After which I have to code Felix into Julia, a relational optimizer for statistical inference with Tuffy <http://i.stanford.edu/hazy/tuffy/> inside, for enterprise settings.
On Tuesday, August 9, 2016 at 12:07:32 AM UTC-3, Kevin Liu wrote: > > Can I get tips on bringing Alchemy's optimized Tuffy > <http://i.stanford.edu/hazy/tuffy/> in Java to Julia while showing the > best of Julia? I am going for the most correct way, even if it means coding > Tuffy into C and Julia. > > On Sunday, August 7, 2016 at 8:34:37 PM UTC-3, Kevin Liu wrote: >> >> I'll try to build it, compare it, and show it to you guys. I offered to >> do this as work. I am waiting to see if they will accept it. >> >> On Sunday, August 7, 2016 at 6:15:50 PM UTC-3, Stefan Karpinski wrote: >>> >>> Kevin, as previously requested by Isaiah, please take this to some other >>> forum or maybe start a blog. >>> >>> On Sat, Aug 6, 2016 at 10:53 PM, Kevin Liu <kvt...@gmail.com> wrote: >>> >>>> Symmetry-based learning, Domingos, 2014 >>>> https://www.microsoft.com/en-us/research/video/symmetry-based-learning/ >>>> >>>> Approach 2: Deep symmetry networks generalize convolutional neural >>>> networks by tying parameters and pooling over an arbitrary symmetry group, >>>> not just the translation group. In preliminary experiments, they >>>> outperformed convnets on a digit recognition task. >>>> >>>> On Friday, August 5, 2016 at 4:56:45 PM UTC-3, Kevin Liu wrote: >>>>> >>>>> Minsky died of a cerebral hemorrhage at the age of 88.[40] >>>>> <https://en.wikipedia.org/wiki/Marvin_Minsky#cite_note-40> Ray >>>>> Kurzweil <https://en.wikipedia.org/wiki/Ray_Kurzweil> says he was >>>>> contacted by the cryonics organization Alcor Life Extension Foundation >>>>> <https://en.wikipedia.org/wiki/Alcor_Life_Extension_Foundation> seeking >>>>> Minsky's body.[41] >>>>> <https://en.wikipedia.org/wiki/Marvin_Minsky#cite_note-Kurzweil-41> >>>>> Kurzweil >>>>> believes that Minsky was cryonically preserved by Alcor and will be >>>>> revived >>>>> by 2045.[41] >>>>> <https://en.wikipedia.org/wiki/Marvin_Minsky#cite_note-Kurzweil-41> >>>>> Minsky >>>>> was a member of Alcor's Scientific Advisory Board >>>>> <https://en.wikipedia.org/wiki/Advisory_Board>.[42] >>>>> <https://en.wikipedia.org/wiki/Marvin_Minsky#cite_note-AlcorBoard-42> In >>>>> keeping with their policy of protecting privacy, Alcor will neither >>>>> confirm >>>>> nor deny that Alcor has cryonically preserved Minsky.[43] >>>>> <https://en.wikipedia.org/wiki/Marvin_Minsky#cite_note-43> >>>>> >>>>> We better do a good job. >>>>> >>>>> On Friday, August 5, 2016 at 4:45:42 PM UTC-3, Kevin Liu wrote: >>>>>> >>>>>> *So, I think in the next 20 years (2003), if we can get rid of all of >>>>>> the traditional approaches to artificial intelligence, like neural nets >>>>>> and >>>>>> genetic algorithms and rule-based systems, and just turn our sights a >>>>>> little bit higher to say, can we make a system that can use all those >>>>>> things for the right kind of problem? Some problems are good for neural >>>>>> nets; we know that others, neural nets are hopeless on them. Genetic >>>>>> algorithms are great for certain things; I suspect I know what they're >>>>>> bad >>>>>> at, and I won't tell you. (Laughter)* - Minsky, founder of CSAIL MIT >>>>>> >>>>>> *Those programmers tried to find the single best way to represent >>>>>> knowledge - Only Logic protects us from paradox.* - Minsky (see >>>>>> attachment from his lecture) >>>>>> >>>>>> On Friday, August 5, 2016 at 8:12:03 AM UTC-3, Kevin Liu wrote: >>>>>>> >>>>>>> Markov Logic Network is being used for the continuous development of >>>>>>> drugs to cure cancer at MIT's CanceRX <http://cancerx.mit.edu/>, on >>>>>>> DARPA's largest AI project to date, Personalized Assistant that >>>>>>> Learns (PAL) <https://pal.sri.com/>, progenitor of Siri. One of >>>>>>> Alchemy's largest applications to date was to learn a semantic network >>>>>>> (knowledge graph as Google calls it) from the web. >>>>>>> >>>>>>> Some on Probabilistic Inductive Logic Programming / Probabilistic >>>>>>> Logic Programming / Statistical Relational Learning from CSAIL >>>>>>> <http://people.csail.mit.edu/kersting/ecmlpkdd05_pilp/pilp_ida2005_tut.pdf> >>>>>>> (my >>>>>>> understanding is Alchemy does PILP from entailment, proofs, and >>>>>>> interpretation) >>>>>>> >>>>>>> The MIT Probabilistic Computing Project (where there is Picture, an >>>>>>> extension of Julia, for computer vision; Watch the video from Vikash) >>>>>>> <http://probcomp.csail.mit.edu/index.html> >>>>>>> >>>>>>> Probabilistic programming could do for Bayesian ML what Theano has >>>>>>> done for neural networks. >>>>>>> <http://www.inference.vc/deep-learning-is-easy/> - Ferenc Huszár >>>>>>> >>>>>>> Picture doesn't appear to be open-source, even though its Paper is >>>>>>> available. >>>>>>> >>>>>>> I'm in the process of comparing the Picture Paper and Alchemy code >>>>>>> and would like to have an open-source PILP from Julia that combines the >>>>>>> best of both. >>>>>>> >>>>>>> On Wednesday, August 3, 2016 at 5:01:02 PM UTC-3, Christof Stocker >>>>>>> wrote: >>>>>>>> >>>>>>>> This sounds like it could be a great contribution. I shall keep a >>>>>>>> curious eye on your progress >>>>>>>> >>>>>>>> Am Mittwoch, 3. August 2016 21:53:54 UTC+2 schrieb Kevin Liu: >>>>>>>>> >>>>>>>>> Thanks for the advice Cristof. I am only interested in people >>>>>>>>> wanting to code it in Julia, from R by Domingos. The algo has been >>>>>>>>> successfully applied in many areas, even though there are many other >>>>>>>>> areas >>>>>>>>> remaining. >>>>>>>>> >>>>>>>>> On Wed, Aug 3, 2016 at 4:45 PM, Christof Stocker < >>>>>>>>> stocker....@gmail.com> wrote: >>>>>>>>> >>>>>>>>>> Hello Kevin, >>>>>>>>>> >>>>>>>>>> Enthusiasm is a good thing and you should hold on to that. But to >>>>>>>>>> save yourself some headache or disappointment down the road I advice >>>>>>>>>> a >>>>>>>>>> level head. Nothing is really as bluntly obviously solved as it may >>>>>>>>>> seems >>>>>>>>>> at first glance after listening to brilliant people explain things. >>>>>>>>>> A >>>>>>>>>> physics professor of mine once told me that one of the (he thinks) >>>>>>>>>> most >>>>>>>>>> malicious factors to his past students progress where overstated >>>>>>>>>> results/conclusions by other researches (such as premature >>>>>>>>>> announcements >>>>>>>>>> from CERN). I am no mathematician, but as far as I can judge is the >>>>>>>>>> no free >>>>>>>>>> lunch theorem of pure mathematical nature and not something induced >>>>>>>>>> empirically. These kind of results are not that easily to get rid >>>>>>>>>> of. If >>>>>>>>>> someone (especially an expert) states such a theorem will prove >>>>>>>>>> wrong I >>>>>>>>>> would be inclined to believe that he is not talking about literally, >>>>>>>>>> but >>>>>>>>>> instead is just trying to make a point about a more or less >>>>>>>>>> practical >>>>>>>>>> implication. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Am Mittwoch, 3. August 2016 21:27:05 UTC+2 schrieb Kevin Liu: >>>>>>>>>>> >>>>>>>>>>> The Markov logic network represents a probability distribution >>>>>>>>>>> over the states of a complex system (i.e. a cell), comprised of >>>>>>>>>>> entities, >>>>>>>>>>> where logic formulas encode the dependencies between them. >>>>>>>>>>> >>>>>>>>>>> On Wednesday, August 3, 2016 at 4:19:09 PM UTC-3, Kevin Liu >>>>>>>>>>> wrote: >>>>>>>>>>>> >>>>>>>>>>>> Alchemy is like an inductive Turing machine, to be programmed >>>>>>>>>>>> to learn broadly or restrictedly. >>>>>>>>>>>> >>>>>>>>>>>> The logic formulas from rules through which it represents can >>>>>>>>>>>> be inconsistent, incomplete, or even incorrect-- the learning and >>>>>>>>>>>> probabilistic reasoning will correct them. The key point is that >>>>>>>>>>>> Alchemy >>>>>>>>>>>> doesn't have to learn from scratch, proving Wolpert and Macready's >>>>>>>>>>>> no free >>>>>>>>>>>> lunch theorem wrong by performing well on a variety of classes of >>>>>>>>>>>> problems, >>>>>>>>>>>> not just some. >>>>>>>>>>>> >>>>>>>>>>>> On Wednesday, August 3, 2016 at 4:01:15 PM UTC-3, Kevin Liu >>>>>>>>>>>> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>> Hello Community, >>>>>>>>>>>>> >>>>>>>>>>>>> I'm in the last pages of Pedro Domingos' book, the Master >>>>>>>>>>>>> Algo, one of two recommended by Bill Gates to learn about AI. >>>>>>>>>>>>> >>>>>>>>>>>>> From the book, I understand all learners have to represent, >>>>>>>>>>>>> evaluate, and optimize. There are many types of learners that do >>>>>>>>>>>>> this. What >>>>>>>>>>>>> Domingos does is generalize these three parts, (1) using Markov >>>>>>>>>>>>> Logic >>>>>>>>>>>>> Network to represent, (2) posterior probability to evaluate, and >>>>>>>>>>>>> (3) >>>>>>>>>>>>> genetic search with gradient descent to optimize. The posterior >>>>>>>>>>>>> can be >>>>>>>>>>>>> replaced for another accuracy measure when it is easier, as >>>>>>>>>>>>> genetic search >>>>>>>>>>>>> replaced by hill climbing. Where there are 15 popular options for >>>>>>>>>>>>> representing, evaluating, and optimizing, Domingos generalized >>>>>>>>>>>>> them into >>>>>>>>>>>>> three options. The idea is to have one unified learner for any >>>>>>>>>>>>> application. >>>>>>>>>>>>> >>>>>>>>>>>>> There is code already done in R >>>>>>>>>>>>> https://alchemy.cs.washington.edu/. My question: anybody in >>>>>>>>>>>>> the community vested in coding it into Julia? >>>>>>>>>>>>> >>>>>>>>>>>>> Thanks. Kevin >>>>>>>>>>>>> >>>>>>>>>>>>> On Friday, June 3, 2016 at 3:44:09 PM UTC-3, Kevin Liu wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>> https://github.com/tbreloff/OnlineAI.jl/issues/5 >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Friday, June 3, 2016 at 11:17:28 AM UTC-3, Kevin Liu wrote: >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> I plan to write Julia for the rest of me life... given it >>>>>>>>>>>>>>> remains suitable. I am still reading all of Colah's material on >>>>>>>>>>>>>>> nets. I ran >>>>>>>>>>>>>>> Mocha.jl a couple weeks ago and was very happy to see it work. >>>>>>>>>>>>>>> Thanks for >>>>>>>>>>>>>>> jumping in and telling me about OnlineAI.jl, I will look into >>>>>>>>>>>>>>> it once I am >>>>>>>>>>>>>>> ready. From a quick look, perhaps I could help and learn by >>>>>>>>>>>>>>> building a very >>>>>>>>>>>>>>> clear documentation of it. Would really like to see Julia a >>>>>>>>>>>>>>> leap ahead of >>>>>>>>>>>>>>> other languages, and plan to contribute heavily to it, but at >>>>>>>>>>>>>>> the moment am >>>>>>>>>>>>>>> still getting introduced to CS, programming, and nets at the >>>>>>>>>>>>>>> basic level. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> On Friday, June 3, 2016 at 10:48:15 AM UTC-3, Tom Breloff >>>>>>>>>>>>>>> wrote: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Kevin: computers that program themselves is a concept which >>>>>>>>>>>>>>>> is much closer to reality than most would believe, but >>>>>>>>>>>>>>>> julia-users isn't >>>>>>>>>>>>>>>> really the best place for this speculation. If you're actually >>>>>>>>>>>>>>>> interested >>>>>>>>>>>>>>>> in writing code, I'm happy to discuss in OnlineAI.jl. I was >>>>>>>>>>>>>>>> thinking about >>>>>>>>>>>>>>>> how we might tackle code generation using a neural framework >>>>>>>>>>>>>>>> I'm working >>>>>>>>>>>>>>>> on. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> On Friday, June 3, 2016, Kevin Liu <kvt...@gmail.com> >>>>>>>>>>>>>>>> wrote: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> If Andrew Ng who cited Gates, and Gates who cited Domingos >>>>>>>>>>>>>>>>> (who did not lecture at Google with a TensorFlow question in >>>>>>>>>>>>>>>>> the end), were >>>>>>>>>>>>>>>>> unsuccessful penny traders, Julia was a language for web >>>>>>>>>>>>>>>>> design, and the >>>>>>>>>>>>>>>>> tribes in the video didn't actually solve problems, perhaps >>>>>>>>>>>>>>>>> this would be a >>>>>>>>>>>>>>>>> wildly off-topic, speculative discussion. But these >>>>>>>>>>>>>>>>> statements couldn't be >>>>>>>>>>>>>>>>> farther from the truth. In fact, if I had known about this >>>>>>>>>>>>>>>>> video some >>>>>>>>>>>>>>>>> months ago I would've understood better on how to solve a >>>>>>>>>>>>>>>>> problem I was >>>>>>>>>>>>>>>>> working on. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> For the founders of Julia: I understand your tribe is >>>>>>>>>>>>>>>>> mainly CS. This master algorithm, as you are aware, would >>>>>>>>>>>>>>>>> require >>>>>>>>>>>>>>>>> collaboration with other tribes. Just citing the obvious. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> On Friday, June 3, 2016 at 10:21:25 AM UTC-3, Kevin Liu >>>>>>>>>>>>>>>>> wrote: >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> There could be parts missing as Domingos mentions, but >>>>>>>>>>>>>>>>>> induction, backpropagation, genetic programming, >>>>>>>>>>>>>>>>>> probabilistic inference, >>>>>>>>>>>>>>>>>> and SVMs working together-- what's speculative about the >>>>>>>>>>>>>>>>>> improved versions >>>>>>>>>>>>>>>>>> of these? >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> Julia was made for AI. Isn't it time for a consolidated >>>>>>>>>>>>>>>>>> view on how to reach it? >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> On Thursday, June 2, 2016 at 11:20:35 PM UTC-3, Isaiah >>>>>>>>>>>>>>>>>> wrote: >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> This is not a forum for wildly off-topic, speculative >>>>>>>>>>>>>>>>>>> discussion. >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> Take this to Reddit, Hacker News, etc. >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> On Thu, Jun 2, 2016 at 10:01 PM, Kevin Liu < >>>>>>>>>>>>>>>>>>> kvt...@gmail.com> wrote: >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> I am wondering how Julia fits in with the unified tribes >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> mashable.com/2016/06/01/bill-gates-ai-code-conference/#8VmBFjIiYOqJ >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> https://www.youtube.com/watch?v=B8J4uefCQMc >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>> >>>
