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 >>>>>>>>>> >>>>>>>>> >>>>>>>>>
