Russian President Vladimir Putin stated in 2017 that the country that establishes superiority in artificial intelligence will become the ruler of the world. More recently, Tesla CEO Elon Musk and renowned psychologist Steven Pinker engaged in some rather heated debates over AI use, risks and regulation, with Musk warning that while the benefits of the new tech are revolutionary, the exponential increase in hardware ability and software talent also make AI a potential source of incredible risk. Given these disparate opinions, it's important that federal technologists collaborate with industry leaders to determine the best way to introduce AI to their agencies, while mitigating risks and capitalizing on its intended use and benefits. It is also critical that industry re-evaluate the speed and manner with which they bring any new technology to market. While there are many ways AI is being used positively to improve or simplify life, AI is largely open source so the code is publicly available to anyone: ambitious startups, innovative developers and malicious actors. While many of the big tech companies and leaders in the burgeoning AI field make their code available to the public with the goal of stimulating innovation, many would argue more adversaries than legitimate users are leveraging the code for ill-intent, as evidenced by the foreign influence of the 2016 election.

You receive a notification on your phone that a critical shipment from your China factory has missed its filing deadline with the customs broker. Your logistics manager is alerted that there is an 80% chance that the components he's waiting for are likely to be delayed another 48 hours by excessive port traffic and your GTM software advises diverting the shipment to an alternate port facility. Your compliance officer is informed that there is a 95% chance that a shipment of parts from Malaysia is likely to be held for up to three days to be subjected to a detailed customs inspection. If you think this type of information would be of great assistance to your supply chain business planning and operations, you are not alone. It is this type of integrated data and communications that are becoming the backbone of the Big Data led revolution underway in supply chain. The human brain can only process and make use of a limited amount of information before it becomes overwhelmed and unable to effectively recognise patterns and trends.

The lack of credit details has led to the emergence of analytics start-ups that are working on ways to develop alternate data-based lending programs to offer personal loans Now, customers with no prior credit score can easily get loans Priyanka Pani The burgeoning online lending segment in India is also giving rise to a new kind of challenge on sourcing credit score data. To solve this problem, several fintech companies are using Artificial Intelligence (AI) and Machine Learning (ML) to create alternate lending data score for more than 80 per cent of the Indian population who have no credit scores. From the place where people live to the restaurants they visit to their digital footprints on social media, ML captures it all. Live data Mohan Yadav, a 25-year-old software professional in Mumbai, was denied a ₹25,000 personal loan by his bank since he had no credit history.

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on training data, that presents an effective approach to incorporating the evaluation measure (i.e. G-mean) into the objective function. We first optimize the misclassification costs, then apply them to deep belief network. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state-of-the-art on both benchmark datasets and real-world dataset for fault diagnosis in tool condition monitoring.

Google Shopping's trust and safety team reportedly found 5,000 merchant accounts that were scamming users. Google is buckling down on fraud on its shopping site after a company executive was reportedly scammed while purchasing a Bluetooth headset. The executive, CNBC reports, ordered the high-end headset earlier this year when he saw it was selling for a low price. But it was too good to be true: The merchant, despite claiming to be in the US, was actually based in Vietnam, and took the Google employee's credit card information without ever sending the product. When the employee reported the case to his co-workers at Google, they not only banned the bogus seller from listing new products but also kicked off a global probe which identified 5,000 merchant accounts that were scamming users. Google has dealt with a series of scams on its services, ranging from Google Docs to ads.

SoundHound, a voice-enabled artificial intelligence (AI) and conversational intelligence technologies company, announced news on Thursday (May 3) that it has raised $100 million in new funding from a group of strategic investors. In a press release, the company said investors in the round of funding include Tencent Holdings, Daimler, Hyundai Motor Company, Midea Group and Orange. Those companies join existing investors Samsung, NVIDIA, KT Corporation, HTC, NAVER, LINE, Nomura, Sompo Japan Nipponkoa and Recruit. SoundHound plans to use the funding to drive adoption and distribution of Houndify, its voice AI platform in the automotive, Internet of Things, consumer products and enterprise apps and services markets. The funding will also be used to open new offices in China, France and Germany.

News about Machine Learning (ML), Artificial Intelligence (AI) and related research areas. Controlling your gadgets by talking to them is so 2018. In the future, you will not even have to move your lips. A prototype device called AlterEgo, created by Arnav Kapur, a 23-year old MIT Media Lab graduate student, is already making this possible. With Kapur's device -- a 3-D-printed plastic doodad that looks kind of like a skinny white banana attached to the side of his head -- he can flip through TV channels, change the colors of lightbulbs, make expert chess moves, solve complicated arithmetic problems, and order a pizza, all without saying a word or lifting a finger.

As U.S. and Chinese officials engage in highly anticipated trade talks, officials from China have asserted that it will not discuss two of the biggest trade demands from the United States. One is about the U.S. trade deficit; the other is an issue that could become the greatest technology war in history: China's push into artificial intelligence. The United States has good reason to be concerned about China's hard stance. While the ongoing trade war is grabbing all the headlines, it's the tussle for dominance in the A.I. space that could shape the economic fortunes of the two world powers. Overshadowed by the dazzling A.I. advances made by the United States so far, China has been silently but resolutely building an ecosystem that is feeding and fueling its ambition to become a world leader in A.I. by 2030.

There is a general tendency among counterterrorism analysts to understate rather than hyperbolize terrorists' technological adaptations. In 2011 and 2012, most believed that the "Arab Spring" revolutions would marginalize jihadist movements. But within four years, jihadists had attracted a record number of foreign fighters to the Syrian battlefield, in part by using the same social media mobilization techniques that protesters had employed to challenge dictators like Zine El Abidine Ben Ali, Hosni Mubarak, and Muammar Qaddafi. Militant groups later combined easy accessibility to operatives via social media with new advances in encryption to create the "virtual planner" model of terrorism. This model allows online operatives to provide the same offerings that were once the domain of physical networks, including recruitment, coordinating the target and timing of attacks, and even providing technical assistance on topics like bomb-making.

Facial recognition technology used by the UK police is making thousands of mistakes, a new report has found. South Wales Police, London's Met and Leicestershire are all trialling automated facial recognition systems in public places to identify wanted criminals. According to police figures, the system often makes more incorrect matches than correct ones. Experts warned the technology could lead to false arrests and described it as a'dangerously inaccurate policing tool'. South Wales Police has been testing an automated facial recognition system.