13 ms·

Acquisitions accelerate as tech giants seek to build AI smarts

100 points by t23 9y ago

batbomb 9y ago
Lattice wasn't discussed on HN, but they were acquired two weeks ago by Apple.
- aub3bhat 9y ago
  Their source code (DeepDive) is Open and Apache licensed. My guess the DARPA funding prevented FoundationDB type fate. https://github.com/HazyResearch/deepdive https://github.com/HazyResearch/deepdive http://cs.stanford.edu/people/chrismre/papers/dd.pdf http://cs.stanford.edu/people/chrismre/papers/dd.pdf
- vvanders 9y ago
  I had a heart attack and thought this was Lattice Semi. Whew.
  - amelius 9y ago
    It could still happen. Market cap is $855M versus $800B for Apple.
graycat 9y ago
Uh, guys with the big bucks: AL/ML are at most a tiny fraction of the material, tools, power, and value of the pure/applied math on the shelves of the research libraries. If you have a problem you want solved with AI/ML, 99 44/100% of the time you are better off going for what's on the shelves of the libraries, as taught in various high end grad schools, than with anything currently specifically AL/ML. So, broadly, go for work in statistics, optimization, stochastic processes, and optimal control. For a specific application, may want some work that stands on that existing material and is also at least somewhat original. E.g., the crucial technical core of my startup is some original applied math I derived based on pure/applied math that's long been on the shelves of the research libraries. For the valuable work of my startup, what's in AI/ML now is in comparison at best weak, nearly silly early grade school baby talk. Really, guys, 99 44/100% of the good stuff is still where it's long been -- on the shelves of the best research libraries. And for the education for that work, it's definitely NOT in departments of computer science. Instead look at selected programs in pure/applied math in some of the best research universities.
- aub3bhat 9y ago
  This is a spectacularly bad advice, which "off-the-shelves" research libraries that you mention? OpenCV, LibSVM, Weka or Matlab/Octave?? Most examples/implementations in OpenCV are outpaced by Deep Learning methods. TensorFlow, Caffe, PyTorch/Torch which today implement most of the state of the art methods were all written on average a year or two ago. >> E.g., the crucial technical core of my startup is some original applied math I derived based on pure/applied math that's long been on the shelves of the research libraries. For the valuable work of my startup, what's in AI/ML now is in comparison at best weak, nearly silly early grade school baby talk. Rather than vaguely mentioning libraries and startup, I suggest you offer concrete evidence behind your claims. >> And for the education for that work, it's definitely NOT in departments of computer science. Instead look at selected programs in pure/applied math in some of the best research universities. This is just truthiness (Math feels more "hardcore" than CS so Math department must be the better source.). Having known several people at Google/Apple/Facebook-FAIR, CS departments are typically major source of AI/ML researchers.
  - guiambros 9y ago
    > ... which "off-the-shelves" research libraries that you mention? OpenCV, LibSVM, Weka or Matlab/Octave?? I think they meant "library" in the literal sense - the one with books -, and not "software libraries". But yes, while common sense, it's empty of any meaning. Like "it's better to invest in the core math and statistical fundamentals than the specific applications". Right, but so what? Folks like DeepMind, Numenta, etc are developing new core research material, and applying in practice.
  - graycat 9y ago
    > This is a spectacularly bad advice, which "off-the-shelves" research libraries that you mention? Harvard, Princeton, MIT, Stanford, Berkeley, U. Chicago, Johns Hopkins, Cal Tech, need I go on? It's very good advice; sorry you don't understand it. In short, the computer science people know how to write software, but for exploiting pure/applied math they don't know what software to write; that needs a mathematician, and the computer science profs rarely have enough background in math to do well with the subject. The math is based on pure math and is heavily theorems and proofs, and about the only way to be good with that material is to be a BS, MS, and hopefully Ph.D. in pure/applied math. The computer science AI/ML work has obtained some good results; just why the techniques work is too often still a mystery. But the emphasis on gigantic quantities of data make the work a niche and real applications rare.
senatorobama 9y ago
Wish I was in this industry instead of my dead-end job.
- tinbad 9y ago
  On the bright side: every job will be a dead-end when AI is very good.
  - TeMPOraL 9y ago
    When people revolt and start hanging programmers for destroying their jobs, it probably won't matter in which field of the industry any of us were.
    - resf 9y ago
      Fortunately the AI riot police and soldiers will be far more effective at containing unrest than their human predecessors.
      - dkersten 9y ago
        You are right. We need to start building in "protect our programmers" as a high-level AI directive.
  - stouset 9y ago
    You and I must have vastly different definitions of "bright".
  - throwaway29292 9y ago
    This particularly worries me because if technical skills go out of vogue, then people skills would become conversely more important, and the thing I've optimized for all my life would become useless.
- GuiA 9y ago
  6-12 months of self directed learning & self promotion (blog posts, talks at local user groups, open source contributions, etc) can get you in that industry if you're already a semi-competent programmer.
miguelrochefort 9y ago
Yet, no one is tackling the AI UI problem. Why is that? I can't believe people are still thinking natural language is the way to go...
- ethbro 9y ago
  Natural language is always the endgame. Because natural language either removes the physical input system or frees up screen real estate.
  - kobeya 9y ago
    Natural language can be incredibly imprecise though. I expect the ideal UI for AI will meet somewhere in the middle with something that combines the best of programming languages with the specificity and ease of use of natural language -- think "strongly typed natural language". I'd rather my command type-error than the machine go off and do what I say, not what I meant.
    - ethbro 9y ago
      Step 1 was recognizing speech to text accurately. Step 2 is AI that is globally context- and subtext-aware. I expect Step 3 to be AI that is personally situation-, context-, and subtext-aware (IoT & personal info storage-enabled). And I think that's drastically going to improve accuracy. I can see some standardization of input (in the same way that GUIs evolved), but I think it will probably take the form of grammar structure & error behavior more than strongly typing questions.
      - dkersten 9y ago
        That will solve the AI understanding you, but still won't make me more precise and clear in my instructions. Yes, the AI can hopefully infer the rest, but even then, I can currently type faster than I can speak. Will the AI be able to infer enough of what I want that I can say substantially less and have the AI still get it right? At that stage, why not go all the way and remove me from the equation completely?

100 points by t23 9y ago

batbomb 9y ago

Lattice wasn't discussed on HN, but they were acquired two weeks ago by Apple.

aub3bhat 9y ago
Their source code (DeepDive) is Open and Apache licensed. My guess the DARPA funding prevented FoundationDB type fate. https://github.com/HazyResearch/deepdive https://github.com/HazyResearch/deepdive http://cs.stanford.edu/people/chrismre/papers/dd.pdf http://cs.stanford.edu/people/chrismre/papers/dd.pdf
vvanders 9y ago
I had a heart attack and thought this was Lattice Semi. Whew.
- amelius 9y ago
  It could still happen. Market cap is $855M versus $800B for Apple.

graycat 9y ago

Uh, guys with the big bucks: AL/ML are at most a tiny fraction of the material, tools, power, and value of the pure/applied math on the shelves of the research libraries. If you have a problem you want solved with AI/ML, 99 44/100% of the time you are better off going for what's on the shelves of the libraries, as taught in various high end grad schools, than with anything currently specifically AL/ML. So, broadly, go for work in statistics, optimization, stochastic processes, and optimal control. For a specific application, may want some work that stands on that existing material and is also at least somewhat original. E.g., the crucial technical core of my startup is some original applied math I derived based on pure/applied math that's long been on the shelves of the research libraries. For the valuable work of my startup, what's in AI/ML now is in comparison at best weak, nearly silly early grade school baby talk. Really, guys, 99 44/100% of the good stuff is still where it's long been -- on the shelves of the best research libraries. And for the education for that work, it's definitely NOT in departments of computer science. Instead look at selected programs in pure/applied math in some of the best research universities.

aub3bhat 9y ago
This is a spectacularly bad advice, which "off-the-shelves" research libraries that you mention? OpenCV, LibSVM, Weka or Matlab/Octave?? Most examples/implementations in OpenCV are outpaced by Deep Learning methods. TensorFlow, Caffe, PyTorch/Torch which today implement most of the state of the art methods were all written on average a year or two ago. >> E.g., the crucial technical core of my startup is some original applied math I derived based on pure/applied math that's long been on the shelves of the research libraries. For the valuable work of my startup, what's in AI/ML now is in comparison at best weak, nearly silly early grade school baby talk. Rather than vaguely mentioning libraries and startup, I suggest you offer concrete evidence behind your claims. >> And for the education for that work, it's definitely NOT in departments of computer science. Instead look at selected programs in pure/applied math in some of the best research universities. This is just truthiness (Math feels more "hardcore" than CS so Math department must be the better source.). Having known several people at Google/Apple/Facebook-FAIR, CS departments are typically major source of AI/ML researchers.
- guiambros 9y ago
  > ... which "off-the-shelves" research libraries that you mention? OpenCV, LibSVM, Weka or Matlab/Octave?? I think they meant "library" in the literal sense - the one with books -, and not "software libraries". But yes, while common sense, it's empty of any meaning. Like "it's better to invest in the core math and statistical fundamentals than the specific applications". Right, but so what? Folks like DeepMind, Numenta, etc are developing new core research material, and applying in practice.
- graycat 9y ago
  > This is a spectacularly bad advice, which "off-the-shelves" research libraries that you mention? Harvard, Princeton, MIT, Stanford, Berkeley, U. Chicago, Johns Hopkins, Cal Tech, need I go on? It's very good advice; sorry you don't understand it. In short, the computer science people know how to write software, but for exploiting pure/applied math they don't know what software to write; that needs a mathematician, and the computer science profs rarely have enough background in math to do well with the subject. The math is based on pure math and is heavily theorems and proofs, and about the only way to be good with that material is to be a BS, MS, and hopefully Ph.D. in pure/applied math. The computer science AI/ML work has obtained some good results; just why the techniques work is too often still a mystery. But the emphasis on gigantic quantities of data make the work a niche and real applications rare.

senatorobama 9y ago

Wish I was in this industry instead of my dead-end job.

tinbad 9y ago
On the bright side: every job will be a dead-end when AI is very good.
- TeMPOraL 9y ago
  When people revolt and start hanging programmers for destroying their jobs, it probably won't matter in which field of the industry any of us were.
  - resf 9y ago
    Fortunately the AI riot police and soldiers will be far more effective at containing unrest than their human predecessors.
    - dkersten 9y ago
      You are right. We need to start building in "protect our programmers" as a high-level AI directive.
- stouset 9y ago
  You and I must have vastly different definitions of "bright".
- throwaway29292 9y ago
  This particularly worries me because if technical skills go out of vogue, then people skills would become conversely more important, and the thing I've optimized for all my life would become useless.
GuiA 9y ago
6-12 months of self directed learning & self promotion (blog posts, talks at local user groups, open source contributions, etc) can get you in that industry if you're already a semi-competent programmer.

miguelrochefort 9y ago

Yet, no one is tackling the AI UI problem. Why is that? I can't believe people are still thinking natural language is the way to go...

ethbro 9y ago
Natural language is always the endgame. Because natural language either removes the physical input system or frees up screen real estate.
- kobeya 9y ago
  Natural language can be incredibly imprecise though. I expect the ideal UI for AI will meet somewhere in the middle with something that combines the best of programming languages with the specificity and ease of use of natural language -- think "strongly typed natural language". I'd rather my command type-error than the machine go off and do what I say, not what I meant.
  - ethbro 9y ago
    Step 1 was recognizing speech to text accurately. Step 2 is AI that is globally context- and subtext-aware. I expect Step 3 to be AI that is personally situation-, context-, and subtext-aware (IoT & personal info storage-enabled). And I think that's drastically going to improve accuracy. I can see some standardization of input (in the same way that GUIs evolved), but I think it will probably take the form of grammar structure & error behavior more than strongly typing questions.
    - dkersten 9y ago
      That will solve the AI understanding you, but still won't make me more precise and clear in my instructions. Yes, the AI can hopefully infer the rest, but even then, I can currently type faster than I can speak. Will the AI be able to infer enough of what I want that I can say substantially less and have the AI still get it right? At that stage, why not go all the way and remove me from the equation completely?