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 <[email protected]> >> 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 <[email protected]> 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 <[email protected]> >>>>>>>>>>>> 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 >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>
