Open-sourcing DeepMind Lab
deepmind.com
Open-sourcing DeepMind Lab
1–10 of 35 posts
Re: Open-sourcing DeepMind Lab
#2This 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".
Re: Open-sourcing DeepMind Lab
#3Is this meant to be a competitor to the just released OpenAI Universe ? https://news.ycombinator.com/item?id=13103742
Re: Open-sourcing DeepMind Lab
#4This 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".
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Re: Open-sourcing DeepMind Lab
#5Is 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.
Re: Open-sourcing DeepMind Lab
#6This 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".
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.
Re: Open-sourcing DeepMind Lab
#7> 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?
Re: Open-sourcing DeepMind Lab
#8This 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'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.
Re: Open-sourcing DeepMind Lab
#9> 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 tricki…
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.
Re: Open-sourcing DeepMind Lab
#10In 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."