Billionaire tech entrepreneur Mark Cuban has seen a ton of change since he first got in the technology business in 1982, but he argues that artificial intelligence (AI) is going to "change everything, 180 degrees." He warns that if the U.S. allows other countries to take the lead in AI, then it'll be "SOL," an acronym that employs profanity to communicate urgency. "All these things have happened that have changed how we do business, changed how we lived our lives, changed everything, right, the internet. But what we're going to see with artificial intelligence dwarfs all of that," Cuban said in an interview with hedge fund manager J. Kyle Bass of Hayman Capital on RealVision Television, a subscription financial video service. AI is expected to soon bring an increase in productivity, resulting in fewer jobs all while the population continues to grow. "It's not a question of how it plays out over 100 years.

Siemens, to supplement its acquisition of Mentor Graphics, said that it had bought Solido Design Automation, whose software tools use machine learning to chisel rough edges off complex chip designs, optimizing power consumption and verifying that the chips are ready to be manufactured. The acquisition is another smoke signal signifying that Siemens wants to expand into software tools for chips and circuit boards used in everything from factory equipment to airplanes to self-driving cars. Last year, the industrial juggernaut paid $4.5 billion for Mentor Graphics, one of the three major plays in electronic design automation. Solido, like Mentor Graphics, will be folded into the product life cycle management software business of Siemens' digital factory division. The Plano, Texas-based group sells software to help manage the life cycle of products like electric vehicles and wind turbines, from design to production to service to disposal.

TensorFlow is an open source software library, provided by Google, mainly for deep learning, machine learning and numerical computation using data flow graphs. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. At first look, it may look very confusing to the reader.

Over the last few years, the quest to build fully autonomous vehicles has shifted into high gear. Yet, despite huge advances in both the sensors and artificial intelligence (AI) required to operate these cars, one thing has so far proved elusive: developing algorithms that can accurately and consistently identify objects, movements, and road conditions. As Mathew Monfort, a postdoctoral associate and researcher at the Massachusetts Institute of Technology (MIT) puts it: "An autonomous vehicle must actually function in the real world. However, it's extremely difficult and expensive to drive actual cars around to collect all the data necessary to make the technology completely reliable and safe." All of this is leading researchers down a different path: the use of game simulations and machine learning to build better algorithms and smarter vehicles.

The International Telecommunications Union (ITU), the United Nations specialised agency for information and communication technologies (ICTs), has launched a new ITU Focus Group to establish a basis for ITU standardisation to assist machine learning (ML) in bringing more automation and intelligence to ICT network design and management. Machine learning algorithms are helping operators to make smarter use of network-generated data. These algorithms enable ICT networks and their components to adapt their behaviour autonomously in the interests of efficiency, security and optimal user experience. Fixed and mobile networks generate a huge amount of data both at the network infrastructure level and at the user/customer level, which contain a lot of useful information such as data on location, mobility and call patterns. New ML methods for big data analytics in communication networks can extract relevant information from the network data, and then leverage this knowledge for autonomic network control and management as well as service provisioning.

Medical transcription is often seen as one of the more mundane tasks that need to be done in the doctor's office. Yet, it's vitally important for making sure that medical records are accurate, and that all of the physician's observations, orders, and conversations with patients is properly documented. Google wanted to see if the voice recognition technologies already available in Google Assistant, Google Home, and Google Translate could be used to automate the transcription process and help doctors, as well as medical scribes, take notes more quickly. In a recent proof of concept study, Google developed a system that utilized two automatic speech recognition models, a Connectionist Temporal Classification (CTC) phoneme-based model and a Listen Attend and Spell (LAS) grapheme-based model, and trained them with over 14,000 hours of recorded speech. The result was a pretty respectable word error rate of 20.1% for the CTC model and 18.9% for the LAS model, although the CTC model required the researchers to clean up noise in the recordings before processing it. Based on the favorable results, Google will be soon start working with physicians and researchers at Stanford University to investigate what types of clinically relevant information can be automatically extracted from medical conversations to reduce the amount time doing documentation and increase productive time with patients.

Federal and local laws prohibit flying drones near football games, and authorities are examining additional ways to prevent the unmanned aircraft from hovering over crowds of tens of thousands of people after the flights Sunday, Santa Clara police Lt. Dan Moreno said. He declined to discuss the security measures being considered.

In September, Michal Kosinski published a study that he feared might end his career. The Economist broke the news first, giving it a self-consciously anodyne title: "Advances in A.I. Are Used to Spot Signs of Sexuality." But the headlines quickly grew more alarmed. By the next day, the Human Rights Campaign and Glaad, formerly known as the Gay and Lesbian Alliance Against Defamation, had labeled Kosinski's work "dangerous" and "junk science." In the next week, the tech-news site The Verge had run an article that, while carefully reported, was nonetheless topped with a scorching headline: "The Invention of A.I. 'Gaydar' Could Be the Start of Something Much Worse."

A robotic gripper that can screw in lightbulbs or use a screwdriver without needing to'see' them or be pre-programmed to recognize them has been developed at the University of California San Diego (UCSD).

Driverless cars will be on Britain's roads by 2021 as a result of sweeping regulatory reforms that will put the UK in the forefront of a post-Brexit technological revolution, chancellor Philip Hammond will say this week. In his budget on Wednesday Hammond will allow driverless cars to be tested without any human operator inside or outside the car, and without the legal constraints and rules that apply in many other EU nations, and much of the US. The move – welcomed by the UK motor industry – is part of an attempt by Hammond and the Treasury to project a more upbeat message about the prospects for the UK economy after Brexit, and focus on opportunities as well as the risks. Carmakers have warned that they may have to move at least some production abroad if there is no deal to keep Britain inside the EU single market and customs union, at least for a two-year transition period. But Mike Hawes, chief executive of the Society of Motor Manufacturers and Traders, said it was good news that the government was taking a lead by making the UK attractive to those seeking to develop, test and build an entirely new generation of cars.