FDA Commissioner Scott Gottlieb is making the most of his final week at the agency. In the month that has passed since Gottlieb rattled the medical device industry with news of his impending resignation, the commissioner has issued 18 public statements pertaining to nearly all corners of the agency's realm, from food, tobacco, and cosmetics to drugs and devices. Friday is Gottlieb's last day on the job. On Tuesday, Gottlieb said the agency will consider a new regulatory framework for reviewing medical devices that use advanced artificial intelligence algorithms. AI has been making headlines in medtech for a while now, and this is certainly not the first time FDA has turned its attention to how AI-based medical devices should be regulated.

Please click the subscribe button at the top of this article to have articles in our DeepTech series delivered directly to you each week. Researchers at Iowa State University designed an AI system to create personalized prosthetic aortic heart valves. These customized valves can restore normal blood flow for people with aortic valvular disease. Over 90,000 people in the US have valve replacement surgery every year. Traditional drug discovery is a very long and expensive process involving many tests to determine the safety and efficacy of each new drug candidate.

"I was really surprised how primitive information technology is in the hospitals," says Regina Barzilay, a professor at the Massachusetts Institute of Technology who is working on improving mammography with artificial intelligence. "I was really surprised how primitive information technology is in the hospitals," says Regina Barzilay, a professor at the Massachusetts Institute of Technology who is working on improving mammography with artificial intelligence. Regina Barzilay teaches one of the most popular computer science classes at the Massachusetts Institute of Technology. And in her research -- at least until five years ago -- she looked at how a computer could use machine learning to read and decipher obscure ancient texts. "This is clearly of no practical use," she says with a laugh.

Lego has introduced a new coding and robotics set called "Spike Prime" that will help kids gain valuable confidence in team building, STEM skills, and the power of persistence. The sets come with lesson plans for teachers, and kids can build anything from a breakdancing robot to a grasshopper-like racing module. These aren't your childhood legos, but they'll be helping build the foundation for the next generation of kids. Inbox--a Google-powered app that runs Gmail with more niche features--has been moved to the trash. But if you were one of those loyal users of the app, there's still hope: We put together a list of the best email alternatives for you, because email doesn't have to suck.

In 2014, IBM opened swanky new headquarters for its artificial intelligence division, known as IBM Watson. Inside the glassy tower in lower Manhattan, IBMers can bring prospective clients and visiting journalists into the "immersion room," which resembles a miniature planetarium. There, in the darkened space, visitors sit on swiveling stools while fancy graphics flash around the curved screens covering the walls. It's the closest you can get, IBMers sometimes say, to being inside Watson's electronic brain. One dazzling 2014 demonstration of Watson's brainpower showed off its potential to transform medicine using AI--a goal that IBM CEO Virginia Rometty often calls the company's moon shot. In the demo, Watson took a bizarre collection of patient symptoms and came up with a list of possible diagnoses, each annotated with Watson's confidence level and links to supporting medical literature. Within the comfortable confines of the dome, Watson never failed to impress: Its memory banks held knowledge of every rare disease, and its processors weren't susceptible to the kind of cognitive bias that can throw off doctors. It could crack a tough case in mere seconds. If Watson could bring that instant expertise to hospitals and clinics all around the world, it seemed possible that the AI could reduce diagnosis errors, optimize treatments, and even alleviate doctor shortages--not by replacing doctors but by helping them do their jobs faster and better.

Artificial Intelligence (AI) has been a game changer for global businesses, opening doors to innumerable possibilities. With the integration of AI in businesses, the global economy is excepted to grow exponentially in the coming years. Although the introduction of AI into business strategies is considered a revolutionary idea, what most business executives struggle with is the proper application of AI throughout their organization in such a way that it generates maximum ROI and value. This gives rise of several questions, "How do we educate our staff about AIs? How can we acquire AI-trained employees? What is the most suitable AI strategy for our business? How do we certify our AI is trustworthy? Will there be new privacy and cybersecurity threats to deal with?".

NOTE: This is an excerpt of Planet Money's newsletter. You can sign up here. If you shop online, there's a good chance the price you pay for stuff is determined by a computer algorithm. As of 2015, over one third of the 1,600 best-selling items sold on Amazon came from sellers who used algorithms to set their price. Algorithms are spreading like crazy, but are they giving companies too much power over consumers?

Deep neural networks have achieved state-of-the-art performance on a variety of tasks. But they have been shown to be vulnerable to adversarial examples, which are maliciously perturbed examples almost identical to original samples in human perception, but cause models to make incorrect decisions [31]. The vulnerability of neural networks to adversarial examples implies a security risk in applications with real-world consequences, such as self-driving cars, robotics, financial services, and criminal justice, and also suggests a difference between humans and existing machine learning systems. The study of adversarial examples is thus necessary to identify the limitation of current machine learning algorithms, provide a metric for robustness, investigate the potential risk, and suggest ways to improve the robustness of models. Considerable effort has gone into the design of new algorithms for the generation of adversarial examples. Adversarial examples can be categorized according to several criteria: the similarity metric, the attack goal, and the threat model.

As algorithms are increasingly used to make important decisions pertaining to individuals, algorithmic discrimination is becoming a prominent concern. The seminal work of Dwork et al. [ITCS 2012] introduced the notion of individual fairness (IF): given a task-specific similarity metric, every pair of similar individuals should receive similar outcomes. In this work, we study fairness when individuals have diverse preferences over the possible outcomes. We show that in such settings, individual fairness can be too restrictive: requiring individual fairness can lead to less-preferred outcomes for the very individuals that IF aims to protect (e.g. a protected minority group). We introduce and study a new notion of preference-informed individual fairness (PIIF), a relaxation of individual fairness that allows for outcomes that deviate from IF, provided the deviations are in line with individuals' preferences. We show that PIIF can allow for solutions that are considerably more beneficial to individuals than the best IF solution. We further show how to efficiently optimize any convex objective over the outcomes subject to PIIF, for a rich class of individual preferences. Motivated by fairness concerns in targeted advertising, we apply this new fairness notion to the multiple-task setting introduced by Dwork and Ilvento [ITCS 2019]. We show that, in this setting too, PIIF can allow for considerably more beneficial solutions, and we extend our efficient optimization algorithm to this setting.

A group of Google employees have called for the removal of a rightwing thinktank leader from the company's new artificial intelligence council, citing her anti-LGBT and anti-immigrant record. Employees published a letter on Monday criticizing the appointment of Kay Coles James, the president of the Heritage Foundation, to Google's newly formed advisory council for "the responsible development of AI". James has a history of fighting trans rights and LGBT protections, and has advocated for Donald Trump's proposed border wall. "In selecting James, Google is making clear that its version of'ethics' values proximity to power over the wellbeing of trans people, other LGBTQ people and immigrants," the employees wrote in the letter, which was published online and shared internally at the company. "Such a position directly contravenes Google's stated values."