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.

Check out Part I of this article for background information, and to discover the first 12 uses of statistical modeling. Here we list another 12 popular uses of statistical, data science, machine learning, optimization, graph theory, mathematical and operations research techniques. Monte-Carlo simulations are used in many contexts: to produce high quality pseudo-random numbers, in complex settings such as multi-layer spatio-temporal hierarchical Bayesian models, to estimate parameters (see picture below), to compute statistics associated with very rare events, or even to generate large amount of data (for instance cross and auto-correlated time series) to test and compare various algorithms, especially for stock trading or in engineering. Customer churn analysis helps you identify and focus on higher value customers, determine what actions typically precede a lost customer or sale, and better understand what factors influence customer retention. Statistical techniques involved include survival analysis (see Part I of this article) as well as Markov chains with four states: brand new customer, returning customer, inactive (lost) customer, and re-acquired customer, along with path analysis (including root cause analysis) to understand how customers move from one state to another, to maximize profit.

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.

The slides go through the implementation details of Google Deepmind's AlphaGo, a computer Go AI that defeated the European champion. The slides are targeted for beginners in the machine learning area.

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.

Patent lawsuits are costly and time-consuming. An ability to forecast a patent litigation and time to litigation allows companies to better allocate budget and time in managing their patent portfolios. We develop predictive models for estimating the likelihood of litigation for patents and the expected time to litigation based on both textual and non-textual features. Our work focuses on improving the state-of-the-art by relying on a different set of features and employing more sophisticated algorithms with more realistic data. The rate of patent litigations is very low, which consequently makes the problem difficult. The initial model for predicting the likelihood is further modified to capture a time-to-litigation perspective.

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl's theory of causation, and uses Pearl's PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.