NASA has keyed in on a dozen technologies that will help humans on their next mission to the moon in 2024. The payloads which will be sent to the moon aboard one of NASA's landers, will aid the agency in its ambitious Artemis mission. NASA's next lunar landing will not only mark the first attempt to put humans back on the moon since 1972 -- including the first-ever woman -- but will eventually set forth the construction of a lunar base and a satellite called the'lunar gateway.' A new moon rover is among the technologies that are being studied and designed under NASA's direction for application toward its Artemis mission'The selected lunar payloads represent cutting-edge innovations, and will take advantage of early flights through our commercial services project,' said Thomas Zurbuchen, associate administrator of the agency's Science Mission Directorate in Washington in a statement. 'Each demonstrates either a new science instrument or a technological innovation that supports scientific and human exploration objectives, and many have broader applications for Mars and beyond.'

In the last few years, when the cybercrooks have speeded their execution plan on making quick money by ransomware attacks. All enterprises, including banks, government offices, police stations, big and small businesses, have witnessed WannaCry, Petya, NotPetya ransomware attacks. The question for us is what we can do to defend from cyber threats? The cybersecurity industry is pitching heavily to leverage AI to combat cyber threats. Almost every cybersecurity vendor is claiming to have AI in its product.

Amazon has confirmed that its Alexa voice assistant sometimes stores your data indefinitely, even after any corresponding audio clips have been deleted. The admission comes after inquiries from US Senator Chris Coons, who asked the tech firm to explain what happens to voice records and data gathered by Alexa. The senator, a democrat, wrote to Amazon following a CNET investigation in May that revealed that the company retains voice records unless users delete them. The probe had also suggested that, regardless, written transcripts of those voice recordings may also be kept indefinitely. Amazon's device - along with Apple's Siri and, until recently, Google's Assistant - saves every single interaction a person has with the device, with some unintentional snippets also being recorded.

Artificial Intelligence (AI) is poised to improve people's lives worldwide and accelerate progress on the United Nations Sustainable Development Goals (SDGs). Yet, AI can also bring with it a host of unintended consequences. One of the most pernicious areas could be AI's ability to spread misinformation at a pace and scale not seen before. At the recent AI for Good Global Summit, participants from academia, the United Nations, major media outlets and the private sector gathered to discuss the unintended consequences of AI and AI-powered misinformation. To be sure, AI has provided a wealth of task-facilitating tools for media and the field of journalism, where its impact can be seen in everything from the emergence of voice-recognition transcription tools to automatically generated content.

Immigration and Customs Enforcement officials have mined state driver's license databases using facial recognition technology, analyzing millions of motorists' photos without their knowledge. In at least three states that offer driver's licenses to undocumented immigrants, ICE officials have requested to comb through state repositories of license photos, according to newly released documents. At least two of those states, Utah and Vermont, complied, searching their photos for matches, those records show. In the third state, Washington, agents authorized administrative subpoenas of the Department of Licensing to conduct a facial recognition scan of all photos of license applicants, though it was unclear whether the state carried out the searches. In Vermont, agents only had to file a paper request that was later approved by Department of Motor Vehicles employees.

Berkeley Lab researchers (from left) Vahe Tshitoyan, Anubhav Jain, Leigh Weston, and John Dagdelen used machine learning to analyze 3.3 million abstracts from materials science papers. Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials. "Without telling it anything about materials science, it learned concepts like the periodic table and the crystal structure of metals," says Jain. "That hinted at the potential of the technique. But probably the most interesting thing we figured out is, you can use this algorithm to address gaps in materials research, things that people should study but haven't studied so far."

Dr. Mitchell Pryor earned is BSME at Southern Methodist University in 1993. After graduating, he taught math and science courses at St. James School in St. James Maryland before returning to Texas. He completed is Masters (1999) and PhD (2002) at UT Austin with an emphasis on the modeling, simulation, and operation of redundant manipulators. Since earning his PhD, Dr. Pryor has taught graduate and undergraduate courses in the mechanical and electrical engineering departments as well as led and conducted research in the area of robotics and automation in Mechanical Engineering, Petroleum Engineering and the Nuclear Engineering Teaching Laboratory. He has worked for numerous research sponsors including, NASA, DARPA, DOE, INL, LANL, ORNL, Y-12, and many industrial partners.

A link has been posted to your Facebook feed. Despite sending humans to Earth's orbit and the moon, the idea of humans surviving in outer space must seem like science fiction. Creating an environment that can sustain human life in the almost total absence of gravity, as well as no electrical outlets or oxygen, takes a lot of experimentation. That's been the job of teams of dedicated scientists who have facilitated some of the most unforgettable moments in space exploration. We compiled 30 common items that were invented for use in the race for space.

When quantitative models are used to support decision-making on complex and important topics, understanding a model's ``reasoning'' can increase trust in its predictions, expose hidden biases, or reduce vulnerability to adversarial attacks. However, the concept of interpretability remains loosely defined and application-specific. In this paper, we introduce a mathematical framework in which machine learning models are constructed in a sequence of interpretable steps. We show that for a variety of models, a natural choice of interpretable steps recovers standard interpretability proxies (e.g., sparsity in linear models). We then generalize these proxies to yield a parametrized family of consistent measures of model interpretability. This formal definition allows us to quantify the ``price'' of interpretability, i.e., the tradeoff with predictive accuracy. We demonstrate practical algorithms to apply our framework on real and synthetic datasets.

When predictive models are used to support complex and important decisions, the ability to explain a model's reasoning can increase trust, expose hidden biases, and reduce vulnerability to adversarial attacks. However, attempts at interpreting models are often ad hoc and application-specific, and the concept of interpretability itself is not well-defined. We propose a general optimization framework to create explanations for linear models. Our methodology decomposes a linear model into a sequence of models of increasing complexity using coordinate updates on the coefficients. Computing this decomposition optimally is a difficult optimization problem for which we propose exact algorithms and scalable heuristics. By solving this problem, we can derive a parametrized family of interpretability metrics for linear models that generalizes typical proxies, and study the tradeoff between interpretability and predictive accuracy.