In 1985 researchers at the University of Leiden published a paper describing the phase transitions of the heavy fermion alloy uranium ruthenium silicide (URu2Si2). That work sparked numerous studies into this fascinating material, with the phase transition at 17.5K proving particularly puzzling. Despite decades of research, the nature of this phase transition is still unclear. This March a collaboration of researchers from Cornell University, Florida State University, Los Alamos National Laboratory, Max Planck Institute, Dresden, and Leiden University shed further light on the problem by combining resonant ultrasound spectroscopy and machine learning. Their work was published in Science Advances.

Juniper Networks, Inc. JNPR recently announced that its Wireless LAN platform from subsidiary -- Mist Systems -- has been selected by a leading Belgium-based transportation and logistics company, Transports Vervaeke. The latest collaboration is primarily aimed at leveraging Mist's AI-driven platform to enhance the productivity of the European company's workforce with modernized enterprise solutions. Markedly, the innovative network solution will be designed by Juniper's Elite Partner, Infradata, which provides cybersecurity and automation solutions to enterprises as well as network operators. Equipped with avant-garde cloud architecture, Mist is better known for leveraging its AI-driven network to enhance user experience. It also offers best-in-class subscription services like AI-supported virtual assistant Marvin, Asset Visibility, Wi-Fi Assurance and User Engagement for trouble shooting analytics as well as for seamless connectivity with key insight and automation.

Tree-based machine learning models are among the most popular non-linear predictive learning models in use today, with applications in a variety of domains such as medicine, finance, advertising, supply chain management, and more. These models are often described as a "black box" -- while their predictions are based on user inputs, how the models arrived at their predictions using those inputs is shrouded in mystery. This is problematic for some use cases, such as medicine, where the patterns and individual variability a model might uncover among various factors can be as important as the prediction itself. Now, thanks to researchers in the Allen School's Laboratory of Artificial Intelligence for Medicine and Science (AIMS Lab) and UW Medicine, the path from inputs to predicted outcome has become a lot less dense. In a paper published today in the journal Nature Machine Intelligence, the team presents TreeExplainer, a novel set of tools rooted in game theory that enables exact computation of optimal local explanations for tree-based models.

Industrial Intelligence was one of the core topics at this year's Hannover Fair. Due to the importance of this area, we took a closer look at the topic area. Artificial intelligence has the potential to revolutionize the production and energy industries. People teach machines to act logically and purposefully to meet customer needs. AI systems generate knowledge and today, based on data and algorithms, can continuously optimize operating states or reliably predict faults and failures – in production processes, in the power grid or in logistics.

Written by Zest AI CTO Jay Budzik. Zest's ZAML software uses machine learning technology to help lenders make more effective credit decisions safely, fairly and transparently. Founded by Google CIO Douglas Merrill and backed by Matrix Partners, Lightspeed, Upfront, Flybridge and Baidu, Zest works with finance companies worldwide to help more people access fair and transparent credit. It's been a year since MMC Ventures printed the accidental finding that 40 percent of AI startups had no material use of AI in their tech stack. As an AI company CTO, I can tell you the buzz can be deafening.

When we think of human trafficking, we often think about the despondent faces of women and children who live in slums all over the world. What if human trafficking is much closer to home than we think? In 2019, Markie Dell, stood on the TEDx stage to recount her experience of being a domestic human trafficking victim. She was an awkward teenager who was groomed by a girl that she befriended at a birthday party. She was subsequently kidnapped, drugged, sexually violated, intimidated at gunpoint into dancing in strip clubs for an entire year.

Intel, along with the Georgia Institute of Technology (Georgia Tech) recently obtained a multimillion-dollar deal from the Defense Advanced Research Projects Agency (DARPA) in the US. As per the four-year contract, both will work on'Guaranteeing Artificial Intelligence (AI) Robustness against Deception' – or GARD – program for DARPA. According to Intel, it is the main contractor in the multimillion-dollar joint deal, which is targeted at improving cybersecurity defence support facing and spoofing attacks on machine learning (ML) systems. Spoofing attacks can alter and imperil the interpretation of data by the ML algorithms used in an autonomous system. Military systems are vulnerable to security attacks, which can pose risks to extremely sensitive information that can potentially harm military systems.

Artificial intelligence is transforming how we design and build. By 2050, the effects of AI adoption will be widely felt across all aspects of our daily lives. As the world faces a number of urgent and complex challenges, from the climate crisis to housing, AI has the potential to make the difference between a dystopian future and a livable one. By looking ahead, we're taking stock of what's happening, and in turn, imagining how AI can shape our lives for the better. Artificial intelligence is broadly defined as the theory and development of computer systems to perform tasks that normally require human intelligence.

Gary Marcus, founder and CEO of Robust.ai, a company based in Palo Alto, Calif., that is trying to build a cognitive platform for a range of bots, is a proponent of AGI having to work more like a human mind. Speaking at the MIT Technology Review's virtual EmTech Digital conference, he said today's deep learning algorithms lack the ability to contextualize and generalize information, which are some of the biggest advantages to human-like thinking. Marcus said he doesn't specifically think machines need to replicate the human brain, neuron for neuron. But there are some aspects of human thought, like using symbolic representation of information to extrapolate knowledge to a broader set of problems, that would help achieve more general intelligence. "[Deep learning] doesn't work for reasoning or language understanding, which we desperately need right now," Marcus said.

Nuro, the self-driving startup founded by two ex-Google engineers, was approved to test its driverless delivery robots on public roads in California. The company is the second to receive a driverless permit in the state. Nuro, which has tested its driverless grocery delivery service in Arizona and Texas, is authorized to test two light-duty delivery vehicles in nine Bay Area cities, according the California DMV. This includes portions of the cities of Atherton, East Palo Alto, Los Altos, Los Altos Hills, Menlo Park, Mountain View, Palo Alto, Sunnyvale, and Woodside. The vehicles can't exceed 25 mph and are only approved to operate in fair weather conditions on streets with a speed limit of no more than 35 mph. Waymo is the only other company to receive a permit from the DMV for driverless testing -- but has yet to exercise that privilege.