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This week, 25 European countries signed a Declaration of cooperation on AI. Whereas a number of Member States had already announced national initiatives on Artificial Intelligence, they have now declared a strong will to join forces and engage in a European approach to deal therewith. By teaming up, the opportunities of AI for Europe can be fully ensured, while the challenges can be dealt with collectively.
The Member States agreed to work together on the most important issues raised by Artificial Intelligence, from ensuring Europe’s competitiveness in the research and deployment of AI, to dealing with social, economic, ethical and legal questions.
The Serious Fraud Office (SFO) has today announced that AI which can work 2,000 times faster than a human lawyer will be used on all new casework.
Through automatic document analysis, the agency will be able to ‘investigate more quickly, reduce costs and achieve a lower error rate’ than through the work of human lawyers, it said. A pilot ‘robot’ able to process more than half a million documents a day, was implemented by the SFO to scan for legal professional privilege content in its case against Rolls-Royce. The SFO said the new system, called ‘Axcelerate’ and produced by software company OpenText, will be rolled out alongside the ’robot’. The office said this will enable SFO staff to ’better target their work and time in other aspects of investigative and prosecutorial work’.
According to the 2018 Q1 MoneyTree Report published on Wednesday, US AI has a big quarter: Funding to US-based artificial intelligence companies leapt 29% in Q1’18 as $1.9B was invested across 116 deals.
Amidst a modest increase in deal activity, quarterly funding to US-based artificial intelligence companies increased 29% in Q1’18 as $1.9B was invested across 116 deals. This represents an 8-quarter high in funding.
The $1B+ quarter was led by companies such as UiPath ($153M Series B), Pony.ai ($112M Series A), and Nuro ($92M Series A).
San Francisco startup Directly has raised $20 million in a third round of funding to build its customer service offering for companies leveraging AI and crowd-sourced experts.
In an interview, Directly CEO and cofounder Antony Brydon acknowledges that the field of customer service providers is extremely competitive. But Directly fuses together the two main approaches in customer service that haven’t been brought together in one offering, he says: AI and crowd-power.
Researchers working at The Allen Institute for Artificial Intelligence, The University of Illinois Urbana-Champaign, and The University of Washington developed the AI, called Composition, Retrieval and Fusion Network (Craft). It was trained on a database of more than 25,000 Flintstones videos which had been painstakingly annotated.
Craft uses the annotations from videos to determine how the original images correspond to the words used to describe them. Eventually it builds up a set of parameters that enables it to “understand” what makes individual characters and objects from the cartoon match their plain-language counterparts. Once it understands this relation, it’s able to generate video clips based on novel text inputs that look a lot like the cartoon it was trained on.
Cruise recently brought on seven team members from Zippy.ai, which develops robots for last-mile grocery and package delivery, for an undisclosed amount of money.
“Their expertise in machine learning, computer vision, and simulation is among the best in the industry,” Cruise CEO Kyle Vogt wrote in a Medium post. “But perhaps more importantly, their commitment to working on a team — and doing things the right way — strengthens our ability to safely test, validate, and deploy our self-driving technology at scale.”
In new research, Hyejin Youn, an assistant professor of management and organisation at Kellogg, and colleagues seek to understand how machines will disrupt the economies of individual cities. By carefully analysing the workforces of American metropolitan areas, the team calculated what portion of jobs in each area is likely to be automated in coming decades.
They found that, in general, small cities will have higher portions of their workforce replaced by machines than large cities. The reason: While cities of all sizes have many easily automated jobs (like card dealers, fisherman, cashiers, and accountants), large cities like Boston also have larger shares of managerial and knowledge professions (like lawyers, scientists, and software developers). Since these jobs require knowledge and skills that cannot easily be taught to a machine, they will offset the total impact of automation. In smaller cities, fewer of those offsetting jobs exist.
A brewery in Virginia has even used this technology to create what it hopes could be the perfect IPA—and the methodology they used is certainly intriguing. Charlottesville’s Champion Brewing company recently teamed up with the nearby machine learning company Metis Machine to brew their new ML IPA—a computer’s vision of what should essentially be the ideal IPA.
“We provided the parameters on which IPAs are judged at the Great American Beer Festival and matched that range with the 10-best-selling IPAs nationally, as well as the 10 worst selling IPAs at a local retailer and Metis came up with the results,” Hunter Smith, owner of Champion Brewing Company said announcing the beer. “We are stoked to be the first to use this method of creating a beer recipe.”