There's no better way of ensuring you win a race than by setting the rules yourself. That may be behind the recent rush by countries, international organizations, and companies to put forward their visions for how the AI race should be governed. China became the latest to release a set of "ethical standards" for the development of AI last month, which might raise eyebrows given the country's well-documented AI-powered state surveillance program and suspect approaches to privacy and human rights. But given the recent flurry of AI guidelines, it may well have been motivated by a desire not to be left out of the conversation. The previous week the OECD, backed by the US, released its own "guiding principles" for the industry, and in April the EU released "ethical guidelines."

As artificial intelligence becomes a standard laboratory tool, scientists are quickly discovering both the promise and perils of algorithmically driven research. Artificial intelligence (AI) is cropping up everywhere these days, according to major news sources that are themselves increasingly driven by computer algorithms. Marketers use AI to target advertisements, engineers use it to anticipate device failures, and AI-driven social media platforms wield outsize influence on everything from fashion to politics. While all types of AI--also called machine learning--entail programming a computer to learn from examples and make inferences, practitioners distinguish different forms of it. Within the broader field of AI, a subset of strategies employ artificial neural networks. These mimic biological brains, with elements of a program connecting to each other like neurons.

In Asia, no economic or business sector is as vibrant or commands more attention of policymakers as the digital sphere. This trend is well documented in a landmark study by United Nations Conference on Trade and Development (UNCTAD)'s first annual Digital Economy Report. The timely study was released earlier this month as countries across Asia grapple with a complex digital future. Digital technologies help cut costs, enable delivery of services without leakages, reduce opportunities for graft, promote ease of doing business, leverage an increasingly non-tactile world, grow economies, have the potential to create millions of new jobs and, it appears, even help fight fake news. On the flip side, there are concerns of the cost of the emerging digital economy in terms of loss of traditional employment sectors, eroding the right to privacy, abetting authoritarian statecontrol of citizens' lives, causing a spike in cybercrimes and, according to some, promoting a deracinated, atomised existence for human beings.

Top Stories, Tweets Top Stories, Aug 5-11: Knowing Your Neighbours: Machine Learning on Graphs; What is Benford's Law and why is it important for data science? Top KDnuggets tweets, Jul 31 - Aug 06: NLP vs. NLU: from Understanding a Language to Its Processing News Exploratory Data Analysis Using Python Meetings The slow, startling triumph of Reverend Bayes - John Elder's 2019 Keynote at PAW in London Cambridge Analytica whistleblower Chris Wylie to headline Big Data LDN 2019 keynote programme Academic Postdoctoral position (2 years) in multivariate analysis and deep learning PhD student position in computational science with focus on chemistry Monash University: Research Fellow - Computer Vision [Melbourne, Australia] Image of the week 12 NLP Researchers, Practitioners, Innovators to Follow Learn how to do Machine Learning on Graphs; Follow these 12 amazing leaders in NLP; Read the explanation of Deep Learning for NLP, including ANNs, RNNs and LSTMs; Understand what is Benford's Law and why is it important for data science; Find the 6 key concepts in Andrew NG Machine Learning Yearning; and more. Knowing Your Neighbours: Machine Learning on Graphs 12 NLP Researchers, Practitioners & Innovators You Should Be Following Deep Learning for NLP: ANNs, RNNs and LSTMs explained! What is Benford's Law and why is it important for data science?

Should we be expecting an enterprising entrepreneur to invent a true self-driving car while toiling away inside their garage, it's a question often asked. One of the most popular tropes in high-tech seems to be the notion that an enterprising entrepreneur could miraculously invent the next-gen earth-shattering doodad in their garage. In fact, the start-up whiz might not even be using their own garage and instead might be toiling away in someone else's garage, such as one owned by a dear aunt or a neighbor that is okay with having a resident tinkerer that is aiming to change the world. Time and again, we've all heard the stories about the Silicon Valley millionaires and zillionaires that began their arduous journey by using a soldering iron and a cheap keyboard to craft some breakthrough hardware and software for an ingenious system that ultimately took the marketplace by storm. Here's a question worth considering: Could a start-up entrepreneur invent a true self-driving car in their garage?

Amazon.com's Alexa has mastered Hindi in just a few years. The voice assistant introduced to India in 2017 gets a major local makeover for one of the largest retail markets. From Wednesday, Amazon launches a version that now speaks Hindi and Hinglish -- a unique blend with English. It can also switch automatically between all three. The new, improved Alexa and Echo speakers hit the market in time for the Diwali shopping season.

The Directorate General of Training (DGT), under the skill development and entrepreneurship ministry, has signed an agreement with IT major IBM to carry out a nationwide Train-the-Trainer programme in basic artificial intelligence, an official statement said on Wednesday. As part of the programme, ITI trainers will be trained on basic artificial intelligence (AI) skills towards using the technology in their day-to-day training activities, the ministry said in a statement. This programme, it said, aims at enabling the trainers with basic approach, workflow and application of artificial intelligence that they can apply in their training modules. "IBM aims at training 10,000 faculty members from ITIs across the country and the programme will be executed over a period of one year with 14 trainers across 7 locations with over 200 workshops," it added. Mahendra Nath Pandey, Minister for Skill Development and Entrepreneurship said, many more training programmes will be initiated for the trainers.

Artificial intelligence (AI) has long been promoted as a tool which could transform the way clinicians work in the NHS. However there is an awareness that the space between the promise and the reality is often large. Great Ormond Street Hospital (GOSH), in common with other NHS hospitals, has a problem with patients not attending appointments. Could artificial intelligence (AI) – which uses software to perform tasks usually requiring human intelligence – make a difference? By analysing operational data, GOSH hopes to develop an algorithm that can predict which patients are likely to miss an appointment, enabling the hospital to find ways to maximise the likelihood of them attending.

The study was also presented at the Heart Failure Society of America Annual Scientific Meeting in Philadelphia. Type 2 diabetes is a global epidemic that is expected to affect over 592 million people globally by 2035, a dramatic increased from 382 million people with diabetes mellitus in 2013, a prevalence that is likely to be underestimated. Type 2 diabetes patients are at more than double the risk of developing heart failure resulting in disability or death among such patients. Earlier this month, late-breaking trial results revealed that a new class of medications known as SGLT2 inhibitors may be helpful for patients with heart failure. These therapies may also be used in patients with diabetes to prevent heart failure from occurring in the first place.

Professor Lawrence joins the University's Department of Computer Science and Technology from Amazon Cambridge, where he has been Director of Machine Learning for the past three years. He is also Professor of Machine Learning at the University of Sheffield, where he will retain a visiting position. Professor Lawrence's research interests are in probabilistic models with applications in computational biology, personalised health and developing economies. At Sheffield, he led the ML@SITraN group, and helped to develop an Open Data Science Initiative, an approach to data science designed to address societal needs. "There's so much expertise at Cambridge, in all aspects of systems and data: that's why I'm so excited about joining," Lawrence said.