The U.S. Congress should create a federal agency for artificial intelligence. In 1906, in response to shocking reports about the disgusting conditions in U.S. meat-packing facilities, Congress created the Food and Drug Administration (FDA) to ensure safe and sanitary food production. In 1934, in the wake of the worst stock market crash in U.S. history, Congress created the Securities and Exchange Commission (SEC) to regulate capital markets. In 1970, as the nation became increasingly alarmed about the deterioration of the natural environment, Congress created the Environmental Protection Agency (EPA) to ensure cleaner skies and waters. When an entire field begins to create a broad set of challenges for the public, demanding thoughtful regulation, a proven governmental approach is to create a federal agency focused specifically on engaging with and managing that field.

The U.S. Weather Service has always phrased rain forecasts as probabilities. I do not want a classification of "it will rain today." There is a slight loss/disutility of carrying an umbrella, and I want to be the one to make the tradeoff. This is coming from personal experience and from multiple contexts, but it seems that many data scientists simply do not understand logistic regression, or binomials and multinomials in general. The problem arises from logistic regression often being taught as a "classification" algorithm in the machine learning world.

Zone7 bases its analysis on more than five million hours of performance data. While it has started pilot programs in MLB and the NHL, its focus is on global soccer, with about three dozen clients spanning Bundesliga, Serie A, Ligue 1 and the English Football League Championship, which is the second division below the Premier League. Its most high-profile success (that it is able to disclose) has been Getafe CF, which is currently in fifth place in Spain's La Liga despite a team wage bill in the league's bottom half. By some measures, they've reduced injuries by 65% with Zone7.

If you have ever used Python and scikit-learn to build machine learning (ML) models from large data sets, you may have also wished that you could make these computations go faster. What if I told you that altering a single line of code could accelerate your ML computations? What if I also told you that getting faster results doesn't require specialized hardware? In this article, I will teach you how to train ridge regression models using a version of scikit-learn that is optimized for Intel CPUs, then compare the performance and accuracy of these models trained with the vanilla scikit-learn library. This article continues our series on accelerated ML algorithms.

AI, or artificial intelligence, is influencing some fundamental aspects of our lives, from our democracy to employment prospects. It is transforming industries and changing societies. Behavior is being tracked, traced and analyzed -- whether people know it or not -- and some are frustrated by the ever-growing number of chatbots we have to engage with. Like many acronyms, AI is now an all-encompassing marketing phrase that is thrown around, much like 5G. AI is really software running on powerful computers that are programmed to interpret data and act on it intelligently.

Last week, I wrote about Apple's App Store controversy, which I'm kind of revisiting this week through the lens of how Apple's WWDC announcements tease a change to what apps fundamentally look like in the future. If you're reading this on the TechCrunch site, you can get this in your inbox here, and follow my tweets here. Apple's App Store has had a controversial month with developers demanding changes to how apps are monetized, but as Apple detailed the next versions of its operating systems at WWDC, it's clear they believe third-party apps themselves have room to be fundamentally revamped. This week at WWDC, Apple debuted App Clips, a snappy new segment of third party experiences that scales down the idea of an app around just a single feature or two. A user can quickly call up an App Clip via a URL, NFC tag or visual code and download when the right context arises.

Members of Congress introduced a new bill on Thursday that would ban government use of biometric technology, including facial recognition tools. Pramila Jayapal and Ayanna Pressley announced the Facial Recognition and Biometric Technology Moratorium Act, which they said resulted from a growing body of research that "points to systematic inaccuracy and bias issues in biometric technologies which pose disproportionate risks to non-white individuals." The bill came just one day after the first documented instance of police mistakenly arresting a man due to facial recognition software. There has been long-standing, widespread concern about the use of facial recognition software from lawmakers, researchers rights groups and even the people behind the technology. Multiple studies over the past three years have repeatedly proven that the tool is still not accurate, especially for people with darker skin.

The world is in the midst of a historical turning point. The COVID-19 pandemic has effectively halted life as we once knew it, and left the open question, "what will our world look like when'normal' life resumes?" While we don't have a crystal ball that allows us to peer into the future, history has given us a template on what to expect. Past pandemics have shaped politics, crashed economies, purred revolutions and produced other profound societal transformations. In the 14th century, the bubonic plague killed more than 60 percent of Europe's population – a dramatic population decline that actually improved living standards for the survivors and marked the decline in serfdom.

Deep learning (DL) models are known for tackling the nonlinearities associated with data, which the traditional estimators such as logistic regression couldn't. However, there is still a cloud of doubt with regards to the increased use of computationally intensive DL for simple classification tasks. To find out if DL really outperforms shallow models significantly, the researchers from the University of Pennsylvania experiment with three ML pipelines that involve traditional methods, AutoML and DL in a paper titled, 'Is Deep Learning Necessary For Simple Classification Tasks.' The UPenn researchers stated that a support-vector machine (SVM) model might predict more accurately susceptibility to a certain complex genetic disease than a gradient boosting model trained on the same dataset. Moreover, choosing different hyperparameters within that SVM model can vary performances.

During the Covid-19 crisis, Shibata has corresponded with people all over the world who have recently turned to Paro robots as a therapeutic tool. In addition to their increased prominence in elderly and memory care, Shibata says the pandemic has created some novel use cases. Workers at a high-volume call center in Tokyo who dealt with calls about coronavirus testing were given a Paro as a stress-relief tool this May. And Shibata has been emailing with a 34-year-old nurse in an Atlanta intensive care unit who started using a Paro this April as a way to cope with being isolated from his loved ones and pet. "He used to live with his family and a dog at the home, but in order to avoid any risks of infection from him to them, they moved to a different house," Shibata says.