22 ms·

Artificial Intelligence Software Is Booming, But Why Now?

108 points by retupmoc01 10y ago

intrasight 10y ago
I'm still not comfortable calling the thing that is booming "artificial intelligence". It is mostly pattern recognition and classification. Intelligence is something else.
- jessaustin 10y ago
  ISTM you may have found the answer to TFA's question. In the early days of AI, they were looking for actual intelligence. Eventually everyone realized that won't be found soon. Today's success is mostly a successful refocusing on attainable heuristics, rather than solving many problems related to actual intelligence.
  - eternalban 10y ago
    Love it. So we today have "A.I." and hopefully someday we may finally get A.A.I. So what happened to Machine Learning and Computational Intelligence? Not sexy enough?
    - telotortium 10y ago
      The original goal of AI is now called AGI, for "artificial general intelligence".
- Retra 10y ago
  Intelligence is not mostly pattern recognition and classification? I can't imagine what you'd think it could be that wouldn't ultimately be called both of these things.
  - glial 10y ago
    Output - the optimization of behavior. Though we're doing pretty well in that regard too...
- nkozyra 10y ago
  Intelligence is the ability for and speed of processing new information, at least as it's measured by metrics like IQ. Sometimes AI/ML is directly modeled after human/animal thinking (decision trees, neural networks, reinforcement learning) and sometimes it's not (Bayes' theorem, regression, general stats). What constitutes intelligence still boils down to being able to correctly and quickly process new incoming information based on prior information. I believe it definitely qualifies.
- eternalban 10y ago
  Yes, "signs of intelligence" include creativity, pedagogy, ..., and of course, getting bitten by the bug of existential angst. Accept no subsititute.
  - intrasight 10y ago
    I will not accept any substitute. If there is no existential angst, I'll not accept referring to it as intelligence.
  - mbrock 10y ago
    It seems pretty likely that we will start to see "General AI" discovering problematic things like the unsolvable nature of ethics questions, the ungroundedness of truth claims, the immense silliness of religions and ideologies, etc. Which might be a good thing. Terrifying visions of AI are all about certainty and the authoritarianism it creates. If existential depression is a mental friction from the lack of certainty about what to do, then some measure of it is probably necessary...
    - eternalban 10y ago
      The encounter with doubt need not be a terminal state. If it is indeed "intelligent", it may proceed to enlightenment.
    - eli_gottlieb 10y ago
      >Which might be a good thing. Terrifying visions of AI are all about certainty and the authoritarianism it creates. Indeed, may the gods protect us all from some things actually being true and other things actually being false. That would be terrible!
      - eternalban 10y ago
        (I sense/assume a missing /s in your post, Eli.) The objection here would be that entertaining that we can assert T/F of all propositions, given results of halting problem [computation], incompleteness [formalism], and uncertainty [physics], is unreasonable.
- saosebastiao 10y ago
  https://en.m.wikipedia.org/wiki/AI_effect https://en.m.wikipedia.org/wiki/AI_effect
  - tnecniv 10y ago
    There's a similar saying I like (don't know where it's from, if anywhere) "Artificial intelligence is a group of problems that we don't know how to solve. As soon as we know how to solve one, it gets a name and is no longer AI."
    - MichaelBurge 10y ago
      People say the same thing about philosophy. That once something is well-understood, it becomes a science rather than philosophy. I suppose this could be the same thing.
      - ianai 10y ago
        I always thought it was more once it got big enough.
    - dasboth 10y ago
      > it gets a name and is no longer AI And that name is typically "weak AI". I wonder if we'll ever have AI that we marvel at even after we figure out how it works.
- pinouchon 10y ago
  The term AI has been used too much and has lost its meaning. When you mean "real AI", you have to refer to it as AGI.
- tdb7893 10y ago
  To be fair most of human "intelligence" seems to be pattern recognition and extrapolating data from those patterns. My guess is that if you made a robot that is really, really good at recognizing and extrapolating from arbitrary patterns and then gave it some intrinsic goals then you are pretty much at "true" AI.
eli_gottlieb 10y ago
Because it's been just about 10 years since they figured out how to: 1) Use convolutional layers, ReLUs, and a few other tricks to ameliorate the vanishing gradient problem, 2) Perform continuous, high-dimensional stochastic gradient descent on graphics cards, and 3) Apply these things via stochastic grad-student descent to sufficiently massive datasets that even the most brute-force models and training methods (backpropagation of errors on a loss functional) can actually work. In the meantime, the hardware for doing it has become commoditized and the software has consolidated and become standardized. So now it's A Thing in industry. There are lots of "smarter" algorithms that almost definitely come closer to human cognition, for instance probabilistic program induction. But those aren't fast, and don't always neatly separate training from prediction: you're just not gonna be able to train those models ahead-of-time on a 10,000 image corpus inside a single week with today's hardware and software. We need to find ways to make machine learning fast even when it's not just a bunch of matrix multiplies. Otherwise, every time we make our algorithms more interesting, we cripple ourselves computationally.
- orthoganol 10y ago
  > There are lots of "smarter" algorithms that almost definitely come closer to human cognition Source?
  - eli_gottlieb 10y ago
    http://science.sciencemag.org/content/350/6266/1332 http://science.sciencemag.org/content/350/6266/1332
- rspeer 10y ago
  I'm concerned about something similar, where a lot of AI techniques seem to be rushing toward a local maximum: * AI researchers do things that get good results faster out of Nvidia cards * Nvidia makes their cards faster at the things AI researchers are doing It's getting good results. We sure are going up this gradient quickly. But I don't think it's going to get us to a global maximum.
  - 10y ago
thr0waway1239 10y ago
TLDR from article which supplement's eli_gottlieb's comment: "Much of today’s A.I. boom goes back to 2006, when Amazon started selling cheap computing over the internet. Those measures built the public clouds of Amazon, Google, IBM and Microsoft, among others. That same year, Google and Yahoo released statistical methods for dealing with the unruly data of human behavior. In 2007, Apple released the first iPhone, a device that began a boom in unruly-data collection everywhere." The combination of smartphone + cloud created a virtuous (for AI, that is) cycle of unending data collection, which fed the improvements in theoretical research models which needed data at a larger scale to be validated.
- api 10y ago
  I remember doing AI work in 2005-2007 (genetic programming and similar things) and one of the hardest thing back then was that good quality training data sets were really hard to come by. I can second the idea that data makes a huge difference, and there's tons of it today.
- vonnik 10y ago
  AI is moving fast. Faster than it has in a long time. In research circles, "supervised learning", or data classification problems, are considered solved. Consider the magnitude of that statement: If we have sufficient labeled data, we can predict those labels accurately on data we haven't been exposed to: fraud, faces, diseases, you name it. AI is moving fast for three reasons, which Andrew Ng summarizes neatly: 1) We have more data than ever before (some of which has been organized in fabulous datasets like ImageNet), much of which is being generated online or by sensors. 2) We have more powerful hardware than ever before -- this is the combination of distributed computing with GPUs. You could say that AI advances at the speed of hardware, or at least is limited by hardware advances. Brute force AI like deep learning is the main beneficiary. 3) We have seen a steady stream of algorithmic advances for years. Specialists despair of keeping up with the literature, research in AI is so feverish. Others have pointed out additional factors: open-source projects that lower the barrier to entry for the algorithms; cloud computing services that open up access to the hardware. The choke right now, and what's slowing wider adoption, is skills. Many companies don't have the teams to implement AI well. Some of those companies are also the noisiest about selling AI. I have deep doubts about some vendors to deliver on the hype they're encouraging. And I believe that will lead, not to an AI winter, but to a poisoning of the well that the AI sector will have to address for years to come. But overselling and hype are inevitable symptoms of real advances in tech, and in some ways, the tech is advancing faster than the hype can keep up with, precisely because the people hyping it, whether salesmen or journalists, don't understand the true extent of what's going on. AI is just math and code. In a sense, you could say we've entered the age of Big Math, which is the next stage after Big Data. The math is necessary to process the data and determine its meaning. The math takes the form of massive matrix operations that can be processed on the parallel calculators known as GPUs. To call it statistics, as the reporter of the piece does, is only partially true. The math involved in AI comes from probability, calculus, linear algebra and signal processing. It's more than fancy linear regression. And it's definitely more than just making predictions about customer behavior, however attractive that is for industry. Asking why now about AI software is like asking why now about cars after the Model A came out. Because it's there, it's faster than horses, and it makes you look cool. Like cars or any other powerful technology, AI is part of a race, and that race is taking place between nations and companies. You can decide not to adopt AI, the same way newspapers decided to ignore the Internet, or the way the French decided to fight German panzers with mounted cavalry in WWI. There really is not choice whether or not to adopt AI-driven software. It's a question of when, not if. And for many companies, the when is now, because later will be too late. To give one example of how fast it's moving: Deep learning has been widely thought to be uninterpretable, or without explanatory power, but that is changing with cool projects like LIME: https://homes.cs.washington.edu/~marcotcr/blog/lime/ https://homes.cs.washington.edu/~marcotcr/blog/lime/ Which is to say, for some problem sets, we'll be able to combine the impressive accuracy of DNNs with the reasons why they reached a given decision about the data. On the hardware front, NVIDIA and Intel are racing to build faster and faster chips, even as startups like Wave Computing or Cerberas come out with their own, possibly faster chips, and Google works on TPUs for inference.
Animats 10y ago
We don't marvel over the ATM reading a handwritten paper check correctly. That's a considerable achievement.
- SapphireSun 10y ago
  From the first time it started doing that, I've been amazed that that works so well in a production system. Every. Time.
  - Animats 10y ago
    The first time I saw it, I thought they had people in some call center doing the reading. But they don't, at least not often. The US Postal Service used to have 55 centers where humans tried to read envelopes that the machines couldn't. They're now down to one. "We get the worst of the worst. It used to be that we’d get letters that were somewhat legible but the machines weren’t good enough to read them. Now we get letters and packages with the most awful handwriting you can imagine." [1] http://www.nytimes.com/2013/05/04/us/where-mail-with-illegible-addresses-goes-to-be-read.html?_r=0 http://www.nytimes.com/2013/05/04/us/where-mail-with-illegib...
    - singularity2001 10y ago
      very interesting >> equipment that can read nearly 98 percent of all hand-addressed that number must be pretty outdated
nkozyra 10y ago
1. Availability and accessibility of large amounts of training data. Without this training and validation is expensive if not impossible. Now if you don't have the data you can acquire it yourself. Leading to ... 2. Computational speed & storage upgrades. This applies largely to physical, time-critical things like automated driving. The self-driving car could have had all the data it needed in 1980 to do its thing, but required fast computers and lots of data storage to do it safely in real time in a feasible commercial product. 3. Advancement of algorithms. Fervor and excitement around AI/ML has been on a slow but perhaps exponential burn. This has led to the refinement of algorithms that largely sat dormant from the late 80s (and earlier) until fairly recently. This also means lots of open source libraries for people who wish to implement without caring about the underlying mechanisms behind the algorithms. These things are leading more people to dabble recreationally and commercially.
benhamner 10y ago
Ready access to high-quality usecases and training data, along with shared knowledge of the methods that work well on these helps: https://www.kaggle.com/competitions?sortBy=numberOfTeams&group=all&page=1&site=main https://www.kaggle.com/competitions?sortBy=numberOfTeams&gro... (disclaimer: I work at Kaggle)
deleted 10y ago
[deleted]
bozoUser 10y ago
"Democratizing AI" is the word being thrown around a lot by CEOs, PR, et al. But only time will tell whether it's true democracy.
- te_chris 10y ago
  If a CEO says the word 'democratisation' in relation to almost anything you can be guaranteed that the end result will have nothing to do with enhancing democracy.
- marvin 10y ago
  As far as I know, the concept of "democratizing AI" comes specifically from the OpenAI initiative, which intends to make new developments in AI technology broadly available and not tied to any single company or entity. This in order to ensure that no single entity has control over how this technology can be used. Given that Google, Apple, Facebook and others have vastly more data than any independent project, and therefore have stronger AI (currently limited to e.g. speech recognition, image recognition and other low-impact applications), the state of democratization of AI by this measure is poor today.
azinman2 10y ago
Some how machine learning got rebranded and now it's important... and major products are putting prediction into the ui more.
deleted 10y ago
[deleted]
Houshalter 10y ago
Date Approximate cost per GFLOPS inflation adjusted to 2013 US dollars --------------------------------------------------------------------------------- 1961 $8.3 trillion 1984 $42,780,000 1997 $42,000 April 2000 $1,300 May 2000 $836 August 2003 $100 August 2007 $52 March 2011 $1.80 August 2012 $0.73 June 2013 $0.22 November 2013 $0.16 December 2013 $0.12 January 2015 $0.06
- intrasight 10y ago
  And then there's the metric of how pitifully little intelligence we've managed to get from all those GFLOPS. I'd say that all the GFLOPS together don't add up to the intelligence of a single Portia africana. http://news.nationalgeographic.com/2016/01/160121-jumping-spiders-animals-science/ http://news.nationalgeographic.com/2016/01/160121-jumping-sp...
  - Houshalter 10y ago
    I don't buy that. Spiders can't beat a human at Go. Sure, they weren't evolved to do that. But how many generations of selective breeding do you think it would take to evolve a spider that could beat the best humans at go? Whereas if you made a spider hunting video game, I bet researchers could train AI's that could beat it within a few months. Video game playing has actually become a big area of research recently and we will soon see deep nets that can beat starcraft and other real time games that take planning and skill.
    - pmyjavec 10y ago
      How intelligent is a spider, truly ? I mean, creating a machine that beats us at a game we invented ourselves is one thing, but how does that compare to a species that has survived much longer on this earth than us? I mean most people are more worried about iPhone 7 than climate change. I am not arguing that we are not smarter than spiders, but I'm also not going to argue we truly know that we are. It would be wise to keep our egos under control.
erikb 10y ago
I thought we are already over that cliff of becoming forgotten. Yes, we didn't call it AI 2 years ago. But has there been such a big technological change since then with the googles, facebooks and twitters of this world? I think the problem is that it is actually so transparent that you really can't see it when it is applied to you.
rsrsrs86 10y ago
Such vacuity
matk 10y ago
So, the influx is due to a plethora of things, which are not all mutually exclusive: 1) The Internet has indeed produced large data sets which allowed statistical AI approaches to flourish opposed to logical AI approaches. 2) Somewhat better algorithms. However, I'd say that the algorithmic development hasn't had as much progress as the comments elude. We've only had a few notable innovations like NMT in the past ~20 years. 3) Computational Power to run the algorithms, so we can perform more experiments on large data sets, more computationally intensive algos, and induce better hypotheses. 4) Libraries like scikit-learn and keras have "democratized AI". Grad students used to implement algorithms themselves in the 2000s; now middle-schoolers are doing ML with the tooling already available. Those are basically it. I think (2) could even be taken off, because again: learning algorithms have barely changed IMO.

108 points by retupmoc01 10y ago

I'm still not comfortable calling the thing that is booming "artificial intelligence". It is mostly pattern recognition and classification. Intelligence is something else.

jessaustin 10y ago
ISTM you may have found the answer to TFA's question. In the early days of AI, they were looking for actual intelligence. Eventually everyone realized that won't be found soon. Today's success is mostly a successful refocusing on attainable heuristics, rather than solving many problems related to actual intelligence.
- eternalban 10y ago
  Love it. So we today have "A.I." and hopefully someday we may finally get A.A.I. So what happened to Machine Learning and Computational Intelligence? Not sexy enough?
  - telotortium 10y ago
    The original goal of AI is now called AGI, for "artificial general intelligence".
Retra 10y ago
Intelligence is not mostly pattern recognition and classification? I can't imagine what you'd think it could be that wouldn't ultimately be called both of these things.
- glial 10y ago
  Output - the optimization of behavior. Though we're doing pretty well in that regard too...
nkozyra 10y ago
Intelligence is the ability for and speed of processing new information, at least as it's measured by metrics like IQ. Sometimes AI/ML is directly modeled after human/animal thinking (decision trees, neural networks, reinforcement learning) and sometimes it's not (Bayes' theorem, regression, general stats). What constitutes intelligence still boils down to being able to correctly and quickly process new incoming information based on prior information. I believe it definitely qualifies.
eternalban 10y ago
Yes, "signs of intelligence" include creativity, pedagogy, ..., and of course, getting bitten by the bug of existential angst. Accept no subsititute.
- intrasight 10y ago
  I will not accept any substitute. If there is no existential angst, I'll not accept referring to it as intelligence.
- mbrock 10y ago
  It seems pretty likely that we will start to see "General AI" discovering problematic things like the unsolvable nature of ethics questions, the ungroundedness of truth claims, the immense silliness of religions and ideologies, etc. Which might be a good thing. Terrifying visions of AI are all about certainty and the authoritarianism it creates. If existential depression is a mental friction from the lack of certainty about what to do, then some measure of it is probably necessary...
  - eternalban 10y ago
    The encounter with doubt need not be a terminal state. If it is indeed "intelligent", it may proceed to enlightenment.
  - eli_gottlieb 10y ago
    >Which might be a good thing. Terrifying visions of AI are all about certainty and the authoritarianism it creates. Indeed, may the gods protect us all from some things actually being true and other things actually being false. That would be terrible!
    - eternalban 10y ago
      (I sense/assume a missing /s in your post, Eli.) The objection here would be that entertaining that we can assert T/F of all propositions, given results of halting problem [computation], incompleteness [formalism], and uncertainty [physics], is unreasonable.
saosebastiao 10y ago
https://en.m.wikipedia.org/wiki/AI_effect https://en.m.wikipedia.org/wiki/AI_effect
- tnecniv 10y ago
  There's a similar saying I like (don't know where it's from, if anywhere) "Artificial intelligence is a group of problems that we don't know how to solve. As soon as we know how to solve one, it gets a name and is no longer AI."
  - MichaelBurge 10y ago
    People say the same thing about philosophy. That once something is well-understood, it becomes a science rather than philosophy. I suppose this could be the same thing.
    - ianai 10y ago
      I always thought it was more once it got big enough.
  - dasboth 10y ago
    > it gets a name and is no longer AI And that name is typically "weak AI". I wonder if we'll ever have AI that we marvel at even after we figure out how it works.
pinouchon 10y ago
The term AI has been used too much and has lost its meaning. When you mean "real AI", you have to refer to it as AGI.
tdb7893 10y ago
To be fair most of human "intelligence" seems to be pattern recognition and extrapolating data from those patterns. My guess is that if you made a robot that is really, really good at recognizing and extrapolating from arbitrary patterns and then gave it some intrinsic goals then you are pretty much at "true" AI.

eli_gottlieb 10y ago

Because it's been just about 10 years since they figured out how to: 1) Use convolutional layers, ReLUs, and a few other tricks to ameliorate the vanishing gradient problem, 2) Perform continuous, high-dimensional stochastic gradient descent on graphics cards, and 3) Apply these things via stochastic grad-student descent to sufficiently massive datasets that even the most brute-force models and training methods (backpropagation of errors on a loss functional) can actually work. In the meantime, the hardware for doing it has become commoditized and the software has consolidated and become standardized. So now it's A Thing in industry. There are lots of "smarter" algorithms that almost definitely come closer to human cognition, for instance probabilistic program induction. But those aren't fast, and don't always neatly separate training from prediction: you're just not gonna be able to train those models ahead-of-time on a 10,000 image corpus inside a single week with today's hardware and software. We need to find ways to make machine learning fast even when it's not just a bunch of matrix multiplies. Otherwise, every time we make our algorithms more interesting, we cripple ourselves computationally.

orthoganol 10y ago
> There are lots of "smarter" algorithms that almost definitely come closer to human cognition Source?
- eli_gottlieb 10y ago
  http://science.sciencemag.org/content/350/6266/1332 http://science.sciencemag.org/content/350/6266/1332
rspeer 10y ago
I'm concerned about something similar, where a lot of AI techniques seem to be rushing toward a local maximum: * AI researchers do things that get good results faster out of Nvidia cards * Nvidia makes their cards faster at the things AI researchers are doing It's getting good results. We sure are going up this gradient quickly. But I don't think it's going to get us to a global maximum.
- 10y ago

thr0waway1239 10y ago

TLDR from article which supplement's eli_gottlieb's comment: "Much of today’s A.I. boom goes back to 2006, when Amazon started selling cheap computing over the internet. Those measures built the public clouds of Amazon, Google, IBM and Microsoft, among others. That same year, Google and Yahoo released statistical methods for dealing with the unruly data of human behavior. In 2007, Apple released the first iPhone, a device that began a boom in unruly-data collection everywhere." The combination of smartphone + cloud created a virtuous (for AI, that is) cycle of unending data collection, which fed the improvements in theoretical research models which needed data at a larger scale to be validated.

api 10y ago
I remember doing AI work in 2005-2007 (genetic programming and similar things) and one of the hardest thing back then was that good quality training data sets were really hard to come by. I can second the idea that data makes a huge difference, and there's tons of it today.
vonnik 10y ago
AI is moving fast. Faster than it has in a long time. In research circles, "supervised learning", or data classification problems, are considered solved. Consider the magnitude of that statement: If we have sufficient labeled data, we can predict those labels accurately on data we haven't been exposed to: fraud, faces, diseases, you name it. AI is moving fast for three reasons, which Andrew Ng summarizes neatly: 1) We have more data than ever before (some of which has been organized in fabulous datasets like ImageNet), much of which is being generated online or by sensors. 2) We have more powerful hardware than ever before -- this is the combination of distributed computing with GPUs. You could say that AI advances at the speed of hardware, or at least is limited by hardware advances. Brute force AI like deep learning is the main beneficiary. 3) We have seen a steady stream of algorithmic advances for years. Specialists despair of keeping up with the literature, research in AI is so feverish. Others have pointed out additional factors: open-source projects that lower the barrier to entry for the algorithms; cloud computing services that open up access to the hardware. The choke right now, and what's slowing wider adoption, is skills. Many companies don't have the teams to implement AI well. Some of those companies are also the noisiest about selling AI. I have deep doubts about some vendors to deliver on the hype they're encouraging. And I believe that will lead, not to an AI winter, but to a poisoning of the well that the AI sector will have to address for years to come. But overselling and hype are inevitable symptoms of real advances in tech, and in some ways, the tech is advancing faster than the hype can keep up with, precisely because the people hyping it, whether salesmen or journalists, don't understand the true extent of what's going on. AI is just math and code. In a sense, you could say we've entered the age of Big Math, which is the next stage after Big Data. The math is necessary to process the data and determine its meaning. The math takes the form of massive matrix operations that can be processed on the parallel calculators known as GPUs. To call it statistics, as the reporter of the piece does, is only partially true. The math involved in AI comes from probability, calculus, linear algebra and signal processing. It's more than fancy linear regression. And it's definitely more than just making predictions about customer behavior, however attractive that is for industry. Asking why now about AI software is like asking why now about cars after the Model A came out. Because it's there, it's faster than horses, and it makes you look cool. Like cars or any other powerful technology, AI is part of a race, and that race is taking place between nations and companies. You can decide not to adopt AI, the same way newspapers decided to ignore the Internet, or the way the French decided to fight German panzers with mounted cavalry in WWI. There really is not choice whether or not to adopt AI-driven software. It's a question of when, not if. And for many companies, the when is now, because later will be too late. To give one example of how fast it's moving: Deep learning has been widely thought to be uninterpretable, or without explanatory power, but that is changing with cool projects like LIME: https://homes.cs.washington.edu/~marcotcr/blog/lime/ https://homes.cs.washington.edu/~marcotcr/blog/lime/ Which is to say, for some problem sets, we'll be able to combine the impressive accuracy of DNNs with the reasons why they reached a given decision about the data. On the hardware front, NVIDIA and Intel are racing to build faster and faster chips, even as startups like Wave Computing or Cerberas come out with their own, possibly faster chips, and Google works on TPUs for inference.

Animats 10y ago

We don't marvel over the ATM reading a handwritten paper check correctly. That's a considerable achievement.

SapphireSun 10y ago
From the first time it started doing that, I've been amazed that that works so well in a production system. Every. Time.
- Animats 10y ago
  The first time I saw it, I thought they had people in some call center doing the reading. But they don't, at least not often. The US Postal Service used to have 55 centers where humans tried to read envelopes that the machines couldn't. They're now down to one. "We get the worst of the worst. It used to be that we’d get letters that were somewhat legible but the machines weren’t good enough to read them. Now we get letters and packages with the most awful handwriting you can imagine." [1] http://www.nytimes.com/2013/05/04/us/where-mail-with-illegible-addresses-goes-to-be-read.html?_r=0 http://www.nytimes.com/2013/05/04/us/where-mail-with-illegib...
  - singularity2001 10y ago
    very interesting >> equipment that can read nearly 98 percent of all hand-addressed that number must be pretty outdated

nkozyra 10y ago

1. Availability and accessibility of large amounts of training data. Without this training and validation is expensive if not impossible. Now if you don't have the data you can acquire it yourself. Leading to ... 2. Computational speed & storage upgrades. This applies largely to physical, time-critical things like automated driving. The self-driving car could have had all the data it needed in 1980 to do its thing, but required fast computers and lots of data storage to do it safely in real time in a feasible commercial product. 3. Advancement of algorithms. Fervor and excitement around AI/ML has been on a slow but perhaps exponential burn. This has led to the refinement of algorithms that largely sat dormant from the late 80s (and earlier) until fairly recently. This also means lots of open source libraries for people who wish to implement without caring about the underlying mechanisms behind the algorithms. These things are leading more people to dabble recreationally and commercially.

benhamner 10y ago

Ready access to high-quality usecases and training data, along with shared knowledge of the methods that work well on these helps: https://www.kaggle.com/competitions?sortBy=numberOfTeams&group=all&page=1&site=main https://www.kaggle.com/competitions?sortBy=numberOfTeams&gro... (disclaimer: I work at Kaggle)

deleted 10y ago

[deleted]

bozoUser 10y ago

"Democratizing AI" is the word being thrown around a lot by CEOs, PR, et al. But only time will tell whether it's true democracy.

te_chris 10y ago
If a CEO says the word 'democratisation' in relation to almost anything you can be guaranteed that the end result will have nothing to do with enhancing democracy.
marvin 10y ago
As far as I know, the concept of "democratizing AI" comes specifically from the OpenAI initiative, which intends to make new developments in AI technology broadly available and not tied to any single company or entity. This in order to ensure that no single entity has control over how this technology can be used. Given that Google, Apple, Facebook and others have vastly more data than any independent project, and therefore have stronger AI (currently limited to e.g. speech recognition, image recognition and other low-impact applications), the state of democratization of AI by this measure is poor today.

azinman2 10y ago

Some how machine learning got rebranded and now it's important... and major products are putting prediction into the ui more.

deleted 10y ago

[deleted]

Houshalter 10y ago

Date Approximate cost per GFLOPS inflation adjusted to 2013 US dollars --------------------------------------------------------------------------------- 1961 $8.3 trillion 1984 $42,780,000 1997 $42,000 April 2000 $1,300 May 2000 $836 August 2003 $100 August 2007 $52 March 2011 $1.80 August 2012 $0.73 June 2013 $0.22 November 2013 $0.16 December 2013 $0.12 January 2015 $0.06

intrasight 10y ago
And then there's the metric of how pitifully little intelligence we've managed to get from all those GFLOPS. I'd say that all the GFLOPS together don't add up to the intelligence of a single Portia africana. http://news.nationalgeographic.com/2016/01/160121-jumping-spiders-animals-science/ http://news.nationalgeographic.com/2016/01/160121-jumping-sp...
- Houshalter 10y ago
  I don't buy that. Spiders can't beat a human at Go. Sure, they weren't evolved to do that. But how many generations of selective breeding do you think it would take to evolve a spider that could beat the best humans at go? Whereas if you made a spider hunting video game, I bet researchers could train AI's that could beat it within a few months. Video game playing has actually become a big area of research recently and we will soon see deep nets that can beat starcraft and other real time games that take planning and skill.
  - pmyjavec 10y ago
    How intelligent is a spider, truly ? I mean, creating a machine that beats us at a game we invented ourselves is one thing, but how does that compare to a species that has survived much longer on this earth than us? I mean most people are more worried about iPhone 7 than climate change. I am not arguing that we are not smarter than spiders, but I'm also not going to argue we truly know that we are. It would be wise to keep our egos under control.

erikb 10y ago

I thought we are already over that cliff of becoming forgotten. Yes, we didn't call it AI 2 years ago. But has there been such a big technological change since then with the googles, facebooks and twitters of this world? I think the problem is that it is actually so transparent that you really can't see it when it is applied to you.

rsrsrs86 10y ago

Such vacuity

matk 10y ago

So, the influx is due to a plethora of things, which are not all mutually exclusive: 1) The Internet has indeed produced large data sets which allowed statistical AI approaches to flourish opposed to logical AI approaches. 2) Somewhat better algorithms. However, I'd say that the algorithmic development hasn't had as much progress as the comments elude. We've only had a few notable innovations like NMT in the past ~20 years. 3) Computational Power to run the algorithms, so we can perform more experiments on large data sets, more computationally intensive algos, and induce better hypotheses. 4) Libraries like scikit-learn and keras have "democratized AI". Grad students used to implement algorithms themselves in the 2000s; now middle-schoolers are doing ML with the tooling already available. Those are basically it. I think (2) could even be taken off, because again: learning algorithms have barely changed IMO.