5 ms·

This Startup Has Developed a New Artificial Intelligence That Can Beat Google

20 points by aaron_p 10y ago

staticelf 10y ago
How do you measure intelligence? What do they mean by "100 times more efficient" than tensorflow? Isn't Tensorflow just a framework that you create AIs with? From my understanding we don't even have a good definition on human intelligence, so how can we measure it or/and artificial intelligence? If someone has answers from me who have zero experience in the AI-field I would be happy.
- miltondts 10y ago
  Here is an interesting definition and way to measure intelligence http://www.vetta.org/documents/AIQ.pdf http://www.vetta.org/documents/AIQ.pdf an informal definition from the paper: “intelligence measures an agent’s ability to achieve goals in a wide range of environments” There is also a formal definition and a method of measuring it. EDIT: a blog with a summary of the paper and source code http://www.vetta.org/2011/11/aiq/ http://www.vetta.org/2011/11/aiq/
rahimnathwani 10y ago
" If you want to train the system to recognize a cat, for example, show it 10,000 pictures over and over again of every possible variation of what a cat looks like. But don't show it a dog, because then the AI will get really confused." Umm, that's not how machine learning (or human learning) works. You can't learn to recognise something after seeing only positive examples.
- candiodari 10y ago
  Humans seem to be pretty good at it, so why not ? If I show a kid 100 elephants, and one dolphin, it will in fact be aware that a dolphin is not an elephant. In fact, I bet it'd still be aware of that even if I were to include one dolphin in the sequence of elephants and claim it's an elephant. In fact I was curious and I have a kid. I was pretty sure they didn't know the difference between a bee and a praying mantis, so I used those (google images makes that really quick). "That's a different kind of bee daddy" is what came out. Now of course, you can say kids have seen lots of examples of animals already and therefore they have a lot of negative training data "stored in their memory". That's not how the mind works though. It doesn't go away and train itself at all. And it's easy to devise an algorithm that does that too. Instead of using the positive examples to draw a decision boundary directly you use it to train both positive and negative features. You can have independent features, negative features and positive features. Deep learning as it's commonly used gets you positive features, but really backprop just moves features away from independence of the input data. Learning the opposite question is easy as well. So combine this, running half-half positive and negative features. That gets you positive and negative features. So yes you can train, even with deep learning, on just a few examples. Backprop will convert independent features to either positive or negative features. So now let's say I do that with 1000 9x9 convolutions and I train half of them positive and half of them negative. Would it be able to tell me a 64x64 dolphin is not an elephant ? Interesting question. I need to try, but I have little doubt that this will succeed, and get a decent success rate on identifying dolphins as non-elephants. It is even possible to do deep online learning, especially if you don't care about different images having unequal weights. And there's plenty of algorithms that can do online learning by themselves as well. In fact, the quintessential reinforcement learning algorithm is pretty much the answer to the question "how can I update my probability distribution exactly given this extra piece of data".
  - majewsky 10y ago
    > If I show a kid 100 elephants, and one dolphin, it will in fact be aware that a dolphin is not an elephant. Yes, because it has learned to distinguish the class "elephant" from the thousands of other things that it has already seen. If all the kid had ever seen were 100 elephants, I'm pretty sure it would have a darn hard time telling dolphins and elephants apart. You dismiss this line of thinking, but consider the following: Imagine yourself as having an entirely untrained sense. For example, let's say that I convert the image data of the elephant images into a sound pattern, and play them to you. After hearing all 100 elephants, it would be impossible for you to recognize a dolphin image's sound because there is no negative sample to tell elephant sounds apart from. All you could tell is that it's definitely an image as sound, as opposed to all the other sensory inputs that you've seen examples of.
    - candiodari 10y ago
      Why wouldn't I be able to train negative classifiers ? Note that I do always have "negative" training data available : background noise for one, and I'm sure I can generate 10 other kinds but I don't think that's even necessary. Backprop moves things away from equilibrium. It can do so in both directions. It can make a network more accurate, or more "inversely" accurate. I guess what you mean is that seeing only positive examples will lead to a network that always answers "yes". And that is indeed a concern. So perhaps I should adjust my answer to hedge for that: If the network trains a good positive feature for elephants, it will be able to train a good negative feature as well.
      - rahimnathwani 10y ago
        "Note that I do always have "negative" training data available" Yes, you do, but the model being trained with only cat pictures doesn't. "If the network trains a good positive feature for elephants, it will be able to train a good negative feature as well." I'm not sure what you mean here. Let's say a positive feature for an elephant is a patch of grey. What's a good negative feature? And how would the model learn that the presence of that feature should make it less likely what it's seeing is an elephant.
- todd8 10y ago
  Well actually, learning without negative reenforcement is possible. Experiments with pigeons over half a century ago showed that this was effective. See for example [1] and [2]. Interestingly, learning under training with inconsistent rewards (i.e. rewarding correct responses only some of the time) results in training that is stronger in the sense that birds after the training phase will keep up the trained behavior longer. It's like they don't get discouraged as quickly when they sometimes don't get the rewards they expect. I'm not sure what the implications are for machine learning, but especially for neural net learning it's interesting to consider biological models for learning. [1] Errorless Learning of a Conditional Temporal Discrimination https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ [2] Errorless Learning https://en.m.wikipedia.org/wiki/Errorless_learning https://en.m.wikipedia.org/wiki/Errorless_learning
  - rahimnathwani 10y ago
    In the pigeon example, there was no negative reinforcement, but there were negative examples used during training. They first trained the pigeons to peck bright red buttons. But introducing bright green buttons would have had the pigeons pecking those, too. So they first trained them with negative examples that were even more different from the ones they'd seen: dimly-lit green buttons that were lit for only a short time. The dimly-lit green buttons were the negative examples here, just like dogs would be if you were training a machine to recognise cats.
- todd8 10y ago
  Well actually, learning without negative reenforcement is possible. Experiments with pigeons over half a century ago showed that this was effective. See for example [1] and [2]. Interestingly, learning under training with inconsistent rewards (i.e. rewarding correct responses only some of the time) results in training that is stronger in the sense that birds after the training phase will keep up the trained behavior longer. It's like they don't get discouraged as quickly when they sometimes don't get the rewards they expect. I'm not sure what the implications are for machine learning, but especially for neural net learning it's interesting to consider biological models for learning. [1] Errorless Learning of a Conditional Temporal Discrimination https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ [2] Errorless Learning https://en.m.wikipedia.org/wiki/Errorless_learning https://en.m.wikipedia.org/wiki/Errorless_learning
majewsky 10y ago
When I visit that site, it blurs out everything and shows me a Quote of the Day that I don't care about. How can I actually read the article?
- matart 10y ago
  Continue to article button in the top right corner
amai 10y ago
Seems to be related to https://news.ycombinator.com/item?id=13644441 https://news.ycombinator.com/item?id=13644441
mchahn 10y ago
> Maybe down the line it will consider offering its Bayesian machine learning technique to outsiders, but it would be a hard sell as the tech industry has adopted deep learning systems fanatically. I call BS. Build a better mousetrap and ...

20 points by aaron_p 10y ago

How do you measure intelligence? What do they mean by "100 times more efficient" than tensorflow? Isn't Tensorflow just a framework that you create AIs with? From my understanding we don't even have a good definition on human intelligence, so how can we measure it or/and artificial intelligence? If someone has answers from me who have zero experience in the AI-field I would be happy.

miltondts 10y ago
Here is an interesting definition and way to measure intelligence http://www.vetta.org/documents/AIQ.pdf http://www.vetta.org/documents/AIQ.pdf an informal definition from the paper: “intelligence measures an agent’s ability to achieve goals in a wide range of environments” There is also a formal definition and a method of measuring it. EDIT: a blog with a summary of the paper and source code http://www.vetta.org/2011/11/aiq/ http://www.vetta.org/2011/11/aiq/

rahimnathwani 10y ago

" If you want to train the system to recognize a cat, for example, show it 10,000 pictures over and over again of every possible variation of what a cat looks like. But don't show it a dog, because then the AI will get really confused." Umm, that's not how machine learning (or human learning) works. You can't learn to recognise something after seeing only positive examples.

candiodari 10y ago
Humans seem to be pretty good at it, so why not ? If I show a kid 100 elephants, and one dolphin, it will in fact be aware that a dolphin is not an elephant. In fact, I bet it'd still be aware of that even if I were to include one dolphin in the sequence of elephants and claim it's an elephant. In fact I was curious and I have a kid. I was pretty sure they didn't know the difference between a bee and a praying mantis, so I used those (google images makes that really quick). "That's a different kind of bee daddy" is what came out. Now of course, you can say kids have seen lots of examples of animals already and therefore they have a lot of negative training data "stored in their memory". That's not how the mind works though. It doesn't go away and train itself at all. And it's easy to devise an algorithm that does that too. Instead of using the positive examples to draw a decision boundary directly you use it to train both positive and negative features. You can have independent features, negative features and positive features. Deep learning as it's commonly used gets you positive features, but really backprop just moves features away from independence of the input data. Learning the opposite question is easy as well. So combine this, running half-half positive and negative features. That gets you positive and negative features. So yes you can train, even with deep learning, on just a few examples. Backprop will convert independent features to either positive or negative features. So now let's say I do that with 1000 9x9 convolutions and I train half of them positive and half of them negative. Would it be able to tell me a 64x64 dolphin is not an elephant ? Interesting question. I need to try, but I have little doubt that this will succeed, and get a decent success rate on identifying dolphins as non-elephants. It is even possible to do deep online learning, especially if you don't care about different images having unequal weights. And there's plenty of algorithms that can do online learning by themselves as well. In fact, the quintessential reinforcement learning algorithm is pretty much the answer to the question "how can I update my probability distribution exactly given this extra piece of data".
- majewsky 10y ago
  > If I show a kid 100 elephants, and one dolphin, it will in fact be aware that a dolphin is not an elephant. Yes, because it has learned to distinguish the class "elephant" from the thousands of other things that it has already seen. If all the kid had ever seen were 100 elephants, I'm pretty sure it would have a darn hard time telling dolphins and elephants apart. You dismiss this line of thinking, but consider the following: Imagine yourself as having an entirely untrained sense. For example, let's say that I convert the image data of the elephant images into a sound pattern, and play them to you. After hearing all 100 elephants, it would be impossible for you to recognize a dolphin image's sound because there is no negative sample to tell elephant sounds apart from. All you could tell is that it's definitely an image as sound, as opposed to all the other sensory inputs that you've seen examples of.
  - candiodari 10y ago
    Why wouldn't I be able to train negative classifiers ? Note that I do always have "negative" training data available : background noise for one, and I'm sure I can generate 10 other kinds but I don't think that's even necessary. Backprop moves things away from equilibrium. It can do so in both directions. It can make a network more accurate, or more "inversely" accurate. I guess what you mean is that seeing only positive examples will lead to a network that always answers "yes". And that is indeed a concern. So perhaps I should adjust my answer to hedge for that: If the network trains a good positive feature for elephants, it will be able to train a good negative feature as well.
    - rahimnathwani 10y ago
      "Note that I do always have "negative" training data available" Yes, you do, but the model being trained with only cat pictures doesn't. "If the network trains a good positive feature for elephants, it will be able to train a good negative feature as well." I'm not sure what you mean here. Let's say a positive feature for an elephant is a patch of grey. What's a good negative feature? And how would the model learn that the presence of that feature should make it less likely what it's seeing is an elephant.
todd8 10y ago
Well actually, learning without negative reenforcement is possible. Experiments with pigeons over half a century ago showed that this was effective. See for example [1] and [2]. Interestingly, learning under training with inconsistent rewards (i.e. rewarding correct responses only some of the time) results in training that is stronger in the sense that birds after the training phase will keep up the trained behavior longer. It's like they don't get discouraged as quickly when they sometimes don't get the rewards they expect. I'm not sure what the implications are for machine learning, but especially for neural net learning it's interesting to consider biological models for learning. [1] Errorless Learning of a Conditional Temporal Discrimination https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ [2] Errorless Learning https://en.m.wikipedia.org/wiki/Errorless_learning https://en.m.wikipedia.org/wiki/Errorless_learning
- rahimnathwani 10y ago
  In the pigeon example, there was no negative reinforcement, but there were negative examples used during training. They first trained the pigeons to peck bright red buttons. But introducing bright green buttons would have had the pigeons pecking those, too. So they first trained them with negative examples that were even more different from the ones they'd seen: dimly-lit green buttons that were lit for only a short time. The dimly-lit green buttons were the negative examples here, just like dogs would be if you were training a machine to recognise cats.
todd8 10y ago
Well actually, learning without negative reenforcement is possible. Experiments with pigeons over half a century ago showed that this was effective. See for example [1] and [2]. Interestingly, learning under training with inconsistent rewards (i.e. rewarding correct responses only some of the time) results in training that is stronger in the sense that birds after the training phase will keep up the trained behavior longer. It's like they don't get discouraged as quickly when they sometimes don't get the rewards they expect. I'm not sure what the implications are for machine learning, but especially for neural net learning it's interesting to consider biological models for learning. [1] Errorless Learning of a Conditional Temporal Discrimination https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014774/ [2] Errorless Learning https://en.m.wikipedia.org/wiki/Errorless_learning https://en.m.wikipedia.org/wiki/Errorless_learning

majewsky 10y ago

When I visit that site, it blurs out everything and shows me a Quote of the Day that I don't care about. How can I actually read the article?

matart 10y ago
Continue to article button in the top right corner

amai 10y ago

Seems to be related to https://news.ycombinator.com/item?id=13644441 https://news.ycombinator.com/item?id=13644441

mchahn 10y ago

> Maybe down the line it will consider offering its Bayesian machine learning technique to outsiders, but it would be a hard sell as the tech industry has adopted deep learning systems fanatically. I call BS. Build a better mousetrap and ...