5 ms·

Wasn't MSRA behind that amazing live English-to-Chinese translator [0]? It's interesting to contrast Google's and Microsoft's approach with ours at Wolfram Res

Wasn't MSRA behind that amazing live English-to-Chinese translator [0]? It's interesting to contrast Google's and Microsoft's approach with ours at Wolfram Research (at 0.3% their size, of course [1]). We've come to see that it is very fruitful to live in a murky space somewhere between "commercial product" and "research project". I'm thinking specifically about Wolfram|Alpha. It's an enormous and daunting project. We've tackled a lot of very hard problems, and obviously have a long way still to go. It has the weird property of being a thing that ships every week, but not one that the parent company depends on financially. And so we kind of do our own thing, encoding domain knowledge, adding content, curating data, and designing frameworks, even if some if it won't ever make us any money (e.g. who will ever pay for dog vision [2]). We mostly just do things because they're cool or fun. But it's been incredibly useful to drive innovation elsewhere in the company. For example: 1. Alpha's unit system (the best in the world, the authors claim) is already in Mathematica 9. 2. Alpha is inspiring us to bring high-level semantic data and reasoning to Mathematica 10. 3. The automatic analysis in Pro is being souped up and will form part of Mathematica's predictive interface (automatic suggestions to "perform logistic regression", and so on). 4. We're working on taking the natural language understanding frameworks we've built for Alpha and using them to translate natural language queries into structured SQL or hierarchical document queries. 5. The domain specific languages we've invented to represent things in the real world are going to be exposed in a soon-to-be open format we're calling the Wolfram Data Format (which I hope will succeed where RDF is failing). We wouldn't have though of any of this stuff without Alpha. And it wasn't explicitly driven by either commercial or basic research, but rather some kind of eccentric blend of the two. [0] http://www.youtube.com/watch?v=Nu-nlQqFCKg http://www.youtube.com/watch?v=Nu-nlQqFCKg [1] http://www.wolframalpha.com/input/?i=600+people+%2F+%28number+of+employees+at+microsoft+%2B+google%29 http://www.wolframalpha.com/input/?i=600+people+%2F+%28numbe... [2] http://www.wolframalpha.com/input/?i=apply+dog+vision+to+image+of+a+lady+bug http://www.wolframalpha.com/input/?i=apply+dog+vision+to+ima...

seanmcdirmid 13y ago
Yes, a semi-research project with lots of peel offs works well. It is nice that Wolfram has a nice product with good profits that can bankroll something like Alpha. Seemingly inefficient (not directly profitable) experimentation is a great way of driving innovation and invention. Contrast with Apple, who has a razor sharp focus on profitable products: they don't make many mistakes, and they aren't taking any risks these days. As an aside, I don't think the mostly constructed knowledge representation approach taken by Alpha will scale in the long run. You guys might want to look at playing around more with machine learning. On the other hand, there are gaps in machine learning that are best filled by DSLs and explicit construction.
- taliesinb 13y ago
  Yes. Fewer arrows has worked well for Apple. We _are_ taking machine learning more seriously (I claim some credit here). My team is integrating ML into Mathematica 10 as we speak. And we've experimented with deep learning. I think some interesting things will come out of that in the future. I think a hybrid approach is pragmatic. There is much computational knowledge that can't be assembled from web scrapes, as Google's purchase of Metaweb indicates (anyone remember Google Squared?).
  - seanmcdirmid 13y ago
    I am seriously looking forward to see what you guys come up with!
    - taliesinb 13y ago
      Thanks! Likewise, my eyes are always peeled for the latest stuff from MSR.

seanmcdirmid 13y ago

Yes, a semi-research project with lots of peel offs works well. It is nice that Wolfram has a nice product with good profits that can bankroll something like Alpha. Seemingly inefficient (not directly profitable) experimentation is a great way of driving innovation and invention. Contrast with Apple, who has a razor sharp focus on profitable products: they don't make many mistakes, and they aren't taking any risks these days. As an aside, I don't think the mostly constructed knowledge representation approach taken by Alpha will scale in the long run. You guys might want to look at playing around more with machine learning. On the other hand, there are gaps in machine learning that are best filled by DSLs and explicit construction.

taliesinb 13y ago
Yes. Fewer arrows has worked well for Apple. We _are_ taking machine learning more seriously (I claim some credit here). My team is integrating ML into Mathematica 10 as we speak. And we've experimented with deep learning. I think some interesting things will come out of that in the future. I think a hybrid approach is pragmatic. There is much computational knowledge that can't be assembled from web scrapes, as Google's purchase of Metaweb indicates (anyone remember Google Squared?).
- seanmcdirmid 13y ago
  I am seriously looking forward to see what you guys come up with!
  - taliesinb 13y ago
    Thanks! Likewise, my eyes are always peeled for the latest stuff from MSR.