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To put it a little bit bluntly, if you need 300 million pictures of cats in order to be able to say it's a cat, a horse, or a cow, I do not deem that very intelligent. So, whenever I read deep learning or artificial intelligence, I get a little bit skeptical. To me, it would be much more about the small data. The brain is definitely a small-data machine and not a big-data machine. Dan Patterson: I'm so glad you said big data because it sounds as though that runs counter to the notion that well, with more data we can create better AI and we can have better systems.

Artificial intelligence is stuck today because companies are likening the human brain to a computer, according to Swiss neuroscientist and co-founder of Starmind Pascal Kaufmann. However, the brain does not process information, retrieve knowledge, or store memories like a computer does. When companies claim to be using AI to power "the next generation" of their products, what they are unknowingly referring to is the intersection of big data, analytics, and automation, Kaufmann told ZDNet. "Today, so called AI is often just the human intelligence of programmers condensed into source code," said Kaufmann, who worked on cyborgs previously at DARPA. "We shouldn't need 300 million pictures of cats to be able to say whether something is a cat, cow, or dog. Intelligence is not related to big data; it's related to small data. If you can look at a cat, extract the principles of a cat like children do, then forever understand what a cat is, that's intelligence."

The basic of neuronal networks are neurons. Neurons consists of a cell body, dendrites and axons. Neurons ceaselessly scan their environment for chemical gradients and for other neurons to connect. If a neuron cannot connect to other neurons it dies (no worries, we produce daily around 80'000 new neurons, no matter how old we are). The scanning is nothing else as the growing of the axons.