Your phone might soon be getting AI-assisted upgrades to benefit your health. Google Health has introduced research projects that promise to turn smartphones into disease screening tools. One promising avenue involves using the onboard microphones on a phone as a stethoscope to detect circulatory irregularities like murmurs. The innovations could be deployed through telehealth, saving the need and time for patients to travel to a doctor. "At Google, we're focused on unlocking the potential of everyday devices to support people's health and wellness," Greg Corrado, head of Health AI at Google, told Digital Trends in an interview.

The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore's Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today.

Google is looking to artificial intelligence as a way to make a mark in health care. Google is looking to artificial intelligence as a way to make a mark in health care. One of the biggest corporations on the planet is taking a serious interest in the intersection of artificial intelligence and health. Google and its sister companies, parts of the holding company Alphabet, are making a huge investment in the field, with potentially big implications for everyone who interacts with Google -- which is more than a billion of us. The push into AI and health is a natural evolution for a company that has developed algorithms that reach deep into our lives through the Web.

When Greg Corrado, Ph.D., an artificial intelligence researcher, took the stage at the TedMed Conference last year, he was frank. "Doctors who partner with artificial intelligence as a decision-making aid will see their healing powers expand more than they have in the past 100 years," he told the audience of medical professionals. Corrado is a principal scientist at Google AI and an expert in machine learning. "To practice medicine today," he continued, "is to weather an information hurricane...AI [and machine learning] is our best opportunity to tame the data beast and actually scale care to meet demand." Companies are creating algorithms to sort medical records, determine treatments, diagnose sepsis in as little as 12 hours, and even predict who will skip their next doctor's appointment.

Some of Google's top scientists today discussed the future of artificial intelligence and the message was one of tempered expectations – something we hadn't seen much of at the Google I/O event. The field of artificial intelligence exists in two states which, upon first glance, appear diametrically opposed. In one, here in 2018, we have computers that can usually figure out what a cat looks like with only a few hints – something most toddlers can get right with near-perfect accuracy. Yet in the other state, fully autonomous vehicles and superhuman AI-powered diagnostic tools for doctors are functionally available now. Figuring out what's possible today, when it comes to artificial intelligence, is a full-time job in and of itself.

Scientists in Google's health division are developing technology they believe can help doctors better diagnose, treat and prevent vision impairment caused by diabetic retinopathy -- a common eye disease among diabetics that can lead to blindness. The technology is a cloud-based algorithm that analyzes photographic images of the eye for signs of the disease and grades the severity of the problem. Signs include abnormal growth of blood vessels in the back of the eye that causes scarring and detachment of the retina. The algorithm is being tested in clinical trials and has not been approved by federal regulators. It is considered a medical device and would have to be approved by the U.S. Food and Drug Administration to be used in clinical care.

Google employs some of the world's smartest researchers in deep learning and artificial intelligence, so it's not a bad idea to listen to what they have to say about the space. One of those researchers, senior research scientist Greg Corrado, spoke at RE:WORK's Deep Learning Summit on Thursday in San Francisco and gave some advice on when, why and how to use deep learning. His talk was pragmatic and potentially very useful for folks who have heard about deep learning and how great it is -- well, at computer vision, language understanding and speech recognition, at least -- and are now wondering whether they should try using it for something. The TL;DR version is "maybe," but here's a little more nuanced advice from Corrado's talk. You can also watch the presentations from our Future of AI meetup, which was held in late 2014.)

The recent revelation that Google is using machine learning to help process some of its search results is attracting interest and questions about this field within artificial intelligence. What exactly is "machine learning" and how do machines teach themselves? Here's some background drawn from those involved with machine learning at Google itself. Yesterday, Google held a "Machine Learning 101" event for a variety of technology journalists. I was one of those in attendance.

There's a high-stakes race under way in Silicon Valley to develop software that makes it easy to weave artificial intelligence technology into almost everything, and Google has sprinted into the lead. Google computer scientists including Jeff Dean and Greg Corrado built software called TensorFlow, which simplifies the programming of key systems that underpin artificial intelligence. That helps Google make its products smarter and more responsive. It's important for other companies too because the software makes it dramatically easier to create computer programs that learn and improve automatically. What's more, Google gives it away.

"Machine learning is really not dark magic, it's just another tool." As the leading professor involved in the development of Google Brain, and Google's Director of Augmented Intelligence Research, he worked on their release of their open-source library for machine learning, Tensorflow. "Our main hurdle is to get people educated on how this works in practice." He shared that practical advice last week at TQ in Amsterdam (disclaimer: TQ is part of TNW), alongside three startups that each have their own hands-on experience with machine learning as well. For years now, the decreasing costs of computation power and data storage have gone hand in hand with the explosive birth of tech startups.