Important note on nomenclature: While often referred to as "AI" registers, the more appropriate term should be "Algorithmic Decision System" (ADS) registers. A register is an official record of information. In its simplest form, it can provide visibility over algorithmic systems in use, which is the most basic form of transparency. It can also be designed to enable meaningful transparency that meets stakeholder information needs. It's the latter use case that I explore throughout this article. Meaningful transparency is an important concept in AI ethics.

It is a law of physics that everything that is not prohibited is mandatory. They are everywhere: in language, cooking, communication, image processing and, of course, computation. Mitigating and correcting them keeps society running. You can scratch a DVD yet still play it. QR codes can be blurred or torn yet are still readable. Images from space probes can travel hundreds of millions of miles yet still look crisp. Error correction is one of the most fundamental concepts in information technology.

Artificial Intelligence (AI) can unlock huge opportunities for both enterprises and governments, but AI needs to be deployed at large scale for the same. According to Rama Devi Lanka, director, Emerging Technologies and officer on special duty at ITE&C, Government of Telangana, the government is looking to deploy AI at the same scale as India's Unified Payments Interface (UPI). "If you look at the unified payments interface (UPI), that is the scale at which we are looking to deploy AI," she added. Speaking at the Mint AI Summit on April 29, Lanka said that Telangana has identified agriculture, healthcare, education, transportation, smart cities, and climate change as some of the focus areas for AI implementation. However, she noted that the lack of availability of data can be a problem.

As more and more companies are developing and/or utilizing artificial intelligence (AI), it is important to consider risk management and best practices to address issues like bias in AI. The National Institute of Standards and Technology (NIST) recently released a draft of its AI Risk Management Framework (Framework) and guidance to address bias in AI (Guidance). The voluntary Framework addresses risks in the design, development, use, and evaluation of AI systems. The Guidance offers considerations for trustworthy and responsible development and use of AI, notably including suggested governance processes to address bias. The Framework and Guidance will be useful for those who design, develop, use, or evaluate AI technologies.

If you enjoy articles about A.I. at the intersection of breaking news join AiSupremacy here. I cannot continue to write without community support. This content is borderline NSFW, as I don't agree with all the Ukraine posting. But still I believe it's a story that needs to be told. On March 30th, 2022 the U.S. included 100 killer drones called Switchblades in an arms package to Ukraine.

When Russia invaded Ukraine, the US Department of Defense turned to a team of machine learning and artificial intelligence experts to make sense of an avalanche of information about the conflict. "We have surged data scientists forward," Deputy Secretary of Defense Kathleen Hicks told WIRED in a recent interview. These tech experts crafted code and machine learning algorithms, creating systems that are "especially valuable for synthesizing the complex logistics picture," she said. Due to the sensitive nature of operations in Ukraine, Hicks says she cannot provide details of what the data team has done. But Hicks says this helps prove a point that she and others have been making within the Pentagon for some time--that technology is fundamentally changing the nature of war, and the US needs to adapt in order to maintain its edge.

The Pentagon has committed to combating bias in artificial intelligence but may not be asking the right questions of industry partners to be successful. AI has become a bedrock of 21st century military strategy, and the Defense Department argues that machine learning is integral to the U.S. military's competitive edge. But traditional AI models are "brittle" and "not ready for deployment," Nisheeth Vishnoi, a Yale professor of computer science, said. Wrong results on the battlefield include incorrect identification of human and environmental targets. The department has spent billions of dollars on AI development, including establishment of a Joint Artificial Intelligence ...

The Allen Telescope Array, used by Northern California's SETI Institute in its often difficult-to-fund search for extraterrestrial life.Redding Record Searchlight / Zuma Press This story was originally published by Undark and is reproduced here as part of the Climate Desk collaboration. Science is built on the boldly curious exploration of the natural world. Astounding leaps of imagination and insight--coupled with a laser like focus on empiricism and experimentation--have brought forth countless wonders of insight into the workings of the universe we find ourselves in. But the culture that celebrates, supports, and rewards the audacious mental daring that is the hallmark of science is at risk of collapsing under a mountain of cautious, risk-averse, incurious advancement that seeks merely to win grants and peer approval. I've encountered this problem myself.

'… Europe is a unique aspiration. Since then questions of new technologies and European values have been at the forefront of political discussions in Brussels and member states regarding Artificial Intelligence (AI), including preparations for the forthcoming AI Act and recently adopted Digital Services Act. These discussions have not only addressed technocratic questions of economic indicators and legal instruments but also involved soul-searching and reflections on European identity: What is Europe? What does Europe stand for? And how does Europe want to project its identity and power to the rest of the world?

Drones and robots could form part of a new radiation-monitoring system at the Chernobyl power station in Ukraine, as scientists at the plant fear that existing sensor networks built after the nuclear accident in 1986 have been at least partially destroyed by Russian troops. When Russia seized the Chernobyl plant in February, the sensors monitoring gamma radiation levels quickly went offline and most remained that way.