Fifty years ago, Mike Sanders watched with awe and anticipation as the crew of Apollo 11--Neil Armstrong, Buzz Aldrin, and Michael Collins--splashed down in the Pacific Ocean. Landing men on the moon and returning them safely to the earth was a seminal moment in the history of flight, and it had a profound effect on then 7-year-old Sanders, who now heads the Lone Star UAS Center of Excellence & Innovation at Texas A&M University–Corpus Christi. Looking back, Sanders says he never expected the day to come when he would be working with NASA on anything, let alone another chapter in the history of flight. But this year, he landed in the middle of one of the most important aeronautical projects of this generation: an effort to build a safe and effective unmanned aircraft system traffic management (UTM) platform. In August, Texas A&M–Corpus Christi's Lone Star UAS Center of Excellence and its partners' workers stood alongside NASA scientists and engineers as they flew 22 small physical and digital drones above and between tall buildings in five areas of Corpus Christi. The low-altitude test culminated a five-year effort to learn what it would take to build a nationwide system for managing low-altitude drone traffic.

We've trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization--all without task-specific training. Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets. On language tasks like question answering, reading comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no task-specific training data.

CHICAGO – PhysIQ, a leader in applying artificial intelligence to wearable sensor data, today announced that it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for their algorithm to continuously determine respiration rate in ambulatory patients. This clearance adds to their expanding portfolio of FDA-cleared cloud-based analytics, which also include QRS detection, heart rate, heart rate variability, atrial fibrillation detection, and their personalized physiology change detection analytic. The latest clearance advances physIQ's strategy to offer a deep portfolio of FDA-cleared analytics that can be applied to wearable sensor data. To enable this, physIQ's platform collects raw telemetry from the device and uploads it to the cloud where FDA-cleared analytics use the raw biosignals to produce vital signs. With this approach physIQ is able to provide vital sign analytics that benefit from the superior computing power of the cloud and fuel the higher-level analytics that further characterize dimensions of human physiology.

Let's be clear, Artificial Intelligence, in particular in its latest development, deep learning that mimics the way the human mind works, first emerged in America. This gave the U.S. a huge head start over the rest of the world – including China, putting the U.S. firmly in the lead of the race for AI. What Americans didn't develop at home, they bought from Europe. In this respect, two British firms stand out with groundbreaking contributions to AI development: ARM and DeepMind. While all eyes are trained on the AI race between China and America, is there a role left for Europe?

Let's be clear, Artificial Intelligence, in particular in its latest development, deep learning that mimics the way the human mind works, first emerged in America. This gave the U.S. a huge head start over the rest of the world – including China, putting the U.S. firmly in the lead of the race for AI. What Americans didn't develop at home, they bought from Europe. In this respect, two British firms stand out with groundbreaking contributions to AI development: ARM and DeepMind. While all eyes are trained on the AI race between China and America, is there a role left for Europe?

Deepfake artist Hao Li created this Putin deepfake, which was shown at an MIT conference this week.AP Photo/Alexander Zemlianichenko; MIT Technology Review A deepfake pioneer said in an interview with CNBC on Friday that "perfectly real" digitally manipulated videos are just six to 12 months away from being accessible to everyday people. "It's still very easy you can tell from the naked eye most of the deepfakes," Hao Li, an associate professor of computer science at the University of Southern California, said on CNBC's Power Lunch. "But there also are examples that are really, really convincing." He continued: "Soon, it's going to get to the point where there is no way that we can actually detect [deepfakes] anymore, so we have to look at other types of solutions." Li created a deepfake of Russian president Vladimir Putin, which was showcased at an MIT tech conference this week.

Candidates must provide evidence that is related to the skills and abilities outlined under the Specialized Experience. Specialized experience must have been at a sufficiently high level of difficulty to clearly show that the candidate possesses the required qualifications set forth below. Candidates must provide evidence that is related to the skills and abilities outlined under the Professional Technical Qualifications (PTQ). Experience must have been at a sufficiently high level of difficulty to clearly show that the candidate possesses the required professional/technical qualifications set forth below. Professional Technical Qualifications: Applicants must clearly demonstrate in their application materials that they possess technical attributes in the Professional/Technical Qualifications (PTQs).

A Google research team has recently created an artificial intelligence (AI) system that can detect 26 different skin diseases with the same accuracy as a licensed dermatologist. This deep learning technology evaluates images and metadata, such as self-reported symptoms and demographic information, to generate a ranked list of possible diagnoses just as a trained professional would. The Google team's findings were covered in a paper titled "A deep learning system for differential diagnosis of skin diseases" and in a blog post penned by, Yuan Liu, PhD, Software Engineer and Peggy Bui, MD, Technical Program Manager, Google Health. With nearly 2 billion patients having some form of skin condition globally and many areas lacking dermatologists, patients must often take such concerns up with their primary care physicians. Research has shown that while dermatologists diagnose these skin conditions with accuracies between 77-96%, the general practitioner does so with only 24-70% accuracy.

Deepfake videos could be commonplace and found across the media and online platforms within six months, according to a leading expert. The idea of the videos is to look completely real and show people doing things they never did. These are created by complex computing and artificial intelligence and have caused outrage recently. Moving images can be created from just a single image of a person and US politician Nancy Pelosi, Facebook founder Mark Zuckerberg and even the Mona Lisa have been used in the convincing clips already. The video that kicked off the concern last month was a doctored video of Nancy Pelosi (pictured), the speaker of the US House of Representatives.

Amazon.com Inc. AMZN -1.50% is the latest example of a large employer committing to help its workers gain new skills. The online retailer said Thursday it plans to spend $700 million over about six years to retrain a third of its U.S. workforce as automation, machine learning and other technology upends the way many of its employees do their jobs. Companies as varied as AT&T Inc., Walmart Inc., WMT -0.11% JPMorgan Chase & Co. and Accenture ACN -0.81% PLC have embarked on efforts to prepare workers for new roles. At a time of historically low unemployment, coupled with rapid digital transformation that requires high-tech job skills, more U.S. companies said they want to help their employees transition to new positions--and they have their bottom line squarely in focus. Many have concluded that they must coach existing staff to take on different types of work, or face a dire talent shortage, said Ryan Carson, founder and chief executive of Treehouse, a firm that pairs tech apprentices, often from underrepresented groups, with employers and helps train them.