Python has actually had concurrency since about 2019: https://docs.python.org/3/library/asyncio.html. Having used it a few times, it seems fairly sane, but tbf my experience with concurrency in other languages is fairly limited.
Iraq peak US troops: range from 168,000 - 192,000 (2007)
Vietnam peak US troops: 543,000 (1969)
Korea peak US troops: 320,000, unclear what year
edit: obviously, Russia's involvement in Ukraine over the last few days would be by far the biggest operation in Europe since WW2. Just not globally (by a long shot).
The sim2real approach can work pretty well as long as you are very in tune with where your simulator falls short relative to the real world and take steps to circumvent those shortcomings.
We were able to train the robot to climb stairs completely by feel/proprioception without any sort of vision. We trained it in simulation, and then transferred it to the real world without issue.
Actually, I think it is conventional neural networks which can only approximate finite state machines. RNNs are (in theory, not so much in practice) Turing complete.
Hey everyone, I am one of the authors of the paper described in this article. We used reinforcement learning and recurrent neural networks to learn a controller which can ascend and descend stairs without any vision-based perception, meaning that it must rely entirely on proprioception to walk. This is the first time (to our knowledge) that a human-sized bipedal robot has been able to climb real-world stairs blind to the world, and we're pretty excited about the results of this research.
I was always very unimpressed with VR until I borrowed a headset to play Half Life Alyx. That game convinced me VR is the inevitable future of gaming. The rest of the industry may not be there yet, but it is an obviously superior experience.
This is mostly true for supervised and unsupervised learning models, but for reinforcement learning the LSTM is king because of the convenient fact that it can be evaluated one time step at a time, instead of just outputting a sequence like a transformer. For things like robotic control, etc, attention-based models are pretty nonsensical.
Wow. I have been looking for a good resource on implementing self-attention/transformers on my own for the last week - can't wait to read this through.
I've been writing a C-based deep learning library for the past year or so, which I've recently been trying to clean up to be a bit more presentable. It was pretty heavily inspired by Darknet, although it admittedly has far fewer features: https://github.com/siekmanj/sieknet