Sign in to report inappropriate content. With experts from academia, industry, civil society, and government, we'll explore critical and emerging issues around understanding and guiding AI's human and societal impact to benefit humanity.

No one wants to be a nine-to-five robot. People want to feel inspired, find meaning, and see the impact their work has on others. And when they do, they're more engaged, innovative, and productive. If you're a leader, helping others feel a sense of purpose can be a powerful tool. So, why then do so many leaders have trouble lighting up their employees? The simple answer is it's extremely difficult to instill purpose in others.

In China, AI helps sort rubbish, catch criminals and improve traffic – now it's used to stop toilet paper theft

When Warner Brothers acquired movie discovery site, Flixster, and its reviews site, Rotten Tomatoes, in 2011, its President and COO, Steve Polsky, set out to explore how scoring consumer behavior data from mobile activity could be leveraged to solve a growing global financial crisis, one where two-thirds of the world's population have been excluded from transacting in the digital economy because they lack formal financial identity. To address this problem, Polsky founded Juvo to serve as a data-driven micro-financing provider that uses machine learning to extend emergency airtime loans to prepaid phone users when they run low on their balance. As customers make timely payments, they unlock access to progressively larger loans and build credit that they can use to qualify for a credit card and other financial instruments. Inspired by the World Bank challenge to achieve Universal Financial Access for a billion people by 2020, this San Francisco-based Series B startup has raised $54 million with rounds led by NEA and Wing, and strategic partnerships with Samsung NEXT, Nokia, Cable & Wireless and other leading mobile operators around the world. At the heart of their offerings is the data set they're mining which boasts over 5.6 billion data points being constructed real time for over 250 million people daily across 26 countries and 4 continents.

Agriculture is set to become'smarter' with digitisation and new technologies such as facial recognition for animals promising to provide the sector with greater control over its processes. Dr. Venkat Maroju, Chief Executive Officer of'SourceTrace' a provider of software solutions to the agriculture and allied sectors, said that digital technologies have an "enormous potential" to impact agriculture in several aspects by enabling farmers, on the one hand, to produce more while at the same time reducing the environmental impact of agricultural production. "Digital technology comes with several solutions to make this happen from farm management and traceability to certification and market linkage," he said. In September, itelligence AG the SAP software and technologies services company in partnership with the German Technical University OWL and HARTING Foundation & Co. KG a provider of industrial interconnection technology announced the launch of HARTING MICA . This is a system designed to enable the most efficient method of farming a given acreage of wheat using data collected via sensors from the soil, agricultural machinery and satellite images.

"When considering potential risks from future technology, one should not be content with merely analyzing what's likely to happen--instead, one should look at what's possible, even if unlikely." I'm a big believer in that quote – the reason I spend so much time painting pictures of possible futures. Mr. Tallinn expects the backbone of technology in the 2020s to be defined by gradual improvements in biotechnology, nanotechnology, and Artificial Intelligence. What else can we expect in the next decade? A recent Article by George Dvosrsky – a senior staff reporter at Gizmodo – explores the futuristic developments in the next ten years.

NEWPORT NEWS, VA – From new particle accelerator technology, to the exploration of new ways to treat wastewater, to applications of artificial intelligence, six cutting-edge projects are getting a jumpstart on research and development at the Department of Energy's Thomas Jefferson National Accelerator Facility. The projects are supported by the Laboratory Directed Research and Development program, which recently announced the continuation of four projects and new funding for two more for fiscal year 2020, which began October 1. The LDRD program provides resources for Jefferson Lab personnel to make rapid and significant contributions to critical science and technology problems that further the goals of the laboratory and the DOE. "We are delighted with the progress that was made on the ongoing LDRD projects, and we look forward with great interest to the results of the fiscal year 2020 projects and the boost they will give to long-term strategic directions of the laboratory," said Jefferson Lab Director Stuart Henderson. Of the six funded projects, four include aspects of artificial intelligence and machine learning: Three projects aim to develop machine learning to assist physicists in monitoring and/or analyzing large volumes of scientific data, while the last has the goal of improving up-time of Jefferson Lab's Continuous Electron Beam Accelerator Facility, a DOE User Facility.

WASHINGTON, D.C. – Today, the U.S. Department of Energy (DOE) announced $13 million in funding for five research projects in artificial intelligence (AI) aimed at improving AI as a tool of scientific investigation and prediction. The projects involve scientists at nine research institutions, including both DOE national laboratories and universities. "Artificial intelligence, including machine learning, provides an extremely powerful way of tackling the most pressing issues facing our scientists today," said U.S. Secretary of Energy Rick Perry. "This research will help us adapt AI to the specific scientific challenges that DOE-supported scientists are addressing today and in the process help sustain U.S. leadership in this critical and growing field." Of the $13 million, $11.1 million is reserved for two three-year projects focused on the development of new AI algorithms and software adapted to specific scientific problems.

We are living through a new industrial revolution in which artificial intelligence (AI) and machine learning (ML) permeate all industries and sectors of our economy, enhancing the impact and value of businesses worldwide. The UK has acknowledged this, and consequently is heavily investing in these technologies. But despite numerous successes, there have also been many failed AI projects that simply did not provide the expected return on investment. In many cases, the reasons for these failures are mismatched expectations and the overselling of AI's capabilities. Businesses must be better informed and trained to understand the limitations and expectations of what can be achieved when harnessing AI to solve their problems.

BENGALURU: When food tech company, Zomato, let go of around 540 of its support staff last week, it said that improvement in its after-sales technology had forced its hand. The automation made almost 10% of the workforce in certain support roles across Zomato's customer, merchant, and delivery partner teams, redundant. While routine jobs will continue to give way to automation, food tech firms like Swiggy and Zomato are increasingly turning to machine learning (ML) and automation to drive their businesses, using years of data accumulated from food orders and user-level consumption patterns. And each customer order is now being influenced by the customer's own previous history of order preferences. Swiggy boasts of more than 130,000 restaurant partners on its platform, while Zomato claims to have added around 150,000 restaurants. With such a large supply base in place, both the food tech apps are now solving for demand, mainly using data.