WIRE)--International Data Corporation (IDC) has published a 2016 IDC Innovators report recognizing pioneering players in the machine learning-based text analytics market. IDC Innovators are companies with under 50M in revenue that offer an inventive technology and/or groundbreaking new business model. Kira Systems, Loop AI Labs, NetBase, and Taste Analytics were all named as IDC Innovators in the machine learning-based text analytics market for 2016. "Organizations are continually looking to improve their handling of data, especially unstructured data, given the explosion of information that is available via the Internet today," said David Schubmehl, Research Director, IDC's Content Analytics, Discovery and Cognitive Systems research. "Understanding and utilizing this human-generated data is a significant challenge for most organizations and the use of machine learning based text analytics is rapidly becoming the best approach to dealing with this type of data."

I did a webcast earlier today about Bayesian statistics. Some time in the next week, the video should be available from O'Reilly. In the meantime, you can see my slides here: And here's a transcript of what I said: Thanks everyone for joining me for this webcast. At the bottom of this slide you can see the URL for my slides, so you can follow along at home. I'm Allen Downey and I'm a professor at Olin College, which is a new engineering college right outside Boston. Our mission is to fix engineering education, and one of the ways I'm working on that is by teaching Bayesian statistics. Bayesian methods have been the victim of a 200 year smear campaign. If you are interested in the history and the people involved, I recommend this book, The Theory That Would Not Die.

The idea of having sex with a robot might seem more like something out of a science fiction film, but one in five of us are now open to the idea, according to new research. A recent survey found 21 per cent of British people would have sex with a droid, and one in three would go on a date. It comes as a leading expert on future technology claims human-on-robot sex will be more common than human-on-human sex by 2050. The survey was done VoucherCodesPro who asked 2,816 sexually active Brits aged 18 to describe which activities they would then carry out with a cyborg. Researchers asked those participants who said they would have sex with a robot why they would do it.

During his company's first-quarter earnings call, Google CEO Sundar Pichai said delivering artificial intelligence and machine learning as cloud services to enterprise customers "is going be a huge source of differentiation for us." "We are at an exceptionally interesting tipping point where these technologies are really taking off," Pichai told financially grounded Wall Street investors. Google, Mountain View, Calif., is engineering solutions that can enable services partners to help customers use machine learning to understand their data, he said. A couple of weeks later, at Google's I/O developers' conference, Pichai revealed a new custom chip, the Tensor Processing Unit, or TPU, that will power machine-learning workloads. One notable customer, Snapchat, already is using Google's platform to learn more about its users by studying the content of the photos they post on the social media site. And in March, Google enjoyed its own board game coup.

Data is rarely clean and often you can have corrupt or missing values. It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance. In this post you will discover how to handle missing values in your machine learning data using Weka. How To Handle Missing Data For Machine Learning in Weka Photo by Peter Sitte, some rights reserved. The problem used for this example is the Pima Indians onset of diabetes dataset.

Many datasets can be viewed as a noisy sampling of an underlying space, and tools from topological data analysis can characterize this structure for the purpose of knowledge discovery. One such tool is persistent homology, which provides a multiscale description of the homological features within a dataset. A useful representation of this homological information is a persistence diagram (PD). Efforts have been made to map PDs into spaces with additional structure valuable to machine learning tasks. We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs. The discriminatory power of PIs is compared against existing methods, showing significant performance gains. We explore the use of PIs with vector-based machine learning tools, such as linear sparse support vector machines, which identify features containing discriminating topological information. Finally, high accuracy inference of parameter values from the dynamic output of a discrete dynamical system (the linked twist map) and a partial differential equation (the anisotropic Kuramoto-Sivashinsky equation) provide a novel application of the discriminatory power of PIs.

We consider the problem of approximating partition functions for Ising models. We make use of recent tools in combinatorial optimization: the Sherali-Adams and Lasserre convex programming hierarchies, in combination with variational methods to get algorithms for calculating partition functions in these families. These techniques give new, non-trivial approximation guarantees for the partition function beyond the regime of correlation decay. They also generalize some classical results from statistical physics about the Curie-Weiss ferromagnetic Ising model, as well as provide a partition function counterpart of classical results about max-cut on dense graphs \cite{arora1995polynomial}. With this, we connect techniques from two apparently disparate research areas -- optimization and counting/partition function approximations. (i.e. \#-P type of problems). Furthermore, we design to the best of our knowledge the first provable, convex variational methods. Though in the literature there are a host of convex versions of variational methods \cite{wainwright2003tree, wainwright2005new, heskes2006convexity, meshi2009convexifying}, they come with no guarantees (apart from some extremely special cases, like e.g. the graph has a single cycle \cite{weiss2000correctness}). We consider dense and low threshold rank graphs, and interestingly, the reason our approach works on these types of graphs is because local correlations propagate to global correlations -- completely the opposite of algorithms based on correlation decay. In the process we design novel entropy approximations based on the low-order moments of a distribution. Our proof techniques are very simple and generic, and likely to be applicable to many other settings other than Ising models.

Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.

Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a database, ignoring class information. Instead, NN searches are performed within each class, generating a score per class. A key problem with NN techniques, including NBNN, is that they fail when the data representation does not capture perceptual (e.g.~class-based) similarity. NBNN circumvents this by using independent engineered descriptors (e.g.~SIFT). To extend its applicability outside of image-based domains, we propose to learn a metric which captures perceptual similarity. Similar to how Neighbourhood Components Analysis optimizes a differentiable form of KNN classification, we propose "Class Conditional" metric learning (CCML), which optimizes a soft form of the NBNN selection rule. Typical metric learning algorithms learn either a global or local metric. However, our proposed method can be adjusted to a particular level of locality by tuning a single parameter. An empirical evaluation on classification and retrieval tasks demonstrates that our proposed method clearly outperforms existing learned distance metrics across a variety of image and non-image datasets.

We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) potential misspecification, overfitting, and otherwise bias-prone or inefficient use of a large identifying covariate set in the estimation of causal effects. Our method targets the analysis toward a well defined ``retrospective intervention effect'' (RIE) based on hypothetical population interventions and applies a machine learning ensemble that allows data to guide us, in a controlled fashion, on how to use a large identifying covariate set. We illustrate with an analysis of policy options for reducing ex-combatant recidivism in Colombia.