Showing posts from April, 2016

Flu + teaching AI to play games

Flu and strange Ideas I have a cold/flu thing at the moment and feel rotten, due to interaction with my general health when I get even a mild cold or flu I get pain everywhere and due to the levels of pain killer I take normally, I just have to grin and bare it. The way I tend to cope is to keep my mind occupied to try and not think about it. Strangely this is often a creative time in terms of random thoughts, I guess the body pushes more natural drugs into me to try and counteract the pain leading to me being a bit 'drugged'. Thinking about teaching (not training) AIs Last night I was deep in thought about Deep AIs, as you may have noticed from my recent blog posts is something I'm really enjoying and TBH can see myself working in the field. Before the fame of the recent AlphaGo wins, Deepmind were tackling other simpler games from the Atari 2600 machine. The paper "Human-level control through deep reinforcement learning" successfully learnt to play a number

The greatest lie ever told: Premature Optimisation is the root of all evil!

Its a lie because it implies there is a 'premature' aspect to writing code, that you shouldn't worry about performance or power at some phase of the research/project. Its simple not true, there are time when its not the greatest priority but thinking about how it affects these areas is never wasted. At some fundamental level, software is something that takes power (electricity) into math. Anytime you don't do it optimally you waste power.  The only time that doesn't matter is when you have a small problem and lots of power, in practise few interesting problems are lucky to get away with that. If you work in machine learning or big data, the likely limit to what you can do is related to how many processors (wether they are CPU, GPU or FPGA) you can throw at the problem. Assuming you can't scale infinitely, then the results you can get out of a finite set of HW will largely be determined by how performant your code is. When you've designed your latest

Teaching machines to render

I've been studying AI tech a fair bit recently for a variety of reasons. There are lots of areas I want to explore using AI as solvers/approximations but as someone whom is generally employed to do graphics stuff, there always an interest in the application of AI technology to rendering. Currently except for a few papers on copying artistic styles to photos, its not yet a major discipline. The real big thing to take aware from AI like deep neural nets, is that they are approximate function solvers that are taught via showing them the data rather than be explicitly programmed. Once trained they evaluate the inputs, through there learnt 'function' and give you a result. With enough nodes and layers in the network and a lot of training, they can give a solution to any equation you have existing data for. In rendering, the classic Kajiya equation is the solution real and offline renderers attempt to solve. The reason why rendering takes some much compute power, is t