Tech companies Microsoft and Google are sounding the alarm on just how harmful artificial intelligence can be for investors and brands alike. A.I. is still the most disputed part of technology and is becoming increasingly more commonplace as companies look to incorporate it across their platforms. While critics call for justification on the use of the technology and in some cases an all-out ban, A.I. continues to be a billion dollar industry, with many tech companies willing to withstand a tarnished brand reputation for lucrative profits. In Google's recently released 2018 SEC annual report it highlighted their brand issues around A.I. that could impact the company's bottom line. "New products and services, including those that incorporate or utilize artificial intelligence and machine learning, can raise new or exacerbate existing ethical, technological, legal, and other challenges, which may negatively affect our brands and demand for our products and services and adversely affect our revenues and operating results."

Physician burnout is one of the most serious conditions in today's medical profession. The Agency for Healthcare Research and Quality defines the condition as "a long-term stress reaction caused by emotional exhaustion [and] depersonalization," among other factors. According to the American Medical Association, physicians suffer from considerable stress caused by facets of their job that have little to do with actually providing personalized patient care. The AMA reports that physicians spend up to six hours daily working with electronic health records (EHRs) to adhere to government and hospital documentation requirements. That's six hours not spent seeing patients, and thus not having the time to listen carefully and diagnose, empathize, hold a hand, speak with family members, or explain conditions and next steps.

In many social situations, a discrepancy arises between an individual's private and expressed opinions on a given topic. Motivated by Solomon Asch's seminal experiments on social conformity and other related socio-psychological works, we propose a novel opinion dynamics model to study how such a discrepancy can arise in general social networks of interpersonal influence. Each individual in the network has both a private and an expressed opinion: an individual's private opinion evolves under social influence from the expressed opinions of the individual's neighbours, while the individual determines his or her expressed opinion under a pressure to conform to the average expressed opinion of his or her neighbours, termed the local public opinion. General conditions on the network that guarantee exponentially fast convergence of the opinions to a limit are obtained. Further analysis of the limit yields several semi-quantitative conclusions, which have insightful social interpretations, including the establishing of conditions that ensure every individual in the network has such a discrepancy. Last, we show the generality and validity of the model by using it to explain and predict the results of Solomon Asch's seminal experiments.

Increased federal funding and ethical inquiry are needed to best develop America's artificial intelligence capabilities, argued three Duke experts in a congressional briefing on Capitol Hill on Feb. 15. Duke Professors Vincent Conitzer, Nita Farahany and Walter Sinnott-Armstrong spoke broadly about the ethical implications of what the advent of A.I. means for medicine, lethal weapons, automobiles and unemployment. Conitzer's presentation offered a definition of A.I., Sinnott-Armstrong explored the ethics of lethal autonomous weapons and Farahany dove into the legal questions A.I. will challenge. Artificially intelligent systems already excel at games of probability and prediction but fail at games of context and interpretation, said Conitzer in his presentation. This program began a three-part Duke in DC series for congressional staff exploring policy implications for human-A.I. collaboration.

After years of promises about a physical wall stretching along the United States-Mexico border, president Donald Trump declared a state of emergency last week in an attempt to secure wall funding in spite of Congressional opposition. But physical barriers alone have always been both ineffective and expensive. And the constant debate around that singular aspect has distracted from a much more pressing issue: how the US can expand its use of technology for screening and enforcement at the border without overstepping already strained privacy rights. Border security technologies, like surveillance drones and biometric identity schemes, received funding in Congress's most recent spending bill as an alternative to Trump's physical wall. But privacy advocates have long argued that a "smart wall," often called a "smart fence," can pose real threats to human rights not just at checkpoints and processing facilities, but for anyone within the 100-mile-wide "border zone" in which US Customs and Border Protection has jurisdiction. "The way that this debate has been weaponized has really shut down a big portion of the conversation that we should be having," says Evan Greer, deputy director of the digital rights group Fight for the Future.

In December, Microsoft President Brad Smith urged lawmakers to set rules on facial-recognition technology to prevent a privacy-threatening "race to the bottom." Now the company has joined a legislative fight in its home state against rules it says would be too restrictive. Microsoft is pushing back on a bill sponsored by a bipartisan group of Washington state lawmakers that would ban local and state government from using facial recognition until certain conditions are met, including a report by the state attorney general certifying that systems in use are equally accurate for people of differing races, skin tones, ethnicities, genders, or age. Microsoft has endorsed a different bipartisan privacy bill, modeled on European data laws. It contains less restrictive facial recognition rules, which closely mirror Smith's proposals from December.

Securing patents for inventions that use artificial intelligence (AI) and machine learning can be challenging for innovators of these ground-breaking technologies, which attempt to use the processing power of computers to replicate the intelligence and learning capabilities of humans. Without patent or other intellectual property protection, they may be unable to commercialise their inventions, which could undermine investment in this dynamic field of research and development. To clear the way for innovators, the European Patent Office has recently amended its'Guidelines for Examination' by including a new section containing advice about how patents related to AI and machine learning technologies should be assessed. The guidance clarifies that whilst algorithms are regarded as'computational' and abstract in nature, which means they are not patentable per se, once applied to a technical problem they may become eligible for patent protection. Beneficially, the approach outlined in the guidance is similar to that currently used to assess the patentability of computer-implemented inventions.

You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like? You're also not reassured by the vendors the city interacts with.

This paper presents DIALOG (Digital Investigation Ontology); a framework for the management, reuse, and analysis of Digital Investigation knowledge. DIALOG provides a general, application independent vocabulary that can be used to describe an investigation at different levels of detail. DIALOG is defined to encapsulate all concepts of the digital forensics field and the relationships between them. In particular, we concentrate on the Windows Registry, where registry keys are modeled in terms of both their structure and function. Registry analysis software tools are modeled in a similar manner and we illustrate how the interpretation of their results can be done using the reasoning capabilities of ontology