Andrew Ng on the state of deep learning at Baidu
medium.com
medium.com
It is the period between sentience and "advanced" artificial intelligence that should be worrying for two reasons: First, it is close at hand. More importantly, the unintended consequences of tech are never well thought out in the initial stages of adoption.
I'm reading Eric Schlosser's book on the early days of nuclear weapons and the many, many near misses the US experienced as it adopted nuclear arms without much thought to risk management. I see parallels in the race to develop and deploy pre-sentient A.I. Link below to Schlosser's book.
http://www.amazon.com/Command-Control-Damascus-Accident-Illu...
The metaphor is that you could have "sedentary" superintelligences that solve a relatively narrow problem. They take in huge amounts of data, and spit out ingenious answers to well-defined problems. Things like traffic-routing, energy allocation, or photo/video recognition, etc. They are tools for humans.
By what mechanism do these superintelligences involve into organisms motivated to extinguish humans? Reproduction, which leads to independent evolution. That process is inherently unpredictable.
But right now we're firmly in the stage of humans creating AI. Our AI is nowhere near capable enough to create more AI. And I would argue that there is no possibility of this happening by "accident"; you would have to specifically engineer AI to able to create more AI.
I agree with Ng in that it's more of a distraction now than anything else. The jobs issue is a lot more pertinent.
It reminds me of this book I got at Google 8 or 9 years ago. This guy asked a robotic professors how they would avoid being killed by a robot. And the professor said: "Climb up one step" (on a staircase).
Are you sure? Lots of machine-learning/optimization processes already work much like evolution.
How many people thought an email worm could bring down thousands of machines around the world in 1988?
http://en.wikipedia.org/wiki/Morris_worm
If one (or several) of the robots depicted in the below video wants someone dead, the "stair step" defense is not going to be enough:
http://futureoflife.org/static/data/documents/research_prior... (section 3.4, Control, although the general skepticism is noted)
But I will say that there is a difference between feedback and unconstrained evolution. Machine learning mechanisms are feedback loops. But that doesn't mean they will break out of their own paradigm without human intervention.
Malware is actually a good example.
The Morris worm is a static piece of program text, invented by a human. It took advantage of a homogeneous and static environment to spread widely (lots of computers running the same program with the same vulnerability.) There's a huge difference between spreading in this environment and adapting / reproducing so as to spread in novel environments.
Think about Stuxnet. AFAICT, this was a completely novel solution to a problem that had never been seen before: jumping an air gap and destroying a piece of hardware via software.
What do you think is more fruitful approach to designing something like Stuxnet?
1) Assemble the best and brightest minds from multiple domains, painstakingly build simulation environments based on the most plausible intelligence, test custom malware laboriously in those environments, etc.
2) Start with some existing malware (not unlike the Morris worm), and design a meta-algorithm to evolve it into something capable of destroying a nuclear plant from afar?
#2 is science fiction, as far as I can tell. The level of ingenuity required for a task like this is just qualitatively outside the domain of computers.
This is closer to what would be required for us to lose control: for AI to be designing AI, rather than humans designing AI. And keep in mind that AI designing AI is harder than AI designing malware.
I have seen the demos from Deep Mind where it learns to play video games without any knowledge of the rules. I don't claim to fully understand these techniques. (I have however worked in a research lab on robotics, and seen the vast gulf between what the media portrays and reality.)
But I would be interested if anyone knowledgeable in those techniques sees a realistic path from deep learning to something like #2.
Pretty much every academic that I'm aware of who works in the field of AI has similar views to Ng, i.e., suggestions of an impending AI cataclysm are wildly overblown and don't fit the facts. The people predicting doom are those who for the most part aren't involved with current research and as such don't have a good perspective on the limitations of current techniques.
There is no magic in modern AI advances and there is no reason to expect that they'll yield anything like generalized intelligence. By the nature of the methods they work well in the specific domains they're trained on and poorly everywhere else.
And a very precise detonation device strapped around said ball of uranium.
Software is not by itself dangerous. It only exists in the most abstract sense. What is dangerous is creating physical devices that have the ability to cause physical damage if manipulated in a certain way. Allowing those devices to be controlled autonomously through a software-hardware interface is where the danger from "AI" comes in.
We're much closer to runaway biological threats than we are to AI-inspired ones. Or maybe that's my knowledge gap in the two domains speaking and we're equally far away in each.
And no one is arguing that software can't be dangerous. It clearly can, just like poorly constructed bridges or cars can be dangerous. But the idea of machines thinking for themselves is science fiction. There is no indication that we're anywhere close to that happening.
These are just advances in doing things human can do, i.e. recognizing in pictures, driving a car. Deep learning is known since long, calculation were just too long.
Fundamentally nothing has changed, we're not closer to an "intelligent machine".
If "evil killer robots" are created, it won't be because of advances in big data or deep learning or any other buzzword. It'll happen independently, potentially aided by current state of the art techniques but not because of them. It could happen at any time, some guy in his basement could be on the verge of a breakthrough as we speak. But google's ability to detect cat faces or Watson's winning on jeopardy are not signs of a coming apocalypse. It's highly unlikely that we can cluster or gradient descent our way to human level of intelligence, no matter how many cores or hidden layers are used.
This whole "famous people are worried about AI" thing is understandably media friendly, but it's a bit of a distraction. Fortunately the majority of active researchers aren't taking it seriously at all.
You don't need to fear much, by the time the engine evolves data into something scary, the universe is long gone.
Source? How can you possibly know how AI will be developed? Machine learning is likely to be a huge component in any AI. Deep learning has been shown to beat all sorts of tasks thought to require strong AI. it can learn complicated patterns and heuristics from raw data, which is the hardest part of AI.
It's quite possible we are 99% of the way there, and we just need someone to figure out how to use it for general intelligence.
Regardless, this is all irrelevant. The people concerned about AI are mostly worried for the long term. No one is claiming we will have AI in ten years for certain. I'm not sure why people keep equating these two totally different predictions.
That's why ML researchers saying the stuff they work on isn't dangerous, isn't very reassuring. No one is claiming that it is.
Eh? A human baby normally doesn't even start to learn text until well after it shows a complete mastery of every phoneme in its native language along with some basic vocabulary and a decent understanding of syntax.
I find it baffling that such a distinguished AI expert would assert that phonemes are unnecessary invented constructs. Are there many who share the same belief?
As Ng noted, phonemes are artificial constructs. If they are necessary for a particular language then they should be learned in the same way other constructs are, not imposed on the learning algorithm from the outside.
Are phonemes imposed on human babys? Maybe if you were raised by Hooked on Phonics[1], but I'd wager that most humans did not have the idea imposed on them.
However, no serious NLP researcher would suggest building a text-to-speech system by getting rid of the middle OCR layer and just hooking the raw image directly to the expected sound. (Well, at least I think so, but I'd be glad to know if I'm wrong.)
Phonemes are absolutely imposed on human babies. Sure, they wouldn't know the word "phoneme", but that doesn't make the concept any less real. Imagine how a typical education on reading/writing would start like: "This is G. It is used for words like grass, game, and girl. Actually, it can also be used for gem or genie!"
Can you imagine an English-speaking child listening to this and not immediately understanding that there are two distinct sounds involved, that the first sounds for "grass", "game", and "girl" are somehow the same (even though the waveforms are different), and that this sound is somehow different from that of "gem" or "genie"? If a child doesn't understand it, then the child probably needs a speech therapy.
Perhaps you're thinking mainly of Western writing systems?
Presumably since Andrew now works for Baidu, he is particularly interested in reading Chinese. The interesting thing about Chinese is that, as I understand it, the same symbols can have different pronunciations in different dialects, but all pronunciations mean the same thing.
I.e. the symbol has a unique meaning, but multiple possible sounds. Which implies the sound isn't really what's important here.
While the concept of phonemes is certainly valid, the specific mappings created by linguists could be considered ad hoc. E.g. English has a huge number of pure vowels. Maybe there is some order order to English vowels them that is hard to capture in the representations linguists use, but easy in these models.
In speech recognition, people were taking audio segments of different phonemes, manually labeling them, and having the algorithm predict them. Rather than just giving it the raw audio and desired output.
Is this an effective way to start projects? Sure its the end goal but using this as the starting point might be unnecessarily limiting. It might limit ideas to things that seem safe at the beginning.
The seeding of most novel ideas often seem very niche/minor and then evenutally exploded in popularity - then their importance starts to become apparent (see Twitter).
I'd rather invest in a large group of people broken up into small teams trying a varied approach from bold darpa-style ideas to more practical localized problem, similar to Valve or YC.
A "project" is usually an application of existing technology done in an innovative way. It may well end up being the next Facebook, but it isn't something that usually requires breakthroughs in scientific fields.
Ng's (and Baidu's) "tech" is a much more basic level of research. Baidu isn't interested in funding research into translating English into Latin because - while it is novel - it is unlikely to impact 100 million users.
OTOH: Chinese/English translation? Yes. World leading voice recognition? Yes[1]. Understanding images? Of course![2]
[1] http://www.forbes.com/sites/roberthof/2014/12/18/baidu-annou...