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Comment by hacker_9 | Hacker News Reader

This is about 100 years too early. Seriously why do people think neural networks are the answer to AI? They are proven to be stupid outside of their training data. We have such a long way to go. This fear-mongering is pointless.

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In terms of artificial general intelligence, the kind of stuff that gets associated with 'the singularity' etc. - I agree - there is seemingly nothing at all out there that appears to even be on a trajectory, I mean even theoretically, to coming close. But sure, for more narrow specializations, disrupting certain industries, there is a lot that could be advanced on a short time scale.

I don't think the big breakthroughs in artificial general intelligence are going to come from well funded scientific researchers anyways, they are going to come out of left field from where you least expect it.

That's the thing. It's not necessarily about an evolution. If I "accidentally" make a break though right now. The AI could evolve into a super intelligence before dinner time.

Simply stated, an AI that writes AI.(Forget the halting problem for a moment) How many iterations can it create in 3 hours?

Think about how long it takes a baby to learn (a few years). There is no reason an AI would self-improve multiplicatively in hours or days or even weeks.

With a powerful enough computer the learning curve of a simulated baby could hypothetically be condensed to minutes, or seconds. Which would appear to us in normal time to be an intelligence explosion.

The simulated baby would extremely tax to the limit the entire resources of whatever supercomputer trained it. People have to run deep learning programs on multiple GPUs right now, and performance is constrained by memory bandwidth, which does not follow Moore's law.

It's a hypothetical, not expected to happen in conventional supercomputers with the current state of machine learning.

Imagine a massively parallel optical computer with the same transistor density as the human brain, the size of an olympic swimming pool running at the speed of light, and networked with 1000s of other similar computers around the world.

Foomp, superintelligence, you won't even be able to pinpoint the source.

Sure, but you would get the baby situation I posited years before what you're envisioning. You're skipping a bunch of steps. My hypotheticals (if they ever do happen) would occur way before your hypotheticals. Progress does not go in "Foomp" steps.

Everything goes in foomp steps if you condense time enough. Condense the last 200,000 years of human evolution to a couple minutes, and it goes 'Foomp', and the super foompy part doesn't happen until the last 2/10ths of a second with industrial civilization wherein the development of global networks compounds our collective intelligence exponentially, yielding unforeseeable emergent properties.

So certainly you would get the baby situation first, but going from manageable baby to astral foetus could potentially happen rapidly and unexpectedly as the rate of progress accelerates to unfathomable speeds, which is what's happened already in going from tribal man to modern civilization, and if you extrapolate that very consistent and reliable trendline, it leads to progress happening in a foomp step perceived as a foomp in real time. Really, all life is just one big accelerating foomp.

This makes no logical sense, You're not addressing my point about the fact that the intermediate progress will be chronologically ordered in time.

> If I "accidentally" make a break though right now. The AI could evolve into a super intelligence before dinner time.

We don't need to rely only on humans to design every aspect of neural networks. We are already computationally searching for AI designs that work better. In a recent paper, hundreds of variations of design for the neuron of the LSTM network have been tried to see which one is the best and which of its components are important.

Also, we can play with networks like clay - starting from already trained networks, we can add new layers, make layers wider, transfer knowledge from one complex network into a simpler one and initialize new networks from old networks so as they don't start from scratch every time. We can download models trained by other groups and just plug them in (see word2vec for example). This makes iterative experimenting and building on previous success much faster.

I don't think evolving a super intelligence will happen by simple accident, it will be an incremental process of search. The next big things I predict will be capable robots and decent dialogue agents.

Probably dependent on your computer ;)

It's not necessarily the answer to AI, but it works remarkably well. Is it Skynet or the Terminator yet? No.

There are different ideas for what constitutes AI. Expert systems and knowledge-based reasoners? Pattern-recognizer black boxes? Chatbots? AGI?

Over the years the concept of AI shifted. Until recent years "AI" was mostly used for things like A* search, creating algorithms that play turn-based table games for example (see the Russell-Norvig book), symbolic manipulation, ontologies etc., a few years ago it began to also refer to machine learning like neural networks again.

Neural networks are good at what they are designed for. Whether they will lead to the path to human-like artificial intelligence is a speculative question. But symbolic manipulation alone won't be able to handle the messiness of sensory data for sure. I think neural nets are much better suited for open-ended development than hand engineered pipelines that were state of the art until recently (like extracting corner points, describing them with something like SIFT, clustering the descriptors and using an SVM over bag-of-words histograms). Hand engineering seems too restrictive.

Quibble with the timeline: AI as a field has included a big chunk devoted to machine learning research pretty much continuously, especially since the '80s or so. The specific methods in vogue do change: decision trees, neural networks, SVMs, boosting, association-rule learning, genetic algorithms, Bayesian networks, etc. go through periods of waxing and waning in popularity. A few years ago boosting/bagging and other ensemble methods were very hot and neural networks were out of fashion; now neural networks are hot and the boosting hype has quieted down a bit. But ML is pretty much always there in some form, since learning from data is an important component of AI.

ML was there but at least when I started learning about these things around 8 years ago, the label "AI" was mostly used for symbolic stuff. Courses named "AI" taught from the Russell-Norvig book. Things like resolution, planning in the block world, heuristic graph search, min-max trees, etc. ML existed but it wasn't really under the label of "AI" as far as I can remember. I think it's something of a marketing term that big companies like Google and Facebook reintroduced due to the scifi connotations. But that's just my guess.

I can see that for intro courses, especially because of the book, though it varies a lot by school and instructor. On the research side it's been a big part of the field, though. The proceedings of a big conference like AAAI [1] are a decent proxy for what researchers consider "AI", and ML has been pretty well represented there for a while.

[1] http://www.aaai.org/Library/AAAI/aaai-library.php

> I think it's something of a marketing term

you're not alone in thinking so

https://news.ycombinator.com/item?id=10483846

i get the impression that terminology bifurcated into "AI" and "cognitive science" around the time Marr published Vision in the 80's.

quibbles and q-bits aside, i was glad to see the announcement from the perspective of a if-not-free-then-at-least-probably-open-source-ish software appreciator.

>>This is about 100 years too early.

More fundamentally, we are trying to achieve what we can't even define. Define AI, and implementing it should be quite easy.

"Human level AI" seems like trying to define problems through observed characteristics.

I think it was Douglas Hofstadter who had said something to the order that we don't even exactly understand what 'intelligence' means, let alone a clear definition reducible to a mathematical equation or a implementable program.

Your chess programs, are really not 'thinking' in pure sense, there are trying to replace 'thinking' with an algorithm that resembles the outcome of 'thinking'.

I don't see any "fear-mongering" in this announcement?

The creation of what is essentially an ethics committee for a technology that doesn't even exist yet? With people such as Elon Musk on board who have publicly said 'AI is our biggest existential threat' ?

Additionally the second paragraph:

We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as is possible safely.

This infers they think AI will be used for hostile means. Such as wiping out the human race maybe? It is just un-informed people making un-informed decisions and then informing other un-informed people of said decisions as if they were informed.

Sutskever, Schulman, Karpathy and Kingma are experts in machine learning.

And yes, AI will definitely be used for all sorts of purposes including hostile means. Just like anything else, really. Financial manipulation, spying, intelligent military devices, cracking infrastructure security, etc.

These are realistic concerns, we shouldn't fall for the Skynet red herring. We can have problems with ethical AI use, even if it's not a self-aware super-human superintelligence.

I hope I'm wrong, but given the composition of the donors I'd be surprised if they really put much scrutiny on near-term corporate/government misuse of AI, apart perhaps from military robots. There are definitely interesting ethics questions already arising today around how large tech companies, law enforcement, etc. are starting to use AI, whether it's Palantir, the FBI, Google, or Facebook, so no argument that it's a timely subject at least in some of its forms. It'll be interesting to see if they get into that. I'd guess they probably want to avoid the parts that overlap too much with data-privacy concerns, partly because a number of their sponsors are not exactly interested in data privacy, and partly because the ethical debate then becomes more complex (it's not purely an "ethics of AI" debate, but has multiple axes).

I share your concerns. It also worries me that the brightest ML researchers choose to work at companies like Facebook, Google and Microsoft instead of public universities. One reason is probably that academia and public grants are too sluggish to accommodate this fast paced field. Another is that these companies have loads of data that these researchers can use to test their ideas.

The downside is that much of the research is probably held secret for business advantages. The public releases are more of a PR and hiring strategy than anything else in my opinion. By sending papers to conferences, Google's employees can get to know the researchers and attract them to Google.

Others say there's nothing to worry about, Google and Facebook are just today's equivalent of Bell Labs, which gave numerous contributions to computer technologies without causing much harm.

I doubt they are strictly targeting "strong AI", and a lot of the things we use and call AI right now also benefit from open work and discussion. Just because it is just "Machine learning" doesn't mean it isn't used for questionable or bad purposes.

EDIT: I have to agree with _delirium's skepticism towards them doing much in that regard though.

What do you see as the downside of creating this organization now, as opposed to in 100 years? Artificial Intelligence in its current state has shown itself to be incredibly useful and effective at small tasks. I see no harm in researching and expanding this field (besides opportunity cost).

Also, I see a distinct lack of "fear-mongering" in this post.

The linked site says nothing about neural networks.

"we've also started to see what it might be like for computers to be [creative], to [dream], and to [experience the world]."

All three of those links are about neural networks.

So you've turned "what it might be like" with a couple examples of mild AI-ish tasks that caught the public's attention this year into "the answer"?

The site straight up says "deep learning." Which is neural networks.

There's a paragraph basically saying "there's been some cool stuff happening (you may have even heard about) in this particular field". Extrapolating that to the entire organization thinking that "neural networks are the answer to AI" as OP claimed is silly.

Of the researchers they hired (some of them don't appear to be active researchers right now, like Trevor Blackwell), they're all are deep learning researchers...

Humans are really stupid without training too.

Not true. Humans can adapt to many environments by pulling in training from other related environments and applying it to the new data. Some call this generalised learning, but I think it points more to our ability to shape everything we know to any given problem - something 'beyond' just generalising.

You were born with millions of years of training and still couldn't care for yourself without many years of personalized training.

There is a reason for this: it makes us able to adapt to _any_ environment on earth. We learn everything from our parents/guardians who have learnt all the 'right' ways of doing things. Look around, are we not the dominant species?

From our point of view we're the most dominate, but from other points of view you could easily argue that bacteria are the most dominate. There are a lot more of them and they will almost certainly outlive us.

This doesn't really seem relevant when we're talking about the development of knowledge through training in humans.

Instinct isn't "training", really. Don't conflate neural networks and genetic algorithms in people or AI... :)

You're over-glorifying humans. We're still just a savannah ape and our skills reflect that.

What? The brain has not only solved the vision problem (reconstructing depth from still images, recognising the objects in the scene, and filling in the occluded parts), it has also solved the motion problem of coordinated the movement of our ~300 muscles (given constraints, how do I move from A to B, or pickup the cup, or do a handstand), as well as solved the memory problem (basically infinite memory, with some sort of priority system for removing unused/old memories so we can always learn more). Additionally it solved the communication problem with language that computers still can't parse properly. It is so smart it is even conscious, and self-aware as well as death-aware.

This is not over-glorifying. That is fact.

The brain doesn't "solve" tasks. That's cart before horse thinking. Our whole concept of "vision" only exists because eyes and visual cortices exist. I know it seems like a philosophical nitpick, but saying that the visual cortex is good at vision is like saying that water is good at being wet or being surprised that your soup is perfectly fitting the shape of your bowl.

Now, the human brain is definitely a complicated thing to study and understand (by whom? by itself!), but framing it as if the brain was a computer that received a task that it then solved, is the wrong way of thinking about this.

I know, I was approaching it from your angle, the current state of ML, and explaining it from that context. You support ML but then refer to humans as 'just apes' in a derogatory fashion. I was just pointing out that in fact these dumb apes solved all your ML problems a very long time ago.

If you have to forget, your memory is not infinite. My computer's HD is infinite, but you have to remove programs that you don't use.

Well technically yes, but I could counter argue that we have 'living memories' that we can not only replay at any time (any of them, without any buffering or delay), but also change and combine with other memories to create new memories. Additionally if someone tells you something the brain can search all your memories in what seems to be a microsecond and pull up the relevant ones (file search on steroids, that can even search every single frame in all your recorded movies).

Much more useful than static data on a hard drive.

Except when you can't remember something and you have to spend seconds, or even minutes trying to remember it.

A hard drive would find it much faster.

There's evidence that our savannah ape ancestors experienced unpredictable climate conditions, so it's not that humans are adapted to savannah life, but rather constantly changing conditions that require an adaptable brain:

http://humanorigins.si.edu/research/climate-research/effects

However, humans have amazing ability to generalize from a very small number of examples, which is still a very challenging computational task.

So throw more data at it then.

What does it feel like to be so confident in an opinion while so many brilliant scientists disagree with you?

Please let's resist taking this thread flameward. The GP contains both a substantive point and provocation, which isn't great. In such cases, the helpful way to respond is to de-escalate, by addressing the substantive point and ignoring the provocation.

I'm not the same guy, but I'd like to answer: Familiar, that's how it feels. I'm confident that I'm right about the other matter, and subjects are related, so I'm not bothered.

I don't think openai was formed to concentrate on neural networks, and I think we can assume the members are well aware of the limitations of neural nets.

I just don't understand the folks that are so confident that strong AI is either not possible, or not achievable within our lifetimes.

If you're in the camp that thinks it's not possible, then you must ascribe some sort of magical or spiritual significance to the human brain.

If you don't think it's possible inside of 100 years, then you're probably just extrapolating on history. The thing about breakthroughs is they never look like they're coming. Until they do.

I ascribe spiritual significance to the mind (not the brain).

If consciousness is more than a mere product of brain's functioning, Strong AI does not have to be beyond the horizon.

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