Indeed, DL-based AI is likely to continuue - rightfully so - for non-safety critical domains (translation, games, ...) It's crazy that few people notice that real world applications with safety issues (where 99.9% accuracy is a joke) - ex: self driving vehicles - require much more that what DL has to offer. The AI community's sloppiness when it comes to safety and robustness engineering will lead to the some serious problems (when it comes to real-world industrial applications). But yeah there will still be jobs for people playing around.
"Especially when your opinion goes against what pretty much everyone else in the field thinks" -> Most Deep learning people are consumers or hardware manufacturers, very few researchers in the field, and most people that have deep knowledge of it would agree with this guy.
I believe he has much more insight than most AI researchers/engineers. What's going on in AI right know looks exactly like what happened in the 1960s, sucking all the resources into something (i.e Deep Learning) without looking at the big picture, without looking at epistemological questions, and not training new grads on solid AI foundations anymore like probability theory and logic. Not asking the right questions in terms of expressivity of models, sensitivity analysis, robustness and manifold learning. The next AI winter is coming.
I cited Lyapunov, more as the ABC of nonlinear controls. Much more can be done in an analytical fashion, the "end-to-end" system here does not "solve" anything. It is a trained steering command regressor, nothing fancy, it's likely to work in this guy's living room, under certain lighting conditions, there is no way of predicting its accuracy, sensibility or anything else. Engineers have been breaking down systems into sub systems for a reason -> tractability of testing and improvement. End-to-end systems like that have close to zero value if you need something reliable.
When falling down actually corresponds to killing a pedestrian, then I'd rather try to understand the complexity of robust walking rather than observing a bunch of humans, mimic their behavior and hope for the best.
Indeed, I was not really talking about the vision pipeline. But once you decouple the problem (use ML for vision, planning for the trajectory, controls for the rest), you'll get much more stability, guarantees and insight into how to improve your problem. These kinds of end-to-end approaches are very hard to evaluate, they have zero educational value, are not parsimonious and tend to reduce people's analytical skills.
Obviously, my message was slightly provocative, deep learning methods and classical controls (which by the way are able to quantify robustness to plant uncertainties and noisy signals) are all very useful but shall be used in combination. End-to-end techniques that bundle perception, planning and control in an opaque net are fun to play with (like in this article), it just very sad to see people believing this produces robust and safety-critical systems and we see too much of such articles on HN.
Obviously, "Lane Following Autopilot using my brain and controls theory" would not make it to the top of HN. Welcome to the new era where Tensorflow replaces Lyapunov and ML spares you the need of understanding hard problems... until you need guarantees and safety... but but it's ok let's add more data.
Completely agree with this viewpoint. Explanatory power and reproducibility are two of the most important aspects of a scientific paper. Most deep learning based papers lack both these properties.
In aeronautics, we were able to increase safety to an insane level by understanding the physics of the environment, formally proving and certifying algorithms, using Robust control theories that allow to formally deal with uncertainty. The power of mathematical modeling together with robust software testing and a limited/controlled use of learning algorithms is - in my opinion - likelier to bring safety to such systems rather than such an opaque use of CNNs. And I'm not even talking about human machine interaction or liability issue that would emerge from such extreme approaches (which node of the net or which training sample will be blamed?). I guess CNNs allow a lot of wannabe engineers to play with real world problems and dream that their 10 lines python code would match semantic models if fed with more training data, but I'm pretty sure Aircraft/Car manufacturers will/should not replace formally certified controls algorithms and redundant architectures built on the top of hundreds years of analytical results with a rack of NVIDIA GPUs.
This is actually completely incorrect. Many cognitive biases occur at low levels of cognition without involving language, take professional pilots and confirmation biases despite of a ton of training data.
You're so right, actually there is nothing new in this idea. Kalman filters (so basically belief propagation based on gaussian observations) were already used by DL Kleinman in 1970 to model aircraft pilots behavior. We are way past this simplistic hypothesis. Actually, quantum dynamics (yeah why not, it's just a model) seem to better describe human decision making than classical stochastic models (Quantum dynamics of human decision-making - http://www.sciencedirect.com/science/article/pii/S0022249606...)
I feel like frameworks like Meteor promote systematic use of websockets, and more and more developers don't even try to implement scalable stateless server code.