A DARPA Perspective on Artificial Intelligence [pdf]
darpa.mil
darpa.mil
As I've said for years, the big lack in AI is in the "common sense" and unstructured manipulation area. Nobody can build something with squirrel levels of manipulation and agility, even in simulation. Robot manipulation in unstructured situations is still very poor. The people trying to simulate C. elegans at the neuron level can't get that to work, despite a full wiring diagram and years of effort.
Something very low level is not understood. There's a Nobel Prize waiting for whomever figures that out.
Isn’t that what alpha go or the StarCraft league are? Organic strategies in well-defined contexts (action options of the squirrel at Tn)? “Squirrel” is a nice reference frame.
That was the original intent, but it appears the dropped it before public demos.
But if you want it control a robot that's going to bring out a pot of tea while you play, you'll be wiping the floor.
For all of our mastery of information processing, our ability to fabricate and motivate fine differentiated structures is really quite incredibly poor relative to biology. The little beetle is basically alien technology relative to the toy. But add in the fact that it can feed itself, repair itself and reproduce itself with a system that can scale 3-5 orders of magnitude up and down based on need is pretty mindblowing.
The mRNA vaccines might be one of the first times where we've leveraged biology to directly fabricate a discrete part with desired properties. Certainly in raw tonnage of output I can't think of anything that comes close. I feel like this is only going to advance with time, to the point where we may be growing robots instead of milling and printing them.
You have a room sized super computer that trains for zillions of hours and can’t even pick out pedestrians with especially good precision.
Then you have a bee with a minuscule brain that can do complex pattern recognition to find flowers, complex manipulation to get nectar and pollen out of them, object avoidance, swarming behaviour, and fancy communicative dances (both performing and interpreting), plus all the other stuff bees do to get by in the world.
Something is up there for sure.
Still, my position is that NO progress has ever been made in the area of AI, and no progress will be made any time soon.
I'll take it a step further (in case I don't get enough downvotes for what I've written so far). I maintain that you CAN NOT build intelligence. You can only TAP INTO it. So the very direction in which all of our AI efforts are headed is a dead-end.
I wouldn't go exactly this far, but I would say that whatever process might exist to create artificial intelligence, it might be closer to gardening than to engineering.
My vague feeling is that there might be some sort of (non-supernatural) "mysterious" component to intelligence that we won't be able to engineer and that might just emerge under the right circumstances.
In that case we would just have to "grow" AI, without being completely sure that our effort will work.
While not squirrel level, this impressed the heck out of me: https://ashish-kmr.github.io/rma-legged-robots/
A reasonable first step would be autonomous small cheap drones, but I can't say what their missions would be. AlphaStar and Openai Five are mentioned elsewhere here, and these demonstrate that the problem isn't unapproachable.
There probably isn't enough confidence yet to arm autonomous drones, or there isn't a meaningful tactical purpose.
Tay had a 'repeat after me' feature (an ancient feature of any IRC chatbot to just echo or print a given string). That's all this is. A troll account issued the command 'Repeat after me' and then tweeted '@TayandYou HITLER DID NOTHING WRONG!', and the Tay daemon dutifully repeated it back to the troll in that thread.
Anyway, long story short, the Tay incident is either entirely or mostly bogus in the way people want to use it (as an AI safety parable). The real story, of an `echo` gone wrong, is vastly less interesting, and is about as important as typing '8008' into your calculator and showing it to your teacher.
If I go through 'tay ai' (as I have before), I see much the same thing: I see a lot of clearly edited squirrely images, which remove all of the context, and when they appear to include context, the UI looks wrong (some of these are clearly using the 'Replies' tab on the Tay page, instead of being on the actual convo thread; why? to remove the context with the repeat-after-mes or which would show Tay is just spitting out lots of canned generic responses, of course), and like some tweets are being edited out and the remainder spliced together. Do you really think that a 2016-era mass market chatbot (sub-char-RNN in power, often relying on heavily engineered template/script databases) really knows how to do Trump memes complete with clap emoji? What dataset has those? Of course not. It's just a repeat-after-me where the initial tweets were edited out. (What? Someone edit a screenshot, especially where no one can check the original, to score political points? You really think someone would do that - just go on the Internet and tell lies?)
If we look at ones which seem semi-legitimate, like the genocide one, then all we are seeing is typical chatbot evasiveness and generic Eliza-level responses, cherrypicked out of the 100k or so tweets Tay made before they were all deleted. "Do you support $X?" "I do indeed". Wow! Stunning evidence that 'Microsoft turned an advanced AI loose on the Internet and trolls taught it to be evil', as the narrative goes.
So, like I said. All of the long, coherent, strikingly detailed and most offensive ones appear to be either blatantly or probably repeat-after-mes, while the plausible ones are empty chatbot spacefilling cherrypicked out of countless thousands of tweets and made to look more offensive by implying they are the same as the troll-written repeat-after-mes. Neither one supports the broad narrative Tay is used for by OP. And we can note that, beyond the points I've raised here like "how on earth did it 'learn' so quickly" and "there is no evidence it even had 'learning' turned on", there is generally a striking lack of replications of Tay. Which is exactly what you would expect if all the really offensive ones were due to a single ill-considered repeat-after-me feature left enabled, and the rest are just chatbot phaticisms.
> then all we are seeing is typical chatbot evasiveness and generic Eliza-level responses
Having found an archive of replies (https://imgur.com/a/iBnbW) "plausible ones are empty chatbot spacefilling cherrypicked out of countless thousands of tweet" seems apt.
In my mind this was the "lesson to be learned" is a HCI one rather than a technical one.
Also:
> First wave: Handcrafted knowledge
> Second wave: Statistical learning
> Third wave: Contextual adaptation
I understood clearly enough the first two, but the slides become increasingly ambiguous and fuzzy towards the end; and it seems to me they are mixing up a bunch of not self-evidently related desiderata.
It is not immediate, for instance, that small "generative" models that are easy to interpret necessarily lead to better "abstraction" (whatever that means). And whatever this all has to do with "contextual adaptation" is to me anyone's guess.
Highly alarming (but sadly, from experience, unsurprising) to see such fuzzy position document from such an important funding agency for AI.
As a researcher, I like their non-hype way of defining AI as "programmed ability", which is accurate and realistic -- also puts AI further apart from real intelligence, which means unanticipated activities.
I would like to know more what they see as "abstracting", from their perspective.
We haven't got much further in our scientific understanding of intelligence - if you bought a psychology text book today and ten years ago there wouldn't be much of a breakthrough change detectable in terms of modeling cognition. And as impressive as some computer science AI models perform certain tasks, I haven't been taken by surprise by them asking me a question out of the blue, which is one of my personal litmus tests for intelligence.
This is the first point of advice in the Heilmeier Catechism
So no, its a bit more than a house plant
I've worked at companies whose vehicles you've probably seen and have never heard anything like what you're suggesting is "the truth". Can you provide more details on how current solutions don't remotely approach reasonable safety levels?
Practitioners work with much more tightly defined objectives and methods for improving their systems than "conceptual and causal understanding of anything". From the perspective of folks in the industry we've been making remarkable progress, well beyond what we could have expected. We're saturating major benchmarks, sometimes in one to two years. Back in the day, during the "AI Winter", it was often 10 years before a new breakthrough happened.
https://machinelearning.technicacuriosa.com/2017/03/19/a-dar...
"Systems construct contextual explanatory models for classes of real world phenomena" is the next goal. That is, understanding + being able to describe the reasoning for the understanding.
No technical depth, really, but lots of words to google if you want to learn more.
- error rate too high
- you can trick a classifier with noise
- it's racist sometimes
Actual dangers of AI:
- stop problem
- infeasibility of sandboxing
- difficulty of aligning black boxes with human values