101–110 of 139 posts
Related but slightly off-topic, there is a great sci-fi story by Ted Chiang (the same author who made the story behind Arrival film) about humans raising AIs in an artificial world. The premise is that if we want AIs to act like humans, we must teach them like we teach humans: http://subterraneanpress.com/magazine/fall_2010/fiction_the_...
Earlier quoted context omitted.
I take the point to be that there aren't "deeper" fundamental principles at play in these models. Tremendous progress has comes from simply tweaking of the numbers of layers, or how the feed forward to each other (skipping layers, etc), or by throwing more computer power or data at the same basic algorithm. Where might we look for deeper principles? One idea is to consider what brains do and how they might be doing i…
As a physicist judging from the outside, I share some your feeling. Are there general laws governing "learning"? Theorems? Are there "deeper" things to learn as humans? The thing is people in the field don't need heavy intuition or math. In some ways that's good (if you just want a result to utilize) and in others, it's bad (if you are a curious person).
If so I think a layman law of ML is already understood.
To draw an analogy, some programmers might use trial-and-error to produce programs by fiddling with a few lines of code, seeing if it pass more unit tests, repeat.
If you believe that a human brain can be represented by machine code, then given infinite time, that trial-and-error programmer can write down the "source code" of the brain.
Then machine learning is just a "turing complete programming language"(i.e., a neural network architecture) with "source code" (in the form of matrix weights) where "passing more unit tests" is done by numerically following a gradient to update the "source code".
Everything else is just finding a better "programming language" that can make this run very fast on our current machines.
...That being said...
Instead of presenting the agent with a 2d plane of pixels, they should be presented with a sphere of pixels, with their POV inside.
Earlier quoted context omitted.
> Google's AlphaGo also learned from screen pixels. Source? That literally seems to make zero sense to me. Go can be represented in a super-simple state. Why make it spend millions of cycles learning to categorize pixels into that state you already have?
I would guess that they trained AlphaGo from many thousands of hours of match footage. Writing a computer vision script to segment / extract the data may cost cycles as you say, but would save many human hours by eliminating the need to re-watch the footage and literally type out state information for each move.
Disclaimers: I cannot see the future. These are just my opinions. I really appreciate the work and money that SamA, Elon, and others have put into the OpenAI project. The Universe work in particular might help encourage young people, many of whom love video games, to study AI. But I feel that contrarians, such as myself, have an ethical commitment to young people to voice our doubts and criticisms, so that they can a…
Experimentation does not always follow theory in science. I would argue that many of the great discoveries of physics in the 20th century followed directly from startling results of experiments conducted at the end of the 19th century (the photoelectric effect for example). I agree that there seems to be an awful lot of experimenting going on and that hardware and large datasets have helped tremendously but there are also many researchers poking and prodding at deep learning theory [1, 2].
So from a glass-half-full perspective we have rapid (sometimes iterative) experimentation coupled with yawning gaps in theory to explain surprising results. In other words, the opposite of previous AI booms and a big reason to be optimistic despite all the hype.
I'd love to see AI, using games, master the art of determining a depth for objects in the scene. If you ask a person, "about how far away is that car?", they often give you an okay answer that is at least in the same magnitude as the actual distance 1 m, 10 m, 100 m, 1000 m. If AI could do that, you could then navigate an environment in the real world better using only a camera or two. So you start with a virtual wor…
That's a great (and hard) problem! More generally, imagine AI that could learn the physics of the world. For example, if the ball is rolling away, the AI should be able to predict that the ball will look smaller on the next frame. Going further, if the ball is about to roll under a shadow, the AI should predict that the ball will become a darker shade of green. (After several years working in a robotics research comp…
These are like unit tests of AI (basic shapes and transforms) and I agree physical reckoning is at the top, one of the big tests that is a capstone and something beautiful to behold in nature (eg. sports). Maybe the a virtual soccer game at the end?
From my lidar experience, I wanted to reach for a model rather than deal with noisy sensor data. I want to generate the output (3d world) with my model, then the NN learns the inverse (eg. the scene graph used to generate the scene).
I enjoy thinking about this stuff, though it really makes my head spiral sometimes when I relate it to my own reality. It's easy to feel like you're losing touch.
Related but slightly off-topic, there is a great sci-fi story by Ted Chiang (the same author who made the story behind Arrival film) about humans raising AIs in an artificial world. The premise is that if we want AIs to act like humans, we must teach them like we teach humans: http://subterraneanpress.com/magazine/fall_2010/fiction_the_...
I 100% agree we need to teach them like humans, so they at least can build a model of how humans interact and participate with one another. At the least this will teach them about us, more than it will teach them about anything else. And if we want to participate and collaborate with that future of AI we need to have these models.
I noticed the OpenAI team wrote their own VNC driver in Go for performance reasons[0]. I would love to hear more about how they were able to achieve increased performance over other VNC drivers. [0] https://github.com/openai/go-vncdriver
We'd started by adapting an existing Python driver in Twisted, implementing additional encodings and offloading to threads for calls into C libraries like zlib. We got this working reasonably on small environments like Atari, but for environments which generated many update rectangles, we started to be bitten by the GIL. I still believe that one could make Python work, but it'd take quite a lot of effort.
libvncserver is a fast C driver, but it's GPL, and doesn't have any particular support for parallelization. We wanted Universe to be usable by everyone, from hobbyists to companies, so GPL was a no-go. (We actually talked to the libvncserver maintainers, who said that they would be interested in dropping GPL restriction, but there have been far too many contributors over its long history to figure out how to do so.)
Our Go driver, based on https://github.com/mitchellh/go-vnc, has scaled quite well. It takes advantage of Go's lightweight thread model: each connection runs in its own goroutine, which makes it easy to run hundreds of connections in parallel without needing hundreds of threads.
>You can keep your own VNC connection open, and watch the agent play, or even use the keyboard and mouse alongside the agent in a human/agent co-op mode.
Earlier quoted context omitted.
> Google's AlphaGo also learned from screen pixels. Source? That literally seems to make zero sense to me. Go can be represented in a super-simple state. Why make it spend millions of cycles learning to categorize pixels into that state you already have?
I would guess that they trained AlphaGo from many thousands of hours of match footage. Writing a computer vision script to segment / extract the data may cost cycles as you say, but would save many human hours by eliminating the need to re-watch the footage and literally type out state information for each move.