On a recent research trip, I found myself in the back of a taxi in New Delhi, amid a dizzying mix of car horns and exhaust.

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight global mesh network technology platform for both mobile and fixed solutions, today announced that GBT Technologies, S.A., a Costa Rican company ("GBT CR"), has completed its written design of the deep nanometer range 3D microchip patent, clearing the way for GBT to file for international protection (PCT) for the patent. The application has been assigned serial number PCT/US19/50266 and the international filing date is September 10, 2019. This microchip is targeted for GBT's planned future chipset and derivative integrated circuits. The technology is based on a new concept for a microchip's die structure and orientation and is specifically designed for deep nanometer range. It is designed to work with Digital, Analog and Mixed technologies to achieve much higher performance and die yield.

Heart failure is an important potential complication of type 2 diabetes that occurs frequently and can lead to death or disability. Earlier this month, late-breaking trial results revealed that a new class of medications known as SGLT2 inhibitors may be helpful for patients with heart failure. These therapies may also be used in patients with diabetes to prevent heart failure from occurring in the first place. However, a way of accurately identifying which diabetes patients are most at risk for heart failure remains elusive. A new study led by investigators from Brigham and Women's Hospital and UT Southwestern Medical Center unveils a new, machine-learning derived model that can predict, with a high degree of accuracy, future heart failure among patients with diabetes.

Admitted patients often have to wait a number of hours for a radiologist to review their chest X-ray, even though it may be marked as urgent or STAT. That's because way too many are marked as such in most clinics. Pneumothorax, or collapsed lung, can go unnoticed in the meantime, leading to a dangerous amount of time to wait while inside the hospital. GE Healthcare's Critical Care Suite automatically processes chest scans right on the X-ray machine and flags those where it detects potential signs of pneumothorax. The attending radiologist immediately gets a copy of the scan via the hospital's PACS system and the technologist performing the scan is alerted as well, to help make sure that the patient is triaged properly.

LOS ANGELES--(BUSINESS WIRE)--Eyenuk Inc., a global artificial intelligence (AI) medical technology and services company and the leader in real-world applications for AI Eye Screening, announced today that the EyeArt AI Eye Screening System was deployed successfully for Italy's first national prevention and diagnosis campaign for retinal and diabetic maculopathy. A total of 2,200 patients were screened at 30 centers across Italy, with more than half chosen for EyeArt AI Eye Screening. The Month of Prevention of Diabetic Retinopathy and Maculopathy was sponsored by the Italian Ministry of Health, the city of Milan and the Italian Ophthalmology Society in collaboration with the Ambrosian Ophthalmic Center (CAMO), San Raffaele Hospital and Eyenuk. In Italy, an estimated 3.2 million patients have diabetes. As many as 25% are estimated to be affected by diabetic retinopathy (DR),1 the main cause of vision impairment and blindness among working-age adults.

First invented in the 1980s by Chuck Hull, an engineer and physicist, 3D printing technology – also called additive manufacturing – is the process of making an object by depositing material, one layer at a time. Similarly to how an inkjet printer adds individual dots of ink to form an image, a 3D printer adds material where it is needed, based on a digital file. Many conventional manufacturing processes involved cutting away excess materials to make a part, and this can lead to wastage of up to 30 pounds (13.6 kilograms) for every one pound of useful material, according to the Energy Department's Oak Ridge National Laboratory in Tennessee. By contrast, with some 3D printing processes about 98 per cent of the raw material is used in the finished part, and the method can be used to make small components using plastics and metal powders, with some experimenting with chocolate and other food, as well as biomaterials similar to human cells.

McDonald's announced it will McBuy the Bay Area voice-recognition startup Apprente for an undisclosed amount. According to McDonald's, Apprente's "sound-to-meaning" technology handles "complex, multilingual, multi-accent and multi-item conversational ordering," and believes the technology will help streamline the drive-thru process -- even faster food, you say?? As the earth turns and the centuries change, so does the way people wish to order a Big Mac, and Micky D's has the cash to listen. Back in March, the company bought Dynamic Yield, which customizes drive-thru menus based on factors like weather, time of day, and customer order profiles. A month later, it invested in New Zealand app-designer Plexure, which will help connect customers to its new smart drive-thrus, among other things.

D. Tyler McQuade, Ph.D, professor in the Department of Chemical and Life Science Engineering at Virginia Commonwealth University College of Engineering, is principal investigator of a multi-university project seeking to use artificial intelligence to help scientists come up with the perfect molecule for everything from a better shampoo to coatings on advanced microchips. The project is one of the first in the U.S. to be selected for $994,433 in funding as part of a new pilot project of the National Science Foundation (NSF) called the Convergence Accelerator (C-Accel). McQuade and his collaborators will pitch their prototype in March 2020 in a bid for additional funding of up to $5 million over five years. Adam Luxon, a Ph.D. student in the Department of Chemical and Life Science Engineering who has been involved from the beginning, explained it this way: "We want to essentially make the Alexa of chemistry." Just as Amazon, Google and Netflix use data algorithms to suggest customized predictions, the team plans to build a platform and open knowledge network that can combine and help users make sense of molecular sciences data pulled from a wide range of sources including academia, industry and government.

You have to step outside of your own limitations, your own beliefs, your own flawed and fragmented angle on the world and see it from a thousand different perspectives. You have to see giant abstract patterns and filter through human nature, politics, technology, social dynamics, trends, statistics and probability. It's so mind-numbingly complex that our tiny little simian brains stand very little chance of getting it right. Even predicting the future five or ten years out is amazingly complicated. So what am I going to do? Yes, I realize this is utterly insane. It's like climbing Mount Everest, with no shoes, no jacket, no Sherpa, and no oxygen after having barely climbed a small hill! Of course, I'm going to do it anyway. When someone asked George Mallory why he climbed Mt Everest, he said "because it was there." Like many famous quotes, he probably never really said it but who cares? The quote was so good we had to invent it anyway! Let's dive in and take a look at how AI will change society in the next few years, and by the time you're old and grey, and when you're long since turned to dust.

On August 29, 2019 I put on a workshop on Emerging Memories and Artificial Intelligence at Stanford University put on by the Stanford Center for Magnetic Nanotechnology and Coughlin Associates. We had several interesting speakers talking about various types of artificial intelligence and the role that new non-volatile memories will play in both training AI models and implementing them in the field using inference engines. This piece will talk about some of the material presented at this workshop. Dr. Shan Wang, co-organizer of the event gave a introduction, talking about emerging non-volatile memories and in particular on Magnetic Random Access Memory (MRAM). He spoke about how various new memories work--in particular Resistive RAM (RRAM), Phase Change Memory (PCM), MRAM and Ferrroelectic RAM (FRAM).