In a bid to tackle the spread of fake news and misleading videos created using artificial intelligence (AI) and bots, China has released new rules that ban online video and audio providers from using deep learning to produce fake news without a proper disclosure. Failing to provide a disclosure that the post in question was created with AI or VR technology is now a criminal offence, according to the Chinese government. The rules go into effect on January 1st, 2020, and will be enforced by the Cyberspace Administration of China, The Verge reported. The regulation comes about one-and-a-half months after California introduced legislation to make political deepfakes illegal, outlawing the creation or distribution of videos, images, or audio of politicians doctored to resemble real footage within 60 days of an election. The new regulation published said that both providers and users of online video news and audio services are not allowed to use new technologies such as deep learning and virtual reality to create, distribute and broadcast fake news, according to South China Morning Post.

Brexit supporters are more likely to live in areas most threatened by the economic impact of automation, according to a study of the impact of robots and artificial intelligence in the workplace. A map of the parts of the UK likely to be hit by automation fits more closely with the map of leave voters than any other factor, said the Institute for the Future of Work (IFW). Up to 15 million workers are expected to have their employment prospects endangered by automation over the next decade, according to a series of reports that have tried to gauge the impact of new technology in the workplace. In 2015, the Bank of England estimated as many as 15m jobs would need to change or be lost through automation. A report by the consultancy firm PwC found that 10m, or 30%, of jobs in Britain were potentially under threat from breakthroughs in artificial intelligence In some sectors half the jobs could go, it warned.

Recent improvements in artificial intelligence have significantly increased the gap between government policy and leading practices. AI is changing norms and business models throughout society, demanding new and effective policy responses from government on subjects with little real precedence. While government struggles to adapt to the rapid pace of change AI brings new solutions as well, offering the potential to change how policy is made by offering new tools and methods of policy development. This one-day course is designed for policy practitioners in government who are looking to get ahead of the curve and put AI to work for Canadians. Designed by policy practitioners, for policy practitioners, the course is easily accessible for those with a limited baseline knowledge of AI while also providing relevant policy insights for those who have an intermediate skill level.

AWS is excited to introduce Amazon SageMaker Operators for Kubernetes, a new capability that makes it easier for developers and data scientists using Kubernetes to train, tune, and deploy machine learning (ML) models in Amazon SageMaker. Customers can install these Amazon SageMaker Operators on their Kubernetes cluster to create Amazon SageMaker jobs natively using the Kubernetes API and command-line Kubernetes tools such as'kubectl'. Many AWS customers use Kubernetes, an open-source general-purpose container orchestration system, to deploy and manage containerized applications, often via a managed service such as Amazon Elastic Kubernetes Service (EKS). This enables data scientists and developers, for example, to set up repeatable ML pipelines and maintain greater control over their training and inference workloads. However, to support ML workloads these customers still need to write custom code to optimize the underlying ML infrastructure, ensure high availability and reliability, provide data science productivity tools, and comply with appropriate security and regulatory requirements.

Earlier this year, San Francisco-based venture capital firm Riverwood Capital invested US$ 20 million in Omie, a Brazilian start-up that provides small and medium businesses (SMBs) with an AI-powered business management software. Omie's genius idea was to focus on small firms, not served by larger management software services. By automating business functions, the company essentially eliminates the massive amount of paper work required in Brazil, a country notorious for red tape. "Cloud management platforms are revolutionising small and medium businesses in Brazil," says Marcelo Lombardo, Omie's CEO and founder. He spoke with LSE Business Review managing editor Helena Vieira on 5 November during the Web Summit conference in Lisbon. Starting from the beginning, what does Omie do? Omie is a cloud management software for small and midsize businesses. We put together pretty much everything a small business owner needs for his daily life (financials, invoicing, inventory, manufacturing, etc).

Washington D.C.: With the help of machine learning and artificial intelligence researchers are accelerating the power of batteries. Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery, according to the study published in -- Chemical Science. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes. To do so, they used a computationally intensive model called G4MP2. This collection of molecules, however, represented only a small subset of 166 billion larger molecules that scientists wanted to probe for electrolyte candidates.

Sign in to report inappropriate content. Hava Siegelmann, Microsystems Technology Office Program Manager DARPA, gives a keynote at the Human-Level AI Conference in Prague in August 2018. The conference combined three major conferences AGI, BICA, and NeSy and was organized by AI research and development company GoodAI.

Rapid advances in AI and machine learning are defining cybersecurity's future daily. Identities are the new security perimeter and Zero Trust Security frameworks are capitalizing on AI's insights to thwart breaches in milliseconds. Advances in AI and machine learning are also driving the transformation of endpoint security toward greater accuracy and contextually intelligence. Gartner predicts $137.4B will be spent on Information Security and Risk Management in 2019, increasing to $175.5B in 2023, reaching a CAGR of 9.1%. Cloud Security, Data Security, and Infrastructure Protection are the fastest-growing areas of security spending through 2023.

New Delhi: Delhi government's VAT department will soon take the help of a machine learning tool to identify bogus firms and tax evasion to plug leaks, which is estimated to be around Rs 300 crore annually. AAP government has shared thousands of VAT returns registered in Delhi between 2012-17, which are being scrutinised by two research scholars -- Aprajit Mahajan and Shekhar Mittal -- who are associated with University of California, Berkeley. Government officials said the researchers are likely to submit their first set of findings by December on the basis of which the VAT department would plan its action against firms that might be involved in systematic evasion of taxes. "It is going to be a first-ever systematic study of tax evasion in an economy with weak compliance," Delhi Dialogue and Development Commission vice-chairman Jasmine Shah said, referring to the researchers' paper'Who is Bogus? According to officials, the VAT department currently carries out surprise inspections to nab defaulters or traders resorting to unfair means to avoid paying taxes by generating bills of fraudulent transactions with firms that exist only on paper.

The researchers, based at UBC's faculty of applied science, designed three types of stealth attack on robotic vehicles that caused the machines to crash, miss their targets or complete their missions much later than scheduled. The attacks required little to no human intervention to succeed on both real and simulated drones and rovers. "We saw major weaknesses in robotic vehicle software that could allow attackers to easily disrupt the behaviour of many different kinds of these machines," said Karthik Pattabiraman, the electrical and computer engineering professor who supervised the study. "Especially worrisome is the fact that none of these attacks could be detected by the most commonly used detection techniques." Robotic vehicles use special algorithms to stay on track while in motion, as well as to flag unusual behaviour that could signal an attack.