MIT researchers discovered hidden magnetic properties in multi-layered electronic material by analyzing polarized neutrons using neural networks. An MIT team incorporates AI to facilitate the detection of an intriguing materials phenomenon that can lead to electronics without energy dissipation. Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, "topological materials," has offered an alternative but promising means for achieving electronics without energy dissipation (or loss). Compared to superconductors, topological materials provide a few advantages, such as robustness against disturbances.

Artificial intelligence and machine learning … make it easier to analyse the large data sets used in defence intelligence and cyber security.

SUNY Empire State College has recently launched a Master of Science in applied analytics degree program to prepare students for careers as data analysts, business analysts, data scientists, and machine-learning engineers. SUNY Empire's M.S. degree in applied analytics comes as modern industry is recognizing the value of its data, which is quickly becoming a commodity across all economic sectors. Data scientists and data engineers are rated among some of the best tech jobs in the U.S. At the same time, a growing number of organizations are raising the red flag over significant shortages of tech workers. The U.S. Bureau of Labor and Statistics predicts job growth in applied analytics and related fields will reach 20%, a rate that is much faster than other occupations. Professionals in applied analytics can expect a mean average annual wage of $103,930, which is significantly higher than the national average in other jobs.

Researchers at the University of Texas at Austin are leading the way when it comes to fine-tuning the accuracy of artificial intelligence. UT's Machine Learning Lab held a public lecture Friday titled, "AI for Accurate and Fair Imaging." Researchers with the lab's Institute for Foundations of Machine Learning (IFML) have been working to improve the algorithm that in 2020 produced an internet-famous image of former President Barack Obama, dubbed "White Obama." AI that was meant to enhance a pixelated, low-resolution photo of the 44th president instead transformed him into a white man. "Even though it looked like a good image, a high resolution, realistic image of a person, it had a bias," said Alex Dimakis, IFML co-director.

Artificial Intelligence and its inherent bias seems to be an ongoing contributing factor in slowing minorities home loan approvals. An investigation by The Markup found lenders were more likely to deny home loans to people of color than to white people with similar financial characteristics. Specifically, 80% of Black applicants are more likely to be rejected, along with 40% of Latino applicants, and 70% of Native American applicants are likely to be denied. How detrimental is the secret bias hidden in mortgage algorithms? It's important to note that 45% of the country's largest mortgage lenders now offer online or app-based loan origination, as FinTech looks to play a major role in reducing bias in the home lending market, CultureBanx reported.

This automatically-generated transcript is taken from the IT Pro Podcast episode'Do we need AI regulation?'. To listen to the full episode, click here. The AI industry has been going from strength to strength over the past several years, with machine learning technology becoming increasingly widely available to businesses, along with a stream of breakthroughs in research and development. However, this explosion of AI capabilities has also brought its share of problems. Questions of model transparency, implicit bias and ethical deployments have frequently been levelled at efforts in this space. And numerous campaigners have called for governments to introduce legislation, which will place greater controls on the development and implementation of AI systems. Joining us this week to discuss the issue of AI regulation, whether it's necessary and how it might be implemented without stifling innovation is Cindi Howson, chief data strategy officer for analytics software vendor ThoughtSpot. Cindi, great to have you on the show. Great to be here, Sabina, and Adam.

One of the imaging technologies that many robotics companies are integrating into their sensor packages is Light Detection and Ranging, or LiDAR for short. Currently commanding great attention and investment from self-driving car developers, the approach essentially works like radar, but instead of sending out broad radio waves and looking for reflections, it uses short pulses of light from lasers. Traditional time-of-flight LiDAR, however, has many drawbacks that make it difficult to use in many 3D vision applications. Because it requires detection of very weak reflected light signals, other LiDAR systems or even ambient sunlight can easily overwhelm the detector. It also has limited depth resolution and can take a dangerously long time to densely scan a large area such as a highway or factory floor.

SUNY Empire State College has recently launched a Master of Science in applied analytics degree program to prepare students for careers as data analysts, business analysts, data scientists, and machine-learning engineers. SUNY Empire's M.S. degree in applied analytics comes as modern industry is recognizing the value of its data, which is quickly becoming a commodity across all economic sectors. Data scientists and data engineers are rated among some of the best tech jobs in the U.S. At the same time, a growing number of organizations are raising the red flag over significant shortages of tech workers. The U.S. Bureau of Labor and Statistics predicts job growth in applied analytics and related fields will reach 20%, a rate that is much faster than other occupations. Professionals in applied analytics can expect a mean average annual wage of $103,930, which is significantly higher than the national average in other jobs.

A new DARPA initiative aims to ultimately give AI systems the same complex, rapid decision-making capabilities as military medical staff and trauma surgeons who are in the field of battle. The In the Moment (ITM) program, which is right now soliciting research proposals, aims to develop the foundations of expert machine-learning models that can make difficult judgment calls – where there is no right answer – that humans can trust. This study could lead to the deployment of algorithms that can help medics and other personnel make tough decisions in moments of life and death. "DoD missions involve making many decisions rapidly in challenging circumstances and algorithmic decision-making systems could address and lighten this load on operators … ITM seeks to develop techniques that enable building, evaluating, and fielding trusted algorithmic decision-makers for mission-critical DoD operations where there is no right answer and, consequently, ground truth does not exist," DARPA said. At the heart of this problem is that these sorts of AI systems need to be trained even when there is no ground truth or consensus among experts.

Ukraine's defense ministry on Saturday began using Clearview AI's facial recognition technology, the company's chief executive said after the US startup offered to uncover Russian assailants, combat misinformation, and identify the dead. Ukraine is receiving free access to Clearview AI's powerful search engine for faces, letting authorities potentially vet people of interest at checkpoints, among other uses, added Lee Wolosky, an adviser to Clearview and former diplomat under US presidents Barack Obama and Joe Biden. The plans started forming after Russia invaded Ukraine and Clearview Chief Executive Hoan Ton-That sent a letter to Kyiv offering assistance, according to a copy seen by Reuters. Clearview said it had not offered the technology to Russia, which calls its actions in Ukraine a "special operation". Ukraine's Ministry of Defense did not reply to requests for comment.