The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption is not always fulfilled. An alternative approach is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistics, which favors splitting variables with many possible split points. Conditional inference forests avoid this split point selection bias. However, linear rank statistics are utilized in current software for conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. We therefore use maximally selected rank statistics for split point selection in random forests for survival analysis. As in conditional inference forests, p-values for association between split points and survival time are minimized. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split point selection is possible. However, there is a trade-off between unbiased split point selection and runtime. In benchmark studies of prediction performance on simulated and real datasets the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used.

Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. In this paper, a method to obtain deformation factors with local support is presented. The benefits of such models include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. For that, based on a well-grounded theoretical motivation, we formulate a matrix factorisation problem employing sparsity and graph-based regularisation terms. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation ability and sparse shape reconstruction, whereas for human body shapes our method gives more realistic deformations.

We continue our analysis of volume and energy measures that are appropriate for quantifying inductive inference systems. We extend logical depth and conceptual jump size measures in AIT to stochastic problems, and physical measures that involve volume and energy. We introduce a graphical model of computational complexity that we believe to be appropriate for intelligent machines. We show several asymptotic relations between energy, logical depth and volume of computation for inductive inference. In particular, we arrive at a "black-hole equation" of inductive inference, which relates energy, volume, space, and algorithmic information for an optimal inductive inference solution. We introduce energy-bounded algorithmic entropy. We briefly apply our ideas to the physical limits of intelligent computation in our universe.

In October 2011, Apple added the emoji keyboard to iOS as an international keyboard. Since then, digital language has evolved such that nearly half of comments and captions on Instagram contain emoji characters. And earlier this week, Instagram also added support for emoji characters in hashtags, which allows people to tag and search content with their favorite emoji # . In Part 1 of this blog post series, we will take a deep dive into emoji usage on Instagram. By applying machine learning and natural language processing techniques, we'll discover the hidden semantics of emoji.

In a conference on Monday, U.S. Deputy Defense Secretary Bob Work outlined a key component of modern warfare strategy called Third Offset. The military intends to take advantage of cutting-edge R&D to incorporate AI-human teams to overcome an enemy's network. At the 2016 Global Strategy forum on Monday, Mr. Work noted that products with potential military applications are fast-tracked to enter the global market. "R&D is going down in the public sector, but up in the private sector. Most things that have to do with AI [artificial intelligence] and autonomy are happening in the private sector. And so all competitors are going to have access to it, it's going to be a world of fast-followers. You're going to have an instance where you're not going to have a lasting advantage."

Update: readers of the post have also pointed out this critique by Ernest Davis and this response to Davis by Rob Bensinger. Update 2: Both Rob Bensinger and Michael Tetelman rightly pointed out that my intelligence definition was sloppily defined. I've added a clarification that the defintion is'for a given task'. This post is a discussion of Nick Bostrom's book "Superintelligence". The book has had an effect on the thinking of many of the world's thought leaders. In that light, and given this series of blog posts is about the "Future of AI", it seemed important to read the book and discuss his ideas. In an ideal world, this post would certainly have contained more summaries of the books arguments and perhaps a later update will improve on that aspect. For the moment the review focuses on counter-arguments and perceived omissions (the post already got too long with just covering those). Bostrom considers various routes we have to forming intelligent machines and what the possible outcomes might be from developing such technologies. He is a professor of philosophy but has an impressive array of background degrees in areas such as mathematics, logic, philosophy and computational neuroscience. So let's start at the beginning and put the book in context by trying to understand what is meant by the term "superintelligence" In common with many contributions to the debate on artificial intelligence, Bostrom never defines what he means by intelligence. Obviously, this can be problematic. On the other hand, superintelligence is defined as outperforming humans in every intelligent capability that they express.

Who here remembers taking computer programming in school? Whether you learned programming by punching holes in a never ending series of cards, or by writing simple DOS or other computer language commands, the fact remained that computers needed an incredibly precise set of instructions to accomplish a task. The more complicated the task, the more complicated your instructions had to be. Machine learning is inherently different. Rather than telling a computer exactly how to solve a problem, the programmer instead tells it how to go about learning to solve the problem for itself.

Jurgi Camblong plans to work with "liquid biopsies" making the process less invasive and faster This article was first published in the June 2016 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online. Jurgi Camblong is diagnosing cancer using thousands of people's DNA. The 38-year-old Sophia Genetics co-founder detects cancer in the lungs, skin, ovaries and breast, as well as congenital diseases, by sequencing the genomes of patient's tissue samples – then uses machine learning to compare the results and suggest the most effective treatments. "The problem is not producing the content or the data but really analysing to find the important information so you can act on a disease," says Camblong.

French officials have unveiled a humanoid diving robot that they hope will give a big artificial hand to the practice of underwater archaeology. Ocean One, which looks like something out of a scuba-diving sequel to "Transformers," is the work of a team of roboticists, including Oussama Khatib of Stanford University. It is intended to help researchers explore underwater archaeological sites that are too deep to be explored by human divers. It was unveiled by culture officials on Thursday in the French city of Marseille following a trial sifting through the wreckage of "The Moon," a 17th century warship, where it had managed to collect a delicate ceramic pot and bring it back to the surface.

Watch our founder and CEO, Parsa Ghaffari (@parsaghaffari) and Kevin Koidl (@koidl), a Research Fellow at Trinity College Dublin's Department of Computer Science and the ADAPT research centre, talk Computer intelligence at a recent talk they gave at Science Gallery, Dublin. This interactive discussion, takes you from general AI, right through to the modern day applications of narrow AI paying, particular attention to a real life example, the Bigfoot App, which was created as part of the Lifelogging exhibition, currently running at Science Gallery, Dublin.