he did not call it GAN, he called it curiosity, it’s actually famous work, many citations in all the papers on intrinsic motivation and exploration, although I bet many GAN people don’t know this yet

I learned about it through his inaugural tweet on their miraculous year. I knew LSTM, but I did not know that he and Sepp Hochreiter did all those other things 30 years ago.

The blog sums it up in section 5 Artificial Curiosity Through Adversarial Generative Neural Networks (1990)

The first NN is called the controller C. C (probabilistically) generates outputs that may influence an environment. The second NN is called the world model M. It predicts the environmental reactions to C’s outputs. Using gradient descent, M minimises its error, thus becoming a better predictor. But in a zero sum game, C tries to find outputs that maximise the error of M. M’s loss is the gain of C.

That is, C is motivated to invent novel outputs or experiments that yield data that M still finds surprising, until the data becomes familiar and eventually boring. Compare more recent summaries and extensions of this principle, e.g., [AC09].

GANs are an application of Adversarial Curiosity [AC90] where the environment simply returns whether C’s current output is in a given set [AC19].

So I read those referenced papers. AC19 is kinda modern guide to the old report AC90 where the adversarial part first appeared in section: Implementing Dynamic Curiosity and Boredom, and the generative part in section: Explicit Random Actions versus Imported Randomness, which is like GANs versus conditional GANs. AC09 is a survey from 2009 and sums it up: maximise reward for prediction error.

I know that Ian Goodfellow says he is the inventor of GANs, but he must have been a little boy when Jurgen did this in 1990. Also funny that Yann LeCun described GANs as “the coolest idea in machine learning in the last twenty years” although Jurgen had it thirty years ago

No, it is NOT the same as predictability minimisation, that’s yet another adversarial game he invented, in 1991, section 7 of his explosive blog post which contains additional jaw-droppers

Following the Rubik’s cube video and paper, seems OpenAI is doing their usual business: Brute force search/learning with $$$ GPU hours, viral a video and PR, call it AGI “progress”.

I’ve seen better previous works, in sample efficiency and/or intepretability:

DeLaNet, Generalize robots as learnable Lagrangian, able to derive and seperate robot kinematics and external forces, e.g. Friction and Coriolis forces. https://arxiv.org/abs/1907.04489 https://openreview.net/forum?id=BklHpjCqKm

TossingBot, Residual dynamics model learning, able to near-online adapt and generalize to arbitrary-weight objects throwing to targets with perturbations, https://arxiv.org/abs/1903.11239 https://ai.googleblog.com/2019/03/unifying-physics-and-deep-learning-with.html

One may argue they did prior-less learning, but their sample efficiency is big problem (adaptive randomized environment is controlled data augmentation). They are going to the opposite direction of Yoshua Bengio’s composable latents and composable disentanglement approach.

I can’t see any discussion post for AAAI2020 author feedback.

I think this year, the reviews are good.

As announced at the PyTorch Developer Conference earlier this month, there is now (public alpha) support for Cloud TPUs in PyTorch 1.3.

The repo https://github.com/pytorch/xla links to a few Google Cloud blog posts for example tutorials that should be useful to dive in:

Training FairSeq Transformer on Cloud TPU using PyTorch

Training Resnet50 on Cloud TPU with PyTorch

Training PyTorch models on Cloud TPU Pods

Does this mean we can use Google Cloud TPUs for PyTorch 1.3, but not yet for TensorFlow 2.0?

Can anyone see their AAAI Feebacks? On that page I just see Reviewer #1 / #2 / #3 but no text or anything.

Sorry for the stupid question, this is my first submission and I’m really nervous.

EDIT: I’m not the only one. Thanks for the quick response.