Many of us like to think that artificial intelligence could help eradicate biases, that algorithms could help humans avoid hiring or policing according to gender or race-related stereotypes. But a new study suggests that when computers acquire knowledge from text written by humans, they also replicate the same racial and gender prejudices--thus perpetuating them. Researchers at Princeton University and Britain's University of Bath found that machine learning "absorbs stereotyped biases" when trained on words from the internet. Their findings, published in the journal Science on Thursday, showed that machines learn word associations from written texts that mirror those learned by humans. "Don't think that AI is some fairy godmother," said study co-author Joanna Bryson.

If you're headed to the UEFA Champions League final in Cardiff on June 3rd, you might just be part of a massive experiment in security -- and a privacy uproar. South Wales Police are conducting a face recognition trial that could scan every one of the 170,000 visitors expected to show up in the city for the match, whether or not they're heading to the stadium. Cameras around both the stadium and Cardiff's main train station will compare faces against a police database of 500,000 people of interest. If there's a match, police will get a heads-up that could help them stop a terrorist or frequent hooligan. The UK's surveillance camera commissioner, Tony Porter, tells Motherboard that the South Wales Police will have to honor the country's usage guidelines.

A true lie is a lie that becomes true when announced. In a logic of announcements, where the announcing agent is not modelled, a true lie is a formula (that is false and) that becomes true when announced. We investigate true lies and other types of interaction between announced formulas, their preconditions and their postconditions, in the setting of Gerbrandy's logic of believed announcements, wherein agents may have or obtain incorrect beliefs. Our results are on the satisfiability and validity of instantiations of these semantically defined categories, on iterated announcements, including arbitrarily often iterated announcements, and on syntactic characterization. We close with results for iterated announcements in the logic of knowledge (instead of belief), and for lying as private announcements (instead of public announcements) to different agents. Detailed examples illustrate our lying concepts.

Facebook must obtain the permission of German users of WhatsApp before processing their personal data, a German court confirmed on Tuesday. Last August, Facebook subsidiary WhatsApp changed its privacy policy to allow the transfer of its users' personal information to Facebook for processing. That angered the Hamburg Commissioner for Data Protection and Freedom of Information, which in September ordered the companies to stop the transfer until they had obtained users' consent, and to delete any data they had already transferred. Facebook challenged the order in Hamburg's administrative court, and on Tuesday the court handed down its ruling. The court upheld the Commissioner's requirement to obtain consent, but threw out the order to delete the data on procedural grounds.

Babylon Health, the U.K. startup that offers a digital healthcare app using a mixture of artificial intelligence (AI) and video and text consultations with doctors and specialists, has raised $60 million in new funding. The company says it plans to use the new capital to continue building out its AI capabilities, including offering diagnosis by AI (rather than a more simple triage functionality), which is pegged to roll out later this year. Babylon's previous investors also include Hoxton Ventures, Richard Reed, Adam Balon and Jon Wright (co-founders of Innocent Drinks), and, perhaps most notably, Demis Hassabis and Mustafa Suleyman, founders of Deepmind, the AI group bought by Google for $500m, who are also advising the digital health startup. Earlier this year, Babylon starting working with a number of health authorities in London to trial its AI-powered chatbot'triage' service as an alternative to the NHS's 111 telephone helpline that patients call to get healthcare advice and be directed to local and out-of-hours medical services. It built on top of the same AI-enabled symptom checker feature in the main Babylon app that was released last July and which the company says has provided medical advice to over 250,000 people to date.

The paper submissions for ICLR 2017 in Toulon France deadline has arrived and instead of a trickle of new knowledge about Deep Learning we get a massive deluge. This is a gold mine of research that's hot off the presses. Many papers are incremental improvements of algorithms of the state of the art. I had hoped to find more fundamental theoretical and experimental results of the nature of Deep Learning, unfortunately there were just a few. There was however 2 developments that were mind boggling and one paper that is something I've been suspecting for a while now and has finally been confirm to shocking results.

ABB and IBM on Tuesday announced a strategic collaboration that brings together ABB's digital offering -- ABB Ability -- with IBM Watson Internet of Things cognitive capabilities to unlock new value for customers in utilities, industry, transport and infrastructure. Customers will benefit from ABB's deep domain knowledge and extensive portfolio of digital solutions combined with IBM's expertise in artificial intelligence and machine learning as well as different industry verticals. The first two joint industry solutions powered by ABB Ability and Watson will bring real-time cognitive insights to the factory floor and smart grids. "This powerful combination marks truly the next level of industrial technology, moving beyond current connected systems that simply gather data, to industrial operations and machines that use data to sense, analyze, optimize and take actions that drive greater uptime, speed and yield for industrial customers," said ABB CEO Ulrich Spiesshofer. "With an installed base of 70 million connected devices, 70,000 digital control systems and 6,000 enterprise software solutions, ABB is a trusted leader in the industrial space, and has a four decade long history of creating digital solutions for customers. IBM is a leader in artificial intelligence and cognitive computing. Together, IBM and ABB will create powerful solutions for customers to benefit from the Fourth Industrial Revolution."

Through my work, I get to meet with lots of interesting people who perform all types of roles and come from all sorts of backgrounds. Sometimes, we get to discuss issues that are very much in the tactical area. At other times, it is more future focused and strategic. The subject of AI – Artificial Intelligence – comes up quite frequently. Actually, I believe it is an unspoken influencing factor to many of the strategic conversations that businesses are having today. Almost every week in the UK there is the seemingly ever present problem of a train company holding a strike on the basis of jobs being lost due to a change in, basically, technology.

Exponential progress in artificial intelligence (AI) and machine learning, fuelled by the combination of cloud, big data and new algorithms, is transforming the retail industry. As AI leverages big data to automate, predict and personalize, retail is testing and implementing these applications to garner robust competitive advantages. The key focus for AI in retail is customer relationships. In times of concerns for the retail sector in the UK where sales posted biggest quarterly fall since 2010, the refashioning of this industry comes as a breath of fresh air with many opportunities to come. Global Artificial Intelligence Opportunities in Retail, 2017, new research from Frost & Sullivan's Connected Industries Growth Partnership Service, offers an overview of AI and its relevance to business in 2017.

This can be seen by the fact that a single repeated word commonly triggers an endless repetitive cycle. For example, a single substitution error Germany beat Germany leads to the catastrophic Germany beat Germany beat Germany beat…, and not the less-wrong Germany beat Germany 2-0. Our solution for Problem 1 (inaccurate copying) is the pointer-generator network. This is a hybrid network that can choose to copy words from the source via pointing, while retaining the ability to generate words from the fixed vocabulary. Let's step through the diagram! This diagram shows the third step of the decoder, when we have so far generated the partial summary Germany beat. As before, we calculate an attention distribution and a vocabulary distribution. However, we also calculate the generation probability, which is a scalar value between 0 and 1. This represents the probability of generating a word from the vocabulary, versus copying a word from the source.