Researchers from the University of Valladolid (Spain) have created a computer model based on neural networks that calculates the probability in Spanish provinces of corruption, as well as the conditions that favor it. This alert system confirms that the probabilities increase when the same party stays in government for more years. The study, published in Social Indicators Research, does not mention the provinces most prone to corruption so as not to generate controversy, explains one of the authors, Ivan Pastor, who says, "A greater propensity or high probability does not imply corruption will actually happen." The data indicate that the real estate tax, the exaggerated increase in the price of housing, the opening of bank branches and the creation of new companies are some of the variables that seem to induce public corruption, and when they are added together in a region, more rigorous control of public accounts might be warranted. "In addition, as might be expected, our model confirms that an increase in the number of years of government by the same political party increases the chances of corruption, regardless of whether or not the party governs with majority," says Pastor.

The person or company that posted the challenged material can counter with a statement that the material isn't illegal. But challenging the takedown notice can give U.S. courts jurisdiction over foreign residents in the dispute.

After nearly a year of legal wrangling, dramatic last-minute delays and uncooperative witnesses, a jury will soon hear arguments in Waymo's high-profile lawsuit accusing Uber of stealing driverless car technology. The trial, which is scheduled to start with jury selection on Wednesday in federal court in San Francisco, pits Waymo, a spinoff of Google and one of the most successful companies from the dot-com boom, against Uber, the ride-hailing giant and today's most valuable start-up. At stake is a leading role in the intense competition among tech and auto companies to create autonomous vehicles. The dispute hinges on the actions of a former star engineer at Google who started his own company and then sold it to Uber within a year. Did he steal thousands of Google computer files as he headed out the door and bring those files with him to Uber?

System detects patterns in Email About likely markers of spam Detected Pattern Emails with "Earn Cash" More likely to be spam email Can use such detected patterns to make automated decisions about future emails Example: Email Spam Filter "Earn Cash" "Earn Cash" detected in 10% of Spam emails 0% of wanted emails Identification Improves Algorithm improves in performance In auto-identifying spam As it is able to examine more data And find additional indicia of spam Algorithm is "learning" over time from additional examples Example: Email Spam Filter "Free" Probability of Spam Contains "Free" 70% Spam Contains "Earn Cash" 90% Spam From Belarus 85% Spam For some (not all) complex tasks Requiring intelligence Intelligent Results Without Intelligence Can get "intelligent" automated results without intelligence By finding suitable Proxies or Patterns People use advanced cognitive skills to translate Proxies for Intelligent Results Without Intelligence Google finds statistical correlations by analyzing previously translated documents Statistical Machine Translation Produces automated translations using statistical likelihood as a "proxy" for underlying meaning Detecting Patterns Proxy Principle for Automation That can serve as Proxies For some underlying Cognitive Task Learning Machine Learning Main Points Pattern Detection Data Self-Programming Summary Major AI Approaches Two Major AI Techniques • Logic and Rules-Based Approach • Machine Learning (Pattern-Based Approach) Hybrid Systems • Many successful AI systems are hybrids of • Machine learning & Rules-Based Hybrids • e.g. Self-driving cars employ both approaches • Human intelligence AI Hybrids • Also, many successful AI systems work best when • They work with human intelligence • AI systems supply information for humans Humans Computers Technology Enhancing (Not Replacing) Humans Humans Alone Computers Alone Examples of AI in Law Today • Machine Learning • AI in Litigation - E-Discovery and "Predictive Coding" • Natural Language Processing (NLP) of Legal Documents • Automated contract analysis • Predictive Analytics for Litigation • Machine Learning Assisted Legal Research • Logic and Rules-Based Approaches • Compliance Engines • Expert Systems • Attorney Workflow Rule Systems • Automated Document Assembly Limits on Artificial Intelligence • Artificial Intelligence Accomplishments • Automate many things that couldn't do before • Limits • Many things still beyond the realm of AI • No thinking computers • No Abstract Reasoning • Often AI systems Have Accuracy Limits • Many things difficult to capture in data • Sometimes Hard to interpret Systems Questions Harry Surden Associate Professor of Law University of Colorado Law School Affiliated Faculty, Stanford CodeX Center Twitter: @HarrySurden Email: hsurden@colorado.edu

The choice has consequences for taxpayers. If no company is found financially responsible, the EPA pays the bill for about 10 years and then turns it over to the state. Colorado currently pays about $1 million a year to operate a treatment plant at one Superfund mine. By 2028, it will pay about $5.7 million annually to operate plants at three mines, not including anything at the Bonita Peak site.

William Gibson wrote that the future is here, just not evenly distributed. The phrase is usually used to point out how the rich have more access to technology, but what happens when the poor are disproportionately subject to it? In Automating Inequality, author Virginia Eubanks argues that the poor are the testing ground for new technology that increases inequality. The book, out this week, starts with a history of American poorhouses, which dotted the landscape starting in the 1660s and were around into the 20th century. From there, Eubanks catalogues how the poor have been treated over the last hundred years, before coming to today's system of social services that increasingly relies on algorithms.

In a cramped and dark venue in a sleazy Tokyo district, dozens of middle-aged men cheer at a performer on stage: The object of their adoration is a 6-year-old girl. Decked out in makeup with ribbons in her hair, Ai is dressed like an adult but still looks very much a child. Even though Ai is so young, she is technically considered an "idol" singer. More typically, idols are in their teens. The idol phenomenon is common in Japan, where rights groups have complained that society's sometimes permissive view of the sexualization of young girls puts minors at risk.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Excerpted from Experience on Demand: What Virtual Reality Is, How It Works, and What It Can Do by Jeremy Bailenson. In his 1984 cyberpunk thriller Neuromancer--a canonical literary journey in virtual reality--William Gibson introduced the terms "cyberspace" and the "matrix." The words are familiar now, but the way he discusses them still feels new: He calls them "a consensual hallucination." What Gibson suggests is that it won't be the graphics or photorealistic avatars that will make virtual worlds feel real--it will be the community of people interacting within them, bringing the world alive through their mutual acknowledgment of its reality.

YOKOHAMA – The Kanagawa Prefectural Police plan to become the first in the nation to introduce predictive policing, a method of anticipating crimes and accidents using artificial intelligence, sources said Sunday. The Kanagawa police will seek research expenses under the prefecture's budget for fiscal 2018 starting April, hoping to put a predictive policing system in place on a trial basis before the 2020 Tokyo Olympics, prefectural government sources said. A system that can determine whether a single perpetrator is behind several crimes, predict an offender's next move and detect where and when crimes or accidents are likely to occur would help police officers investigate crimes and prevent some from happening, they said. It would allow them to patrol the suggested places at the most likely times to ensure safety and would also help speed up probes, the sources said. The AI-based system would employ a "deep learning" algorithm that allows the computer to teach itself by analyzing big data.

William Gibson wrote that the future is here, just not evenly distributed. The phrase is usually used to point out how the rich have more access to technology, but what happens when the poor are disproportionately subject to it? In Automating Inequality, author Virginia Eubanks argues that the poor are the testing ground for new technology that increases inequality. The book, out this week, starts with a history of American poorhouses, which dotted the landscape starting in the 1660s and were around into the 20th century. From there, Eubanks catalogues how the poor have been treated over the last hundred years, before coming to today's system of social services that increasingly relies on algorithms.