I am attending the Cognitive Neuromorphic Engineering Workshop which is organized by the University of Zürich and is being held in Capo Caccia, Sardinia.

The place is fantastic, and the talks are excellent.

One of the first things I noticed, when I started reading about Bio-Inspired AI and neuroscience, is the notion that we cannot say that we "really" understand something unless we find a suitable "traditional" mathematical model that approximates it in a way or another.

While reading about Neuroscience, Biology, Bio-inspired Artificial Intelligence and Evolutionary Robotics, I was struck by the fact that it seems like in each of these fields, mathematics is stretched to the point that it is no longer useful to model system complexity. It instead gets in the way of simple and elegant explanations about emergence and models that might be able to link different levels of complexity within a system.

I finally got an answer, which of course is derived from a model (inspired by Engineering).

But at least here is an answer, which I could not get before, and I got it reading the Neural Engineering book.

Here is a little illusion game that you can play online. It illustrates how vision works.

I am reading a book called "Neural Engineering". It is a framework or theory of how the brain can be modeled in terms of Engineering, with theories derived mostly from Electrical Engineering.

... Bio inspired Artificial Intelligence and Bayesian Networks.

In the past few months I met researchers from Rome's CNR, Stanford and EPFL (Lausanne). They are great researchers, all involved in this Bio-Inspired quest.

I like what I am reading, I like brainstorming about it and the idea of applying these techniques to the Net/Web is stimulating ...

Completely agree with this: "This means that AdWords ads shown in search results drive Google’s earnings, but Google searches access less than 0.02% of the world’s information. Imagine what the other 99.98% is worth.

I studied Engineering and graduated with honors, but I must admit that I never liked mathematical analysis, and was never too good at it. I found it as being too abstract and hard to visualize; on the other hand, I loved computer science, it was my second nature, I could naturally visualize it without efforts. My thesis work, when I graduated, was about a simulation of an automatic storage system at a car factory, which I simulated using what are now generically called cellular automata.

I personally prefer a bottom up approach to Artificial Intelligence.

I have been having many conversations about this in the past. A few days ago I decided to take sometime off "normal study" and take sometime to browse the Internet querying Google for different keywords.

I stumbled upon this old article which basically summarizes some of the stuff I have been thinking about lately. I do not agree with some of it, but very interesting read nevertheless.

We now give it for granted that neurons fire, and basically work (mostly) digitally. It is amazing, though, that they work digitally, given the fact that the incoming patterns they are supposed to work on are not digital.

There is certainly a great incentive in being digital :-)