Regulators are beginning to teach robots who's the boss. After spending billions of dollars on cutting-edge artificial intelligence technologies, Europe's banks and insurers face tougher scrutiny of the tools they use to help root out fraud, check borrowers' creditworthiness and automate claims decisions. European Union rules starting this week will stress human oversight and consumer protection, which may hamper companies trying to build the tools of the future. "Companies developing AI technologies will have to consider and embed the data protection issues into the design process," David Martin, senior legal officer at Brussels-based consumer advocate BEUC, said in an interview. "It's not something where they can just tick a box at the end."

Google has released its manifesto of principles guiding its efforts in the artificial intelligence realm – though some say the salvo isn't as complete as it could be. AI is the new golden ring for developers, thanks to its potential to not just automate functions at scale but also to make contextual decisions, based on what it learns over time. This experiential aspect has the capacity to bring immense good to the proceedings of life, in the form of weeding out cyber-threats before they happen, offering smarter recommendations to consumers and improving algorithms, even tracking wildfire risk and monitoring the environments of endangered species – or, on the back-end, it can speed along manufacturing processes or evaluate open-source code for potential flaws. What we don't want, of course, is a Matrix-y, Skynet-y, self-aware network interested in, say, enslaving humans. Google is looking to thread this needle with its latest weigh-in on the AI front, its principles for guiding AI development.

DeepMind, a leading artificial intelligence (AI) research company, has filed a series of international patent applications, which have now been published for the first time. The applications relate to a number of the fundamental aspects of modern day machine learning, and are therefore of potential significance to anyone operating in the commercial AI sector. Background to DeepMind DeepMind is a London based artificial intelligence (AI) research company, widely recognized as being at the forefront of the field. DeepMind was founded in 2010 and acquired by Google in 2014 for £400m. In 2017, DeepMind famously developed AI capable of defeating a world-champion at Go (Silver et al.

Problematic transportation outfit Uber is thinking about a way to use your phone to determine if you've been drinking. A patent application was uncovered by CNN, entitled "Predicting user state using machine learning," which outlines the general idea. Essentially, by watching how you behave day-to-day, the system can pick up when your behavior is normal (for you) or abnormal. That could be, for instance, how you use your phone, the angle at which you hold it, and even how you're walking. Obviously there are some common sense elements to this, too, especially if you're requesting a ride in the small hours from a notorious night spot.

Amazon is being called out for the poor working conditions in the China-based factory that manufactures its products. New York-based China Labor Watch published over the weekend its report on the factory that produces Amazon's Echo speakers and Kindle e-readers. The report, which summarizes what the watchdog group found throughout its investigation from August 2017 to April 2018, seemingly paints CEO Jeff Bezos and Amazon in a bad light for allegedly taking advantage of low-paid laborers. "[Our] investigation revealed a number of rights violations at the Hengyang Foxconn factory, which manufactures Amazon's Kindle, Echo Dots and tablets," the report stated. "The investigation revealed that dispatch workers made up more than 40 percent of the workforce, a clear violation of the legally mandated 10 percent."

Against the background of the emerging debate about personalized law, this book chapter explores how Big Data and algorithm-based regulation could fundamentally change the design and structure of legal norms: impersonal law based on typifications could be replaced by a more personalized law, based on "granular legal norms". We argue that the use of legal typifications which is a hallmark of impersonal law can be conceptualized as the answer to an information problem, a concession to the imperfections of a legal system administered by humans. The emergence of super-human capacities of information-processing through artificial intelligence could make it possible to personalize the law and achieve a level of "granularity" that has hitherto been unachieved.

IBM's Watson is one of the most relevant AI systems at the moment. Marc Andrews gives us an insider's view on how the company sees the current landscape for the development of this technology as companies start embracing it A year ago, financial institutions started to realise that Artificial Intelligence (AI) should have applicability in their space but didn't fully understand the use cases or the potential implications to their business. Looking back, we needed the view of an expert on the field of AI, so we reached out to IBM's Vice President of Watson Financial Services Solutions, Marc Andrews, to ask him about how he saw the past year. Because of the unknown, we saw a lot of trepidation from these financial institutions: how will we get real value from AI? which activities can AI be applied to, and will it replace jobs? will AI be a black box that drives decisions which can't be explained? "Watson is being trained on regulatory citations to help financial institutions navigate an increasingly complex regulatory environment."

This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks. Moreover, we decided to follow the very recent suggestions of datasheets for dataset, trying to standardize all the available information of the dataset, with a transparency objective.

The first batch of flamethrowers sold by Elon Musk's tunnel construction business The Boring Company have been handed out to customers - with some people traveling thousands of miles to pick one up. The Tesla entrepreneur had suggested the idea of selling a flamethrower at the end of 2017, with the project aiming to raise $10m for The Boring Company, which was founded with the intention of building a network of tunnels to help reduce traffic congestion across the US. Mr Musk claimed that the company had sold 20,000 of the $500 in four days in during January this year, with the first flamethrowers handed out at Boring's Hawthorne, California offices over the weekend. The event took place in a car park adjacent to another of Mr Musk's companies - SpaceX - with the tech billionaire announcing on Twitter that the first 1,000 flamethrowers were bring picked up. Mr Musk has called the item "Not-a-flamethrower" to get around any legal issues of shipping items called flamethrowers, but some customers could not wait to get it into their hands.

Voices for the need of an ethical Artificial Intelligence are getting louder. AI has a great many applications, and has been solving various problems for humans. But such a powerful technology raises equal concerns about its possible misuse. And there have already been multiple cases of AI being used for malicious purposes. It is an advanced AI software that creates an artificial version of a person's face accurately matching that of another.