Note: the original article has been split into two since I think the two points were only vaguely related, I will leave it as is here, since I'd rather not re-post stuff and I think the audience on LW might see the "link" between the two separate ideas presented here. Let's begin with a gentle introduction in to the field of AI risk - possibly unrelated to the broader topic, but it's what motivated me to write about the matter; it's also a worthwhile perspective to start the discussion from. I hope for this article to be part musing on what we should assume machine learning can do and why we'd make those assumptions, part reference guide for "when not to be amazed that a neural network can do something". I've often had a bone to pick against "AI risk" or, as I've referred to it, "AI alarmism". When evaluating AI risk, there are multiple views on the location of the threat and the perceived warning signs. I would call one of these viewpoints the "Bostromian position", which seems to be mainly promoted by MIRI, philosophers like Nick Bostrom and on forums such as AI Alignment.

Or is it just another hyped innovation? It comes with no surprise how AI today becomes a catchall term that is said out loud in the job market. The US and China are in nip and tuck in the AI race for supremacy. Although China aims to be the technology leader by 2030, the economy is still at a struggle phase with a slowdown and trade war with the US. Emerging trends in artificial intelligence (AI) significantly points toward having a geopolitical disruption in the foreseeable future.

The Global Artificial Intelligence in Healthcare Market is expected to grow from USD 2,178. The positioning of the Global Artificial Intelligence in Healthcare Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital). The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Healthcare Market including are Google, IBM, Intel, Microsoft, NVIDIA, Amazon Web Services, General Electric Company, Medtronic, Micron Technology, and Siemens Healthineers. On the basis of Offering, the Global Artificial Intelligence in Healthcare Market is studied across Hardware, Services, and Software. On the basis of Technology, the Global Artificial Intelligence in Healthcare Market is studied across Computer Vision, Context-Aware Computing, Machine Learning, Natural Language Processing, and Querying Method.

Researchers at Google have open-sourced a new framework that can scale up artificial intelligence model training across thousands of machines. It's a promising development because it should enable AI algorithm training to be performed at millions of frames per second while reducing the costs of doing so by as much as 80%, Google noted in a research paper. That kind of reduction could help to level the playing field a bit for startups that previously haven't been able to compete with major players such as Google in AI. Indeed, the cost of training sophisticated machine learning models in the cloud is surprisingly expensive. One recent report by Synced found that the University of Washington racked up $25,000 in costs to train its Grover model, which is used to detect and generate fake news.

A team of scientists in the United States has developed a blood test that uses machine learning to hunt for the telltale signs of lung cancer. The system, which is still early in its development, could eventually replace CT scans as a first-line screening measure for suspected lung cancer patients. The test hunts for tumor DNA that is circulating in a person's blood, and is far less expensive than CT scans which are typically used to diagnose lung cancer. It's not yet ready to be used on a widespread basis or relied upon in real-world settings, but the research is incredibly promising and could prove to be a powerful weapon against one of the deadliest types of cancer. Medical researchers and doctors already know that cancer DNA circulating in a patient's bloodstream could serve as a tool for diagnosing the disease.

What's happening in Japan is written all over our faces -- our blank, expressionless, masked faces. Never before, it seems safe to say, have so many people gone about masked. Thus we confront the microbes that assault us. "As self-protection, your mask is practically useless," says Shukan Gendai magazine this month. Commercial face masks, medical authorities say, can block particles measuring 3 to 5 micrometers.

Modern capitalism has never seen anything quite like the novel coronavirus SARS-CoV-2. In a matter of months, the deadly contagious bug has spread around the world, hobbling any economy in its path. In the United States, where consumer spending accounts for more than two-thirds of economic activity, commerce has come to a standstill as people stay home to slow the virus' spread. Hotels and restaurants and airlines have taken massive hits; Delta has cut its flight capacity by 70 percent. One in five US households has already lost work.

The concept of smart homes is not something new. In 1975, a Scotland company developed a communication protocol that enables smart home devices to talk to each other with the help of existing electrical wires of a home. Over the years, numerous technological developments made smart homes accessible to a large number of people around the globe. If you have yet to try smart home technology a try, here are some of the main reasons why you should have it. No matter what type of neighborhood you live in, it's of the utmost importance to do everything you can to keep your home secure.

Our guest today is Marta Halina, a University Lecturer (Assistant Professor) in the Department of History and Philosophy of Science at the University of Cambridge. Marta's current focus is the philosophy of artificial intelligence. We discuss what philosophers can contribute to AI. We talk about AlphaGo and its stunning defeat of one of the world's most celebrated Go champions. We puzzle over whether artificial minds can think creatively.

Canadian scientists have developed an innovative new technique that uses artificial intelligence to better define the different sections of the brain in newborns during a magnetic resonance imaging (MRI) exam. The results of this study--a collaboration between researchers at Montreal's CHU Sainte-Justine children's hospital and the ÉTS engineering school--are published today in Frontiers in Neuroscience. "This is one of the first times that artificial intelligence has been used to better define the different parts of a newborn's brain on an MRI: namely the grey matter, white matter and cerebrospinal fluid," said Dr. Gregory A. Lodygensky, a neonatologist at CHU Sainte-Justine and professor at Université de Montréal. "Until today, the tools available were complex, often intermingled and difficult to access," he added. In collaboration with Professor Jose Dolz, an expert in medical image analysis and machine learning at ÉTS, the researchers were able to adapt the tools to the specificities of the neonatal setting and then validate them.