We first discuss certain problems with the classical probabilistic approach for assessing forensic evidence, in particular its inability to distinguish between lack of belief and disbelief, and its inability to model complete ignorance within a given population. We then discuss Shafer belief functions, a generalization of probability distributions, which can deal with both these objections. We use a calculus of belief functions which does not use the much criticized Dempster rule of combination, but only the very natural Dempster-Shafer conditioning. We then apply this calculus to some classical forensic problems like the various island problems and the problem of parental identification. If we impose no prior knowledge apart from assuming that the culprit or parent belongs to a given population (something which is possible in our setting), then our answers differ from the classical ones when uniform or other priors are imposed. We can actually retrieve the classical answers by imposing the relevant priors, so our setup can and should be interpreted as a generalization of the classical methodology, allowing more flexibility. We show how our calculus can be used to develop an analogue of Bayes' rule, with belief functions instead of classical probabilities. We also discuss consequences of our theory for legal practice.

Microsoft today announced that it is making it easier for developers to use its Computational Network Toolkit (CNTK) to build their own deep learning applications. The company first open sourced this toolkit in April 2015, but at the time, it was hosted on Microsoft's own CodePlex site and was only available under a restrictive academic license. Now, the team is moving the project to GitHub and to the MIT open source license. CNTK is an open-source deep-learning toolkit that became available back in April 2015. However, when it was still on CodePlex, it was restricted by an academic license, which means that it was virtually unused beyond scholarly use.

Last year Google was awarded a patent for a system that would enable the user to allocate tasks to "a plurality of robotic devices" via the cloud, giving rise to speculation that the company is building a robot army. In a patent registered with the United States Patent and Trademark Office, Google describes how a "computing component" (such as a PC or mobile phone) could communicate with robots over a network to allocate tasks and receive information.

A report shown at the 2016 World Economic Forum in January says millions of jobs will be lost to robots in the next few years. When thinking about who is most vulnerable, factory workers, drivers, and pilots come to mind. Surely the jobs requiring a human touch, such as artists, entertainers, and managers, will stick around, right? Maybe some of those jobs will be safe. Managers, not so much; very soon, robots will be replacing humans in top management positions, even up to the CEO level.

Moreover, it is fantasy to suggest that the accelerating development and deployment of technologies that taken together are considered to be A.I. will be stopped or limited, either by regulation or even by national legislation. A.I. is increasingly critical to competitive performance in most economic sectors, whether manufacturing or services; it is a significantly valued consumer product (how many people rely on softly spoken directions from their cellphone when they are driving?); and in some sectors in which it is highly prized, such as pornography and cybercrime, regulations are unlikely to be effective. And it is not just economics driving a more cognitive future: Every major military organization in the world knows that one way or another cognition in integrated techno-human systems--A.I. in one form or another--will be critical for military competence and national security in an increasingly complex and uncertain geopolitical environment. If the U.S. wants to stop military A.I. research, it is a dangerous whimsy to think that China, Russia, and others, public, private, and nongovernmental, necessarily will follow its lead.

On March 15, DeepMind's AlphaGo, a computer powered by a self-learning artificial intelligence computer program, defeated Go grandmaster Lee Sedol. As the AI community celebrates this major milestone in making machines smart, the debate of "man vs. machine" is heating up. Over the past 25 years -- especially the last five years -- the AI community has transformed theoretical machine learning constructs to solve useful problems. AI techniques such as self-learning, reinforcement learning, and deep neural networks were developed to recognize traffic signs and classify images. The recent rapid progress in AI was powered by the dramatic increase in financial investments in AI.

From the increasing use of contingent freelance workers to the growing role of robotics and smart machines, the corporate workforce is changing--radically and rapidly. These changes are no longer simply a distraction; they are now actively disrupting labor markets and the economy. Three years ago, Deloitte introduced the concept of the open talent economy, predicting that new labor models--on and off the balance sheet--would become increasingly important sources of talent.2 Granted, respondents to this year's survey rated workforce management the least important of the trends we explored. At an even more basic level, companies are struggling to understand who (and what) their workforces are composed of and how to manage today's incredibly diverse combination of worker types.

IT HAS wormed its way into almost every sphere of life, and the law is no exception. Artificial intelligence can now handle a lot of the drudgery of legal work: sifting mountains of documents for relevant titbits, for example, or automatically drafting and checking boilerplate contracts. There's even a "superintelligent attorney" app, ROSS, powered by IBM's Watson supercomputer, that fields legal queries by speed-reading legislation and other resources. But what does it mean for the law when an algorithm, rather than a person, calls the shots? Frank Levy at the Massachusetts Institute of Technology and Dana Remus at the University of North Carolina School of Law have been on the case, exploring the potential ramifications of robotic legal assistants.

Non-human employees are filling positions in all sorts of workplaces, and they are proving themselves to be fast, accurate, and reliable--more so than their human counterparts. That's why Apple's supplier Foxconn is reported to be replacing up to one million workers with robots in order to meet expected demand for the iPhone 6. And it's why Amazon deploys an army of robots to fetch items in its warehouses. It's also why machines powered by artificial intelligence (AI) are now reading MRIs, sorting through thousands of legal cases to identify pertinent information, and writing news articles. The displacement of workers by technology is nothing new, of course, but the nature of our rapidly advancing technology is, as is the wide variety of roles it's poised to replace.

Microsoft has decided to pull back its first publicly available artificial intelligence (AI) robot, after a horrible test run. Earlier this week, Microsoft released an artificial intelligence named Tay, who ran through an official Twitter Account, @Tayandyou. Within 24 hours of Microsoft releasing the AI on Twitter, Tay was shut down by Microsoft because of the offensive subject matter the bot was tweeting out. In a CNN Money Article by Hope King titled "After racist tweets, Microsoft muzzles teen chat bot Tay," a comment was made by Microsoft on the incident. "Microsoft blamed Tay's behavior on online trolls," according to the article, "saying in a statement that there was a coordinated effort to trick the program's commenting skills."