Open-sourcing DeepMind Lab(deepmind.com)
deepmind.com
Open-sourcing DeepMind Lab
https://deepmind.com/blog/open-sourcing-deepmind-lab/
34 comments
I find it interesting that they specify "3D vision from a first person viewpoint". Can somebody explain to me the significance of first person viewpoint vs 3rd person (or other)?
I assume it is because a first person perspective would have more applications in the field of robotics - e.g. twin forward-facing cameras on something like Boston Robotics robotic dog.
This sounds ambitious.
I wonder if they can also address the following problem. Currently, deep learning toolkits need thousands of training images to classify images of, e.g., dogs and cats. A human, in contrast, could learn the difference between a dog and a cat by looking just at a single example (or perhaps a few). So right now, deep learning is too much "simple" pattern matching, and too little real "AI".
I wonder if they can also address the following problem. Currently, deep learning toolkits need thousands of training images to classify images of, e.g., dogs and cats. A human, in contrast, could learn the difference between a dog and a cat by looking just at a single example (or perhaps a few). So right now, deep learning is too much "simple" pattern matching, and too little real "AI".
I'm not convinced that a person who's never seen animals before could tell the difference between all future dogs and cats from a single training example. Humans draw upon a lifetime of learning and experience to achieve this 'one shot learning' capability.
If you take a pre-trained convnet (which, by analogy is like a person who has had 'life experience' of looking at objects), and extract activations for unseen object categories, in many cases you CAN one-shot-learn these new object categories. Try feeding them into a SVM or use L2 distance between test images and the one-shot exemplar image.
On top of this, there's a lot of work on memory-augmented nets and meta-learning for learning new categories on the fly.
If you take a pre-trained convnet (which, by analogy is like a person who has had 'life experience' of looking at objects), and extract activations for unseen object categories, in many cases you CAN one-shot-learn these new object categories. Try feeding them into a SVM or use L2 distance between test images and the one-shot exemplar image.
On top of this, there's a lot of work on memory-augmented nets and meta-learning for learning new categories on the fly.
I'd argue that it's less beneficial to learn new categories as it is to simply recognize when categories differ between samples.
For example, with bears -- I personally know of black bears and polar bears. I can be a little more detailed with fish but with dogs there are dozens of "different" [easily recognizable] types within the same category of "dog".
For example, with bears -- I personally know of black bears and polar bears. I can be a little more detailed with fish but with dogs there are dozens of "different" [easily recognizable] types within the same category of "dog".
Anyone who had raised a child would tell you that, a human, requires years of training to learn the difference between a dog and a cat, by looking countless examples.
Of course, the disparity between deep neural nets and human brains remains unknown. A human learns the difference between a cat and a dog, while at the same time, learns so many different things, yet a neural net only learns the difference between a cat and a dog. We don't know how much we don't know.
Of course, the disparity between deep neural nets and human brains remains unknown. A human learns the difference between a cat and a dog, while at the same time, learns so many different things, yet a neural net only learns the difference between a cat and a dog. We don't know how much we don't know.
One shot learning is such an active area of research there's a long Wikipedia page[1] about it.
I think the SOTA is probably [2], which came out of DeepMind. There's still a way to go before it matches ResNet performance on ImageNet (or even human performance on any real task) though.
[1] https://en.wikipedia.org/wiki/One-shot_learning
[2] https://arxiv.org/abs/1605.06065
I think the SOTA is probably [2], which came out of DeepMind. There's still a way to go before it matches ResNet performance on ImageNet (or even human performance on any real task) though.
[1] https://en.wikipedia.org/wiki/One-shot_learning
[2] https://arxiv.org/abs/1605.06065
Keep in mind that it literally takes human beings years before they can perform basic intelligence tasks. I do agree that AI right now is too focused on pattern matching from large data sets, but Deepmind has definitely been exploring other ways to think about memory or attention in artificial neural networks, and they tend to be more biologically inspired.
[deleted]
Is this meant to be a competitor to the just released OpenAI Universe ? https://news.ycombinator.com/item?id=13103742
No, not directly. DeepMind Lab is a 3D environment that can be highly customized -- looks like its built on an old Quake engine. Their pitch seems to include a lot of real world task simulation. OpenAI Universe is made to sandbox and emulate existing PC software being used with mouse and keybaord input.
At least, that's my non-expert understanding.
At least, that's my non-expert understanding.
So if I want to make an AI for a game, I should use OpenAI?
> There are two parts to this research program: (1) designing ever-more intelligent agents capable of more-and-more sophisticated cognitive skills, and (2) building increasingly complex environments where agents can be trained and evaluated.
I find this puzzling. If your goal were to create an human-like AI (which I always assume is at least partly implicit in these ambitious projects), it seems to me that the trickiest part is to determine what rewards make an optimization algorithm "human". How rewards weight and interact amongst themselves is where the mistery is, isn't it? So why isn't this part of the research program? Any deepminder wants to weight in on this?
I find this puzzling. If your goal were to create an human-like AI (which I always assume is at least partly implicit in these ambitious projects), it seems to me that the trickiest part is to determine what rewards make an optimization algorithm "human". How rewards weight and interact amongst themselves is where the mistery is, isn't it? So why isn't this part of the research program? Any deepminder wants to weight in on this?
There are plenty of domains where the objective is quite well defined. Video games are excellent examples. An agent that achieved superhuman performance on, say, Starcraft, would have a very impressive suite of capabilities involving some significant advances from the current state of the art. As such these tasks are great drivers of research.
But you're absolutely right that humans don't optimize any 'simple' reward function, and achieving human-like behavior in real-world domains will likely require learning reward functions. There are a few people starting to think about this, e.g. this paper https://arxiv.org/abs/1606.03137 (disclaimer: work from my research group though I'm not involved) proposing a framework by which a robot can learn a human's (implicit, complex) values through interaction. This is also related to concerns over AI safety, since naive reward-optimizing agents are (like drug addicts) willing to do arbitrarily bad things in service of maximizing their prescribed "reward", while agents that maintain uncertainty over reward functions are willing to ask for guidance when confused or unsure. However this line of research is much more preliminary and academic -- there are probably people at DeepMind thinking along these lines, but certainly their focus is more on directions that will produce new breakthroughs and practical capabilities within forseeable timeframes.
But you're absolutely right that humans don't optimize any 'simple' reward function, and achieving human-like behavior in real-world domains will likely require learning reward functions. There are a few people starting to think about this, e.g. this paper https://arxiv.org/abs/1606.03137 (disclaimer: work from my research group though I'm not involved) proposing a framework by which a robot can learn a human's (implicit, complex) values through interaction. This is also related to concerns over AI safety, since naive reward-optimizing agents are (like drug addicts) willing to do arbitrarily bad things in service of maximizing their prescribed "reward", while agents that maintain uncertainty over reward functions are willing to ask for guidance when confused or unsure. However this line of research is much more preliminary and academic -- there are probably people at DeepMind thinking along these lines, but certainly their focus is more on directions that will produce new breakthroughs and practical capabilities within forseeable timeframes.
which I always assume is at least partly implicit in these ambitious projects
No serious researcher is even contemplating that problem yet, except as a thought experiment. These projects are more about working out how to work out what questions to ask to direct research which might lead to more generalised AI.
No serious researcher is even contemplating that problem yet, except as a thought experiment. These projects are more about working out how to work out what questions to ask to direct research which might lead to more generalised AI.
Alright thanks.
Do you want to recommend reading regarding the state of the art? Technical papers Ok.
Do you want to recommend reading regarding the state of the art? Technical papers Ok.
SOTA on what specifically? DeepMind is probably the world leader on reinforcement learning, so https://deepmind.com/research/publications/ isn't a bad place to start.
In case it's not obvious, DeepMind is Google:
>> "DeepMind was founded in London in 2010 and backed by some of the most successful technology entrepreneurs in the world. Having been acquired by Google in 2014, we are now part of the Alphabet group. We continue to be based in our hometown of London, alongside some of the country's leading academic, cultural and scientific organisations in the King's Cross Knowledge Quarter."
>> "DeepMind was founded in London in 2010 and backed by some of the most successful technology entrepreneurs in the world. Having been acquired by Google in 2014, we are now part of the Alphabet group. We continue to be based in our hometown of London, alongside some of the country's leading academic, cultural and scientific organisations in the King's Cross Knowledge Quarter."
rahrahrah(1)
Where is the source code?
> The assets will be hosted on GitHub alongside all the code, maps and level scripts.
Looks like OpenAI set some standards. E.g. OpenAI Gym that encourage others like DeepMind to open-soure more training sets.
Also gaming seems to be driving a lot of innovation. In 1990s games drove CPU/GPU advances, while now they seems to be perfect training for future AI deep-learning algorithms.
Also gaming seems to be driving a lot of innovation. In 1990s games drove CPU/GPU advances, while now they seems to be perfect training for future AI deep-learning algorithms.
DeepMind Lab is built on top of id software’s Quake III Arena (id software, 1999) engine using the ioquake3 (Nussel et al., 2016) version of the codebase, which is actively maintained by enthusiasts in the open source community. DeepMind Lab also includes tools from q3map2 (GtkRadiant, 2016) and bspc (bspc, 2016) for level generation. The bot scripts are based on code from the OpenArena (OpenArena, 2016) project.