It treats an incredibly important and foundational problem (fairness), proposes a creative but simple new definition, gives techniques for achieving the definition, proves theorems with regards to optimality, and even provides empirical results. As learning algorithms are used more and more broadly in situations where their decisions affect people's lives, fairness of these algorithms becomes a critical technical, social, and legal problem. While there is certainly no single "right" definition and paradigm when it comes to fairness, this definition seems to clearly be *a* right definition. It's so clean and simple that in retrospect, it seems obvious--a sign of an excellent idea. One of the many things I love about this definition and this work is how it shifts the structure of power and incentives--once a learner is constrained to be fair, under either of the definitions proposed, she is immediately incentivised to gather more data or make other efforts to do a better job of understanding protected populations.

The report describes the discussions from the Workshop on Mathematical Opportunities in Digital Twins (MATH-DT) from December 11-13, 2023, George Mason University. It illustrates that foundational Mathematical advances are required for Digital Twins (DTs) that are different from traditional approaches. A traditional model, in biology, physics, engineering or medicine, starts with a generic physical law (e.g., equations) and is often a simplification of reality. A DT starts with a specific ecosystem, object or person (e.g., personalized care) representing reality, requiring multi -scale, -physics modeling and coupling. Thus, these processes begin at opposite ends of the simulation and modeling pipeline, requiring different reliability criteria and uncertainty assessments. Additionally, unlike existing approaches, a DT assists humans to make decisions for the physical system, which (via sensors) in turn feeds data into the DT, and operates for the life of the physical system. While some of the foundational mathematical research can be done without a specific application context, one must also keep specific applications in mind for DTs. E.g., modeling a bridge or a biological system (a patient), or a socio-technical system (a city) is very different. The models range from differential equations (deterministic/uncertain) in engineering, to stochastic in biology, including agent-based. These are multi-scale hybrid models or large scale (multi-objective) optimization problems under uncertainty. There are no universal models or approaches. For e.g., Kalman filters for forecasting might work in engineering, but can fail in biomedical domain. Ad hoc studies, with limited systematic work, have shown that AI/ML methods can fail for simple engineering systems and can work well for biomedical problems. A list of `Mathematical Opportunities and Challenges' concludes the report.

Machine Learning: It is the process by which a machine can become intelligent and behave like a human for the assigned tasks.

NASA's Curiosity Rover is living its best life through a massive dust storm on Mars, while Opportunity has been forced to hunker down. An artist's conception of a Martian dust storm, which might also crackle with electricity. A giant dust storm has enveloped the entire planet of Mars, with dust clouds reaching up to 40 miles high, NASA announced Wednesday. The dust storm has silenced NASA's solar-powered rover Opportunity since last week, by obscuring the sun. The robot rover has gone to sleep because its solar panels are unable to provide or recharge its batteries.

What is Intel doing in the area of artificial intelligence/machine learning? Artificial intelligence is causing a technological revolution. Intel recognizes the power AI has to transform society and industries. We are committed to democratizing AI and machine-learning innovations so that everyone has the opportunity to benefit. To that end, we've been doing a number of things: This group focuses on solutions that make it easy to incorporate custom AI solutions into existing infrastructure.

The quest to give machines human-level intelligence has been around for decades, and it has captured imaginations for far longer -- think of Mary Shelley's Frankenstein in the 19th century. Artificial intelligence, or AI, was born in the 1950s, with boom cycles leading to busts as scientists failed time and again to make machines act and think like the human brain. But this time could be different because of a major breakthrough -- deep learning, where data structures are set up like the brain's neural network to let computers learn on their own. Together with advances in computing power and scale, AI is making big strides today like never before. Frank Chen, a partner specializing in AI at top venture capital firm Andreessen Horowitz, makes a case that AI could be entering a golden age.

Any AI effort will rely on three main building blocks: data, infrastructure, and talent. The following is a guest post by Rita C. Waite, a Growth Strategy & Investments Manager at Juniper Networks. Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation. Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings. AI pioneers like Google and Amazon, who have adopted these new technologies to create a growing competitive advantage, have already witnessed bottom-line benefits from their AI strategies.

It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us. One algorithm meant to shine a light in the darkness is AdVerif.ai,

While you were looking the other way, Toronto humbly produced some of the globe's top artificial intelligence and deep learning experts, companies, and innovations. Now is the time for the city to stand up tall and loudly proclaim what local folks already know: Toronto is at the center of AI innovation and its real-world applications. The city is home to world-class academic institutions like the University of Toronto and nearby to the University of Waterloo, both of which constantly churn out bright computer and data scientists, engineers, and developers building next-generation AI technologies. These institutions are world leaders in scientific research, creating an ecosystem ripe with opportunities for novel applications for AI, particularly in the fields of health and life sciences. My own company actively recruits staff from both schools.

Any AI effort will rely on three main building blocks: data, infrastructure, and talent. The following is a guest post by Rita C. Waite, a Growth Strategy & Investments Manager at Juniper Networks. Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation. Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings. AI pioneers like Google and Amazon, who have adopted these new technologies to create a growing competitive advantage, have already witnessed bottom-line benefits from their AI strategies.