforts as they are perceived to have little value. Yet, its the 'generality' and 'fuzzy' stuff that underlies our very intelligence. From general to specific or specific to general... So, the industry wants to brute force this w/ money/PHDs/Buzzwords/industry names... The more complex and disjoint a problem space is, the harder it becomes to brute force.... Time will tell. All roads eventually lead to Rome. Though, some will take considerably longer.
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astazangasta 11y ago
>It’s hard to imagine anything more amazing and positively impactful than successfully creating AI, so long as it’s done in a good way. Really, what? This is horseshit of the highest order. Why would you think this is true? The vast majority of automation tasks don't require advanced AI. The vast majority of human work can be removed without recourse to AI. In addition, AI produces enormous problems, especially in the colossally power-imbalanced society we live in. It's overwhelmingly likely that an AI would be used first for surveillance and oppression; this is probably already happening with the most sophisticated intelligences we have available to us. Finally, there is a superabundance of "intelligence" available to us already, in the form of human intelligence. These intelligences are already highly capable of understanding and solving problems that an AI would take generations to appreciate on human terms (if it ever could) To me, the most positively impactful thing that we can do as human beings is to enable the full application of human intelligence to solving human problems, currently going to waste in most corners of the globe.
- tangled_zans 11y ago
  Very wisely said. People in AI try to replicate the "power of the human brain" when there are billions of "human brains" that are surviving at a subsistence level or working in mind-numbing menial tasks. I can't understand the cognitive dissonance that makes a person marvel at how "amazing the brain is" while simultaneously ignore the suffering of billions of those brains.
  - tariqali34 11y ago
    >I can't understand the cognitive dissonance that makes a person marvel at how "amazing the brain is" while simultaneously ignore the suffering of billions of those brains. I can. Humans are good at coming up with brilliant ideas (such as, say, the concept of translation). But they are absolutely poor at executing them effectively and "at scale" (translating arbitrary works from one language to another). So "AI advocates" can talk about how amazing the brain is in coming up with ideas (as if ideas were all that were needed), but what they really want are mechanical brutes that are able to execute those ideas quickly and effectively. At least some people hope that the proliferation of AI labor could mean a reduction of human labor, potentially reducing the "suffering of billions of those brains". This, of course, hinges on whether if the gains of productivity can get redistributed fairly (or if they just accumulate to those who already have capital). And then, there's all the social turmoil that occurs during the transition phase: humans may not want to be obsolete, humans may actually like working, AI accidentally becoming an 'exisitenal threat' due to human error, etc. The brains will suffer more in the short-term, in the faint hope that they will suffer less in the future. EDIT: "The vast majority of automation tasks don't require advanced AI. The vast majority of human work can be removed without recourse to AI." I would argue that when you get to the point when we have automation tasks and human work unnecessary, that we already have AI. We may never reach the stage of Strong AI because it turns out that what we actually do is not intelligent enough to require Strong AI.
YeGoblynQueenne 11y ago
> Mapping images to categories, speech to text, text to categories, go boards to good moves, and the like, is extremely useful, and cannot be done as well with other methods. [citation needed]
- nl 11y ago
  Which part do you need the citation for? Images to Categories: http://image-net.org/challenges/LSVRC/2015/results http://image-net.org/challenges/LSVRC/2015/results Speech to Text: https://en.wikipedia.org/wiki/Speech_recognition#Deep_Feedforward_and_Recurrent_Neural_Networks https://en.wikipedia.org/wiki/Speech_recognition#Deep_Feedfo... gives a good set of citations of different benchmarks text to categories: https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf https://papers.nips.cc/paper/5782-character-level-convolutio... (Note that DL methods only improve on manual feature engineering on the more difficult tasks here) go boards to good moves: The recent DeepMind games?
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YeGoblynQueenne 11y ago
>>I then pulled out the Google Translate app from my pocket, put it in airplane mode, and demonstrated how it translates the text under the camera directly on the image. So I had a look at the google reseach blog cited [1] and it turns out that Deep Learning is not used to do translation in the Google Translate app. It's only used to do Optical Character Recognition- OCR. Note, that's not handwritten digit recognition, I haven't seen any claims that the Google Translate app can do that, so it most probably can't, and can only recognise print characters. OCR Is not something you absolutely need a deep network to do, in fact it's one of those cases were you really don't want to deploy such an expensive system because there are far cheaper alternatives, like the logistic regression mentioned in the article. The google research blog typically doesn't say anything about how the actual translation is done, but it seems to me, from playing around with the app a bit, that it does word-for-word translation, possibly with some probabilistic heuristic to figure out the most common/likely such translation. That's very reasonable, given the app has to run on possibly weak hardware but it's also not as marketable as "Machine Translation with Deep Learning on Google Translate App". So this is not a very good example of the superiority of Deep Learning. It's more a good example of the superiority of the Google hype machine. [1] http://googleresearch.blogspot.co.uk/2015/07/how-google-translate-squeezes-deep.html http://googleresearch.blogspot.co.uk/2015/07/how-google-tran...
- deleted 11y ago
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- nl 11y ago
  Google Translate uses the CNN to extract the text from the image. That's much harder than usual OCR. It's more a good example of the superiority of the Google hype machine. That's pretty unfair. They managed to get this working on a mobile, which is where the real work was done.
  - YeGoblynQueenne 11y ago
    I totally agree that it's a delightful engineering feat to get all that to run on a smartphone and I'm suitably impressed actually. The hype I'm concerned about is the one about Deep Learning. The article is careless with the technology details and makes it sound as if Google is running deep neural networks for machine translation on peoples' phones, which would be, at this time, bigger news than AlphaGo beating Lee Sedol.
deepnet 11y ago
A Hinton inspired speculation about a Robot that plans & communicates. I posit that general A.I. must be embodied to develop, it must learn from the real world by interacting with it and learn by trial and error. Deep learning is a huge leap for AI. It learns best from raw data. Babies learn motor control & input interpretation first[1]. Moravec's paradox proposes learning these basics are the hard foundation that higher levels of reason build on. Abeel and Levine's work[2] where robots learn robust functions to map from pixels to motor torques for domestic tasks show solutions built on features learnt from raw data are more resilient than hand-engineered ones. These trajectories through motor-torque space to produce a desired goal are intriguingly like planning. Mikolov's[3] Word2vec & Radford's Image Vectors[4] produce semantic spaces where vector math is akin to reasoning. Higher level control could be achieved through vector algebras deriving actions and things directly from the robots internal vector space of motor-torques and raw-pixels Thus internal vectors are like thoughts[5] which can interpret, reason, plan, and control. Karpathy shows internal vectors can bridge modalities from words to pictures[6]. If the higher level control vectors of actions and things could translate to the modality of language as verbs and nouns, perhaps the robot could discuss its plans, and receive orders. [1] - Thelen's Infant Development http://www.ncbi.nlm.nih.gov/pubmed/17107442 http://www.ncbi.nlm.nih.gov/pubmed/17107442 [2] - Levine, Finn, Darrel, Abeel http://arxiv.org/abs/1504.00702 http://arxiv.org/abs/1504.00702 & [2b] - Distributed Grasp Learning http://googleresearch.blogspot.co.uk/2016/03/deep-learning-for-robots-learning-from.html http://googleresearch.blogspot.co.uk/2016/03/deep-learning-f... [3] - Mikolov, Chen, Corrado, Dean http://arxiv.org/abs/1301.3781 http://arxiv.org/abs/1301.3781 [4] - Radford, Metz, Chintala https://github.com/Newmu/dcgan_code#arithmetic-on-faces https://github.com/Newmu/dcgan_code#arithmetic-on-faces [5] - Hinton https://youtu.be/IcOMKXAw5VA?t=32m55s https://youtu.be/IcOMKXAw5VA?t=32m55s [6] - Karpathy https://youtu.be/ZkY7fAoaNcg?t=38m31s https://youtu.be/ZkY7fAoaNcg?t=38m31s
brendyn 11y ago
I'm also interested in getting into AI, as it is dream of mine to work on a problem of gread importance. I'm not sure where to start though. I have seen many Neural Network posts on HN but they end up taking me to some Github repo full of code with no documentation at all. Is there some resource that starts from zero and codes up a learning algorithm? Currently I'm reading through some Juder Pearl and E. T. Jaynes books because I'm fascinated by the theory, but I'm none of this show me how to get hands-on coding up an algorithm. If anyone here could recommend something to help get this newbie's foot in the door, I'd be grateful.
- allenleein 11y ago
  1. Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced https://medium.com/humanizing-technology/tool-31-resources-to-learn-ai-deep-learning-from-beginner-to-advanced-ed41b3fc1ae#.301fm9kdf https://medium.com/humanizing-technology/tool-31-resources-t... 2. Getting Up to Speed on Deep Learning https://medium.com/life-learning/getting-up-to-speed-on-deep-learning-20-resources-efec21e0aaf9#.hgct30hbs https://medium.com/life-learning/getting-up-to-speed-on-deep... 3. What are the best talks/lectures related to big data/algorithms/machine learning? https://www.quora.com/What-are-the-best-talks-lectures-related-to-big-data-algorithms-machine-learning https://www.quora.com/What-are-the-best-talks-lectures-relat... Hope it help dude :)

233 points by craigkerstiens 11y ago

Would be interested to hear about his role at Open AI - I would have thought you'd need at least a PhD in Statistics/CS to be able to contribute to AI research, no?

wasd 11y ago
He's written a post about how he defined CTO role at Stripe (http://blog.gregbrockman.com/figuring-out-the-cto-role-at-stripe http://blog.gregbrockman.com/figuring-out-the-cto-role-at-st...). I imagine he'll approaching the role similarly.
eachro 11y ago
I imagine a guy as talented as him(published papers on combinatorics in undergrad) would have no trouble learning all the things necessary to contribute to AI research.
j2kun 11y ago
I'm sure a big part of it is who you know.
gdb 11y ago
In the earliest days, I was doing everything required to make sure everyone else could focus on research: recruiting, operations, spinning up the cluster, taking out the garbage; really whatever needed to be done. There's a surprising amount of traditional engineering work needed to make AI systems happen. Over the past month or two, I've focused largely on launching OpenAI Gym: https://twitter.com/gdb/status/726099677716713472 https://twitter.com/gdb/status/726099677716713472. That being said, I'm planning on working on a research project next. As Ilya likes to say, deep learning is a shallow field — it's actually relatively easy to pick up and start making contributions.
- throwaway6497 11y ago
  Greg, I don't know you. It is pretty incredible reading the blog post and what you have signed up to do. Kudos to Sama for the vision, and all the amazing people you were able to get together for the shared mission. Making the org non-profit is the right model for this. It is all the more surprising given that the whole VC model is to make profit. I am curious to see what you guys come up with. All the best.
  - throwaway6497 11y ago
    This comment at best should be neutral. Why the down voting. Just curious.

grey-area 11y ago

This is an illuminating definition of deep learning from the end of the article: The goal of supervised deep learning is to solve almost any problem of the form “map X to Y”. X can include images, speech, or text, and Y can include categories or even sentences. Interesting to think about the implications when such systems become commonplace.

tlrobinson 11y ago
I know what they mean, but you could say the goal of any program is to "map X to Y" where X are the inputs and Y are the desired outputs :)
- grey-area 11y ago
  It's a bit more precise than that because of the examples given, but also the interesting bit is that it does it with only general guidance from the programmer, on a set of x which is not previously known. Up to now algorithms have encoded domain knowledge, but that's about to change if deep learning lives up to its promise.
- zardo 11y ago
  That's kind of the point. If you can solve the general case, you can solve all the instances too.
- chubot 11y ago
  One thing I've heard talked about is to use deep learning to re-learn arbitrary programs from their inputs and outputs... Then you can throw away the program and just run the neural network. I'm sort of a skeptic, so I think that would just make the programs massively less efficient (usually), more incorrect, and harder to maintain, but I guess it's a research direction. Maybe programmers need to be put out of a job :)
  - alphydan 11y ago
    > arbitrary programs It would be really fun to see a web app built on those principles! User clicks on /login/ link -> neural network {"hum ... I wonder what she's after ... let's send her a login form"} -> response. Trained on millions of MechanicalTurkers ... optimizing the function "which user can you keep engaged the longest". :)

kovek 11y ago

Commenting about the footnote. It seems like significant credit is given to the backpropagation method. I see backpropagation as a core mechanism in how neural networks learn. Are there other ideas present (or that could be explored) for a different core mechanism for learning? The backpropagation algorithm makes sense, but I still feel like there are other core mechanisms through which natural intelligence has developped. These natural mechanism might not be applicable to artificial intelligence, but I think we could learn from them.

jimfleming 11y ago
The core learning method in biological neurons is believed to be something like STDP (Spike-Timing-Dependent Plasticity). Basically, the arrival time of a spike at the post-synaptic end of a neuron is compared to the arrival time of a spike at the pre-synaptic end. The sign and scale of the difference of arrival times will cause a change in the respective synaptic strengths. It has similarities to backprop, and depends on backpropagated signals but backprop is simpler, faster and can exploit knowledge about the loss function (as far as we know STDP cannot). The downside (?) to backprop is that it cannot exploit temporal information like STDP but several recurrent models have found ways around that.
Houshalter 11y ago
There is a theory by Hinton that the brain uses some variation of backprop for learning, and that it explains STDP. However backprop is difficult to use on recurrent signals, as opposed to feed forward ones. I have been thinking about methods to train RNNs in real time. As opposed to saving every time step and iterating over it later. It's a much more difficult problem because you lose the nice properties of backproping error signals over long time spans, and instead have to save any information that might be relevant in the future. One idea to do that is autoencoders. They can be done recurrently, compressing the information from previous time steps into a single vector. But they don't work very well, or at least no one has figured out how to do it well. There's also HTM which is a totally different system inspired by models of the neocortex, but it also isn't as good as LSTM yet.
deepnet 11y ago
Stochastic Gradient Descent is a core mechanism for generalising from many examples in (non-biological) Neural Nets.

studentrob 11y ago

Best of luck. Do you have a hypothesis? More experienced ML folks don't seem to be on board [1] [2]. I haven't seen anyone posit how to build a machine that can set its own goals. I'm sure you'll build and learn something cool nonetheless Another organization that tried to build AI was called Thinking Machines [3]. They were good at parallel programming. A few of them would later form "Ab Initio Software" [4]. Their motto is "from first principles," which Elon likes to say. Socrates did too. My first job out of college trained us on that software. It's easy to use even if you're not a programmer, which was impressive for parallel software in 2004. I still get job emails for having it on my resume. The company is private and does not share its training resources. Boston mentality, I guess. Despite being so closed, they appeared to be quite successful in 2004 and have managed to stick around. [1] http://hunch.net/?p=3692542 http://hunch.net/?p=3692542 [2] https://www.facebook.com/yann.lecun/posts/10153442884182143?__mref=message_bubble https://www.facebook.com/yann.lecun/posts/10153442884182143?... [3] https://www.technologyreview.com/s/406781/thinking-machines/ https://www.technologyreview.com/s/406781/thinking-machines/ [4] http://www.abinitio.com/ http://www.abinitio.com/

joe_the_user 11y ago
The question of how to create general AI is fascinating but I don't think OpenAI is actually aiming for anything like a leap to full general intelligence at the moment. Rather, it seems like their goal is to follow the same incremental path mainstream AI is following but without having to hide it's methods. "OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return." "In the short term, we're building on recent advances in AI research and working towards the next set of breakthroughs." -- Which is to say, ordinary progress, not extraordinary progress. https://openai.com/about/ https://openai.com/about/
- studentrob 11y ago
  Preface: I wish OpenAI the best of luck. That said, the quoted verbiage is vague and different from what Altman and Musk said in interviews [0]. > Altman: The organization is trying to develop a human positive AI > Musk: There’s two schools of thought — do you want many AIs, or a small number of AIs? We think probably many is good. And to the degree that you can tie it to an extension of individual human will, that is also good. OpenAI's about page is also different from what was written in the AI open letters signed by Musk and presumably other backers of OpenAI last year, > Artificial Intelligence (AI) technology has reached a point where the deployment of such systems is — practically if not legally — feasible within years, not decades, and the stakes are high: autonomous weapons have been described as the third revolution in warfare, after gunpowder and nuclear arms. [1] > The key question for humanity today is whether to start a global AI arms race or to prevent it from starting ... We therefore believe that a military AI arms race would not be beneficial for humanity [1] Then, in another letter, > We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do It sounds like Musk either thinks we are approaching AI, or he is misusing the term AI. The AI letter signees and backers of OpenAI may benefit from more discussion with experienced AI researchers such as those I linked above. What Altman and Musk said about the founding goals of OpenAI is not grounded in reality. It's great to have more funding in this field. [0] https://backchannel.com/how-elon-musk-and-y-combinator-plan-to-stop-computers-from-taking-over-17e0e27dd02a#.g33qtn9ni https://backchannel.com/how-elon-musk-and-y-combinator-plan-... [1] http://futureoflife.org/open-letter-autonomous-weapons/ http://futureoflife.org/open-letter-autonomous-weapons/ [2] http://futureoflife.org/ai-open-letter/ http://futureoflife.org/ai-open-letter/

studentrob 11y ago

> Before I finalized my decision to leave, Patrick asked me to go talk to Sam Altman. He said Sam had a good outsider’s perspective, had seen lots of people in similar circumstances, and would probably have a good recommendation on what I should do. > Within five minutes of talking to Sam, he told me I was definitely ready to leave. He said to let him know if he could be helpful in figuring out my next thing. Wow. Probably not what Patrick had in mind.

pc 11y ago
Actually, it was. (Structurally speaking.) Greg and I had had plenty of conversations about what he was looking for and I asked Sam to share his honest assessment with him. It's no good for Stripe to have a CTO who wants to be something else :-).
- studentrob 11y ago
  That's not movie making material =(

infyr 11y ago

"Very few people today would have the audacity to explicitly try building human-level AI." Hmmm, there are a considerable number of people/groups who have this audacity... Have for decades... and they have been explicitly trying with much incremental success. One thing such people speak about is ridding the space of outlandish buzz words/promotions that mask the true nature of how things function. This 'hype' creates barriers to contribution, learning, and progress. Furthermore, the difficult efforts have been overshadowed by statistically mapped input/output flow models currently being called "A.I". There is no mystery w.r.t how deep learning/etc work I.M.O. You have inputs 'X'. You take a known solution space 'Y' (supervised learning) or you create an arbitrary one (unsupervised learning) 'Z'. You break apart the input space and map it to nodes in a graph. You break apart the output space and map it to nodes in a graph. Input flows are decomposed into minimal component parts and recomposed into higher orders of correlation. This is then compared (via flow restricted weighting) to increasing orders of the output space. How does this magical 'piece apart and and piece back together' process work during active flows? It works based on guided encoding of 'importance' weights on the partial information represented by individual nodes in the flow graph network. Thus why under/over fitting can occur if you have too many/too little nodes. How are the weights codified? By encoding the partial derivative (partial contribution) a node has w.r.t to the accuracy of the solution ... Error function (Desired - Actual). Curve fitting. It's essentially distributed brute force statistical gradient descent which is why you have to beat on it, tune it, anneal it, and cram hoards of data through for it for it to yield anything of value. "throw enough dirt and it will stick" Frankly, there is nothing to understand ... NNs/etc are distributed optimizers guided by partial objective information. The resultant network/weights are a spaghetti jumble of 'whatever gets the right output out the other end'. You're throwing a bunch of 'agents' at a solution space and having them gradually combine their results to a final solution. This was previously known as constraint optimization before it got the silicon valley treatment of buzzwords : Distributed constraint optimization... http://mirlab.org/jang/matlab/toolbox/machineLearning/image/gdDemo01.png http://mirlab.org/jang/matlab/toolbox/machineLearning/image/... This is not A.I. I don't feel anyone who has a grain of integrity ever thought it was. It's very slimmed down version of cortical Algorithms with lots of missing pieces at best. Strong A.I is being developed far from such thinking and is a totally different animal. People who work in this space are necessarily guarded and un open as there is a lack of appreciation, value, and funding for their 'audacious' efforts. Of course, once more solid systems are developed, I'm sure you'll hear from them again in the form of 'blackbox' capability presentations. Currently, the spotlight and money are being thrown at PHDs and names as no one has a clear understanding of what they're looking for. Namely because no one wants to spend the time/money on defining that. People are moreso interested in getting products/results out the door. ".... lets get the best minds, throw them in a room, throw money at them and hopefully a solution will come about" Seems very similar to distributed brute forcing of a problem space with a made up objective function...."throw enough dirt and it will stick" Most of the time should be spent on defining what were after ... The method is : https://en.wikipedia.org/wiki/Philosophy_of_science https://en.wikipedia.org/wiki/Philosophy_of_science not cramming mathematical formulas and PHDs into white-papers. There are a lack of generalist being brought into these A.I labs and efforts as they are perceived to have little value. Yet, its the 'generality' and 'fuzzy' stuff that underlies our very intelligence. From general to specific or specific to general... So, the industry wants to brute force this w/ money/PHDs/Buzzwords/industry names... The more complex and disjoint a problem space is, the harder it becomes to brute force.... Time will tell. All roads eventually lead to Rome. Though, some will take considerably longer.

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astazangasta 11y ago

>It’s hard to imagine anything more amazing and positively impactful than successfully creating AI, so long as it’s done in a good way. Really, what? This is horseshit of the highest order. Why would you think this is true? The vast majority of automation tasks don't require advanced AI. The vast majority of human work can be removed without recourse to AI. In addition, AI produces enormous problems, especially in the colossally power-imbalanced society we live in. It's overwhelmingly likely that an AI would be used first for surveillance and oppression; this is probably already happening with the most sophisticated intelligences we have available to us. Finally, there is a superabundance of "intelligence" available to us already, in the form of human intelligence. These intelligences are already highly capable of understanding and solving problems that an AI would take generations to appreciate on human terms (if it ever could) To me, the most positively impactful thing that we can do as human beings is to enable the full application of human intelligence to solving human problems, currently going to waste in most corners of the globe.

tangled_zans 11y ago
Very wisely said. People in AI try to replicate the "power of the human brain" when there are billions of "human brains" that are surviving at a subsistence level or working in mind-numbing menial tasks. I can't understand the cognitive dissonance that makes a person marvel at how "amazing the brain is" while simultaneously ignore the suffering of billions of those brains.
- tariqali34 11y ago
  >I can't understand the cognitive dissonance that makes a person marvel at how "amazing the brain is" while simultaneously ignore the suffering of billions of those brains. I can. Humans are good at coming up with brilliant ideas (such as, say, the concept of translation). But they are absolutely poor at executing them effectively and "at scale" (translating arbitrary works from one language to another). So "AI advocates" can talk about how amazing the brain is in coming up with ideas (as if ideas were all that were needed), but what they really want are mechanical brutes that are able to execute those ideas quickly and effectively. At least some people hope that the proliferation of AI labor could mean a reduction of human labor, potentially reducing the "suffering of billions of those brains". This, of course, hinges on whether if the gains of productivity can get redistributed fairly (or if they just accumulate to those who already have capital). And then, there's all the social turmoil that occurs during the transition phase: humans may not want to be obsolete, humans may actually like working, AI accidentally becoming an 'exisitenal threat' due to human error, etc. The brains will suffer more in the short-term, in the faint hope that they will suffer less in the future. EDIT: "The vast majority of automation tasks don't require advanced AI. The vast majority of human work can be removed without recourse to AI." I would argue that when you get to the point when we have automation tasks and human work unnecessary, that we already have AI. We may never reach the stage of Strong AI because it turns out that what we actually do is not intelligent enough to require Strong AI.

YeGoblynQueenne 11y ago

> Mapping images to categories, speech to text, text to categories, go boards to good moves, and the like, is extremely useful, and cannot be done as well with other methods. [citation needed]

nl 11y ago
Which part do you need the citation for? Images to Categories: http://image-net.org/challenges/LSVRC/2015/results http://image-net.org/challenges/LSVRC/2015/results Speech to Text: https://en.wikipedia.org/wiki/Speech_recognition#Deep_Feedforward_and_Recurrent_Neural_Networks https://en.wikipedia.org/wiki/Speech_recognition#Deep_Feedfo... gives a good set of citations of different benchmarks text to categories: https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf https://papers.nips.cc/paper/5782-character-level-convolutio... (Note that DL methods only improve on manual feature engineering on the more difficult tasks here) go boards to good moves: The recent DeepMind games?

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YeGoblynQueenne 11y ago

>>I then pulled out the Google Translate app from my pocket, put it in airplane mode, and demonstrated how it translates the text under the camera directly on the image. So I had a look at the google reseach blog cited [1] and it turns out that Deep Learning is not used to do translation in the Google Translate app. It's only used to do Optical Character Recognition- OCR. Note, that's not handwritten digit recognition, I haven't seen any claims that the Google Translate app can do that, so it most probably can't, and can only recognise print characters. OCR Is not something you absolutely need a deep network to do, in fact it's one of those cases were you really don't want to deploy such an expensive system because there are far cheaper alternatives, like the logistic regression mentioned in the article. The google research blog typically doesn't say anything about how the actual translation is done, but it seems to me, from playing around with the app a bit, that it does word-for-word translation, possibly with some probabilistic heuristic to figure out the most common/likely such translation. That's very reasonable, given the app has to run on possibly weak hardware but it's also not as marketable as "Machine Translation with Deep Learning on Google Translate App". So this is not a very good example of the superiority of Deep Learning. It's more a good example of the superiority of the Google hype machine. [1] http://googleresearch.blogspot.co.uk/2015/07/how-google-translate-squeezes-deep.html http://googleresearch.blogspot.co.uk/2015/07/how-google-tran...

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nl 11y ago
Google Translate uses the CNN to extract the text from the image. That's much harder than usual OCR. It's more a good example of the superiority of the Google hype machine. That's pretty unfair. They managed to get this working on a mobile, which is where the real work was done.
- YeGoblynQueenne 11y ago
  I totally agree that it's a delightful engineering feat to get all that to run on a smartphone and I'm suitably impressed actually. The hype I'm concerned about is the one about Deep Learning. The article is careless with the technology details and makes it sound as if Google is running deep neural networks for machine translation on peoples' phones, which would be, at this time, bigger news than AlphaGo beating Lee Sedol.

deepnet 11y ago

A Hinton inspired speculation about a Robot that plans & communicates. I posit that general A.I. must be embodied to develop, it must learn from the real world by interacting with it and learn by trial and error. Deep learning is a huge leap for AI. It learns best from raw data. Babies learn motor control & input interpretation first[1]. Moravec's paradox proposes learning these basics are the hard foundation that higher levels of reason build on. Abeel and Levine's work[2] where robots learn robust functions to map from pixels to motor torques for domestic tasks show solutions built on features learnt from raw data are more resilient than hand-engineered ones. These trajectories through motor-torque space to produce a desired goal are intriguingly like planning. Mikolov's[3] Word2vec & Radford's Image Vectors[4] produce semantic spaces where vector math is akin to reasoning. Higher level control could be achieved through vector algebras deriving actions and things directly from the robots internal vector space of motor-torques and raw-pixels Thus internal vectors are like thoughts[5] which can interpret, reason, plan, and control. Karpathy shows internal vectors can bridge modalities from words to pictures[6]. If the higher level control vectors of actions and things could translate to the modality of language as verbs and nouns, perhaps the robot could discuss its plans, and receive orders. [1] - Thelen's Infant Development http://www.ncbi.nlm.nih.gov/pubmed/17107442 http://www.ncbi.nlm.nih.gov/pubmed/17107442 [2] - Levine, Finn, Darrel, Abeel http://arxiv.org/abs/1504.00702 http://arxiv.org/abs/1504.00702 & [2b] - Distributed Grasp Learning http://googleresearch.blogspot.co.uk/2016/03/deep-learning-for-robots-learning-from.html http://googleresearch.blogspot.co.uk/2016/03/deep-learning-f... [3] - Mikolov, Chen, Corrado, Dean http://arxiv.org/abs/1301.3781 http://arxiv.org/abs/1301.3781 [4] - Radford, Metz, Chintala https://github.com/Newmu/dcgan_code#arithmetic-on-faces https://github.com/Newmu/dcgan_code#arithmetic-on-faces [5] - Hinton https://youtu.be/IcOMKXAw5VA?t=32m55s https://youtu.be/IcOMKXAw5VA?t=32m55s [6] - Karpathy https://youtu.be/ZkY7fAoaNcg?t=38m31s https://youtu.be/ZkY7fAoaNcg?t=38m31s

brendyn 11y ago

I'm also interested in getting into AI, as it is dream of mine to work on a problem of gread importance. I'm not sure where to start though. I have seen many Neural Network posts on HN but they end up taking me to some Github repo full of code with no documentation at all. Is there some resource that starts from zero and codes up a learning algorithm? Currently I'm reading through some Juder Pearl and E. T. Jaynes books because I'm fascinated by the theory, but I'm none of this show me how to get hands-on coding up an algorithm. If anyone here could recommend something to help get this newbie's foot in the door, I'd be grateful.

allenleein 11y ago
1. Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced https://medium.com/humanizing-technology/tool-31-resources-to-learn-ai-deep-learning-from-beginner-to-advanced-ed41b3fc1ae#.301fm9kdf https://medium.com/humanizing-technology/tool-31-resources-t... 2. Getting Up to Speed on Deep Learning https://medium.com/life-learning/getting-up-to-speed-on-deep-learning-20-resources-efec21e0aaf9#.hgct30hbs https://medium.com/life-learning/getting-up-to-speed-on-deep... 3. What are the best talks/lectures related to big data/algorithms/machine learning? https://www.quora.com/What-are-the-best-talks-lectures-related-to-big-data-algorithms-machine-learning https://www.quora.com/What-are-the-best-talks-lectures-relat... Hope it help dude :)