Apple is hiring a rising star in the world of deep learning to serve as its first director of AI research. Ruslan Salakhutdinov, an associate professor at Carnegie Mellon University in Pittsburgh, will assume the new position, which is meant to help the company make sure that Siri and its other products make use of the fundamental breakthroughs coming out of academic AI research. Salakhutdinov will talk about his research at EmTech MIT 2016, an MIT Technology Review conference held this week. Salakhutdinov researches very large neural networks used in a technology called deep learning, which lets a computer learn to perform a difficult task by consuming copious training examples. He will continue to work part time at CMU and will hire a team of researchers to work with him at Apple.

For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first increase the error rate before decreasing it. This, possibly counterintuitive, phenomenon is known as peaking. In this work, we observe that a similar but more pronounced version of this phenomenon also occurs in the semi-supervised setting, where instead of labeled objects, unlabeled objects are added to the training set. We explain why the learning curve has a more steep incline and a more gradual decline in this setting through simulation studies and by applying an approximation of the learning curve based on the work by Raudys & Duin.

A former solicitor general has said she is concerned the Ched Evans rape case could discourage victims of sexual offences from coming forward. The 27-year-old footballer was cleared on Friday of raping a 19-year-old woman in a hotel room. Vera Baird told the BBC that details of the woman's sexual past should not have been heard in court. Mr Evans was found guilty of rape in 2012, but that conviction was quashed in April. The Chesterfield striker was accused of attacking the woman at a Premier Inn in Rhuddlan, Denbighshire, on 30 May 2011.

Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down HC with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i:e:, defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art HC approaches.

A key pretrial hearing to determine what evidence prosecutors can use in Bill Cosby's Pennsylvania sex assault case has been scheduled for early November. Prosecutors hope to call 13 other accusers to show the comedian had a pattern of drugging and molesting women. The criminal charges involve an encounter with Andrea Constand in 2004. Prosecutors also want to use Cosby's deposition from Constand's 2005 lawsuit. Cosby acknowledges under oath that he had sexual encounters with a series of women after giving them drugs or alcohol.

To the editor: I empathize with Los Angeles Police Department Chief Charlie Beck and his officers, who are reluctant to quickly release information and videos taken of police shootings. As imperfect human beings, none of us appreciates being exposed to intense public scrutiny. On the other hand, L.A.'s finest should learn from examples set by departments in cities like Las Vegas, where officers quickly post information about shootings online. First, bad things grow in the dark, and you can't set a behavioral standard without oversight. Opening up will create more support for genuine peace officers, who will then be reassured that the public has their back.

The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant of self-learning can be derived by applying block coordinate descent to two related but slightly different objective functions. The resulting soft-label approach is related to an idea about dealing with missing data that dates back to the 1930s. We show that the soft-label variant typically outperforms the hard-label variant on benchmark datasets and partially explain this behaviour by studying the relative difficulty of finding good local minima for the corresponding objective functions.

Many data science competitions suffer from a test set being markedly different from a training set (a violation of the "identically distributed" assumption). It is then difficult to make a representative validation set. We propose a method for selecting training examples most similar to test examples and using them as a validation set. The core of this idea is training a probabilistic classifier to distinguish train/test examples. In part one, we inspect the ideal case: training and testing examples coming from the same distribution, so that the validation error should give good estimation of the test error and classifier should generalize well to unseen test examples.

We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition based on deep neural net- works. As opposed to unsupervised initialisation followed by supervised fine tuning, our method takes advantage of both unlabelled and labelled data simultaneously through mini- batch stochastic gradient descent. We tested the method with varying proportions of labelled vs unlabelled observations in frame-based phoneme classification on the TIMIT database. Our experiments show that the method outperforms standard supervised training for an equal amount of labelled data and provides competitive error rates compared to state-of-the-art graph-based semi-supervised learning techniques.

We can use unsupervised machine learning to identify which drugs are associated with which adverse events. Specifically, machine learning can help us to create clusters based on gender, age, outcome of adverse event, route drug was administered, purpose the drug was used for, body mass index, etc. This can help for quickly discovering hidden associations between drugs and adverse events. Clustering is a non-supervised learning technique which has wide applications. Some examples where clustering is commonly applied are market segmentation, social network analytics, and astronomical data analysis.