"Between 12 to 18 million Americans every year will experience some sort of diagnostic error," said Paul Cerrato, a journalist and researcher. "So the question is: Why such a huge number? And what can we do better in terms of reinventing the tools so they catch these conditions more effectively?" Cerrato is co-author, alongside Dr. John Halamka, newly minted president of Mayo Clinic Platform, of the new HIMSS Book Series edition, Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. At HIMSS20, the two of them will discuss the book, and the bigger picture around CDS tools that are fast being transformed by the advent of artificial intelligence, machine learning and big data analytics.

Bayes' theorem is one of the most important formulae in the field of mathematical statistics and probability, used to calculate the chances of a particular event occurring based on relevant existing information. Bayesian inference meanwhile leverages Bayes' theorem to update the probability of a hypothesis as additional data becomes available. New York University Assistant Professor Andrew Gordon Wilson addressed this question in his recent paper The Case for Bayesian Deep Learning. Paper Abstract: The key distinguishing property of a Bayesian approach is marginalization instead of optimization, not the prior, or Bayes rule. Bayesian inference is especially compelling for deep neural networks.

The AI Now Institute's Executive Director, Andrea Nill Sánchez, today testified before the European Parliament LIBE Committee Public Hearing on "Artificial Intelligence in Criminal Law and Its Use by the Police and Judicial Authorities in Criminal Matters." Her message was simple: "Predictive policing systems will never be safe… until the criminal justice system they're built on are reformed." Sanchez argued that predictive policing systems are built with "dirty data" compiled over decades of police misconduct, and that there's no current method by which this can be resolved with technology. Her testimony was based on a detailed study conducted by the AI Now Institute last year that detailed how predictive policing systems are inherently biased. In a recent study, my colleagues at the AI Now Institute examined 13 US police jurisdictions that had engaged in illegal, corrupt, or biased practices and subsequently built or acquired predictive policing systems. Specifically, my colleagues found that in nine of those jurisdictions, there was a high risk that the system's predictions reflected the biases embedded in the data.

Significant technological advancements and societal shifts occurred during the 2010's decade. Yet many of these developments became so quickly engrained in our daily lives that they often went relatively unnoticed, and their impact all but forgotten. Over this next decade, the 2020s, we expect similar rapid and meaningful advancements to occur. Moore's law suggests that over a 10-year period, semiconductors will advance by 32 times, bringing about mesmerizing innovation in the digital age that should not only change technology but society as well. In this piece, we review the technological advancements over the last decade and anticipate what revolutionary changes may be in store for us over the next 10 years.

In order to train the machine learning algorithms, researchers gathered data on patients from the Indiana Network for Patient Care. The models used information on prescriptions and diagnoses, which are structured fields, as well as medical notes, which are free text, to predict the onset of dementia. Researchers found that the free-text notes were the most valuable to help identify people at risk of developing the disease. The research team, which also included scientists from Georgia State, Albert Einstein College of Medicine and Solid Research Group, recently published its findings on two different machine learning approaches. The paper published in the Journal of the American Geriatrics Society analyzed the results of a natural language processing algorithm, which learns rules by analyzing examples, and the Artificial Intelligence in Medicine article shared the results from a random forest model, which is built using an ensemble of decision trees. Both methods showed similar accuracy at predicting the onset of dementia within one and three years of diagnosis.

On Wednesday the European Commission launched a blizzard of proposals and policy papers under the general umbrella of "shaping Europe's digital future". The documents released included: a report on the safety and liability implications of artificial intelligence, the internet of things and robotics; a paper outlining the EU's strategy for data; and a white paper on "excellence and trust" in artificial intelligence. In their general tenor, the documents evoke the blend of technocracy, democratic piety and ambitiousness that is the hallmark of EU communications. That said, it is also the case that in terms of doing anything to get tech companies under some kind of control, the European Commission is the only game in town. In a nice coincidence, the policy blitz came exactly 24 hours after Mark Zuckerberg, supreme leader of Facebook, accompanied by his bag-carrier – a guy called Nicholas Clegg who looked vaguely familiar – had called on the commission graciously to explain to its officials the correct way to regulate tech companies.

Talk about the weather, generally considered a neutral topic for conversations, is about to get extremely interesting. January 2020 was the Earth's hottest January in the past 141 years of climate records according to scientists at NOAA's National Centers for Environmental Information. Globally, extreme weather and climate disasters pose a threat to public health, economic well-being, and geopolitical stability. Economically, the U.S. has incurred $1.75 trillion in losses since 1980 due to 258 weather and climate disasters according to NOAA figures. Predicting extreme weather is a complex science, and an area where artificial intelligence (AI) machine learning, specifically the pattern-recognition capabilities of deep learning, can make a difference in forecasting accuracy.

The EU is pursuing a digital strategy that builds on our successful history of technology, innovation and ingenuity, vested in European values, and projecting them onto the international stage. The White Paper on Artificial Intelligence (AI) and the European data strategy presented today show that Europe can set global standards on technological development while putting people first. Digital technologies considerably improve our lives, from better access to knowledge and content to how we do business, communicate or buy goods and services. The EU must ensure that the digital transformation works for the benefit of all people, not just a few. Citizens should have the opportunity to flourish, choose freely, engage in society and at the same time feel safe online. Businesses should benefit from a framework that allows them to start up, scale up, pool data, innovate and compete with large companies on fair terms.

Learn about Knightscope's security robots that are scaling nationwide in the U.S. - potential clients can schedule a demo at www.knightscope.com Knightscope is offering securities through the use of an Offering Statement that has been qualified by the Securities and Exchange Commission under Tier II of Regulation A. A copy of the Final Offering Circular that forms a part of the Offering Statement may be obtained both here: www.seedinvest.com/knightscope. This profile and accompanying offering materials may contain forward-looking statements and information relating to, among other things, the company, its business plan and strategy, and its industry. These statements reflect management's current views with respect to future events based on information currently available and are subject to risks and uncertainties that could cause the company's actual results to differ materially. Investors are cautioned not to place undue reliance on these forward-looking statements as they are meant for illustrative purposes and they do not represent guarantees of future results, levels of activity, performance, or achievements, all of which cannot be made.

Research suggests that an AI beat humans to the punch in warning the world about the coronavirus. But it didn't get all the credit, because it needed humans to recognize the danger. Earlier reports had suggested that a Canadian epidemiologist had raised the first warnings of the outbreak, using an algorithm called BlueDot that scanned news reports and airline ticketing to predict the spread of the disease. Associated Press reporters Christina Larson and Matt O'Brien were dubious about the claim, and decided to draw up a timeline of when global alert systems noticed the signals. They determined that the first warning outside China of the virus came from the automated HealthMap system at Boston Children's Hospital, which scans online news and social media reports for signals of spreading disease.