Regional Government


Keeping an Eye on Artificial Intelligence Regulation and Legislation JD Supra

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More and more organizations are beginning to use or expand their use of artificial intelligence (AI) tools and services in the workplace. Despite AI's proven potential for enhancing efficiency and decision-making, it has raised a host of issues in the workplace which, in turn, have prompted an array of federal and state regulatory efforts that are likely to increase in the near future. Artificial intelligence, defined very simply, involves machines performing tasks in a way that is intelligent. The AI field involves a number of subfields or forms of AI that solve complex problems associated with human intelligence--for example, machine learning (computers using data to make predictions), natural-language processing (computers processing and understanding a natural human language like English), and computer vision or image recognition (computers processing, identifying, and categorizing images based on their content). One area where AI is becoming increasingly prevalent is in talent acquisition and recruiting.


We Need You To Help Out The New Arms Race - Praescient Analytics

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Whereas Russia and the United States (U.S.) competed in a traditional arms race through the Cold War, we are now observing a new combative arena where the U.S. government intends to compete with China in the advancement of Industrial Artificial Intelligence (IAI): a new arms race. IAI is defined as a government's motivation to economically invest and advance the commercialization of artificial intelligence within its society. This new arms race may be on the way after U.S. Senators Martin Heinrich (D-NM) and Rob Portman (R-OH) proposed the Artificial Intelligence Act. Believing that Chinese progression in AI technology may soon surpass and threaten American capabilities, the Act calls for a $2.2 billion federal investment strategy over five years in "research, development, demonstration, application to analysis and modeling, and other activities with respect to science and technology in artificial intelligence (AI)." The Act's bi-partisanship nature demonstrates the growing consensus among government officials surrounding the importance of IAI in protecting and bolstering American life against international interference.


Buttigieg fears tech could fuel racially biased credit decisions

FOX News

Fox News Flash top headlines for June 17 are here. Check out what's clicking on Foxnews.com Pete Buttigieg fears that artificial intelligence may further fuel racial bias in America's already challenging credit scoring system. The 37-year-old Democratic presidential candidate was asked by a black business owner about the denial of funding during the Black Economic Alliance Forum that was held in Charleston, S.C. "I'm very worried, living in an era where more and more of this is going to be done by algorithms and by big data, that we're going to automate inequality by failing to be intentional about how some of these algorithms pick up structures and systems and attitudes and assumptions that were already racist in nature," said Buttigieg, who is the mayor of South Bend, Ind., according to Bloomberg. Several Big Tech firms have been criticized over the potential for bias in AI.


Google's chief decision scientist: Humans can fix AI's shortcomings

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Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught.


Google's chief decision scientist: Humans can fix AI's shortcomings

#artificialintelligence

Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught.


Machine Learning for Quantum Design

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In this talk I will discuss some of the long-term challenges emerging with the effort of making deep learning a relevant tool for controlled scientific discovery in many-body quantum physics. The current state of the art of deep neural quantum states and learning tools will be discussed in connection with open challenging problems in condensed matter physics, including frustrated magnetism and quantum dynamics. Variational algorithms for a gate-based quantum computer, like the QAOA, prescribe a fixed circuit ansatz --- up to a set of continuous parameters --- that is designed to find a low-energy state of a given target Hamiltonian. After reviewing the relevant aspects of the QAOA, I will describe attempts to make the algorithm more efficient. The strategies I will explore are 1) tuning the variational objective function away from the energy expectation value, 2) analytical estimates that allow elimination of some of the gates in the QAOA circuit, and 3) using methods of machine learning to search the design space of nearby circuits for improvements to the original ansatz.


California could become first to limit facial recognition technology; police aren't happy

USATODAY - Tech Top Stories

San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. SAN FRANCISCO – A routine traffic stop goes dangerously awry when a police officer's body camera uses its built-in facial recognition software to misidentify a motorist as a convicted felon. At best, lawsuits are launched. That imaginary scenario is what some California lawmakers are trying to avoid by supporting Assembly Bill 1215, the Body Camera Accountability Act, which would ban the use of facial recognition software in police body cams – a national first if it passes a Senate vote this summer and is signed by Gov. Gavin Newsom. State law enforcement officials here do not now employ the technology to scan those in the line of sight of officers.


Keeping an Eye on Artificial Intelligence Regulation and Legislation

#artificialintelligence

More and more organizations are beginning to use or expand their use of artificial intelligence (AI) tools and services in the workplace. Despite AI's proven potential for enhancing efficiency and decision-making, it has raised a host of issues in the workplace which, in turn, have prompted an array of federal and state regulatory efforts that are likely to increase in the near future. Artificial intelligence, defined very simply, involves machines performing tasks in a way that is intelligent. The AI field involves a number of subfields or forms of AI that solve complex problems associated with human intelligence--for example, machine learning (computers using data to make predictions), natural-language processing (computers processing and understanding a natural human language like English), and computer vision or image recognition (computers processing, identifying, and categorizing images based on their content). One area where AI is becoming increasingly prevalent is in talent acquisition and recruiting.


The Global Push to Advance AI

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While different nations often see matters of national policy in very different terms, there are times of nearly universal agreement. That's the case today when it comes to commitments to fuel the advancement of artificial intelligence. Governments around the world agree on the importance of investing in AI initiatives. This point is underscored in a recent report by McKinsey Global Institute. The briefing notes that China and the United States are leaders in AI-related research activities and investments, followed by a second group of countries that includes Germany, Japan, Canada and the United Kingdom.


Disrupting Finance with AI The Future of Energy Exponential Finance

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