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Why Google Hired Ray Kurzweil

Posted by samzenpus on from the getting-the-scoop dept.

An anonymous reader writes "Nataly Kelly writes in the Huffington Post about Google's strategy of hiring Ray Kurzweil and how the company likely intends to use language translation to revolutionize the way we share information. From the article: 'Google Translate is not just a tool that enables people on the web to translate information. It's a strategic tool for Google itself. The implications of this are vast and go beyond mere language translation. One implication might be a technology that can translate from one generation to another. Or how about one that slows down your speech or turns up the volume for an elderly person with hearing loss? That enables a stroke victim to use the clarity of speech he had previously? That can pronounce using your favorite accent? That can convert academic jargon to local slang? It's transformative. In this system, information can walk into one checkpoint as the raucous chant of a 22-year-old American football player and walk out as the quiet whisper of a 78-year-old Albanian grandmother.'"

5 of 117 comments (clear)

  1. He did an interview with NPR on this subject. by Kenja · · Score: 5, Informative

    The main thing he says he will be working on is artificial intelligence that can understand "context". The goal is for Google search to be able to find pages etc based on what you mean rather then on word counts of what you type.

    --

    "Have you ever thought about just turning off the TV, sitting down with your kids, and hitting them?"
    1. Re:He did an interview with NPR on this subject. by Anonymous Coward · · Score: 5, Funny

      I've spent 10 years learning to think like a search engine if this ruins everything and makes me spend another 10 years to learn to think like a normal person again just so the search engine can translate my thoughts correctly I am gonna be pissed.

  2. The Bayesian Bandwagon by qbitslayer · · Score: 5, Interesting

    The problem with people like Kurzweil, Jeff Hawkins, the folks at the Singularity Institute and the rest of the AI community is that they have all jumped on the Bayesian bandwagon. This is not unlike the way they all jumped on the symbolic bandwagon in the last century only to be proven wrong forty years later. Do we have another half a century to waste, waiting for these guys to realize the error of their ways? Essentially there are two approaches to machine learning.

    1) The Bayesian model assumes that events in the world are inherently uncertain and that the job of an intelligent system is to discover the probabilities.
    2) The competing model, by contrast, assumes that events in the world are perfectly consistent and that the job of an intelligent system is to discover this perfection.

    Luckily for the rest of humanity, a few people are beginning to realize the folly of the Bayesian mindset. When asked in a recent Cambridge Press interview, "What was the greatest challenge you have encountered in your research?", Judea Pearl, an Israeli computer scientist and an early champion of the Bayesian approach to AI, replied: "In retrospect, my greatest challenge was to break away from probabilistic thinking and accept, first, that people are not probability thinkers but cause-effect thinkers and, second, that causal thinking cannot be captured in the language of probability; it requires a formal language of its own."

    Read The Myth of the Bayesian Brain for more, if you're interested.

    1. Re:The Bayesian Bandwagon by VortexCortex · · Score: 5, Insightful

      1) The Bayesian model assumes that events in the world are inherently uncertain and that the job of an intelligent system is to discover the probabilities.
      2) The competing model, by contrast, assumes that events in the world are perfectly consistent and that the job of an intelligent system is to discover this perfection.

      Then you have AI (machine intelligence) researchers like myself who realize that the world isn't persistent, perfect or consistent, and neither must intelligent systems be. It's plainly obvious that any sufficiently complex cybernetic (feedback loop) system is indistinguishable from sentience because that's what sentience is (your mind is merely a sentient cybernetic system). I have but to look at the hierarchical neural networks and structures of the human mind to realize it's only a matter of time before the artificial system complexity eclipses our own minds'. The true flaw is top-down thinking. That's not the way complex life was made, that's not the way we achieved sentience, that's not the way to cause it to happen artificially either... It's the bottom up approach that works. You can't design sentient intelligences outright, but you can create self organizing systems that have the capacity to acquire more complexity, and evolve more intelligence. Not all machine intelligence systems have an end to the training process -- These don't fit into your bullshit 1) and 2) classifications.

      Also: The level of intelligence that emerges from any complex system is not artificial, it is real intelligence; That the medium is artificial is not important in terms of intelligence. I think "Artificial Intelligence" is a racist term used by chauvinists that think human intellect is far more special than it really is.

      Want to see something funny? Ask an AI researcher if they believe in Intelligent Design. If they say "Yes" then say, "So you think yourself a god?" If they say, "No" then say, "What do you call yourself doing then?". Those working in emergent intelligence will happily reply that they're modeling the same processes that we already know work in nature, the others will be in quite a state!

  3. Re:Google Could use some Fresh Ideas in AI by raddan · · Score: 5, Insightful

    Yeah, but there's a reason why statistical models are hot now and why the old AI-style of logical reasoning isn't: the AI stuff only works when the input is perfect, or at least, planned for. As we all know, language doesn't really have rules, just conventions. This is why the ML approach to NLP is powerful: the machine works out what was probably meant. That's far more useful, because practically nobody writes well. When Abdur Chowdhury was still Twitter's main NLP guy, he visited our department, and guess what-- people even write in more than one language in a single sentence! Not to mention, in the old AI-style approach, if you fill a big box full of rules, you have to search through them. Computational complexity is a major limiting factor in all AI problems. ML has this nice property that you can often simply trade accuracy for speed. See Monte Carlo methods.

    As you point out, ML doesn't "understand" anything. I personally think "understanding" is a bit of a squishy term. Those old AI-style systems were essentially fancy search algorithms with a large set of states and transition rules. Is that "understanding"? ML is basically the same idea except that transitioning from one state to another involves the calculation of a probability distribution, and sometimes whether the machine should transition is probabilistic.

    I think that hybrid ML/AI systems-- i.e., systems that combine both logical constraints and probabilistic reasoning-- will prove to be very powerful in the future. But does that mean these machines "understand"? If you mean something like what happens in the human brain, I'm not so sure. Do humans "understand"? Or are we also automata? In order to determine whether we've "cracked AI", we need to know the answers to those questions. See Kant and good luck.