Bayesian data analysis is cool, Markov chain Monte Carlo is the cool technique that makes Bayesian data analysis possible, and wouldn't it be coolness if you could do all of this in the browser? That was what I thought, at least, and I've now made bayes.js: A small JavaScript library that implements an adaptive MCMC sampler and a couple of probability distributions, and that makes it relatively easy to implement simple Bayesian models in JavaScript. Well, you can do that right now by clicking "Start sampling" below! This will run an MCMC sampler in your browser implemented in JavaScript.

Machine learning is rapidly becoming one of the most important trends in the enterprise. A combination of Moore's law, the rise of big data, and the evolution of technology stacks have finally delivered the promise of machine learning technologies for many enterprises. However, machine learning extends beyond a standalone industry trend and has the opportunity to power the next wave of innovation in the enterprise. "From the lead technology trends in the market, machine learning appears to be on a trajectory to power the next wave of innovation in the enterprise" The last decade has seen a renaissance of innovation in enterprise software powered by movements like the cloud, mobile, and big data. With these trends established as mainstream technologies in the enterprise, the market is turning its attention to technologies that can become the "next big thing" in enterprise software.

Given the impressive advancement of machine intelligence in recent years, many people have been speculating on what the future holds when it comes to the power and roles of robots in our society. Some have even called for regulation of machine intelligence before it's too late. My take on this issue is that there is no need to speculate – machine intelligence is already here, with greedy robots already dominating our lives. The problem with talking about artificial intelligence is that it creates an inflated expectation of machines that would be completely human-like – we won't have true artificial intelligence until we can create machines that are indistinguishable from humans. While the goal of mimicking human intelligence is certainly interesting, it is clear that we are very far from achieving it.

Quantum physics can fly in the face of human intuition--even that of a physicist such as Mario Krenn at the University of Vienna. This counterintuitive quality makes it difficult for researchers to design experiments to explore the field. Now, to avoid intuitive pitfalls, Krenn and his colleagues have devised a computer program to automatically design new quantum experiments that they would not have thought of themselves. The way that all known particles behave can be explained with quantum physics. A major feature of this branch of physics is that the world becomes a vague, bizarre place at its very smallest levels. For example, atoms and other basic building blocks of the universe can exist in states of flux known as superpositions, meaning they can seemingly be located in two or more places at the same time, or spin in opposite directions simultaneously; and with the phenomenon of quantum entanglement, two or more objects can get connected such that what happens to one instantaneously affects whatever is linked to it, no matter how far apart they are in the universe.

Over three jam packed days, and over one hundred speakers, Interaction 16 explored interaction design of many flavors. Over the years the conversation has evolved, and as Josh Seidan put it, we are no longer asking'What is interaction design?' The discipline is evolving and growing more confident in its place in the world of AI, data, conversational UI as well as web and app work. The overall conference theme was the future of interaction design, and that came through strongly. Topics like data, self-driving cars, conversational UI, algorithms all got plenty of attention.

Most discussions of "robot rights" play out in a seemingly distant, science-fictional future. While skeptics roll their eyes, advocates argue that technology will advance to the point where robots deserve moral consideration because they are "just like us," sometimes referencing the movie Blade Runner. Blade Runner depicts a world where androids have human-like emotions and develop human-like relationships to the point of being indistinguishable from people. But Do Androids Dream of Electric Sheep, the novel on which the film is based, contains a small, significant difference in storyline. In the book, the main character falls in love with an android that only pretends to requite his feelings.

In every prediction about the future of work, artificial intelligence appears pretty close to the top of technology trends for businesses to prepare for. Google and other technology giants are developing algorithms which can learn from human inputs, meaning that they can accelerate their learning in a particular area at an incredibly rapid rate. However, before diving into a new artificial intelligence strategy for business, it's worth taking a look at what the capabilities of AI actually are right now, because the media presents a confusing picture. The first type of AI is highly achievable, and probably shouldn't be called AI at all – it's just a good algorithm. More successful types of this AI are the'recommendation engines' – characterised by you-watched-this-movie so you-may-like-this-TV-show. These are extremely helpful for customer engagement, and bringing people personalised recommendations to make them come back to your product.

Chip Somodevilla / GettyRobot parrots aren't the only reason to look over your shoulder. Artificial intelligence is going bananas right now. Google made headlines with it huge victory in the ancient game of Go a few weeks ago. And AI is entering into the marketplace at a historic rate, changing industries as complex as Wall Street in the process. If you get the feeling that we're at the start of a tidal wave, you might be right -- take it from Nvidia CEO Jen-Hsun Huang.

Optimization is a big part of machine learning. Almost every machine learning algorithm has an optimization algorithm at it's core. In this post you will discover a simple optimization algorithm that you can use with any machine learning algorithm. It is easy to understand and easy to implement. Gradient Descent For Machine Learning Photo by Grand Canyon National Park, some rights reserved.

Open source software is an important piece of the data science puzzle. According to the most recent KDnuggets data science software poll results, 73% of data scientists used free software in the previous 12 months. While there are many sources of such tools on the internet, Github has become a de facto clearinghouse for all types of open source software, including tools used in the data science community. The importance, and central position, of machine learning to the field of data science does not need to be pointed out. The following is an overview of the top 10 machine learning projects on Github.* The top project is, unsurprisingly, the go-to machine learning library for Pythonistas the world over, from industry to academia.