Sébastien Bratières is a PHD candidate in Engineering at the University of Cambridge, where he researches probabilistic machine learning in the Bayesian methods. His background is in natural language processing and speech recognition. Currently he works for Dawin, a company making speech recognition interfaces.

At the NEXT2016 Google cloud platform user conference in San Francisco held this Wednesday, the internet search tech giant commenced a new machine learning platform for developers. The name of this new service called Google Cloud Machine Learning, and will be initially available in a limited preview edition. As the company chairman Eric Schmidt said that Google believes machine learning is "what's next.". With this new cloud platform, Google will make it easier for developers to apply some of the machine learning tactics used by the company to power features like Smart Reply in Inbox services. "Major Google applications use Cloud Machine Learning, including Photos (image search), the Google app (voice search), Translate and Inbox (Smart Reply)," the company says.

We present a new paradigm for speeding up randomized computations of several frequently used functions in machine learning. In particular, our paradigm can be applied for improving computations of kernels based on random embeddings. Above that, the presented framework covers multivariate randomized functions. As a byproduct, we propose an algorithmic approach that also leads to a significant reduction of space complexity. Our method is based on careful recycling of Gaussian vectors into structured matrices that share properties of fully random matrices. The quality of the proposed structured approach follows from combinatorial properties of the graphs encoding correlations between rows of these structured matrices. Our framework covers as special cases already known structured approaches such as the Fast Johnson-Lindenstrauss Transform, but is much more general since it can be applied also to highly nonlinear embeddings. We provide strong concentration results showing the quality of the presented paradigm.

Abstract--Clustering explores meaningful patterns in the non-labeled data sets. Cluster Ensemble Selection (CES) is a new approach, which can combine individual clustering results for increasing the performance of the final results. Although CES can achieve better final results in comparison with individual clustering algorithms and cluster ensemble methods, its performance can be dramatically affected by its consensus diversity metric and thresholding procedure. There are two problems in CES: 1) most of the diversity metrics is based on heuristic Shannon's entropy and 2) estimating threshold values are really hard in practice. The main goal of this paper is proposing a robust approach for solving the above mentioned problems. Accordingly, this paper develops a novel framework for clustering problems, which is called Weighted Spectral Cluster Ensemble (WSCE), by exploiting some concepts from community detection arena and graph based clustering. Under this framework, a new version of spectral clustering, which is called Two Kernels Spectral Clustering, is used for generating graphs based individual clustering results. Further, by using modularity, which is a famous metric in the community detection, on the transformed graph representation of individual clustering results, our approach provides an effective diversity estimation for individual clustering results. Moreover, this paper introduces a new approach for combining the evaluated individual clustering results without the procedure of thresh-olding. Experimental study on varied data sets demonstrates that the prosed approach achieves superior performance to state-of-the-art methods. Clustering, the art of discovering meaningful patterns in the non-labeled data sets, is one of the main tasks in machine learning.

Decision tree learning is a popular data-modeling technique that has been around for over fifty years in the fields of statistics, artificial intelligence, and machine learning. The approach and its innumerable variants have been 1 successfully involved in many challenges requiring classification and regression tasks, and it is no exaggeration to say that many modern predictive algorithms rely directly or indirectly on tree principles. What has greatly contributed to this success is the simplicity and transparency of trees, together with their ability to explain complex data sets. The monographs by Breiman et al. (1984), Devroye et al. (1996), Rokach and Maimon (2008), and Hastie et al. (2009) will provide the reader with introductions to the general subject area, both from a practical and theoretical perspective. The history of trees goes on today with random forests (Breiman, 2001), which are on the list of the most successful machine learning algorithms currently available to handle large-scale and high-dimensional data sets.

Group discussions are essential for organizing every aspect of modern life, from faculty meetings to senate debates, from grant review panels to papal conclaves. While costly in terms of time and organization effort, group discussions are commonly seen as a way of reaching better decisions compared to solutions that do not require coordination between the individuals (e.g. voting)---through discussion, the sum becomes greater than the parts. However, this assumption is not irrefutable: anecdotal evidence of wasteful discussions abounds, and in our own experiments we find that over 30% of discussions are unproductive. We propose a framework for analyzing conversational dynamics in order to determine whether a given task-oriented discussion is worth having or not. We exploit conversational patterns reflecting the flow of ideas and the balance between the participants, as well as their linguistic choices. We apply this framework to conversations naturally occurring in an online collaborative world exploration game developed and deployed to support this research. Using this setting, we show that linguistic cues and conversational patterns extracted from the first 20 seconds of a team discussion are predictive of whether it will be a wasteful or a productive one.

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and noisy observations. This can be a hard problem in complex processes with large state spaces. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF produces exact belief states under certain assumptions and approximate belief states otherwise, where the approximation error is bounded by the degree of uncertainty in the process. We show empirically, in synthetic processes with varying sizes and degrees of passivity, that PSBF is faster than several alternative methods while achieving competitive accuracy. Furthermore, we demonstrate how passivity occurs naturally in a complex system such as a multi-robot warehouse, and how PSBF can exploit this to accelerate the filtering task.

Apple's new robot, Liam, is designed to disassemble iPhones for recycling purposes. Meet Liam, an Apple robot designed to take apart 1.2 million iPhones a year. Mashable reporter Samantha Murphy Kelly got a first look at the robot at Apple's headquarters. It has 29 arms and it was an Apple secret for three years. "Liam is programmed to carefully disassemble the many pieces of returned iPhones, such as SIM card trays, screws, batteries and cameras, by removing components bit by bit so they'll all be easier to recycle. Traditional tech recycling methods involve a shredder with magnets that makes it hard to separate parts in a pure way (you'll often get scrap materials commingled with other pieces)."

Leading investment crowdfunding platform OurCrowd has registered its 6th successful exit as portfolio company Crosswise has been purchased by Oracle. The transaction was revealed in Israeli media on April 15th revealing that Oracle paid 50 million for the "machine learning based cross-device data" company. The transaction closed on April 14th. OurCrowd, and its registered investors, participated in a Series A funding round that provided 670,392 to the young company. Co-investors on the funding round included Giza Venture Capital, Horizons Ventures and a "high profile angel group."

Narratives are made by the artful omission of facts. Never was this maxim more evident than in a gullible feature story that landed on the front page of the New York Times last fall, about a young woman's last-ditch bid for life extension as she succumbed to the ravages of brain cancer. A sober look at the case would have revealed it to be but the latest botched mortuary procedure conducted by a gang of creepy scam artists. Instead, through the good graces of the Times, this grim tale was spun into an inspirational saga of one person's courageous quest for a second chance at life, aided by medical visionaries on the verge of miraculous technological breakthroughs. Kim Suozzi died at age twenty-three in January 2013. After her first diagnosis, twenty-one months earlier, Suozzi chose to become one of the youngest people ever[*] to undergo an expensive form of ritualistic corpse mutilation called cryonic preservation. In pop culture, cryonics is perhaps best known as the plot device that transports the schlubby pizza delivery guy in Matt Groening's animated series Futurama into the thirty-first century. The decades-old quack procedure, which involves freezing corpse parts for later resuscitation, was for a long time apocryphally associated with such wealthy eccentrics as Walt Disney. It then caused a scandal in 2002 when it was widely reported that the body of baseball great Ted Williams had gone into deep freeze against the wishes of some in his family. In recent years, cryonics has regained an entirely undue aura of respectability as the thought leaders of Silicon Valley have trained their enterprising, disruptive vision on the conquest of disease and death.[**] Suozzi, an agnostic libertarian and aspiring neuroscientist, began taking cryonics seriously after discovering the work of the futurologist Ray Kurzweil through a cognitive science class at Truman State University in Missouri. After surgery and other treatments failed to stop the growth of her brain tumor, Suozzi determined that upon death she--or rather, her head--would be frozen and stored for decades, centuries, or millennia in the hope that one day, diligent, wonder-working doctors would transplant her consciousness into a new, healthy body, or perhaps onto a high-capacity hard drive. As a tech-savvy millennial, Suozzi turned to the chat website Reddit for help in raising the 80,000 she needed to fulfill her last wish. That got her well on her way, with about 7,000 reportedly raised.