With the Singularity Summit coming to San Francisco tomorrow, a lot of people are wondering about how close we are to successfully implementing Artificial Intelligence.

Probably you've seen a graph like the above before. Computers are getting faster. Does this mean we're getting closer to true Artificial Intelligence? Not necessarily.

As I'll show in the following paragraphs, both Artificial Intelligence and the less ambitious Machine Learning (ML) aren't computationally but data bounded. This has profound implications for viable avenues for research and development.

Tonight I did a manual graph analysis of my Facebook network: Turns out among nodes with a degree of at least 13, there exist three clearly distinct subgraphs. They probably are most appropriately named The Institute, YCombinator and Future Camp.

Centrality ("who is connected to almost everyone else in the subgraph") seems to follow different rules in all three subgraphs. E.g. in the YC graph very successful founders seem to be more connected than less successful founders, and those in turn are generally more connected than employees.

This was a post I wrote on my 29th birthday

I'd like to thank all of you that have at times walked with me as a friend, besides me as a partner, behind me in support, or in front of me as someone leaving footsteps that I one day hope to fill. Whether now, in the past, or in the future!

Technology speeds everything up - more so in some areas than in others. While it's easy to see that erecting a 15-story building in two days is faster than it's used to be done, it's harder to get a grip on the giant strides we're making in software construction. Nonetheless, software is probably the discipline with the most reliably accelerating productivity.

Just a few years ago software wasn't moving all that fast. In those days most programming time was spent on squeezing the maximum performance from CPU and memory and programmers like John Carmack achieved lasting fame for their knack for teasing just a little bit more performance out of machines. And a few years before that, as prog21 points out, even writing a working basic spellchecker was a major achievement. You simply didn't have the memory to store a list of words. Never mind building a really good spellchecker, you'd be busy with optimizing the base case.

I've recently started using Node.JS and Erlang. Haven't been this unfaithful to Python since 2001, and of course there are consequences:

• IOStream: JS implementation of Tornado's IOStream class for buffered I/O.
• Deque: JS implementation of a Double-ended Queue.

Both are MIT licensed. Hope this is of use!

The long answer comes with history: I've spent years organizing and thinking about conferences and events and what makes them amazing, back in Europe. When you're regularly taking a few hundred thousand Euros in hand to make a conference, every detail starts to matter to you. How many tracks does the conference have, which speakers do you invite, where do you put the buffet, which rooms do you use for the talks. When I first started DeepSec (disclosure: I'm not longer involved), I spent two months looking at every single hotel in Vienna to find the perfect location.