The Department of Veterans Affairs (VA) is teaming-up with Alphabet-owned DeepMind on a medical research partnership to address the global issue of patient deterioration during hospital care, which accounts for 11 percent of in-hospital deaths around the world. The partnership will focus on analyzing patterns from approximately 700,000 historical, de-personalized health records to develop machine learning algorithms that will accurately identify risk factors for patient deterioration and predict its onset. Initially, the focus will be on identifying the most common signs of risk, like acute kidney injury, a problem that can lead to dialysis or death, but is preventable if detected early. "Medicine is more than treating patients' problems," VA Secretary David J. Shulkin said in a statement. "Clinicians need to be able to identify risks to help prevent disease. This collaboration is an opportunity to advance the quality of care for our nation's Veterans by predicting deterioration and applying interventions early."

DeepMind wants to solve the problem of patient deterioration in hospitals. The Google sister-company fed its AI the historical medical records of about 700,000 US veterans in hopes it will learn to predict changes in patient condition that, unchecked, lead to death. The partnership between DeepMind and the Veterans Administration (VA) brings some of the top minds in artificial intelligence research together with "world-renowned clinicians and researchers" working for the government. Basically, the US government is turning to, arguably, the smartest computer on the planet in order to find a cure for human-error. According to the laws of 1980s movies the robots will be attacking by the time you finish reading this sentence.

The team went on to create what would become another master gamer in the AlphaGo family, this one called simply AlphaZero. In a paper posted to the scientific preprint site ArXiv.org in December, DeepMind researchers revealed that after starting again from scratch, the trained-up AlphaZero outperformed AlphaGo Zero--in other words, it beat the bot that beat the bot that beat the best Go players in the world. And when it was given the rules for chess or the Japanese chess variant shogi, AlphaZero quickly learned to defeat bespoke top-level algorithms for those games, too. Experts marveled at the program's aggressive, unfamiliar style. "I always wondered how it would be if a superior species landed on Earth and showed us how they played chess," the Danish grandmaster Peter Heine Nielsen told a BBC interviewer.

OpenAI today updated Gym – its system for training intelligent software – so that developers can teach physical robots to hold pens, pick up and move objects, and so on. Gym was launched in 2016, and is a toolkit for teaching programs new tricks, such as playing Atari games and balancing poles, via reinforcement learning (RL). Now, OpenAI has added a bunch of simulated environments designed to train physical robots how to move and interact with things around them albeit in a virtual world. For example, the simulated environments can be used to teach robotic fingers to play an instrument, or pick and lift an object from the table. This is useful for folks interested in rapidly training intelligent robots over thousands of exercises, without having to rig up a relatively slow-moving physical bot, or before they have a chance to get hold of the hardware.

Alphabet's artificial intelligence arm DeepMind and the U.S. Department of Veterans Affairs have unveiled a research partnership focused on predicting patient deterioration in the hospital setting. The issue is the cause of approximately 11 percent of in-hospital deaths, NHS research shows. Together, the organizations will examine 700,000 historical, depersonalized patient medical records. They'll analyze patterns from the data to see if machine learning can pinpoint risk factors for patient deterioration. To start, the relationship will zoom in on acute kidney injury, a complication related to patient deterioration.

I understand where the article is coming from, but it sounds to me like a classic case of projecting their biases (i.e. the author has very strong feelings about a topic which is pretty marginal to the research papers themselves, and interprets the claims made in the papers through the lens of a worldview in which that topic is very important) For example, AlphaGo Zero makes claims about not using any human (Go) knowledge, which is, by human standards, pretty close to true. It mostly only uses general assumptions which would apply to most turn-based, perfect information board games. While that is certainly "knowledge about Go" in the strictest sense, such a distinction is pretty irrelevant in practice. The paper never claimed it had spawned an AGI that could solve any general problem without human intervention -- the context makes it pretty clear that the research applies to a narrow domain, and I don't believe any claims are made about not making any assumptions which rely on the properties of that narrow domain (indeed, such assumptions existing is pretty much a given -- whether they were implemented on purpose or by pure chance, the fact that it works in a domain and not in another is proof that this is the case)

I'm a High School student with a reasonably basic research project where I am to implement an AI Agent to learn and master games and graph a linear regression of its time to mastery versus the task complexity. My partner and I have decided task complexity is to be based on the number of state spaces (or different inputs) the AI can use. We would like to find a good primary AI and have been using public OpenAi templates. Do any of you guys have suggestions on an efficient and effective way to make a "cookie cutter" algorithm? We'd like for it to be as easy to understand as possible.

We're excited to announce a medical research partnership with the US Department of Veterans Affairs (VA), one of the world's leading healthcare organisations responsible for providing high-quality care to veterans and their families across the United States. This project will see us analyse patterns from historical, depersonalised medical records to predict patient deterioration. Patient deterioration is a significant global health problem that often has fatal consequences. Studies estimate that 11% of all in-hospital deaths are due to patient deterioration not being recognised early enough or acted on in the right way. Alongside world-renowned clinicians and researchers at the VA, we are analysing patterns from approximately 700,000 historical, depersonalised medical records in order to determine if machine learning can accurately identify the risk factors for patient deterioration and correctly predict its onset.

Entrepreneur and CEO of Tesla and SpaceX, Elon Musk may have a little more time on his hands (maybe), as he's departing his spot on the board of the artificial-intelligence safety group OpenAI, according to a blog post. The departure is likely the result of Tesla's move into the realm of A.I., which he said in 2017 would be the "best in the world" and would even be able to "predict your destination." Musk will continue to "donate and advise the organization," OpenAI said in a blog post Feb. 20, adding that "As Tesla continues to become more focused on AI, this will eliminate a potential future conflict for Elon." Musk and Y Combinator CEO Sam Altman co-founded the nonprofit venture in December 2015, with backing from the likes of Peter Thiel (an early backer of Facebook), Reid Hoffman (who co-founded LinkedIn), Jessica Livingston (founding partner of Y Combinator), Greg Brockman and computer scientist Ilya Sutskever, according to the OpenAI website. OpenAI's mission is to develop safe AGI (artificial general intelligence) and ensure those developments are made public; its 60 or so researchers are tasked with long-term research, according to the company.

The Department of Veterans Affairs has announced a research partnership with Alphabet subsidiary DeepMind that will tackle issues concerning patient deterioration during hospital care. Using a dataset comprised of 700,000 historical, de-personalized health records, the machine learning platform will help the VA identify risk factors for deterioration while predicting its onset. "Medicine is more than treating patients' problems," VA Secretary David J. Shulkin, MD, said in a statement. "Clinicians need to be able to identify risks to help prevent disease. This collaboration is an opportunity to advance the quality of care for our nation's veterans by predicting deterioration and applying interventions early."