Donald Trump's chief technology advisor, Michael Kratsios, said that the US government would release any data that might help fuel AI research in the United States, although he didn't specify immediately what kind of data would be released, nor who would be eligible to receive the information. Speaking at EmTech Next, a conference held at MIT this week, Kratsios, deputy assistant to the President and deputy US chief technology officer, said that the government is looking for ways to open up federal data to AI researchers. "Anything that we can do to unlock government data we're committed to," Kratsios told MIT Technology Review. "We'd love to hear from any academic that has any insights." Data has been a key factor behind recent advances in artificial intelligence.

An AI system developed by a team from Germany, France and the US can diagnose skin cancer more accurately than dermatologists. In the study, the software was able to accurately detect cancer in 95% of images of cancerous moles and benign spots, whereas a team of 58 dermatologists was accurate 87% of the time. Chinese researchers have developed an algorithm that can diagnose prostate cancer as accurately as a pathologist. Research leader Hongqian Guo of Nanjing University said: "[This] will help pathologists make better, faster diagnoses, as well as eliminating the day-to-day variation in judgment that can creep into human evaluations." Researchers at the University of Texas, Houston, have developed software to accurately contour the shape of head and neck cancer tumours.

Getty Close-up adult hand typing on laptop A centre for artificial intelligence and public policy is looking to address the unique issues New Zealand will face, and is currently facing, in the era of AI. The centre has been launched in Otago and will explore policy options for managing the introduction of technologies, to maximise their benefits and minimise potential harms. Co-director of the centre, Professor James Maclaurin, said New Zealand's size sets it apart from other countries and it is important to have people acting in an advisory role. "Europe has just passed its general data protection regulations but it is a very big player so if they pass laws Facebook and Google really have to listen to them. "New Zealand is a different environment."

When you think of artificial intelligence (AI), your brain might evoke a gentle robot answering questions accurately. Could that friendly machine disclose information you want to keep private and under your control? For now, AI is a result of human inventiveness: we build, train and influence how AI performs, what we want it to do. In reality, AI is a set of technologies – natural language processing (NLP), machine learning (ML), robotics, etc. – and in many cases it comes down to algorithms, a piece of code with tasks to accomplish. There is an AI-powered algorithm behind most, if not all, digital experiences we have.

Syphilis might be more commonly associated with centuries past. But it's been on the rise for the past decade in England, with more cases last year than in any year since 1949. The disease was, in effect, eradicated in the UK in the mid-80s only to re-emerge around 1999. BBC Reality Check wanted to know why this ancient disease is rearing its head in England in the 21st Century. Syphilis is a bacterial infection that can be treated with antibiotics.

After years of close cooperation, the Boston Consulting Group and KLM Royal Dutch Airlines have agreed to what they describe as a pioneering artificial intelligence partnership that could "revolutionise global airline operations". It is the first time in the Dutch airline's history that it has collaborated with a leading management consultancy to launch an entirely new service. The jointly-developed artificial intelligence (AI) system will digitise KLM's entire commercial aviation process and leverage advanced machine learning technology to streamline operations "in an unprecedented manner". With these and other state-of-the-art analytic and organisational tools, the suite of integrated solutions will be able to minimise daily disruptions caused by human error and unforeseen events. Combining AI, machine learning, and advanced analytics with a joint force of KLM Operational staff and data scientists, engineers and developers from BCG Gamma (the data science consulting arm of BCG), the technology can help optimise airline processes, from front-office to back-office.

Growing up in Egypt in the 1980s, Rana el Kaliouby was fascinated by hidden languages--the rapid-fire blinks of 1s and 0s computers use to transform electricity into commands and the infinitely more complicated nonverbal cues that teenagers use to transmit volumes of hormone-laden information to each other. Culture and social stigma discouraged girls like el Kaliouby in the Middle East from hacking either code, but she wasn't deterred. When her father brought home an Atari video game console and challenged the three el Kaliouby sisters to figure out how it worked, Rana gleefully did. When she wasn't allowed to date, el Kaliouby studied her peers the same way that she did the Atari. "I was always the first one to say'Oh, he has a crush on her' because of all of the gestures and the eye contact," she says.

Techniques for reducing the variance of gradient estimates used in stochastic programming algorithms for convex finite-sum problems have received a great deal of attention in recent years. By leveraging dissipativity theory from control, we provide a new perspective on two important variance-reduction algorithms: SVRG and its direct accelerated variant Katyusha. Our perspective provides a physically intuitive understanding of the behavior of SVRG-like methods via a principle of energy conservation. The tools discussed here allow us to automate the convergence analysis of SVRG-like methods by capturing their essential properties in small semidefinite programs amenable to standard analysis and computational techniques. Our approach recovers existing convergence results for SVRG and Katyusha and generalizes the theory to alternative parameter choices. We also discuss how our approach complements the linear coupling technique. Our combination of perspectives leads to a better understanding of accelerated variance-reduced stochastic methods for finite-sum problems.

We consider semidefinite programs (SDPs) of size n with equality constraints. In order to overcome scalability issues, Burer and Monteiro proposed a factorized approach based on optimizing over a matrix Y of size $n$ by $k$ such that $X = YY^*$ is the SDP variable. The advantages of such formulation are twofold: the dimension of the optimization variable is reduced and positive semidefiniteness is naturally enforced. However, the problem in Y is non-convex. In prior work, it has been shown that, when the constraints on the factorized variable regularly define a smooth manifold, provided k is large enough, for almost all cost matrices, all second-order stationary points (SOSPs) are optimal. Importantly, in practice, one can only compute points which approximately satisfy necessary optimality conditions, leading to the question: are such points also approximately optimal? To this end, and under similar assumptions, we use smoothed analysis to show that approximate SOSPs for a randomly perturbed objective function are approximate global optima, with k scaling like the square root of the number of constraints (up to log factors). We particularize our results to an SDP relaxation of phase retrieval.

LexNLP is an open source Python package focused on natural language processing and machine learning for legal and regulatory text. The package includes functionality to (i) segment documents, (ii) identify key text such as titles and section headings, (iii) extract over eighteen types of structured information like distances and dates, (iv) extract named entities such as companies and geopolitical entities, (v) transform text into features for model training, and (vi) build unsupervised and supervised models such as word embedding or tagging models. LexNLP includes pre-trained models based on thousands of unit tests drawn from real documents available from the SEC EDGAR database as well as various judicial and regulatory proceedings. Keywords: natural language processing, legal, regulatory, machine learning, segmentation, extraction, open source, Python 1. Introduction Over the last two decades, many high-quality, open source packages for natural language processing and machine learning have been released. Researchers and developers can quickly write applications in languages such as Java, Python, and R that stand on the shoulders of comprehensive, well-tested libraries like Stanford NLP ([1]), OpenNLP ([2]), NLTK ([3]), spaCy ([4]), scikit-learn ([5], [6]), Weka ([7]), and gensim ([8]).