In our case, the data was provided by Safecity India, which is a platform launched on 2012, that crowdsources personal stories of sexual harassment and abuse in public spaces [2]. They have collected over 10,000 stories from over 50 cities in India, Kenya, Cameroon, and Nepal. More specifically they provided us a .cvs Additionally to the focal tasks of this project and as part of the NLP channel we decided to automate the category classification based on the sexual harassment case descriptions. Performing this classification task manually is time-consuming and leaving it entirely on the hands of the victim could produce ambiguity in the discrimination of the categories.

Introduced by Conner Prochaska, Chief Commercialization Officer, U.S. Department of Energy This panel highlights the breadth of research in AI across the national laboratory complex and discusses how this research fits into the recently announced National AI Initiative; several notable collaborations in AI between companies and national labs are discussed. Applications of AI to solving core problems in energy grid optimization are discussed. In particular, AI approaches to energy grid security, flexibility, and reliability in light of future energy demands are highlighted. Panel participants include representatives from major utilities. State of applications of AI to transformative problems in drug discovery are discussed; ATOM Partnership is highlighted; future opportunities across disease areas are discussed with participants from life sciences companies.

In Human Compatible, his new book on artificial intelligence (AI), Stuart Russell confronts full on what he calls "the problem of control". That is, the possibility that general-purpose AI will ultimately eclipse the intellectual capacities of its creators, to irreversible dystopian effect. The control problem is not new. But, by 1950, Norbert Wiener, the inventor of cybernetics, was writing (in The Human Use of Human Beings) that the danger to society "is not from the machine itself but from what man makes of it". Russell's book in effect hangs on this tension: whether the problem is controlling the creature, or the creator.

Cyber Security Cooperative Research Centre (CSCRC), together with Data61, the innovation arm of the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and cybersecurity startup Penten, have announced a joint research project that will focus on developing artificial intelligence (AI) enabled cybersecurity defence mechanisms. Under the arrangement announced at D61 Live on Wednesday, Penten will have access to Data61's AI research, which it will use to extend on its existing work to build AI-enabled technology such as "cyber traps" and "decoys". According to Penten CEO Matthew Wilson, using AI will help speed up the creation of cyber traps and make them more realistic. "Our solutions use artificial intelligence to learn the patterns of activity and content from surrounding computers and data. We then use this information to create realistic and believable mimics. This means we can deliver suitable content extremely efficiently, tailored to a customer environment and with minimal effort on the part of the defender," he said.

CALGARY, Alberta--(BUSINESS WIRE)--The Alberta Machine Intelligence Institute (Amii) and Imperial have announced a two-year agreement to collaborate on the development of Imperial's in-house machine learning capabilities, which will enable a range of applied artificial intelligence (AI) projects. Through these projects, Imperial will work to develop more effective ways to recover oil and gas resources, reduce environmental impacts and improve the safety of its workforce. "At Imperial, we are taking action to be a leader in advancing digital and AI technology across the value chain," said John Whelan, Imperial's senior vice-president, upstream. "Amii is not only a leader in the AI space globally, but based locally in Alberta. We believe the institute is a perfect partner to help us showcase Alberta's leadership in technology and digital solutions for responsibly-produced oil and gas."

Seven decades ago, agricultural scientists used high-yielding, dwarf varieties of wheat and rice to revolutionize agriculture across Asia and Latin America – and now European data scientists are teaming up with Kenyan farmers to use the fruits of the Fourth Industrial Revolution to drive the next agricultural one. The Green Revolution produced massive increases in crop yields throughout Asia and Latin America, but even today, many smallholders –farmers who produce crops on small pieces of land – struggle to afford and utilize the mechanized equipment and agricultural chemicals that came with that revolution. When it comes to Africa, there is still great potential for productivity increases in agriculture. The number of small-holder farmers in Kenya could be between 5 million and 9 million people according to some estimates. In order to see how artificial intelligence, machine learning and big data could help those farmers, French consultancy firm Capgemini teamed up with a Kenyan social enterprise in the Kakamega region in Western Kenya.

Amongst a wave of buzzy deep-learning startups in the early 2010s, Tractable has managed to grow steadily by addressing a very specific problem with the technology: helping insurance companies to automatically assess vehicle damage. Founded by Alexandre Dalyac and Razvan Ranca in 2014 after graduating from the company builder programme at Entrepreneur First (EF), Adrien Cohen joined the company soon after as chief business officer, and set out to discover where to apply its computer vision expertise. "If you take a step back, at the time there was a wave of deep learning startups," Cohen told Techworld last week, speaking from a conference room at Tractable's London office, which is an entire floor of a WeWork near Old Street station in east London. "There was a big number of companies with great technology, but no problem to solve. Most of these companies, they ended up being acquired."

Artificial intelligence is going to change how humanity thinks about the role of culture, god, faith, reality and ourselves. Can artificial intelligence solve world hunger and bring eternal peace? We will see that when the time comes but the inevitability of artificial intelligence becoming smarter than human has raised many questions about the long-term survival of the human race. Yes, some are myths and some statements made recently are overhyped but there is no doubt if machine's goals are misaligned with ours then we need to ask ourselves What kind of future we want? What is a good life? The truth behind the harmony of this cosmos as professed by science, the will to take over authority over all things, and with "knowledge is power" philosophy, today's man is a greedy man. Today's man is ready to play with the mind of cosmos. He thinks he has the power to be free and with this power, he will be free eternally.

In the latest regulatory boost for expanded commercial drone services, the company also intends to gradually phase in routine night flights and heavier cargo limits--areas now generally off-limits to most operators. Under the Federal Aviation Administration's announcement Tuesday, the company's Flight Forward unit obtained an immediate green light to ship medical products and specimens in North Carolina across various hospital campuses. But the broad approval for an entire fleet of future drones and pilots on the ground--going beyond what the FAA approved previously--opens the door for many other types of longer-range applications spanning rural and suburban areas. The FAA approval doesn't apply to urban areas. Calling it a major step to enhance services for health-care customers and ultimately an array of other industries, the company said the FAA's approval "has no limits on the size or scope of operations."

Google open source machine learning library TensorFlow 2.0 is now available for public use, the company announced today. The alpha version of TensorFlow 2.0 was first made available this spring at the TensorFlow Dev Summit alongside TensorFlow Lite 1.0 for mobile and embedded devices, and other ML tools like TensorFlow Federated. TensorFlow 2.0 comes with a number of changes made in an attempt to improve ease of use, such as the elimination of some APIs thought to be redundant and a tight integration and reliance on tf.keras as its central high-level API. Initial integration with the Keras deep learning library began with the release of TensorFlow 1.0 in February 2017. It also promises three times faster training performance when using mixed precision on Nvidia's Volta and Turing GPUs, and eager execution by default means the latest version of TensorFlow delivers runtime improvements. The TensorFlow framework has been downloaded more than 40 million times since it was released by the Google Brain team in 2015, TensorFlow engineering director Rajat Monga told VentureBeat earlier this year.