Scientists looking to create a new generation of supercomputers are looking for inspiration from the most complex and energy-efficient computer ever built: the human brain. In some of their initial forays into making brain-inspired computers, researchers are looking at different nonbiological materials whose properties could be tailored to show evidence of learning-like behaviors. These materials could form the basis for hardware that could be paired with new software algorithms to enable more potent, useful and energy-efficient artificial intelligence (AI). In a new study led by scientists from Purdue University, researchers have exposed oxygen deficient nickel oxide to brief electrical pulses and elicited two different electrical responses that are similar to learning. The result is an all-electrically-driven system that shows these learning behaviors, said Rutgers University professor Shriram Ramanathan.

To address the lack of publicly available fish community data for most of US lotic freshwater habitats we develop scientific software modules and databases for predicting fish populations by NHDPlus (National Hydrography Dataset) ComId (Common Identifier) segment. We build predictive models of fish species presence in freshwater streams in CONUS using several customized Scikit-learn (Pedregosa and others 2011) machine learning pipelines. The dataset derives from EPA, USGS, and state agency records and contains 565 fish species observed through electrofishing in 28,519 stream segments identified by their NHDplus ComId sampling locations. We use the observations of fish to develop a binary dataset for each species, labeling as present(1) each species found at least once by electrofishing in sampled ComIds. Then for each species, we use the collection of HUC8's where that species may be found and we label the remaining sampled ComIds as absent(0).

The Defense Digital Service (DDS) will discuss how digital services are supporting the development of AI within the Department of Defense (DoD). From previous and current projects, DDS will discuss how they are supporting this development by pursuing quality data, cybersecurity, and advising organizations within DoD on technical capabilities. Join this engaging and interactive presentation to learn more about how the DDS is supporting AI development in the DoD and stick around for Q&A with the presenters to get your questions answered. AI in Government is where those working in and with the government get together to network, discuss, and interact on topics relating to AI, machine learning, and cognitive technologies. Join us at this monthly event for high-quality content with compelling & informative speakers and opportunities to network and connect with fellow like-minded individuals.

Earlier this year, we announced our vision to empower any developer to become a space developer through Azure. With over 90 million developers on GitHub, we have created a powerful ecosystem and we are focused on empowering the next generation of developers for space. Today, we are announcing a crucial step towards democratizing access to space development, with the preview release of Azure Orbital Space SDK (software development kit)--a secure hosting platform and application toolkit designed to enable developers to create, deploy, and operate applications on-orbit. By bringing modern cloud-based applications to spacecrafts we not only increase the efficiency, value, and speed of insights from space data but also increase the value of that data through the optimization of ground communication. Many of the fundamental technological improvements that have accelerated the growth of Internet of Things (IoT) in the past decade remain untapped by space development missions today.

Grand historical inflection points rarely take the form of long bureaucratic documents, but sometimes they do. On October 7th the Department of Commerce issued its revised policy on AI and semiconductor technology exports to China. The 139 pages of new export control regulations placed a de facto ban on exports to China of the advanced computer chips that power AI algorithms. Since more than 95% of such chips used in China are designed by U.S. semiconductor companies and therefore subject to U.S. export controls, loss of access to U.S. chips puts China's entire future as an AI superpower in jeopardy. AI was the top technology priority listed in the Chinese government's five-year economic plan for 2021-2026, so this action makes clear that the U.S. intends to block China from achieving its top technological goal. Ten days after the new policy came out, Secretary of State Antony Blinken gave a major speech in which he said, "We are at an inflection point.

Best AI Technology leaders are Google, Amazon, IBM, Microsoft, Salesforce, Oracle, NVIDIA, Intel, SAP, and Adobe. The global artificial intelligence market is expected to grow from $2.9 billion in 2019 to $19.6 billion by 2024, at a CAGR of 42.6% during the forecast period. The growth of the artificial intelligence market is driven by the increasing demand for intelligent virtual assistants, such as Amazon Alexa and Google Home, and the increasing adoption of AI-based technologies by enterprises. According to Zion Market Research, The global artificial intelligence market is anticipated to increase from $59.7 billion in 2021 to $422.4 billion by 2028. Robotics, automation, and AI are causing disruption in almost every business.

Growing up in Rhode Island (the Ocean State), I lived very close to the water. Over the years, I have seen the effects of sea level rise and rapid erosion. Entire houses and beaches have slowly been consumed by the tide. I have witnessed first hand how climate change is rapidly changing the ocean ecosystem. Sometimes I feel overwhelmed by the inexorability of climate change.

AI can improve or worsen job quality. What constitutes a quality job? If you were to ask family and friends, they would probably say good pay, benefits, and stable working conditions, but for many workers, workplace technologies, especially AI, are affecting job quality. That's important because the U.S. has a serious job quality problem. The number one ESG challenge companies are grappling with is the treatment of workers.

TuSimple Holdings Inc. co-founder Mo Chen has taken control of the self-driving trucking company as federal authorities continue to investigate TuSimple's relationship with Mr. Chen's other startup, a Chinese hydrogen-trucking company. A TuSimple filing with the Securities and Exchange Commission on Wednesday shows that Mr. Chen has 59% of the voting power at the San Diego-based company, giving him control as of Nov. 9, a day before the company announced it had ousted its board of directors. Mr. Chen acquired the stake through stock purchases using his family trust and British Virgin Islands-based entities, according to the securities filing. TuSimple's newly appointed chief executive officer, Cheng Lu, said, "We have a strong sense of urgency to put our company back on track and regain trust from all stakeholders." A weekly digest of tech reviews, headlines, columns and your questions answered by WSJ's Personal Tech gurus.

Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction that often results in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost.