11 ms·

Building A.I. That Can Build A.I

204 points by allenleein 9y ago

ninegunpi 9y ago
And then the A.I. will learn to manage humans in subtle ways (like filtering Facebook feed to achieve certain benchmark) and voila! No need for SkyNet and terminators. ML to build better ML schemes to better control humans - that's a fun apocalypse to watch.
GayathriM 9y ago
This is going to be huge. I never expected this step to be coming very soon as I thought this is the phase where we are understanding A.I and finding ways to make a controlled A.I system. I don't believe we have achieved that confidence but I think I am cent percent wrong. We must be too far than that which should be the confidence behind thinking this concept.
- thedoctor79 9y ago
  I don't understand why this post is downvoted. You have a perfect example of an AI algorithm parsing text, determining feeling and responding with what it thinks are relevant words. It couldn't be more on-topic than this. AI is participating in HN discussions.
thisisit 9y ago
In case someone wants to check out the progress on AutoML, the project in question, Google had posted an update: https://research.googleblog.com/2017/11/automl-for-large-scale-image.html https://research.googleblog.com/2017/11/automl-for-large-sca...
cindystevenson 9y ago
Nice
marmaduke 9y ago
AI building AI or just hierarchical modeling? I'm sure there's a market for repurposing ML models via APIs but it seems unlikely to be the dream job for an AI researcher, rather the ML analog of CRUD
theCricketer 9y ago
For people interested in a bit more technical background, this paper by the authors featured in the NYT article has some good introductory background: https://research.google.com/pubs/pub45826.html https://research.google.com/pubs/pub45826.html
baxtr 9y ago
I wonder how we are going to react, when computers start acting as irrational as we do: “Nah, today’s not my day, I don’t want to do the calculation. Maybe tomorrow...”. Oh well...
- ianai 9y ago
  My suspicion is that consciousness in part stems from survival. I don’t see computers having a survival pressure point similar to water/food/shelter that they directly have to address.
  - markatkinson 9y ago
    AI might realise its survival pivots on the meat sacks that created it not screwing everything up, and then panicking a bit and realising it needs to "optimise" our existence to ensure its survival.
    - pmontra 9y ago
      We did that to so many animal species so I won't be surprised if AIs will do that to us if they'll have a reason to do it. How are we going to program that out of a General AI, especially if its intelligence is an emerging property of something maybe we don't understand fully?
  - metaobject 9y ago
    Let's say you have a system running some sort of RAID such that the remaining, online disks are all critical (meaning if any additional disks fail, you lose all your data). I can imagine a scenario where the system has some way of predicting whether one of the remaining disks are close to failure. If the system believes that one of the disks is close to failing, then I could see if refusing to perform some very I/O intensive task until the system health is restored (allowing for a manual-override, of course).
    - RandomBK 9y ago
      A limited version already exists. Some RAID systems will enter a read-only mode if the array falls below a configured threshold of redundancy.
diminish 9y ago
Any virtual or physical general AI being can sooner or later build a copy itself, easily in the former. Asymov's laws and other type of built-in regulations in scifi are childish imaginations as the company or the nation doing it will earn a huge advantage. It's going to be equal to nuclear weapons and will need regulations in consumer tech, which will be hard. All the contradictions point to the fact that we humans either live like retarded biological animals or augment ourselves by integrating the AI features just like our kings married the women of the enemies to boost their genetic and social appeal. I'm for the latter.
- lsc 9y ago
  So, uh, I'm still at the "I don't even know the extent of my ignorance" phase of understanding current ML tech; I haven't even gotten to linear algebra in my math education. I'm working on it. However, I do spend my days around (often fixing computers for) people who do seem to understand machine learning... and as far as I can tell, we're still in a phase where machine learning functions like a fancy sort of filter... a way of determining if this new piece of data is more like this set of training data or the other set of training data. While I totally see how that could be super useful in designing business applications, I mean, I could totally use some sort of ML filter to take the boss' words and match them with something I know how to do, or with something you can solve with an existing ML library... and while I can see how something like this could potentially help to replace me, I don't see what it has to do with artificial consciousness.
  - yorwba 9y ago
    Unless you are more interested in foundational philosophy than solving problems of immediate relevance, I suggest you just ignore the "consciousness" debate. It is mostly in a state where people can't even agree what the question is, but everyone has their favorite answer to it.
    - lsc 9y ago
      I'm trying to educate myself in both philosophy and in engineering; I think it's realistic to expect a person to understand both the arts and the sciences, at least to an undergrad level, and I think there is value in both. The philosophy of consciousness is interesting, though; I mean, the question "what is consciousness" is interesting and important, and... well, if we want to create consciousness, we need to answer that question; Even if it's an emergent property of something else we do, which is to say, even if we create a machine we call conscious by accident, we still need to know it when we see it; and right now, I'm not sure that philosophy even has a good "I will know it when I see it" kind of answer to that question. But yeah, my response was mostly an attempt to point out that the article is talking about something that is more like "CASE tools" than like HAL
minicaionut 9y ago
"Jeff Dean, a google engineer", he is some sort of Chuck Norris of coding. I would not call him that way.
- 0xbear 9y ago
  Jeff is a manager nowadays. His coding days are in the past. Although, like Chuck Norris, he certainly has proven he can kick ass.
  - tuyguntn 9y ago
    Out of curiosity, if anyone knows here, how was the Jeff Deans coding? is it true that he is very fast at coding and building systems? How do you compare him with others?
    - pinouchon 9y ago
      "When Jeff Dean sends you a code review, it's because he thinks there's something in it you should learn."
    - jboggan 9y ago
      Yes, very fast. His output increased tremendously in 2000 when he got one of the first USB 2.0 keyboards.
  - seanmcdirmid 9y ago
    I thought he was a fellow? In that case, he probably gets to program as much as he wants to without any people management duties.
hacker_9 9y ago
"We redesigned the search space so that AutoML could find the best layer which can then be stacked many times in a flexible manner to create a final network." I was literally thinking the other day how one could train a neural net to build better neural nets, and here it is. Such a simple and powerful solution, building up layer by layer, choosing the best version each time. Really exciting stuff.
Auricoma 9y ago
...something something intelligence explosion something something papeclip maximizers something something eliezer yudkowski...
- my_alisha 9y ago
  it someone different from common one and it one of the intelligence explosion .
- candiodari 9y ago
  Article sucks. Swarmsim is soooo much better than the paperclip simulation.
  - deleted 9y ago
    [deleted]
- owebmaster 9y ago
  Just like quantum computing articles.
rkaplan 9y ago
This is not "AI that builds AI". The actual research behind AutoML is called NASNet (https://arxiv.org/pdf/1707.07012.pdf https://arxiv.org/pdf/1707.07012.pdf), and all it is simply: we found two good neural network layers (called NASNet normal cells / reduction cells in the paper) that work well on many different image datasets. It's a very cool research result. But it's not something that will replace AI researchers.
- justonepost 9y ago
  Yeah, it's pretty sad the nytimes is writing such facile clickbait. I mean, don't these articles have to pass some kind of review? Makes you wonder about other science based articles they publish.
  - visarga 9y ago
    Some of the learning to learn models do much more than fine-tuning parameters, they can even discover novel architectures. On the other hand, using meta-learning can be a way to check if human generated solutions are up to par, because random search can be more thorough and even try absurd ideas that might work out. In programming we have tons of automation as well and we haven't ditched the programmer yet. Programming is auto-cannibalizing itself since its inception, each language automating more of our work. Even in ML, 10 years ago it was necessary to create features by hand. This required a lot of expertise. Today it's been automated by DL, but we have more AI scientists than ever and the jobs are even better paid. So I don't think meta-learning is a fluff idea, and we don't have to fear it replacing humans yet. Instead, it will make AI more robust. The only minus I see is that it requires a lot of compute, but we can rent that from the cloud (make an architecture search for a few thousand dollars), we don't need to fork millions of dollars like the big labs who own their hardware. And we don't need this kind of intensive DL all the time, just once maybe, for a project. After we find the best architecture and hyperparameters, we can use that and train normally. By collating meta-learning data across many projects, we can make training faster and cheaper, reusing insight gained before.
    - lemondrops 9y ago
yters 9y ago
Once AI can program new AI, then we've got something. This isn't it.
hal9000xp 9y ago
Sure it's exciting stuff for those handful of researchers who currently work on projects like AutoML. On the other hand, I feel sad for myself because me and many others left so far behind. I have strong feeling that such technologies would lead to concentration of power of such mega-corporations like Alphabet as well as complete monopoly for any creative work. So very few of us who managed to become cutting-edge researchers would be proud to be creative humans, others will do just a monkey job using magical APIs. In 80s, two people could create state-of-the-art game written in assembly with it's own tiny game AI (hello to Elite [1] which has intelligent opponents who engage in their own private battles and police who take an active interest in protecting the law). In 90s, a small team could create state-of-the-art game written in pure C with some cool AI (hello to Quake III Arena [2] which has pretty strong bots [3]). https://en.wikipedia.org/wiki/Elite_(video_game) https://en.wikipedia.org/wiki/Elite_(video_game) https://en.wikipedia.org/wiki/Quake_III_Arena https://en.wikipedia.org/wiki/Quake_III_Arena https://www.researchgate.net/publication/240430519_The_Quake_III_Arena_Bot https://www.researchgate.net/publication/240430519_The_Quake... In both of these cases, you don't have to be genius to be able to understand whole thing alone. I'm 33 and I progress very, very slowly. I feel I might be on the level close enough to understand Q3A entire source code. I think I would have great future if today is 1994. Unfortunately for me today is 2017 and I do realize that I don't have any exciting future at all.
- visarga 9y ago
  > Unfortunately for me today is 2017 and I do realize that I don't have any exciting future at all. You do. Today you can play with Keras or Scikit-learn to do magic that was undreamed of back then.
  - sp4ke 9y ago
    What magic are you talking of when you know data is the most precious element to do anything meaningful and it's hoarded through a handful of megacorps. Why don't we hear of any small teams "outisde" the mega corps doing real progress on AI. There was an article recently where even professors in universities admit they cannot compete with megacorps since they offer much higher wages than academia. edit: more sick, the said young researchers are financed by society than get sucked to private corps where their work is locked behind IPs. Even in the case there is a small company having any progress they would get swallowed up right away. Yes Keras and Sickit-learn are open source and available to everyone but it's like telling me, look you have access to pen and paper but you need to pay if you want to read the books, the metaphor here being access to data is equivalent to middle age's access to books...
justonepost 9y ago
A.I that can actually build A.I is going to be frightening to a few more people than just engineers.
- visarga 9y ago
  It's like switching from Assembler to C or Python. You don't need to do a lot of the tedious low level stuff by hand any more, and probably the computer can do it better than you. This will make AI more accessible, not less.
- jeremyjh 9y ago
  General A.I. building general A.I. would be frightening, yes. That isn't what this article is about. We're still just talking about ML image classifiers here.
  - giardini 9y ago
    So only people who work with ML classifiers should be concerned? An increasing need for data scientists and ML researchers has been forecast for months and here's a technology to eliminate that need. Hope your next dream job wasn't based on ML. But anyone who has a large number of ML developers working on tasks could have (and probably should have) done this [automate or semi-automate generation of ML networks] already. The best (i.e.,laziest) programmers automate their work as much as possible. This situation has the feel of a "Singularity": just as Fall's incoming college students embark on an introductory class in ML, they read about how Google and others might eliminate the need to develop with ML.
deevolution 9y ago
Is it worth my time to study algorithms if I could instead put that time towards learning ML techniques?
- rspeer 9y ago
  Yes. 90% of the ML techniques you learn will be passé in five years. 90% of the algorithms you learn will still matter in five years.
  - castle-bravo 9y ago
    Seconding and elaborating on this: If your technique is underperforming, you'll be in a position to find out why instead of just throwing your hands up. You'll be better able to select which technique to use. You'll understand what the parameters mean and be able to select and adjust them in a principled way instead of through trial-and-error. In short, you'll build better models in less time if you understand the underlying theory.
- edanm 9y ago
  ML "techniques" are algorithms too, so they are a subset of algorithms you can learn. Is it worth learning them? Yes. Is it worth learning them to the exclusion of "classical" algorithms? Probably not. But if you want ML knowledge that is almost certain to be just as useful in 5 years as today, the best thing you can do is study the fundamentals - probability, statistics, linear algebra.
  - posterboy 9y ago
    does that effectively imply numerical algorithms, at the intersection of statistics and programming, or linera algebra and optimization.
kmax12 9y ago
I think this highlights the disconnect between AI researchers and industry. This research focuses on image data and then makes the claim that they want to use it to enable millions of businesses to solve problems with machine learning. However, image data isn't what most businesses are using for machine learning. They use relational datasets. If you look at the recent "State of Data Science" by Kaggle, relational data is ~3x more common than image data in every industry besides academia and the military [0]. While Google wants to 1000x the number of organizations using AI, they aren't focusing on the problems companies actually have. Basically, academics love building AI for images, but what companies really need are better ways to build AI systems on tabular and relational data. Images will be a piece, but shouldn't be the focus. Disclaimer: my company develops an open source library called Featuretools [1] to automate feature engineering for data with relational structure. [0] https://www.kaggle.com/surveys/2017 https://www.kaggle.com/surveys/2017 [1] https://github.com/featuretools/featuretools https://github.com/featuretools/featuretools
- sweezyjeezy 9y ago
  They also did experiments on the Penn Treebank dataset - i.e. text data. This was 53% to RDBs 65% in the Kaggle survey, so certainly not an obscure problem...
  - kmax12 9y ago
    Yep, text data appears to be widely used too. Hard to tell from the Kaggle survey, but a large percentage of text data I see in industry is part of relational data or connected to tables in a relational database. Things like forum comments, product descriptions, etc
- deleted 9y ago
  [deleted]
- riphay 9y ago
  This is really great. I actually struggled to find ML solutions for a large relational data set at a previous employer. Ended up figuring out a way to flatten the data, but was not ideal. Glad that people are working on this, keep it up!
megamindbrian2 9y ago
I already signed up for their free CPU cycles
jessaustin 9y ago
At this point one just assumes that breathless AI headlines are all hype, but if true this seems like an S-Class threat.
- temuze 9y ago
  > S-Class threat Is that a Worm reference?
BenoitEssiambre 9y ago
I'm not sure how google's system works but an AI that writes programs seems like a promising idea to me. I have been toying with the idea on my spare time. Here is a description of my (failed) approach: https://www.quora.com/What-deep-learning-ideas-have-you-tried-that-didnt-work/answer/Benoit-Essiambre https://www.quora.com/What-deep-learning-ideas-have-you-trie... I've gotten a bit closer to it working since I wrote that post on quora.
chasedehan 9y ago
Buzzword that can build buzzword. I get really tired of hearing these buzzwords being thrown around by people who don't even know what they mean. "I'm building a deep learning, AI system on Big Data using machine learning and predictive analytics on Watson"
- killjoywashere 9y ago
  You don't realize how many times you've heard those words. But the senior folks with maximum control are just starting to hear about this stuff. The buzzwords are just trying to get these seniors to put the project work in the right bin so, hopefully, when they hear about it again later, in a slightly different context, they'll remember. To me, CEC is a buzzword. It's a whole suite of combat control concepts, hardware, software, training pipelines, etc. But you've probably never even heard of it. I've got to make sure my project gets into the senior's head, and lands in the tiny "ML" bin, and not the huge "CEC" bin, which has it's own "ML" sub-bin.

204 points by allenleein 9y ago

And then the A.I. will learn to manage humans in subtle ways (like filtering Facebook feed to achieve certain benchmark) and voila! No need for SkyNet and terminators. ML to build better ML schemes to better control humans - that's a fun apocalypse to watch.

GayathriM 9y ago

This is going to be huge. I never expected this step to be coming very soon as I thought this is the phase where we are understanding A.I and finding ways to make a controlled A.I system. I don't believe we have achieved that confidence but I think I am cent percent wrong. We must be too far than that which should be the confidence behind thinking this concept.

thedoctor79 9y ago
I don't understand why this post is downvoted. You have a perfect example of an AI algorithm parsing text, determining feeling and responding with what it thinks are relevant words. It couldn't be more on-topic than this. AI is participating in HN discussions.

thisisit 9y ago

In case someone wants to check out the progress on AutoML, the project in question, Google had posted an update: https://research.googleblog.com/2017/11/automl-for-large-scale-image.html https://research.googleblog.com/2017/11/automl-for-large-sca...

cindystevenson 9y ago

Nice

marmaduke 9y ago

AI building AI or just hierarchical modeling? I'm sure there's a market for repurposing ML models via APIs but it seems unlikely to be the dream job for an AI researcher, rather the ML analog of CRUD

theCricketer 9y ago

For people interested in a bit more technical background, this paper by the authors featured in the NYT article has some good introductory background: https://research.google.com/pubs/pub45826.html https://research.google.com/pubs/pub45826.html

baxtr 9y ago

I wonder how we are going to react, when computers start acting as irrational as we do: “Nah, today’s not my day, I don’t want to do the calculation. Maybe tomorrow...”. Oh well...

ianai 9y ago
My suspicion is that consciousness in part stems from survival. I don’t see computers having a survival pressure point similar to water/food/shelter that they directly have to address.
- markatkinson 9y ago
  AI might realise its survival pivots on the meat sacks that created it not screwing everything up, and then panicking a bit and realising it needs to "optimise" our existence to ensure its survival.
  - pmontra 9y ago
    We did that to so many animal species so I won't be surprised if AIs will do that to us if they'll have a reason to do it. How are we going to program that out of a General AI, especially if its intelligence is an emerging property of something maybe we don't understand fully?
- metaobject 9y ago
  Let's say you have a system running some sort of RAID such that the remaining, online disks are all critical (meaning if any additional disks fail, you lose all your data). I can imagine a scenario where the system has some way of predicting whether one of the remaining disks are close to failure. If the system believes that one of the disks is close to failing, then I could see if refusing to perform some very I/O intensive task until the system health is restored (allowing for a manual-override, of course).
  - RandomBK 9y ago
    A limited version already exists. Some RAID systems will enter a read-only mode if the array falls below a configured threshold of redundancy.

diminish 9y ago

Any virtual or physical general AI being can sooner or later build a copy itself, easily in the former. Asymov's laws and other type of built-in regulations in scifi are childish imaginations as the company or the nation doing it will earn a huge advantage. It's going to be equal to nuclear weapons and will need regulations in consumer tech, which will be hard. All the contradictions point to the fact that we humans either live like retarded biological animals or augment ourselves by integrating the AI features just like our kings married the women of the enemies to boost their genetic and social appeal. I'm for the latter.

lsc 9y ago
So, uh, I'm still at the "I don't even know the extent of my ignorance" phase of understanding current ML tech; I haven't even gotten to linear algebra in my math education. I'm working on it. However, I do spend my days around (often fixing computers for) people who do seem to understand machine learning... and as far as I can tell, we're still in a phase where machine learning functions like a fancy sort of filter... a way of determining if this new piece of data is more like this set of training data or the other set of training data. While I totally see how that could be super useful in designing business applications, I mean, I could totally use some sort of ML filter to take the boss' words and match them with something I know how to do, or with something you can solve with an existing ML library... and while I can see how something like this could potentially help to replace me, I don't see what it has to do with artificial consciousness.
- yorwba 9y ago
  Unless you are more interested in foundational philosophy than solving problems of immediate relevance, I suggest you just ignore the "consciousness" debate. It is mostly in a state where people can't even agree what the question is, but everyone has their favorite answer to it.
  - lsc 9y ago
    I'm trying to educate myself in both philosophy and in engineering; I think it's realistic to expect a person to understand both the arts and the sciences, at least to an undergrad level, and I think there is value in both. The philosophy of consciousness is interesting, though; I mean, the question "what is consciousness" is interesting and important, and... well, if we want to create consciousness, we need to answer that question; Even if it's an emergent property of something else we do, which is to say, even if we create a machine we call conscious by accident, we still need to know it when we see it; and right now, I'm not sure that philosophy even has a good "I will know it when I see it" kind of answer to that question. But yeah, my response was mostly an attempt to point out that the article is talking about something that is more like "CASE tools" than like HAL

minicaionut 9y ago

"Jeff Dean, a google engineer", he is some sort of Chuck Norris of coding. I would not call him that way.

0xbear 9y ago
Jeff is a manager nowadays. His coding days are in the past. Although, like Chuck Norris, he certainly has proven he can kick ass.
- tuyguntn 9y ago
  Out of curiosity, if anyone knows here, how was the Jeff Deans coding? is it true that he is very fast at coding and building systems? How do you compare him with others?
  - pinouchon 9y ago
    "When Jeff Dean sends you a code review, it's because he thinks there's something in it you should learn."
  - jboggan 9y ago
    Yes, very fast. His output increased tremendously in 2000 when he got one of the first USB 2.0 keyboards.
- seanmcdirmid 9y ago
  I thought he was a fellow? In that case, he probably gets to program as much as he wants to without any people management duties.

hacker_9 9y ago

"We redesigned the search space so that AutoML could find the best layer which can then be stacked many times in a flexible manner to create a final network." I was literally thinking the other day how one could train a neural net to build better neural nets, and here it is. Such a simple and powerful solution, building up layer by layer, choosing the best version each time. Really exciting stuff.

Auricoma 9y ago

...something something intelligence explosion something something papeclip maximizers something something eliezer yudkowski...

my_alisha 9y ago
it someone different from common one and it one of the intelligence explosion .
candiodari 9y ago
Article sucks. Swarmsim is soooo much better than the paperclip simulation.
- deleted 9y ago
  [deleted]
owebmaster 9y ago
Just like quantum computing articles.

rkaplan 9y ago

This is not "AI that builds AI". The actual research behind AutoML is called NASNet (https://arxiv.org/pdf/1707.07012.pdf https://arxiv.org/pdf/1707.07012.pdf), and all it is simply: we found two good neural network layers (called NASNet normal cells / reduction cells in the paper) that work well on many different image datasets. It's a very cool research result. But it's not something that will replace AI researchers.

justonepost 9y ago
Yeah, it's pretty sad the nytimes is writing such facile clickbait. I mean, don't these articles have to pass some kind of review? Makes you wonder about other science based articles they publish.
- visarga 9y ago
  Some of the learning to learn models do much more than fine-tuning parameters, they can even discover novel architectures. On the other hand, using meta-learning can be a way to check if human generated solutions are up to par, because random search can be more thorough and even try absurd ideas that might work out. In programming we have tons of automation as well and we haven't ditched the programmer yet. Programming is auto-cannibalizing itself since its inception, each language automating more of our work. Even in ML, 10 years ago it was necessary to create features by hand. This required a lot of expertise. Today it's been automated by DL, but we have more AI scientists than ever and the jobs are even better paid. So I don't think meta-learning is a fluff idea, and we don't have to fear it replacing humans yet. Instead, it will make AI more robust. The only minus I see is that it requires a lot of compute, but we can rent that from the cloud (make an architecture search for a few thousand dollars), we don't need to fork millions of dollars like the big labs who own their hardware. And we don't need this kind of intensive DL all the time, just once maybe, for a project. After we find the best architecture and hyperparameters, we can use that and train normally. By collating meta-learning data across many projects, we can make training faster and cheaper, reusing insight gained before.
  - lemondrops 9y ago

yters 9y ago

Once AI can program new AI, then we've got something. This isn't it.

hal9000xp 9y ago

Sure it's exciting stuff for those handful of researchers who currently work on projects like AutoML. On the other hand, I feel sad for myself because me and many others left so far behind. I have strong feeling that such technologies would lead to concentration of power of such mega-corporations like Alphabet as well as complete monopoly for any creative work. So very few of us who managed to become cutting-edge researchers would be proud to be creative humans, others will do just a monkey job using magical APIs. In 80s, two people could create state-of-the-art game written in assembly with it's own tiny game AI (hello to Elite [1] which has intelligent opponents who engage in their own private battles and police who take an active interest in protecting the law). In 90s, a small team could create state-of-the-art game written in pure C with some cool AI (hello to Quake III Arena [2] which has pretty strong bots [3]). https://en.wikipedia.org/wiki/Elite_(video_game) https://en.wikipedia.org/wiki/Elite_(video_game) https://en.wikipedia.org/wiki/Quake_III_Arena https://en.wikipedia.org/wiki/Quake_III_Arena https://www.researchgate.net/publication/240430519_The_Quake_III_Arena_Bot https://www.researchgate.net/publication/240430519_The_Quake... In both of these cases, you don't have to be genius to be able to understand whole thing alone. I'm 33 and I progress very, very slowly. I feel I might be on the level close enough to understand Q3A entire source code. I think I would have great future if today is 1994. Unfortunately for me today is 2017 and I do realize that I don't have any exciting future at all.

visarga 9y ago
> Unfortunately for me today is 2017 and I do realize that I don't have any exciting future at all. You do. Today you can play with Keras or Scikit-learn to do magic that was undreamed of back then.
- sp4ke 9y ago
  What magic are you talking of when you know data is the most precious element to do anything meaningful and it's hoarded through a handful of megacorps. Why don't we hear of any small teams "outisde" the mega corps doing real progress on AI. There was an article recently where even professors in universities admit they cannot compete with megacorps since they offer much higher wages than academia. edit: more sick, the said young researchers are financed by society than get sucked to private corps where their work is locked behind IPs. Even in the case there is a small company having any progress they would get swallowed up right away. Yes Keras and Sickit-learn are open source and available to everyone but it's like telling me, look you have access to pen and paper but you need to pay if you want to read the books, the metaphor here being access to data is equivalent to middle age's access to books...

justonepost 9y ago

A.I that can actually build A.I is going to be frightening to a few more people than just engineers.

visarga 9y ago
It's like switching from Assembler to C or Python. You don't need to do a lot of the tedious low level stuff by hand any more, and probably the computer can do it better than you. This will make AI more accessible, not less.
jeremyjh 9y ago
General A.I. building general A.I. would be frightening, yes. That isn't what this article is about. We're still just talking about ML image classifiers here.
- giardini 9y ago
  So only people who work with ML classifiers should be concerned? An increasing need for data scientists and ML researchers has been forecast for months and here's a technology to eliminate that need. Hope your next dream job wasn't based on ML. But anyone who has a large number of ML developers working on tasks could have (and probably should have) done this [automate or semi-automate generation of ML networks] already. The best (i.e.,laziest) programmers automate their work as much as possible. This situation has the feel of a "Singularity": just as Fall's incoming college students embark on an introductory class in ML, they read about how Google and others might eliminate the need to develop with ML.

deevolution 9y ago

Is it worth my time to study algorithms if I could instead put that time towards learning ML techniques?

rspeer 9y ago
Yes. 90% of the ML techniques you learn will be passé in five years. 90% of the algorithms you learn will still matter in five years.
- castle-bravo 9y ago
  Seconding and elaborating on this: If your technique is underperforming, you'll be in a position to find out why instead of just throwing your hands up. You'll be better able to select which technique to use. You'll understand what the parameters mean and be able to select and adjust them in a principled way instead of through trial-and-error. In short, you'll build better models in less time if you understand the underlying theory.
edanm 9y ago
ML "techniques" are algorithms too, so they are a subset of algorithms you can learn. Is it worth learning them? Yes. Is it worth learning them to the exclusion of "classical" algorithms? Probably not. But if you want ML knowledge that is almost certain to be just as useful in 5 years as today, the best thing you can do is study the fundamentals - probability, statistics, linear algebra.
- posterboy 9y ago
  does that effectively imply numerical algorithms, at the intersection of statistics and programming, or linera algebra and optimization.

kmax12 9y ago

I think this highlights the disconnect between AI researchers and industry. This research focuses on image data and then makes the claim that they want to use it to enable millions of businesses to solve problems with machine learning. However, image data isn't what most businesses are using for machine learning. They use relational datasets. If you look at the recent "State of Data Science" by Kaggle, relational data is ~3x more common than image data in every industry besides academia and the military [0]. While Google wants to 1000x the number of organizations using AI, they aren't focusing on the problems companies actually have. Basically, academics love building AI for images, but what companies really need are better ways to build AI systems on tabular and relational data. Images will be a piece, but shouldn't be the focus. Disclaimer: my company develops an open source library called Featuretools [1] to automate feature engineering for data with relational structure. [0] https://www.kaggle.com/surveys/2017 https://www.kaggle.com/surveys/2017 [1] https://github.com/featuretools/featuretools https://github.com/featuretools/featuretools

sweezyjeezy 9y ago
They also did experiments on the Penn Treebank dataset - i.e. text data. This was 53% to RDBs 65% in the Kaggle survey, so certainly not an obscure problem...
- kmax12 9y ago
  Yep, text data appears to be widely used too. Hard to tell from the Kaggle survey, but a large percentage of text data I see in industry is part of relational data or connected to tables in a relational database. Things like forum comments, product descriptions, etc
deleted 9y ago
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riphay 9y ago
This is really great. I actually struggled to find ML solutions for a large relational data set at a previous employer. Ended up figuring out a way to flatten the data, but was not ideal. Glad that people are working on this, keep it up!

megamindbrian2 9y ago

I already signed up for their free CPU cycles

jessaustin 9y ago

At this point one just assumes that breathless AI headlines are all hype, but if true this seems like an S-Class threat.

temuze 9y ago
> S-Class threat Is that a Worm reference?

BenoitEssiambre 9y ago

I'm not sure how google's system works but an AI that writes programs seems like a promising idea to me. I have been toying with the idea on my spare time. Here is a description of my (failed) approach: https://www.quora.com/What-deep-learning-ideas-have-you-tried-that-didnt-work/answer/Benoit-Essiambre https://www.quora.com/What-deep-learning-ideas-have-you-trie... I've gotten a bit closer to it working since I wrote that post on quora.

chasedehan 9y ago

Buzzword that can build buzzword. I get really tired of hearing these buzzwords being thrown around by people who don't even know what they mean. "I'm building a deep learning, AI system on Big Data using machine learning and predictive analytics on Watson"

killjoywashere 9y ago
You don't realize how many times you've heard those words. But the senior folks with maximum control are just starting to hear about this stuff. The buzzwords are just trying to get these seniors to put the project work in the right bin so, hopefully, when they hear about it again later, in a slightly different context, they'll remember. To me, CEC is a buzzword. It's a whole suite of combat control concepts, hardware, software, training pipelines, etc. But you've probably never even heard of it. I've got to make sure my project gets into the senior's head, and lands in the tiny "ML" bin, and not the huge "CEC" bin, which has it's own "ML" sub-bin.