Alchemy is also less expensive and opaque than Watson's meta learning <http://researcher.watson.ibm.com/researcher/files/il-DAVIDBO/multiobjectiveSOMMOSoptimization_c.pdf>: 'I believe you have prostate cancer because the decision tree, the genetic algorithm, and Naìˆve Bayes say so, although the multilayer perceptron and the SVM disagree.'
On Wednesday, August 3, 2016 at 4:36:52 PM UTC-3, Kevin Liu wrote: > > Another important cool thing I think is worth noting: he added the > possibility of weights to rules (attachment). Each line is equivalent to a > desired conclusion. > > On Wednesday, August 3, 2016 at 4:27:05 PM UTC-3, Kevin Liu wrote: >> >> 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 >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>>
