Technology is evolving at a rapid pace, transforming every business sector. The security industry is no different, as emerging technologies are leveraged to enhance operations. Much has been made of artificial intelligence (AI) and its potential, with companies of all kinds scrambling to implement it. Whilst the hype may presently outweigh the current benefits, AI in the security sector can be truly beneficial. The buzz surrounding facial recognition, in particular, has dominated the public perception of AI in the security space.

Every Machine Learning engineer must know how to use Facets for their project -- The No Code AI Tool. Facets, a project from Google Research, is being used to visualise datasets, find interesting relationships, and clean them for machine learning. The No Code movement is on the rise and an increasing number of companies expect their engineers to quickly deliver results using pre-existing tools. From building web pages in minutes to creating mobile apps from a simple spreadsheet, no-code does it all. The proponents of building products quickly are pushing hard for the no-code movement precisely because it lets you get to the state of the art in a matter of hours instead of weeks.

In response to the coronavirus health crisis, USC researchers have made a hard pivot, adapting labs and lessons learned from treating other diseases to help check the virus and save lives. At their disposal are numerous technologies that give a human advantage, despite the fast-break spread of COVID-19 once it exited central China and spread across the globe. The disease has afflicted thousands of Californians and poses a serious risk to public health and the world economy. Tools such as supercomputers, software apps, virtual reality, big data and algorithms are now in play. They are using the tools to find ways to search and destroy coronavirus DNA, turn smartphones into personal protection devices and use people-friendly simulators to help cope with the crush of medical cases.

Applying artificial intelligence and other advanced digital technologies in the real economy isn't a job for tech nerds. Instead, Europe needs more people who can combine a deep knowledge of industry sectors with some expertise in data analytics. That was the verdict of guests at a Science Business roundtable on digital skills, hosted by Sorbonne University's Center for Artificial Intelligence (SCAI) in Paris on 23 January 2020. The event was part of a series of events and publications by Science Business as part of its Digital Skills project. Conversations about AI's impact on the labour market often assume it will create jobs for a small elite of tech nerds, while destroying many more conventional roles.

ResoluteAI, the Connect to Discover company, announced the addition of a News dataset to their Foundation search platform for scientific content. In partnership with FinTech Studios, the leading AI-based intelligent search and analytics platform for Wall Street, the News database provides ResoluteAI's clients with a robust offering of timely scientific content. Foundation is a multi-source research hub that allows public scientific content to be searched as if it's single-source. ResoluteAI applies the most sophisticated artificial intelligence and machine learning to unstructured content. This AI-driven solution creates structured metadata and organizes it into datasets that include Companies, Patents, Grants, Clinical Trials, Technology Transfer, and Publications.

Nothing is surprising about this list. Despite splashy claims that this place or that is the "new Silicon Valley," the real Silicon Valley is still the place to be. According to the latest report from Burning Glass, California, by far, is still hiring the most AI experts in terms of real numbers, followed by New York, Texas, and Massachusetts. Environmental issues and the perception of unfriendliness to business, and yet people still look to California as the nation's supreme tech hub. You have to look back at the history of the tech spread.

IMAGE: Simulated low temperature (left) and high temperature (right) phase of a 2D Ising model, where blue points are spins pointing up, and the red points are spins pointing down. Tokyo, Japan - Researchers from Tokyo Metropolitan University have used machine learning to study spin models, used in physics to study phase transitions. Previous work showed that image/handwriting classifying AI could be applied to distinguish states in the simplest models. The team showed the approach is applicable to more complex models and found that an AI trained on one model and applied to another could reveal key similarities between distinct phases in different systems. Machine learning and artificial intelligence (AI) are revolutionizing how we live, work, play, and drive.

The list of concerns around the use of artificial intelligence seems to grow with every passing week. Issues around bias, the use of AI for deepfake videos and audio, misinformation, governmental surveillance, security and failure of the technology to properly identify the simplest of objects have created a cacophony of concern about the technology's long-term future. One software company recently released a study which showed only 25% of consumers would trust a decision made by systems using AI, and another report commissioned by KPMG International found that a mere 35% of information technology leaders had a high level of trust in their own organizations' analytics. It's a bumpy journey for AI as the technology world embarks on a new decade and key practitioners in the space are well aware that trust will ultimately determine how widely and quickly the technology becomes adopted throughout the world. "We want to build an ecosystem of trust," Francesca Rossi, AI ethics global leader at IBM Corp., said at the digital EmTech Digital conference on Monday.

MADRID – Some political leaders are hailing a potential breakthrough in the fight against COVID-19: simple pin-prick blood tests or nasal swabs that can determine within minutes if someone has, or previously had, the virus. The tests could reveal the true extent of the outbreak and help separate the healthy from the sick. But some scientists have challenged their accuracy. Hopes are hanging on two types of quick tests: antigen tests that use a nose or throat swab to look for the virus, and antibody tests that look in the blood for evidence someone had the virus and recovered. The tests are in short supply, and some of them are considered unreliable.

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