I found the first comment to this article quite interesting:<p>&quot;Marcus has a point - even is some of what is said about deep neural networks is incorrect (for instance, they can learn and generalize from very few example, one shot learning).<p>However, he got it wrong with the answer. The key for machines to reach the symbolic abstraction level is the way we train them. All training algorithms, supervised, unsupervised or reinforcement learning with LSTM rely on the assumption that there is an &quot;utility function&quot; imposed by some external entity. Problem is, by doing so, we are taking away their  capacity [of the machines] to make questions and create meaning.<p>The most important algorithm for learning is &quot;meaning maximization&quot; not utility maximization. The hard part is that we cannot define what is meaning - maybe we can&#x27;t, I&#x27;m not sure. That is something I will be glad to discuss.&quot;