Accurately predicting patients' risk of 30-day hospital readmission would enable hospitals to efficiently allocate resource-intensive interventions. We develop a new method, Categorical Co-Frequency Analysis (CoFA), for clustering diagnosis codes from the International Classification of Diseases (ICD) according to the similarity in relationships between covariates and readmission risk. CoFA measures the similarity between diagnoses by the frequency with which two diagnoses are split in the same direction versus split apart in random forests to predict readmission risk. Applying CoFA to de-identified data from Berkshire Medical Center, we identified three groups of diagnoses that vary in readmission risk. To evaluate CoFA, we compared readmission risk models using ICD majors and CoFA groups to a baseline model without diagnosis variables. We found substituting ICD majors for the CoFA-identified clusters simplified the model without compromising the accuracy of predictions. Fitting separate models for each ICD major and CoFA group did not improve predictions, suggesting that readmission risk may be more homogeneous that heterogeneous across diagnosis groups.

In recent weeks I managed to decrypt a difficult cipher that, despite expert codebreakers' best efforts, had remained unsolved for 70 years. The code was created by the late Cambridge professor and scientist Robert Henry Thouless, who passed away in 1984. He created it as a "test of survival" to see if he could communicate with the living after his death. Thouless thought if he successfully transmitted cipher keywords to the living through spiritual mediums and the message was received, this would prove he had survived his death. In 2019, I was more interested in seeing whether computer speed, storage and networking capabilities had advanced enough to break a code that had outlived its maker.

Major powers are rushing to strengthen their militaries through artificial intelligence, but the US is hamstrung by certain challenges that rivals like China may not face, giving them an advantage in this strategic competition. Artificial intelligence and machine learning are enabling cutting-edge technological capabilities that have any number of possibilities, both in the civilian and military space. AI can mean complex data analysis and accelerated decision-making -- a big advantage that could potentially be the decisive difference in a high-end fight. For China, one of its most significant advantages -- outside of its disregard for privacy concerns and civil liberties that allow it to gather data and develop capabilities faster -- is the fusion of military aims with civilian commercial industry. In contrast, leading US tech companies like Google are not working with the US military on AI. "If we do not find a way to strengthen the bonds between the United States government and industry and academia, then I would say we do have the real risk of not moving as fast as China when it comes to" artificial intelligence, Lt. Gen. Jack Shanahan said, responding to Insider's queries at a Pentagon press briefing Friday.

Some of the recommendations address getting all EU member states on board and having central oversight through the European Commission to collaborate and ensure consistency. The recommendations primarily deal with identifying funding for AI system development and encouraging regulations to minimize risks to humans and society. AI can enhance economies when used correctly, according to the report. It recommends creating a friendly environment for AI developers and investors. The report stresses that action should begin immediately to harness the opportunities AI can offer the EU and to ensure member states are competitive in the global space.

The U.S. Department of Commerce's National Institute of Standards and Technology (NIST) released a draft of a plan for the federal creation of AI standards. The plan aims to meet the economic and national security needs of the United States and help the country maintain global dominance. "U.S. engagement in establishing AI standards is critical; AI standards developed without the appropriate level and type of involvement of U.S. interests may exclude or disadvantage U.S.-based companies in the marketplace, as well as government agencies," the draft report (PDF) reads. "Moreover, due to the foundational nature of standards, the lack of U.S. stakeholder engagement in the development of AI standards can negatively impact the innovativeness and competitiveness of U.S. interests in the long term." The document, titled "U.S. Leadership in AI: Plan for Federal Engagement in Developing Technical Standards and Related Tools, " was put together in response to a Trump executive order mandating the creation of a federal AI engagement plan within 180 days.

A woman's job application is rejected because of a recruiting algorithm that favors men's résumés. A girl dies by suicide after graphic images of self-harm are pushed up on her feed by social media algorithms. A black teen steals something and gets rated high-risk for committing future crime by an algorithm used in courtroom sentencing, while a white man steals something of similar value and gets rated low-risk. In recent years, advances in computer science have yielded algorithms so powerful that their creators have presented them as tools that can help us make decisions more efficiently and impartially. But the idea that algorithms are unbiased is a fantasy; in fact, they still end up reflecting human biases.

Employed across industries, AI applications unlock smartphones using facial recognition, make driving decisions in autonomous vehicles, recommend entertainment options based on user preferences, assist the process of pharmaceutical development, judge the creditworthiness of potential homebuyers, and screen applicants for job interviews. AI automates, quickens, and improves data processing by finding patterns in the data, adapting to new data, and learning from experience. In theory, AI is objective--but in reality, AI systems are informed by human intelligence, which is of course far from perfect. Humans typically select the data used to train machine learning algorithms and create parameters for the machines to "learn" from new data over time. Even without discriminatory intent, the training data may reflect unconscious or historic bias. For example, if the training data shows that people of a certain gender or race have fulfilled certain criteria in the past, the algorithm may "learn" to select those individuals at the exclusion of others.

Meet Project Overlord: The Marines' Plan for Robot Ships to Move Their Soldiers and Supplies Earlier this year Navy leaders requested $400 million from Congress for two LUSVs in the 2020 proposed defense budget, with eight more to be purchased over the next five years. WASHINGTON – U.S. Marine Corps leaders plan to capitalize on a U.S. Navy plan to develop a Large Unmanned Surface Vessel (LUSV) for long-range resupply missions, and troop transport for Marine Corps warfighters. Smith made his comments today at the Association for Unmanned Vehicle Systems International (AUVSI) Defense, Protection, and Security conference in Washington. The future Navy LUSV could rendezvous with Navy amphibious assault ships offshore to move Marines and supplies quickly where needed, at perhaps lower costs and less risk to human ship crews than is possible today, Smith told AUVSI attendees in a keynote address. Unmanned systems "are less expensive than people," Smith pointed out in his address.

The Defense Department's artificial intelligence center is expecting a transformative year in 2020 with more than double its budget from 2019 and new projects that further ingrain AI into military operations. DoD's Joint Artificial Intelligence Center (JAIC) will embark on a new project next year called AI for maneuver and fires. "The project will focus on individual lines of effort or product lines oriented on warfighting operations like operations/intelligence fusion, joint all-domain command and control, accelerated sensor to shooter timelines, autonomous and swarming systems, target development and operations center workflows," Lt. Gen. Jack Shanahan, JAIC director, said at the Pentagon Friday. That all sounds like a lot of buzzwords, but a real-life example can be found right in your home. Often times you'll open up Netflix and it will say a show is a 95% match for you based on your past watching habits.

Why is AI an emerging cybersecurity threat? Artificial intelligence is a booming industry right now with large corporations, researchers, and startups all scrambling to make the most of the trend. From a cybersecurity perspective, there are a few reasons to be concerned about AI. Your threat assessment models need to be updated based on the following developments. Early cybersecurity AI may create a false sense of security. Most machine-learning methods currently in production require users to provide a training data set.