Some argue that synthetic data can make AI systems better
&gt;&gt; The solution is to just have more data and better data.<p>Nah, sorry, that is just trying to put out the fire by throwing fuel at it. The
big, big weakness of neural networks right now is their reliance on gigantic
datasets that require gigantic computational resources. Neural nets need those
because they can&#x27;t generalise to unseen data. So people try to get them to see
as much data as possible during training. They still overfit, but if they can
overfit to a diverse enough dataset, then they can be useful in practice, even
if that&#x27;s only to solve narrow, specific instances of a problem (like in the
domino recognition system in the article).<p>To address this weakness what is needed is to find ways to make neural nets less
reliant on data, not to find ways to make more data. Make neural nets capable of
generalising robustly to unseen data, from few training instances. Then you
don&#x27;t need to train in a simulation. Of course that would require a radical
rethink of how deep neural nets are trained (even perhaps whether they remain
&quot;deep&quot;, or whether they are trained using gradient descent, the sources of their
data-hunger). Trying to make more data by simulation is only kicking the can
down the road and the only effect it can possibly have is to push the time at
which the real limitation must be really addressed even further down the line so
that an other generation of researchers has to deal with it while the current
generation can keep getting their papers published and their grants granted.<p>See Vladimir Vapnik&#x27;s challenge to the machine vision community:<p><a href="https:&#x2F;&#x2F;youtu.be&#x2F;bQa7hpUpMzM?t=492" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;bQa7hpUpMzM?t=492</a><p>To summarise: learn to identify MNIST digits from 60 examples of each class,
rather than 6000, while retaining current accuracy. (My words now:) Improve
sample efficiency to improve neural nets. Neural nets have shown a remarkable
ability to work well when large amount of resources are available. Now, do like
everyone else does in computer science and try to make them (sample) efficient.