Fox News Flash top headlines for Sept. 12 are here. Check out what's clicking on Foxnews.com California could soon become the largest state to ban the use of facial recognition technology in law enforcement body cameras, a significant milestone in the regulation of the burgeoning technology. The State Assembly on Thursday passed AB 1215, a bill that would impose a three-year moratorium on the technology, garnering praise from privacy and civil liberties advocates. The legislation now heads to Gov. Gavin Newsom's desk.

Amazon has announced a new program for its Echo smart speakers that will put anyone with an Amazon account in charge of answering search queries. The program, called Alexa Answers, will let users browse a list of unanswered questions like'What is the state snack of Texas?' (an example provided by Amazon's own web page) and submit their response. After the answer is entered into the database, the Echo's voice assistant, Alexa, will start relaying it to other users with the same query and an addendum stating that the data is'according to an Amazon customer.' Amazon is now crowd-sourcing answers to users' search queries through its popular smart-speaker, the Echo (pictured above) Participants will enter their answers -- 300 characters or less -- on a dedicated website where they will then compete with other participants to earn points and badges for'good' responses.' The program was officially launched last year, but was invite-only and included a relatively small pool of customers.

In the 1970s, the U.S. Department of Energy poured money into making practical a miraculous technology: the ability to convert sunlight into electricity. Solar energy was a pipe dream, far too expensive and unreliable to be considered a practical power source. But yesterday's moon shot is today's reality. The expense of solar power has fallen more quickly than expected, with installations costing about 80% less today than a decade ago. Alternative energy (like wind and solar) is now often cheaper than conventional energy (like coal and gas).

AI startup Adarga has closed a £5 million Series A fundraising by Allectus Capital. But this news rather cloaks the fact that it has been building up a head of steam since its founding in 2016, building up what they say is a £30 million-plus sales pipeline through strategic collaborations with a number of global industrial partners and gradually building its management team. The proceeds will be used to continue the expansion of Adarga's data science and software engineering teams and to roll out internationally. Adarga, which comes from the word for an old Moorish shield, is a London and Bristol-based startup. It uses AI to change the way financial institutions, intelligence agencies and defence companies tackle problems, helping crunch vast amounts of data to identify possible threats even before they occur.

Over the past few years, researchers have created a growing number of machine learning (ML)-based face recognition techniques, which could have numerous interesting applications, for instance, enhancing surveillance monitoring, security control, and potentially even forensic art. In addition to face recognition, advancements in ML have also enabled the development of tools to predict or estimate specific qualities (e.g., gender or age) of a person by analyzing images of their faces. In a recent study, researchers at the University of Kwazulu-Natal, in South Africa, developed a machine learning-based model to estimate people's age by analyzing images of their faces taken in random real-life environments. This new architecture was introduced in a paper published by Spinger and presented a few days ago at the International Conference on Computational Collective Intelligence (ICCCI) 2019. Most traditional approaches for age classification only perform well when analyzing face images taken in controlled environments, for instance, in the lab or in photography studios.

Iowa State University scientists are working toward a future in which farmers can use unmanned aircraft to spot, and even predict, disease and stress in their crops. Their vision relies on machine learning, an automated process in which technology can help farmers respond to plant stress more efficiently. Arti Singh, an adjunct assistant professor of agronomy, is leading a multi-disciplinary research team that recently received a three-year, $499,845 grant from the U.S Department of Agriculture's National Institute of Food and Agriculture to develop machine learning technology that could automate the ability of farmers to diagnose a range of major stresses in soybeans. The technology under development would make use of cameras attached to unmanned aerial vehicles, or UAVs, to gather birds-eye images of soybean fields. A computer application would automatically analyze the images and alert the farmer of trouble spots.

The key factors driving the GPU market growth include the rising adoption of GPUs in the healthcare sector, rapidly evolving PC gaming landscape with the advent of interactive Virtual Reality (VR) games, and the growing popularity of GPUs for machine learning and neural network training applications. In applications involving machine learning and Big Data analytics, GPUs are highly efficient when compared with CPUs as they enable excellent multiple parallel processing. With 10-100x application throughput and thousands of computational cores compared to CPUs, GPUs are preferred by scientists and data analysts who require massive Big Data processing capabilities. From interactive model-based image registration and MRI connectivity mapping to medical imaging and image reconstruction, the healthcare industry is increasingly leveraging the extensive computational power of GPUs. Technological advancements are giving rise to a persistent expectation from healthcare providers to deliver advanced visualization capabilities.

Humans are inherently risk-averse: We spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair. Still, our measures for controlling the inner workings of our biology can be a little more unruly. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient's risk of cardiovascular death. The system, called "RiskCardio," focuses on patients who have survived an acute coronary syndrome (ACS), which refers to a range of conditions where there's a reduction or blockage of blood to the heart. Using just the first 15 minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories.

"AI is the new electricity." When Andrew Ng speaks, you drop everything and pay attention. That's what I (and thousands of others) did when Andrew Ng compared our age of AI to the discovery of electricity. We are truly living in the age of artificial intelligence. Companies are spending billions of Dollars just to stay relevant in today's ever-changing environment.

Researchers from MIT and elsewhere have developed a system that measures a patient's pain level by analyzing brain activity from a portable neuroimaging device. The system could help doctors diagnose and treat pain in unconscious and noncommunicative patients, which could reduce the risk of chronic pain that can occur after surgery. Pain management is a surprisingly challenging, complex balancing act. Overtreating pain, for example, runs the risk of addicting patients to pain medication. Undertreating pain, on the other hand, may lead to long-term chronic pain and other complications. Today, doctors generally gauge pain levels according to their patients' own reports of how they're feeling.