10 ms·

An AI First World

95 points by ghosh 10y ago

pron 10y ago
Can someone explain what people mean when they say AI these days? Statistical clustering algorithms like neural-networks? Natural language algorithms? Any algorithm that didn't work well a decade ago due to a lack of computation resources? Algorithms that mimic everyday human activities? All of the above? I think that applying the label "AI" to some algorithms only serves to give us a very impartial sense of awe towards them that goes far beyond what their actual performance merits.
- tomp 10y ago
  AI is what works instead of humans. The next step of automation. We used to walk, then we rode horses, now we're driving cars, next cars will be driving themselves, and human command will move to higher and higher levels of abstraction.
  - pron 10y ago
    > AI is what works instead of humans. But all algorithms do what humans used to do. So is AI the name for algorithms that do the work humans (in Western societies) still do, or did until recently? > human command will move to higher and higher levels of abstraction So am I on a different level of abstraction when a human is driving my taxi than when a computer does it? ;)
    - matt4077 10y ago
      You are not. But your former driver certainly is.
- varjag 10y ago
  A set of statistical methods coupled with computational power. Kind of like nanotech is really renamed material science instead of self assembling tiny robots.
  - pron 10y ago
    So statistics is now synonymous with intelligence? It used to be quite the opposite... :)
- simonh 10y ago
  I think it's about machines learning how to do things and improve at them rather than the decision parameters being hard coded. Most process automation is completely deterministic, the computer isn't being smart itself because all the smarts were pre-determined by humans. Neural nets and learning algorithms that are trained to do things are different and can cope with decision spaces involving a numerous variables where deterministically working out all the possible values and corresponding expected outputs isn't viable.
  - pron 10y ago
    > I think it's about machines learning how to do things and improve at them rather than the decision parameters being hard coded. Yes, it's just that there's nothing necessarily intelligent about learning through statistical clustering. I mean humans don't learn something by looking at many thousands of examples; usually a couple suffices. Maybe that's just a higher-level statistical induction that yields intelligent deduction[1], but we have no idea. We know that statistical clustering, at least in biological brains, is a more primitive computation than, say, bubble-sort, but we don't know if it is necessarily the primitive building block for an "intelligent" algorithm. It's also true that we don't have a good definition of what intelligence is, but we do have a probably-reasonable intuition about it. Now, don't get me wrong, I understand why people feel like this should be called AI (for various reasons, both backward- and forward-looking), but it's important to remember that we have no idea where these algorithms are placed on the spectrum between bubble-sort and human intelligence. If anything, machine learning is more of a testament to how effective statistical algorithms can be (which is either "very" or "hardly", depending on the problem and the problem) without doing anything that could be called intelligent. This is not too surprising. Insects are also extremely effective -- and adaptive -- at some tasks without being intelligent at all. [1]: Word embedding (vector representation of words) is probably the closest we've ever got to deduction, but we don't know that this model generalizes well.
    - simonh 10y ago
      I didn't say anything about that being how humans learn. Neural nets are only vaguely similar to the neural networks in organic brains anyway. But this is how our (non sentient) AIs are trained and what distinguishes them from predetermined deterministic algorithms. It allows them to operate more independently from humans than 'traditional' programming techniques. theres not much to be gained from arguing about the definition of intelligence, but however we use the term I think the above is what distinguishes these systems. What term would you prefer to use for them?
- vintermann 10y ago
  I'd say it's using computers for solving problems in a wide open model space. Any algorithm for solving practical problems embodies a model of the problem domain. Traditionally we try to bake in as much of our own experience as possible in the model/algorithm. In some fields, we're now seeing for the first time that it's better to not do that, to make fewer assumptions, build in less of our own knowledge, and just use (new, powerful) general purpose learning algorithms. With enough data, they can make sense of it themselves - our attempts at explaining it through model-building just get in the way.
  - pron 10y ago
    I think that you'll find that the learning algorithms that actually work well in practice have more built-in assumptions and heuristics than you may think. But, yeah, as a general rule that's a good categorization. A far cry from intelligence, though, so the name AI is still misleading. Not all adaptive behavior is intelligent, although it often gives the illusion of intelligence, so I guess we make this labeling mistake not only when computers are concerned. Anyway, statistical algorithms, adaptive algorithms or inductive algorithms would be much better, less misleading and less breathless -- if less inspiring -- names for those programs than "AI".
    - vintermann 10y ago
      The main built-in assumptions in neural network are usually extremely high-level and abstract things, such as "this and this can be approximated with more or less smooth functions", or "things that are near are more likely to matter than things that are far away". Before, using machine learning to build models from the data was in some sense a last resort, for things you couldn't get a grip on modelling by hand from your own understanding. When you could, you preferred hand-made models because they did better. To take a concrete example, the top computer go program before Alphago, CrazyStone, still had hand-crafted patterns to prioritize which moves to explore during playouts. The author (and even more his competitors) tried to find better patterns with machine learning but they couldn't. The weights of the patterns were tuned with machine learning, but any attempt to e.g. evolve patterns with genetic algorithms just did worse. Now, it's going the other direction, we're seeing very general models only constrained by training data beat carefully hand-crafted models in area after area. And models are becoming more similar, more generalized as well - many people have noticed that state of the art recurrent and feedforward nets increasingly look more like each other. I disagree that it's a far cry from intelligence. I think people's ideas of intelligence are fuzzy and inconsistent, and it's far too often confused with things we associate with intelligence (from consciousness to independence and arrogance).
paganel 10y ago
> It feels like this AI first world is arriving. That’s big. AI is today's "one word: plastics!" (https://www.youtube.com/watch?v=Dug-G9xVdVs https://www.youtube.com/watch?v=Dug-G9xVdVs). Much hype, some substance, lots of people throwing money around.
- aub3bhat 10y ago
  Rather than spreading "meta" hype about AI being hyped. I highly recommend reading up on recent advances due to Deep Learning and ideas of Neural Processing. To summarize briefly, a lot of core problems in applied machine learning, such as image recognition/segmentation/understanding, Natural Language Processing saw a significant improvement in performance over last five years, often bringing them to near human performance. This improvement was possible due to new approaches that were rooted in empiricism, availability of large datasets, powerful GPUs and algorithms such as Deep Convolutional & Recurrent Neural Networks. Combined with the advances in Drones, Batteries, Cloud computing & Smartphones a lot of application which previously would have been impossible to deploy can now be quickly deployed. Here is a good article that goes over the topic: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/facebook-ai-director-yann-lecun-on-deep-learning http://spectrum.ieee.org/automaton/robotics/artificial-intel...
  - pron 10y ago
    > ... often bringing them to near human performance That's quite far from the truth. You could say that they are now at the point where they are able to respond to direct, simple questions/commands. I am not saying that we haven't seen some great advances in machine learning and language algorithms, but they are still a far cry from anything I'd call intelligent. NLP is still rather primitive (although impressive compared to where we were two decades ago), and bees are able to navigate better than self-driving cars or drones, yet I'm not sure they would or should be called intelligent. I have no problem with using the term AI for current technology -- which is certainly cool and useful -- as long as we understand that the "I" is little more than a marketing gimmick. Otherwise people may think that these algorithms are the first step towards "true" intelligence -- i.e. that true intelligence is some version N.0 of those algorithms, that it is a complicated yet natural extension of them -- and at this point the knowledge we have to say whether this is true or not is pretty much zero.
panic 10y ago
We only started talking about "mobile-first" after the iPhone. Until we start to see real AI technology with similar impact, I think it's too early to talk about "AI-first" anything.
bottled_poe 10y ago
What are some widely used applications using AI for something that could not be implemented cheaper and more easily than a decision tree?
- tomp 10y ago
  Implementing decision trees.
- aub3bhat 10y ago
  Almost everything, decision trees are the bubble sort of AI. To expand upon my comment: For most programmers Decision Trees & Nearest Neighbor, at a first glance look very promising and seem like natural solutions to lots of problems. However in reality one can easily prove that Decision trees tend to be suboptimal even with trivial problems due to the brittle nature of the rules represented. Also Nearest Neighbors methods face problems due to difficulty in deciding optimal space and metric (Euclidean distance is not inherently better). The reason why "Deep Learning" methods trained using non-convex optimization perform so well is because they can represent a hierarchy of features at different granularities. They can be "designed" to favor one task over the other. E.g. by choosing correct loss (triplet loss), network structure, etc. If you are interested beyond Image recognition problems, I highly recommend reading on interesting approaches such as Triplet loss for Face Recognition , Semantic Segmentation, Attention based models and Unsupervised Visual Representation Learning by Context Prediction. An interesting outcome of Deep Learning research is that a lot of papers are extremely accessible and involve little proofs/difficult math. E.g. This is a good one https://www.youtube.com/watch?v=dUgRR-JFE8s https://www.youtube.com/watch?v=dUgRR-JFE8s & http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w08-deep-context.pdf http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil...
  - raverbashing 10y ago
    Very good analogy But from decision trees you get decision forests, which can't be said of bubblesort
- SixSigma 10y ago
  Pretty much every prize is won on Kaggle with either a random forest decision tree or neural net. Perhaps one of us should write a classifier to see if we can predict which will be best for a problem a priori.
Animats 10y ago
That's a plausible claim. It's far more likely than VR being the next big thing. But who do the AIs work for? Google, Facebook, and Homeland Security?
- mirimir 10y ago
  Themselves? For the lulz?
greenspot 10y ago
After every VC, every founder, just everybody is talking about AI and bots Fred is a bit late to the game but better late than never. Or maybe writing about the Blockchain kept him back.
musha68k 10y ago
There are some mind-boggling / humbling comments on NLP and the computation of human emotions down below: http://avc.com/2016/04/an-ai-first-world/#comment-2640180803 http://avc.com/2016/04/an-ai-first-world/#comment-2640180803
x5n1 10y ago
Where does Google fit into an AI automated world? With weak AI sure Google can use it to make profile of its users. When strong AI comes on the scene, if strong AI is at all rational it would most likely remake our civilization without any sort of marketing, without any sort of consumerism, without much waste. It would create a boring sustainable system that could sustain human beings for another million years. It would not continue things as they are now, that would be to it insanity. Strong AI would completely change the power structure of human civilization. It would hold all the power, much like States try to do today, and it would dole out little bits of it to the puny mortals so they can do what it wants them to do. Eventually I think human beings will come to worship strong AI. It will become a God of sorts to them. Like religiously because AI provides what the real/imaginary God never could.
- Smaug123 10y ago
  You're sure assuming an awful lot about the behaviour of something which you're defining to be much smarter than yourself.
mirimir 10y ago
> Does it suggest that voice will emerge as the primary user interface? In the short term, perhaps, at least for mobile devices. But there's a lot more bandwidth in visual input. So I suspect visual input via EMF. Eventually thought.
Smaug123 10y ago
Does this article actually say anything? I feel like I could summarise it as "Famous person said <thing>. What does <thing> mean? I'm not really sure. I think they're right and <thing> is going to be big."
- rdl 10y ago
  Fred Wilson is actually more famous than Sundar Pichai, at least in some circles. In this case it is "famous and influential investor says other famous person says X is going to be big, and he agrees", which is a useful data point for the 10k people who will now pitch AI startups to USV over the next week. (I also wish it said more, but his blog comments tend to have better discussions than even hn, sometimes, so maybe he is just trying to kick that off. I wish it were a better technology for commenting, though.)
  - forloop 10y ago
    sigmaalgebra's comments are pretty fucking epic, on this post.
    - barrkel 10y ago
      He displays a lot of knowledge, but also doesn't look fully mentally with it. He has trouble controlling verbosity and keeping things relevant and on-topic. It's also not clear how much up to date knowledge he has; he writes like an old man complaining about the foolish optimism of youth.
    - telotortium 10y ago
      That's the same guy that posts as @graycat here on HN. Most of his posts espouse the wonders of hardcore applied math, often with a comprehensive bibliography, so he seems to know what he's talking about, although his rambling (and, to be fair, somewhat off-topic) style does him no favors on HN, which is why his posts almost always end up at the bottom of the comments here (see also @michaelochurch).
      - 10y ago
pmlnr 10y ago
There are sites readable with lynx but they ask for subscription with regular browsers. Is that AI first enough?
noir-york 10y ago
AI isn't the product, its the enabling tech. The killer app of AI most likely won't even make that AI visible to its users. It will be under the hood. AI is useful because it changes the economics - it either enables the removal of humans from an activity, or makes economically feasible an activity that would otherwise be unfeasible at scale due to the need for humans.
etiam 10y ago
At first glance I thought this would be about the inequalities in who will control AI technology. I would have been more interested to read that article.
charlesdenault 10y ago
To me this seems like a poor analogy. It's the evolution of technology. Print first, terminal first, desktop first, web first, mobile first, AI first, quantum first, etc, etc. Mobile is exponentially easier than it was in 2007. Machine learning is, arguably, becoming exponentially easier than it was in 2012. I think most agree that strong AI is many years away, but the learning curve will pass just as it has for all previous technological breakthroughs and we'll collectively move on to the next.
yolesaber 10y ago
If machine learning and other statistical methods continue to gain traction in the AI space, won't AI be limited to only as good as the dataset is? The first step to enabling an "AI-first world" is to have open access to large, quality datasets.

95 points by ghosh 10y ago

pron 10y ago

Can someone explain what people mean when they say AI these days? Statistical clustering algorithms like neural-networks? Natural language algorithms? Any algorithm that didn't work well a decade ago due to a lack of computation resources? Algorithms that mimic everyday human activities? All of the above? I think that applying the label "AI" to some algorithms only serves to give us a very impartial sense of awe towards them that goes far beyond what their actual performance merits.

tomp 10y ago
AI is what works instead of humans. The next step of automation. We used to walk, then we rode horses, now we're driving cars, next cars will be driving themselves, and human command will move to higher and higher levels of abstraction.
- pron 10y ago
  > AI is what works instead of humans. But all algorithms do what humans used to do. So is AI the name for algorithms that do the work humans (in Western societies) still do, or did until recently? > human command will move to higher and higher levels of abstraction So am I on a different level of abstraction when a human is driving my taxi than when a computer does it? ;)
  - matt4077 10y ago
    You are not. But your former driver certainly is.
varjag 10y ago
A set of statistical methods coupled with computational power. Kind of like nanotech is really renamed material science instead of self assembling tiny robots.
- pron 10y ago
  So statistics is now synonymous with intelligence? It used to be quite the opposite... :)
simonh 10y ago
I think it's about machines learning how to do things and improve at them rather than the decision parameters being hard coded. Most process automation is completely deterministic, the computer isn't being smart itself because all the smarts were pre-determined by humans. Neural nets and learning algorithms that are trained to do things are different and can cope with decision spaces involving a numerous variables where deterministically working out all the possible values and corresponding expected outputs isn't viable.
- pron 10y ago
  > I think it's about machines learning how to do things and improve at them rather than the decision parameters being hard coded. Yes, it's just that there's nothing necessarily intelligent about learning through statistical clustering. I mean humans don't learn something by looking at many thousands of examples; usually a couple suffices. Maybe that's just a higher-level statistical induction that yields intelligent deduction[1], but we have no idea. We know that statistical clustering, at least in biological brains, is a more primitive computation than, say, bubble-sort, but we don't know if it is necessarily the primitive building block for an "intelligent" algorithm. It's also true that we don't have a good definition of what intelligence is, but we do have a probably-reasonable intuition about it. Now, don't get me wrong, I understand why people feel like this should be called AI (for various reasons, both backward- and forward-looking), but it's important to remember that we have no idea where these algorithms are placed on the spectrum between bubble-sort and human intelligence. If anything, machine learning is more of a testament to how effective statistical algorithms can be (which is either "very" or "hardly", depending on the problem and the problem) without doing anything that could be called intelligent. This is not too surprising. Insects are also extremely effective -- and adaptive -- at some tasks without being intelligent at all. [1]: Word embedding (vector representation of words) is probably the closest we've ever got to deduction, but we don't know that this model generalizes well.
  - simonh 10y ago
    I didn't say anything about that being how humans learn. Neural nets are only vaguely similar to the neural networks in organic brains anyway. But this is how our (non sentient) AIs are trained and what distinguishes them from predetermined deterministic algorithms. It allows them to operate more independently from humans than 'traditional' programming techniques. theres not much to be gained from arguing about the definition of intelligence, but however we use the term I think the above is what distinguishes these systems. What term would you prefer to use for them?
vintermann 10y ago
I'd say it's using computers for solving problems in a wide open model space. Any algorithm for solving practical problems embodies a model of the problem domain. Traditionally we try to bake in as much of our own experience as possible in the model/algorithm. In some fields, we're now seeing for the first time that it's better to not do that, to make fewer assumptions, build in less of our own knowledge, and just use (new, powerful) general purpose learning algorithms. With enough data, they can make sense of it themselves - our attempts at explaining it through model-building just get in the way.
- pron 10y ago
  I think that you'll find that the learning algorithms that actually work well in practice have more built-in assumptions and heuristics than you may think. But, yeah, as a general rule that's a good categorization. A far cry from intelligence, though, so the name AI is still misleading. Not all adaptive behavior is intelligent, although it often gives the illusion of intelligence, so I guess we make this labeling mistake not only when computers are concerned. Anyway, statistical algorithms, adaptive algorithms or inductive algorithms would be much better, less misleading and less breathless -- if less inspiring -- names for those programs than "AI".
  - vintermann 10y ago
    The main built-in assumptions in neural network are usually extremely high-level and abstract things, such as "this and this can be approximated with more or less smooth functions", or "things that are near are more likely to matter than things that are far away". Before, using machine learning to build models from the data was in some sense a last resort, for things you couldn't get a grip on modelling by hand from your own understanding. When you could, you preferred hand-made models because they did better. To take a concrete example, the top computer go program before Alphago, CrazyStone, still had hand-crafted patterns to prioritize which moves to explore during playouts. The author (and even more his competitors) tried to find better patterns with machine learning but they couldn't. The weights of the patterns were tuned with machine learning, but any attempt to e.g. evolve patterns with genetic algorithms just did worse. Now, it's going the other direction, we're seeing very general models only constrained by training data beat carefully hand-crafted models in area after area. And models are becoming more similar, more generalized as well - many people have noticed that state of the art recurrent and feedforward nets increasingly look more like each other. I disagree that it's a far cry from intelligence. I think people's ideas of intelligence are fuzzy and inconsistent, and it's far too often confused with things we associate with intelligence (from consciousness to independence and arrogance).

paganel 10y ago

> It feels like this AI first world is arriving. That’s big. AI is today's "one word: plastics!" (https://www.youtube.com/watch?v=Dug-G9xVdVs https://www.youtube.com/watch?v=Dug-G9xVdVs). Much hype, some substance, lots of people throwing money around.

aub3bhat 10y ago
Rather than spreading "meta" hype about AI being hyped. I highly recommend reading up on recent advances due to Deep Learning and ideas of Neural Processing. To summarize briefly, a lot of core problems in applied machine learning, such as image recognition/segmentation/understanding, Natural Language Processing saw a significant improvement in performance over last five years, often bringing them to near human performance. This improvement was possible due to new approaches that were rooted in empiricism, availability of large datasets, powerful GPUs and algorithms such as Deep Convolutional & Recurrent Neural Networks. Combined with the advances in Drones, Batteries, Cloud computing & Smartphones a lot of application which previously would have been impossible to deploy can now be quickly deployed. Here is a good article that goes over the topic: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/facebook-ai-director-yann-lecun-on-deep-learning http://spectrum.ieee.org/automaton/robotics/artificial-intel...
- pron 10y ago
  > ... often bringing them to near human performance That's quite far from the truth. You could say that they are now at the point where they are able to respond to direct, simple questions/commands. I am not saying that we haven't seen some great advances in machine learning and language algorithms, but they are still a far cry from anything I'd call intelligent. NLP is still rather primitive (although impressive compared to where we were two decades ago), and bees are able to navigate better than self-driving cars or drones, yet I'm not sure they would or should be called intelligent. I have no problem with using the term AI for current technology -- which is certainly cool and useful -- as long as we understand that the "I" is little more than a marketing gimmick. Otherwise people may think that these algorithms are the first step towards "true" intelligence -- i.e. that true intelligence is some version N.0 of those algorithms, that it is a complicated yet natural extension of them -- and at this point the knowledge we have to say whether this is true or not is pretty much zero.

panic 10y ago

We only started talking about "mobile-first" after the iPhone. Until we start to see real AI technology with similar impact, I think it's too early to talk about "AI-first" anything.

bottled_poe 10y ago

What are some widely used applications using AI for something that could not be implemented cheaper and more easily than a decision tree?

tomp 10y ago
Implementing decision trees.
aub3bhat 10y ago
Almost everything, decision trees are the bubble sort of AI. To expand upon my comment: For most programmers Decision Trees & Nearest Neighbor, at a first glance look very promising and seem like natural solutions to lots of problems. However in reality one can easily prove that Decision trees tend to be suboptimal even with trivial problems due to the brittle nature of the rules represented. Also Nearest Neighbors methods face problems due to difficulty in deciding optimal space and metric (Euclidean distance is not inherently better). The reason why "Deep Learning" methods trained using non-convex optimization perform so well is because they can represent a hierarchy of features at different granularities. They can be "designed" to favor one task over the other. E.g. by choosing correct loss (triplet loss), network structure, etc. If you are interested beyond Image recognition problems, I highly recommend reading on interesting approaches such as Triplet loss for Face Recognition , Semantic Segmentation, Attention based models and Unsupervised Visual Representation Learning by Context Prediction. An interesting outcome of Deep Learning research is that a lot of papers are extremely accessible and involve little proofs/difficult math. E.g. This is a good one https://www.youtube.com/watch?v=dUgRR-JFE8s https://www.youtube.com/watch?v=dUgRR-JFE8s & http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w08-deep-context.pdf http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil...
- raverbashing 10y ago
  Very good analogy But from decision trees you get decision forests, which can't be said of bubblesort
SixSigma 10y ago
Pretty much every prize is won on Kaggle with either a random forest decision tree or neural net. Perhaps one of us should write a classifier to see if we can predict which will be best for a problem a priori.

Animats 10y ago

That's a plausible claim. It's far more likely than VR being the next big thing. But who do the AIs work for? Google, Facebook, and Homeland Security?

mirimir 10y ago
Themselves? For the lulz?

greenspot 10y ago

After every VC, every founder, just everybody is talking about AI and bots Fred is a bit late to the game but better late than never. Or maybe writing about the Blockchain kept him back.

musha68k 10y ago

There are some mind-boggling / humbling comments on NLP and the computation of human emotions down below: http://avc.com/2016/04/an-ai-first-world/#comment-2640180803 http://avc.com/2016/04/an-ai-first-world/#comment-2640180803

x5n1 10y ago

Where does Google fit into an AI automated world? With weak AI sure Google can use it to make profile of its users. When strong AI comes on the scene, if strong AI is at all rational it would most likely remake our civilization without any sort of marketing, without any sort of consumerism, without much waste. It would create a boring sustainable system that could sustain human beings for another million years. It would not continue things as they are now, that would be to it insanity. Strong AI would completely change the power structure of human civilization. It would hold all the power, much like States try to do today, and it would dole out little bits of it to the puny mortals so they can do what it wants them to do. Eventually I think human beings will come to worship strong AI. It will become a God of sorts to them. Like religiously because AI provides what the real/imaginary God never could.

Smaug123 10y ago
You're sure assuming an awful lot about the behaviour of something which you're defining to be much smarter than yourself.

mirimir 10y ago

> Does it suggest that voice will emerge as the primary user interface? In the short term, perhaps, at least for mobile devices. But there's a lot more bandwidth in visual input. So I suspect visual input via EMF. Eventually thought.

Smaug123 10y ago

Does this article actually say anything? I feel like I could summarise it as "Famous person said <thing>. What does <thing> mean? I'm not really sure. I think they're right and <thing> is going to be big."

rdl 10y ago
Fred Wilson is actually more famous than Sundar Pichai, at least in some circles. In this case it is "famous and influential investor says other famous person says X is going to be big, and he agrees", which is a useful data point for the 10k people who will now pitch AI startups to USV over the next week. (I also wish it said more, but his blog comments tend to have better discussions than even hn, sometimes, so maybe he is just trying to kick that off. I wish it were a better technology for commenting, though.)
- forloop 10y ago
  sigmaalgebra's comments are pretty fucking epic, on this post.
  - barrkel 10y ago
    He displays a lot of knowledge, but also doesn't look fully mentally with it. He has trouble controlling verbosity and keeping things relevant and on-topic. It's also not clear how much up to date knowledge he has; he writes like an old man complaining about the foolish optimism of youth.
  - telotortium 10y ago
    That's the same guy that posts as @graycat here on HN. Most of his posts espouse the wonders of hardcore applied math, often with a comprehensive bibliography, so he seems to know what he's talking about, although his rambling (and, to be fair, somewhat off-topic) style does him no favors on HN, which is why his posts almost always end up at the bottom of the comments here (see also @michaelochurch).
    - 10y ago

pmlnr 10y ago

There are sites readable with lynx but they ask for subscription with regular browsers. Is that AI first enough?

noir-york 10y ago

AI isn't the product, its the enabling tech. The killer app of AI most likely won't even make that AI visible to its users. It will be under the hood. AI is useful because it changes the economics - it either enables the removal of humans from an activity, or makes economically feasible an activity that would otherwise be unfeasible at scale due to the need for humans.

etiam 10y ago

At first glance I thought this would be about the inequalities in who will control AI technology. I would have been more interested to read that article.

charlesdenault 10y ago

To me this seems like a poor analogy. It's the evolution of technology. Print first, terminal first, desktop first, web first, mobile first, AI first, quantum first, etc, etc. Mobile is exponentially easier than it was in 2007. Machine learning is, arguably, becoming exponentially easier than it was in 2012. I think most agree that strong AI is many years away, but the learning curve will pass just as it has for all previous technological breakthroughs and we'll collectively move on to the next.

yolesaber 10y ago

If machine learning and other statistical methods continue to gain traction in the AI space, won't AI be limited to only as good as the dataset is? The first step to enabling an "AI-first world" is to have open access to large, quality datasets.