An artificial intelligence algorithm developed by Rochester, Minn.-based Mayo Clinic and cardiac monitoring startup Eko to analyze ECG data for evidence of reduced left ventricular ejection fraction has been designated a "breakthrough device" by the FDA. The algorithm reads ECG data collected by Eko's digital stethoscope to measure LVEF, which refers to the amount of blood pumped out of the heart's left ventricle and can indicate heart failure. The breakthrough device label, presented to technology with potential to address unmet clinical needs, will speed up regulatory review of the algorithm. Eko and Mayo Clinic's partnership to develop the AI algorithm began in late 2018. Since then, studies have shown that the algorithm-equipped stethoscope achieves significant accuracy in detecting low ejection fraction.

Sourcing talent isn't exactly like finding a needle in a haystack -- it is more comparable to panning for gold. Locating the treasure means sifting through many seemingly identical grains of sand to find the perfect hire. When it comes to resumes and candidate profiles, AI can do that sifting at scale, using preset parameters and key terms to surface positive matches from an otherwise unmanageable pile of candidates. By the time the pan gets to the recruiter's hands, the gold has already been separated out, and the recruiter can move more quickly to make further assessments. Video assessments can be useful in sourcing, but they only comprise one small piece of a rich set of AI features that can help staffing firms make positive matches. One practical application already in use is natural language search, which helps find best-fit candidates semantically by grouping like terms to allow recruiters to uncover candidates whose resumes and skills may not match exact search keywords. For example, a recruiter looking for a Java developer may want to see results that return related terms, such as J2EE, Java EE, or Jakarta EE, indicating specific skills within the original search. This kind of AI application serves the intent of a candidate search, rather than the letter.

Editor's note: This is part of a series about ongoing risks and evolving labor issues in the recycling industry. Read more about persistent safety hazards and how MRF operators are responding. Also check out a feature on the firsthand experiences of California workers and the complex medical claim process they face. In MRFs across the U.S., dozens of arms hover over conveyors and appear to be in nearly constant motion sorting incoming materials. Previously, those arms exclusively were attached to humans. Now, a shift is occurring. Increasingly, more of those are arms attached to robots that use pincers or suction cups, instead of fingers, and move much faster. Advanced MRF automation and robotics weren't widely adopted concepts up until about five years ago, according to equipment manufacturers.

Anything you do online can come back to bite you. It's been a decade full of lessons: who to trust, when to speak out and how to stream big events online after you've broken up with your cable company. In 2010, the first iPhone was only three years old. Uber and Lyft didn't exist, and neither did Google Assistant and Siri, Instagram or streaming video. We've come a long way since then, but the next 10 years won't be easy.

Mars pictured in natural color taken by the Rosetta spacecraft's Optical, Spectroscopic, and ... [ ] Infrared Remote Imaging System (OSIRIS). Humans have evolved over millions of years to live on Earth. Now humans are planning long duration space missions that will require them to live in space for extended periods of time. NASA plans to send humans to an asteroid by 2025 and to Mars in the 2030s. NASA's Journey to Mars, the longest manned space mission ever, will require humans to live in space for more than three years.

WEST LAFAYETTE, Ind. – One of the challenges in using machine learning for drug development is to create a process for the computer to extract needed information from a pool of data points. Drug scientists must pull biological data and train the software to understand how a typical human body will interact with the combinations that come together to form a medication. Purdue University drug discovery researchers have created a new framework for mining data for training machine learning models. The framework, called Lemon, helps drug researchers better mine the Protein Data Base (PDB) – a comprehensive resource with more than 140,000 biomolecular structures and with new ones being released every week. The work is published in the Oct. 15 edition of Bioinformatics.

Approvals for AI-based healthcare products are streaming in from regulators around the globe, with medical imaging leading the way. It's just the start of what's expected to become a steady flow as submissions rise and the technology becomes better understood. More than 90 medical imaging products using AI are now cleared for clinical use, thanks to approvals from at least one global regulator, according to Signify Research Ltd., a U.K. consulting firm in healthcare technology. Regulators in Europe and the U.S. are leading the pace. Each has issued about 60 approvals to date.

What is the law but a series of algorithms? Sounds a lot like computer programming, right? The legal system, on the other hand, is not as straightforward as coding. Just consider the complicated state of justice today, whether it be problems stemming from backlogged courts, overburdened public defenders, and swathes of defendants disproportionately accused of crimes. So, can artificial intelligence help?

Google Cloud VP of Retail Carrie Tharp has a long background of embracing disruption. Having joined the Mountain View, Calif.-based tech giant in July, she might be a relative newcomer, but she is no stranger to developing and supporting technology for the changing retail landscape. Tharp spent the past three years at Neiman Marcus Group creating the luxury retailer's digital-first strategy, including shifting to a cloud-centric software stack for marketing and ecommerce and leveraging artificial intelligence and machine learning to drive programs like digital styling, personalized promos and algorithmic buying. Before that, she was immersed in similar tasks at Fossil and Travelocity. One theme that weaves throughout her career: Tharp embraces the challenge of shepherding retail brands through disruption and helping them evolve the way they interact with customers.

The past 10 years have seen a surge of new AI vendors, and the trend isn't likely to end anytime soon, as investors continue to pour money into artificial intelligence. There are thousands of AI startups around the world. Many aim to do similar things -- create chatbots, develop hardware to better power AI models or sell platforms to automatically transcribe business meetings and phone calls. These AI vendors, or AI-powered product vendors, have raised billions over the last decade, and will likely raise even more in the coming years. To help enterprises keep an eye on some of the most promising AI startups, here is a list of those founded within the past five years.