If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Cocky children as young as four have the same levels of overconfidence as city bankers and business leaders, according to a new study. UK researchers demonstrated that high levels of confidence in one's own abilities – a trait common among high achievers – is apparent from an extremely early age. This suggests that cocky city types developed their'cognitive bias' from infancy rather than later life, they say. Researchers conducted a card game with young girls and boys with the objective of collecting as many stickers as possible, and compared their different strategies. More than 70 per cent of four-year-olds and half of five and six-year-olds were overconfident in their expectations - comparable to big shot bankers and traders.
Fear is spreading on social media as people share their thoughts on the deadly coronavirus and the impact of the efforts to combat it. Italian-based artificial intelligence company Expert System has been searching through tens of thousands of social media posts to track feelings towards COVID-19. They used a range of natural language systems to capture the emotional view of different English language social media posts related to the pandemic. The team plan to publish a daily update showing the changing attitudes and emotions surrounding the spread of the virus and efforts to slow it down. For the fourth day in a row fear has been the most dominant emotion expressed in posts, with all negative views increasing across the English-language world.
A Security Operations Center (SOC) as a service is one of the most important aspects of information technology. Our managed detection and response features provide a powerful blend of cutting-edge Concierge Security Team (CST) services in tandem with comprehensive threat intelligence and advanced machine learning. We know what it takes to swiftly and effectively neutralize security threats and adapt safeguards to address potential future infiltration attempts. The last thing you want to do is leave security and regulatory compliance to chance. The financial risk to your business is too great and lawsuits from affected parties can cost you everything.
However, one issue that still persists is how to avoid printing objects that don't meet expectations and thus can't be used, leading to a waste in materials and resources. Scientists at the University of Southern California's (USC's) Viterbi School of Engineering has come up with what they think is a solution to the problem with a new machine-learning-based way to ensure more accuracy when it comes to 3D-printing jobs. Researchers from the Daniel J. Epstein Department of Industrial and Systems Engineering developed a new set of algorithms and a software tool called PrintFixer that they said can improve 3D-printing accuracy by 50 percent or more. The team, led by Qiang Huang, associate professor of industrial and systems engineering and chemical engineering and materials science, hopes the technology can help make additive manufacturing processes more economical and sustainable by eliminating wasteful processes, he said. "It can actually take industry eight iterative builds to get one part correct, for various reasons," said Qiang, who led the research.
They also talk about the launch of a global trial of promising treatments. See all of our News coverage of the pandemic here. See all of our Research and Editorials here. Also this week, Nadine Gogolla, research group leader at the Max Planck Institute of Neurobiology, talks with Sarah about linking the facial expressions of mice to their emotional states using machine learning. This week's episode was produced with help from Podigy.
Machine learning (ML) is the current paradigm for modeling statistical phenomena by harnessing algorithms that exploit computer intelligence. It is common place to build ML models that predict housing prices, aggregate users by their potential marketing interests, and use image recognition techniques to identify brain tumors. However, up until now these models have required scrupulous trial and error in order to optimize model performance on unseen data. The advent of automated machine learning (AutoML) aims to curb the resources required (time and expertise) by offering well-designed pipelines that handle data preprocessing, feature selection, and model creation and evaluation. While AutoML may initially only appeal to enterprises that want to harness the power of ML without consuming precious budgets and hiring skilled data practitioners, it also contains very strong promise to become an invaluable tool for the experienced data scientist.
Codebase Ventures Inc (CSE:CODE) (OTCQB:BKLLF) reported that Love Hemp, a CBD supplier in the UK, and a subsidiary of World High Life Plc, in which the company is invested, has seen record monthly sales via its retail presence and e-commerce site. In an update on the early-stage investor's holdings, Codebase also told investors it is "actively pursuing" pharmaceutical opportunities that could have a positive impact on the current global coronavirus pandemic. World High Life is focused on backing or acquiring companies operating in the CBD wellness and medicinal cannabis industry and its wholly-owned subsidiary, Love Hemp is a leading CBD supplier in the United Kingdom. Elsewhere, the firm said its Arcology investment - an AI (artificial intelligence) blockchain ecosystem - is advancing its presence among dAPP developers by launching a project on GitHub, the world's largest source code sharing platform. DApp stands for decentralized application and such an app has its backend code running on a decentralized peer-to-peer network.
Massive investments in data science teams and machine learning platforms have yet to yield results for most companies. The last mile for AI project success is the deployment and management of models in production requiring new technology and practices. This new area is called Machine Learning Operations or MLOps.
Researchers at the Center for Nanoscale Materials (CNM), a U.S. Department of Energy (DOE) Office of Science User Facility located at the DOE's Argonne National Laboratory, have invented a machine-learning based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers. Researchers can apply this pivotal discovery to the analysis of most structural materials of interest to industry. "What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions," said Subramanian Sankaranarayanan, group leader of the CNM theory and modeling group and an associate professor in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago. "For example, with data analyzed by our 3D tool," said Henry Chan, CNM postdoctoral researcher and lead author of the study, "users can detect faults and cracks and potentially predict the lifetimes under different stresses and strains for all kinds of structural materials." Most structural materials are polycrystalline, meaning a sample used for purposes of analysis can contain millions of grains.
The emergence of the novel coronavirus has left the world in turmoil. COVID-19, the disease caused by the virus, has reached virtually every corner of the world, with the number of cases exceeding a million and the number of deaths more than 50,000 worldwide. It is a situation that will affect us all in one way or another. With the imposition of lockdowns, limitations of movement, the closure of borders and other measures to contain the virus, the operating environment of law enforcement agencies and those security services tasked with protecting the public from harm has suddenly become ever more complex. They find themselves thrust into the middle of an unparalleled situation, playing a critical role in halting the spread of the virus and preserving public safety and social order in the process.