IBM Research, with the help of the University of Texas Austin and the University of Maryland, has created a technology, called BlockDrop, that promises to speed convolutional neural network operations without any loss of fidelity. This could further excel the use of neural nets, particularly in places with limited computing capability. Increase in accuracy level have been accompanied by increasingly complex and deep network architectures. This presents a problem for domains where fast inference is essential, particularly in delay-sensitive and realtime scenarios such as autonomous driving, robotic navigation, or user-interactive applications on mobile devices. Further research results show regularization techniques for fully connected layers, is less effective for convolutional layers, as activation units in these layers are spatially correlated and information can still flow through convolutional networks despite dropout.
Tesla Inc.'s Elon Musk said the carmaker is on the verge of developing technology to render its vehicles fully capable of driving themselves, repeating a claim he's made for years but been unable to achieve. The chief executive officer has long offered exuberant takes on the capabilities of Tesla cars, even going so far as to start charging customers thousands of dollars for a "Full Self Driving" feature in 2016. Years later, Tesla still requires users of its Autopilot system to be fully attentive and ready to take over the task of driving at any time. Tesla's mixed messages have drawn controversy and regulatory scrutiny. In 2018, the company blamed a driver who died after crashing a Model X while using Autopilot for not paying attention to the road.
Researchers at NASA have been hard at work on a pilot AI system intended to help future exploration missions find evidence of life on other planets in our solar system. Machine learning algorithms will help exploration devices analyze soil samples on Mars and return the most relevant data to NASA. The pilot program is currently slated for a test run during the ExoMars mission that will see its launch in mid-2022. As IEEE Spectrum reports, the decision to use machine learning and artificial intelligence to aid the search for life on other planets was driven largely by Erice Lyness, the head of the Goddard Planetary Environments Lab at NASA. Lyness needed to come up with ways of automating aspects of geochemical analyses of samples taken in other parts of our solar system.
AI has become the buzzword of the world, and an integral part of almost every company's digital transformation agenda. AI users have become producers of AI tools and services. Corporate leaders and even the White House have come with forward with a directive on promotion, promulgation, and advancement of artificial intelligence. On February 11, 2019, President Trump signed Executive Order 13859 announcing the American AI Initiative. Executive Order 13859 is the United States' national strategy on artificial intelligence.
Testing for pathogens is a critical component of maintaining public health and safety. Having a method to rapidly and reliably test for harmful germs is essential for diagnosing diseases, maintaining clean drinking water, regulating food safety, conducting scientific research, and other important functions of modern society. In recent research, scientists from University of California, Los Angeles (UCLA), have demonstrated that artificial intelligence (AI) can detect harmful bacteria from a water sample up to 12 hours faster than the current gold-standard Environmental Protection Agency (EPA) methods. In a new study published yesterday in Light: Science and Applications, the researchers created a time-lapse imaging platform that uses two separate deep neural networks (DNNs) for the detection and classification of bacteria. The team tested the high-throughput bacterial colony growth detection and classification system using water suspensions with added coliform bacteria of E. coli (including chlorine-stressed E. coli), K. pneumoniae and K. aerogenes, grown on chromogenic agar as the culture medium.
Dr. Tom Inglesby, director of the Center for Health Security at Johns Hopkins University, joins Chris Wallace on'Fox News Sunday.' A new study published in the Proceedings of the National Academy of Sciences claims compliance in America with social distancing during the early stages of the coronavirus pandemic is linked to working memory. The study, "Working memory capacity predicts individual differences in social-distancing compliance during the COVID-19 pandemic in the United States," assessed the working memory, personality, mood and fluid intelligence of test subjects; the researchers surveyed 850 U.S. residents between March 13 and March 25. The study found a link between working memory and social distancing, and subjects -- noting more benefits than costs -- with higher levels of fluid intelligence, fairness and agreeableness followed the new rules of social distancing compliance, the study found. "The decision of whether or not to follow social distancing guidelines is a difficult one, especially when there is a conflict between the societal benefits (e.g., prevent straining public health resources) and personal costs (e.g., loss in social connection and financial challenges). This decision critically relies on our mental capacity in retaining multiple pieces of potentially conflicting information in our head, which is referred to as working memory capacity," study author Weizhen Xie (Zane) told PsyPost.
Aicha Evans who is the CEO of the self-driving technology development company Zoox, talks about ... [ ] autonomous cars during a keynote session at the Amazon Re:MARS conference on robotics and artificial intelligence at the Aria Hotel in Las Vegas, Nevada on June 6, 2019. On June 26th, Amazon announced via their blog they are acquiring autonomous ride-hailing vehicle startup Zoox. Financial terms of the acquisition were disclosed. However, the Financial Times says Amazon paid $1.2B for Zoox. Launched in 2014, Zoox began with the vision of producing zero-emissions vehicles for autonomous ride-hailing services.
Let's get our James Bond swag on shall we? Defense departments worldwide are betting on AI to deliver the next generation advanced military technology, and the US is no different. In the US of A, this strategy is being orchestrated by the Joint Artificial Intelligence Center (JAIC), a department under the umbrella of the Department of Defense (DoD) led by Acting Director Nand Mulchandani. And he recently gave his first press conference. NLP will play a bigger role in the future of JAIC strategy .
NASA-funded researchers applied artificial intelligence to Facebook user location data captured as two fires wrecked northern California in 2018 and gained new insight into people's evacuation movements and behaviors when disaster strikes, which could strengthen future response. The Defense Innovation Unit and Carnegie Mellon University's Software Engineering Institute are collectively crafting datasets to teach AI tools to assess buildings and structures after natural crises occur, and ultimately augment and increase the accuracy of damage estimates. These are two of many examples detailed in a new report from the Partnership for Public Service and Microsoft that explores how the maturing technology can improve disaster resilience and response, and considerations and actions governments should pursue when adopting AI to boost preparedness, recovery and relief. The report suggests agencies improve data collection and access, make proactive instead of reactive moves, collaborate with other organizations--and more. "While some governments, companies and universities have already used AI in this field, most are still in the early stages of use," officials wrote in the report.