As much as it was like anything, magic was like a language. And like a language, textbooks and teachers treated it as an orderly system for the purposes of teaching it, but in reality it was complex and chaotic and organic. It obeyed rules only to the extent that it felt like it, and there were almost as many special cases and one-time variations as there were rules.

You can catch a taste of the feeling of what might go wrong in the robot pricing wars that elevate the cost of certain used books on Amazon into millions of dollars.

As I amusedly watched the price rise every day, I learned that Amazon retailers are increasingly using algorithmic pricing (something Amazon itself does on a large scale), with a number of companies offering pricing algorithms/services to retailers. Both profnath and bordeebook were clearly using automatic pricing – employing algorithms that didn’t have a built-in sanity check on the prices they produced. But the two retailers were clearly employing different strategies.

a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states. It is a random process characterized as memoryless: the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of "memorylessness" is called the Markov property. Markov chains have many applications as statistical models of real-world processes.

In this article I digested a number of MapReduce patterns and algorithms to give a systematic view of the different techniques that can be found in the web or scientific articles. Several practical case studies are also provided. All descriptions and code snippets use the standard Hadoop’s MapReduce model with Mappers, Reduces, Combiners, Partitioners, and sorting.

The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning

each variable can take one of several discrete values, or the range of possible values is divided to give a finite number of possibilities. Taking the variables together, a huge number of combinations of values must be considered. This effect is also known as the combinatorial explosion.

I’ve been working on evaluating and comparing algorithms, capable of extractinguseful content from arbitrary html documents. Before continuing I encourage you to pass trough some of my previous posts, just to get a better feel of what we’re dealing with; I’ve written a short overview, compiled a list of resources if you want to dig deeper and made a feature wise comparison of related software and APIs.

we turn to YouTube users to help us identify the real hidden gems by playing a voting game called YouTube Slam. We're putting an equal amount of effort into the game itself -- how do people vote? What makes it fun? How do we know when we have a true hit? We're looking forward to your feedback to help us refine this process: give it a try*. You can also check out singer and voter leaderboards. Toggle “All time” to “Last week” to find emerging talent in fresh videos or all-time favorites.

the most powerful way to gain insight into a system is by moving between levels of abstraction. Many designers do this instinctively. But it's easy to get stuck on the ground, experiencing concrete systems with no higher-level view.

What happens when the ideals of net neutrality meet personalization and the filter bubble? How do you regulate an algorithm – and should you, and can you?

Basically, you will find a list which is mostly who Facebook thinks you are Facebook stalking. And if you expand the entry you will see a field called ‘index’. ‘index’ is the number they give to that edge. The lower the number the earlier they show up on your search results.

First, there’s an affinity score between the viewing user and the item’s creator — if you send your friend a lot of Facebook messages and check their profile often, then you’ll have a higher affinity score for that user than you would, say, an old acquaintance you haven’t spoken to in years.

The purpose of this chapter is to provide the reader with an overview over the vast range of applications which have at their heart a machine learning problem and to bring some degree of order to the zoo of problems.

Immune Algorithms belong to the Artificial Immune Systems field of study concerned with computational methods inspired by the process and mechanisms of the biological immune system.

The book "Clever Algorithms: Nature-Inspired Programming Recipes" by Jason Brownlee PhD describes 45 algorithms from the field of Artificial Intelligence.

Watson is a question answering (QA) computing system built by IBM.[2] IBM describes it as "an application of advanced Natural Language Processing, Information Retrieval, Knowledge Representation and Reasoning, and Machine Learning technologies to the field of open domain question answering"

Humans have just two ears, but can locate sounds in three dimensions – in range (distance), in direction above and below, in front and to the rear, as well as to either side. This is possible because the brain, inner ear and the external ears (pinna) work together to make inferences about location.

if this kind of burst had come in at a time when we were getting hit hardest, I guarantee it would have caused delays in the [central quotation system],” Donovan said. That, in turn, could have become one of those dominoes that always seem to present themselves whenever there is a catastrophic failure of a complex system.

On the quantitative trading forum, Nuclear Phynance, the consensus on the patterns seemed to be that they simply just emerged. They were the result of "a dynamical system that can enter oscillatory/unstable modes of behaviour," as one member put it. If so, what you see here really is just the afterscent of robot traders gliding through the green-on-black darkness of the financial system on their way from one real trade to another.

This fascinates me. Most stock market trading is being done by machines, but the stories we tell ourselves are about humans responding to new information. You can’t interview an algorithm about why it made a certain choice.

he has worked on topics in computer science and robotics including artificial curiosity, theories of surprise, incremental program evolution, universal learning algorithms, optimally self-improving theoretical constructs called Gödel machines, artificial ants, robots that are taught how to tie shoelaces using reinforcement learning, and much more