One of the first demonstrations on how powerful Deep Learning can be, used 1,000 pictures per category and needed quite a lot steps to build a model that worked. Without a doubt, this was a seminal work but it also demonstrated that DL only vaguely resembles how humans learn. For instance, if a child would have to look at 1,000 cups to get the concept of it, the lifespan of humans would be too short to survive without strong supervision. Another example are recent breakthroughs in reinforcement learning, but which also come at a certain cost, like a couple of thousand bucks a day for energy. In a lot of cases, data, and even labels, might be no problem, but it often takes days or even weeks to turn them into a useful model. This is also in stark contrast to the brain that uses very little energy and is able to generalize with just a few, or even one example. Again, this is nothing new but begs the question if we spend too little time on fundamental research and try instead too often to beat state-of-art results to get a place in the hall of fame? The viewpoint is probably too easy, since there are examples that there is research that focuses on real-world usage, like WaveNet, but it also shows that you need lots of manpower to do it. Thus, most companies have to rely on global players or public research if they want to build cutting-edge A.I. products. The introduction of GPU clouds definitely helped, because it allows everyone to train larger models without buying the computational machinery, but using the cloud is also not for free and it’s getting worse if the training has to be fast, since you need to buy lots of GPU time then. The topic, in a broader context, has also been recently debated in. In the spirit of the debate, the question is how can we avoid to run against a wall about 1,000 times before we realize it’s not a good idea?
 “Will the Future of AI Learning Depend More on Nature or Nurture?”