Which of these levels of personal detail do you feel comfortable sharing with your smartphone? And should every app on that device have the same level of knowledge about your personal details? Welcome to the concept of siloed sharing. If you want to keep relying on your favorite device to store and automatically sort through your data, it's time to start considering whether you want to trust device-, app-, and cloud-level AI services to share access to all of your information, or whether there should be highly differential access levels with silo-class safeguards in place. Your phone already contains far more information about you than you realize. Depending on who makes the phone's operating system and chips, that information might be spread across storage silos -- separate folders and/or "secure enclaves" -- that aren't easily accessible to the network, the operating system, or other apps.

Charge prediction, determining charges for criminal cases by analyzing the textual fact descriptions, is a promising technology in legal assistant systems. In practice, the fact descriptions could exhibit a significant intra-class variation due to factors like nonnormative use of language, which makes the prediction task very challenging, especially for charge classes with too few samples to cover the expression variation. In this work, we explore to use the charge definitions from criminal law to alleviate this issue. The key idea is that the expressions in a fact description should have corresponding formal terms in charge definitions, and those terms are shared across classes and could account for the diversity in the fact descriptions. Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation. The generated auxiliary representations are created through the interaction of fact description with the relevant charge definitions and terms in those definitions by integrated sentence-and word-level attention scheme. Experimental results on two datasets show that our model achieves significant improvement than baselines, especially for classes with few samples. Introduction The task of charge prediction is to determine appropriate charges, such as theft, seizing or robbery, for criminal cases by analyzing the textual fact descriptions.

Filing a patent is the clerical equivalent of pulling teeth -- at least in the U.S. It first requires inventors to determine the type of intellectual property (IP) protection they require (i.e., utility, design, or plant). Then they're on the hook to conduct a United States Patent and Trademark Office (USPTO) database search for similar inventions. If and only if the novelty of their idea passes muster are they allowed to proceed to the next step, which is preparing an application and fees. The system has motivated people like former aerospace engineer Dr. Stephen Thaler to turn to AI in pursuit of a better way. He, along with a team of legal experts and engineers, developed DABUS, a "creativity machine" that's able to generate ideas without human intervention.

Is artificial intelligence (AI) making the working world better or worse? That was the question explored last week at an interactive symposium hosted by The A.I. Age, a Seattle Times reporting project. AI is seen in workplaces, such as in writing technology used to craft job postings, autonomous floor scrubbers in retail stores and food and service robots in hotels. Yet the impacts of AI on the future of work remains unknown. Experts, including University of Washington public-policy lecturer Akhtar Badshah, co-executive director of the nonprofit United for Respect Andrea Dehlendorf and UW technology law professor Ryan Calo shared their views on the topic during a panel discussion Wednesday evening in downtown Seattle.

It seems that the use of artificial intelligence in facial recognition technology is one that has grown the farthest so far. As ZDNet notes, so far companies like Microsoft have already developed facial recognition technology that can recognize facial expression (FR) with the use of emotion tools. But the limiting factor so far has been that these tools were limited to eight, so-called core states – anger, contempt, fear, disgust, happiness, sadness, surprise or neutral. Now steps in Japanese tech developer Fujitsu, with AI-based technology that takes facial recognition one step further in tracking expressed emotions. The existing FR technology is based, as ZDNet explains, on "identifying various action units (AUs) – that is, certain facial muscle movements we make and which can be linked to specific emotions."

At A Glance: Omdena and Spell collaborate with 30 global AI practitioners in a global challenge to harness the power of machine learning to pioneer new approaches to combatting sexual harassment. Sexual harassment is a severe and ongoing problem that plagues communities worldwide today. The issue particularly rampant in India, where thousands of harassment cases are reported each year. Policymakers have been working to create solutions, yet despite efforts to curb offenders and bring justice to victims, there has been little progress in shifting culture on a societal level, perpetuating a society where women must grapple with fear for their safety in public spaces. Recently, Safecity India, an award-winning NGO with the world's most comprehensive database on sexual harassment cases, hosted an Omdena challenge in effort to bring communities together and create an innovative product to fix the problem.

Imagine you've applied for a job or for a loan, and you're told you're unsuccessful. You're curious as to why, and so you use GDPR legislation to request access to the information the company holds on you. You obtain your data – and at the same time, you discover that the decision was made using artificial intelligence (AI) algorithms that screened out your application for no obvious reason. You discover that AI is being used for surveillance purposes at your place of work – and also that your employer is collecting and processing data relating to your health history using AI algorithms. In neither case has your consent been sought or obtained.

A text-generating artificial intelligence (AI) algorithm whose creators initially deemed too dangerous to release – given its ability to churn out fake news, spam and misinformation after feasting on a mere headline – has been unleashed. So far, so good, says the research lab, OpenAI. In a blog post last week, the lab said that the researchers have seen "no strong evidence of misuse" of the machine-learning language model, which is called GPT-2… at least, not yet. While we've seen some discussion around GPT-2's potential to augment high-volume/low-yield operations like spam and phishing, we haven't seen evidence of writing code, documentation, or instances of misuse […] We acknowledge that we cannot be aware of all threats, and that motivated actors can replicate language models without model release. Exactly how convincing is the output?

In a world where inequality between men and women rules in many sectors of activity, the power of AI could help identify, address and possibly solve those inequalities. Only 22% of AI professionals globally and only 12% of the leading machine-learning researchers are female, according to recent international reports. Because algorithms learn from real-world data, AI can potentially adopt and reinforce existing social biases. Developers could unconsciously integrate gender biases into their AI systems and perpetrate them in recruiting tools, search engines, face recognition systems, medical diagnosis and loan approval tools. AI digital assistants, obedient and obliging machines that pretend to be women are entering our homes, cars and offices and provide a powerful illustration of gender biases coded into mass market products.

Artificial Intelligence (AI) is finding its way into more and more aspects of our daily lives. It is in the algorithms designed to improve our health diagnostics. And it is used in the predictive policing tools used by the police to fight crime. Each of these examples throws up potential problems when it comes to the protection of our human rights. Predictive policing, if not correctly designed, can lead to discrimination based on race, gender or ethnicity.