do we need something else, or just more? - Though deep learning has had some promising results, there are still some things that it simply doesn’t do well at. There are other algorithms that do as well or better at certain tasks. On the other hand,

Use of graphics cards for neural networks - Your graphics card holds a GPU—graphics processing unit. Unlike your CPU, which does one operation at a time, the GPU does many simple operations on many numbers at once.

Diving into deep learning - Neural networks have had their ups and downs. However, in the last decade or so, they’ve really taken off. There were a couple of breakthroughs that made all the difference. Today we look at one of them,

Evolving neural networks with polyworld - Figuring out exactly what structure of a neural network could capture intelligence is a complicated problem. Why not let evolution do it for you? Today we look at Polyworld,

Unsupervised learning - When you are teaching an AI to solve a problem or do some task, you usually have to provide the right answer. However, sometimes, the AI can learn by itself, without being told what the right answer is.

Better and better - Computers are being used to design computers. The better our computers and their tools get, the better computers and tools they can produce. But it wasn’t always an easy path. Today we look at the VAX9000.

An experiment in fuzzy logic - Computers use Boolean logic. Everything is true false, yes no, zero and one. There are plenty of situations when a simple yes or no won’t cover it. To get a computer to handle those situations, one can use fuzzy logic.

Socrates is not a woman - Evolutionary approaches, genetic algorithms, and neural networks aren’t the only approaches to creating artificial intelligence. Today, we look at one of the early and rather successful approaches—expert systems.

Take heart, yee robots shivering in the cold - In the past, new methods of creating an artificial intelligence have garnered interest and enthusiasm. Then, when the over optimistic forecasts fail, nearly all funding and research grinds to a halt.

The perceptron - In 1957, Frank Rosenblatt came up with the perceptron. The perceptron is a simple neural network that was able to recognize simple shapes. Unfortunately, Rosenblatt got a little over excited,