Dr. Benji Maruyama is the Air Force Research Laboratory team lead for Autonomous Materials and the ... [ ] Autonomous Research System also known as ARES. ARES OS, an open-source software program, is now available online as a free download. In the fight to prevail over America's adversaries by out-innovating them - a fight which has all the hallmarks of a Cold War despite President Biden's assertions to the contrary - increasing the speed at which physical lab experiments can be done and iterated is vital. Air Force Research Laboratory (AFRL) scientist, Dr. Benji Maruyama, is reminding his peers and the public that, "Research is a painfully slow process. Being in a lab and doing experiments takes lots of time."

China on Tuesday showed off its increasingly sophisticated air power including surveillance drones and jets able to jam hostile electronic equipment, with an eye on disputed territories from Taiwan to the South China Sea and rivalry with the United States. The country's biggest airshow, in the southern coastal city of Zhuhai, comes as Beijing pushes to meet a 2035 deadline to retool its military for modern warfare. China still lags the United States in terms of tech and investment in its war machine, but experts say it is narrowing the gap. On Tuesday, a prototype of a new surveillance drone able to carry out attacks -- the CH-6 -- was among domestic tech unveiled in Zhuhai. China's WZ-7 high-altitude drone for border reconnaissance and maritime patrol has already entered service with the air force, according to state media Photo: AFP / Noel Celis With a wingspan of 20.5 metres (67 feet) and 15.8 metres long, the drone can carry missiles and is designed for surveillance and strike operations, according to open source intelligence agency Janes. Other debutants include the WZ-7 high-altitude drone for border reconnaissance and maritime patrol, as well as the J-16D fighter jet which can jam electronic equipment.

Almost every aspect of human life is influenced by science and technology. From the smartphone, there are always two sides of technology. As the field of science and technology has advanced, it has fundamentally changed our perspectives of life. Among those advancements, robotics is the most significant development that is trying to get closer to human life. Although it has managed to do make our daily lives easier, they can still create problems.

Tesla is being criticized by motor-safety experts for using YouTubers to beta test its self-driving technology rather than trained safety drivers. After signing non-disclosure agreements, these would-be influencers film their experiences on the road, Vice first reported, using Tesla's Full Self-Driving (FSD) Beta to navigate busy streets. Urban policy expert David Zipper criticized the FSD in a tweet, posting a clip which sees one Seattle beta tester's steering wheel suddenly spin right and the car lurches toward a crosswalk. 'Whoa, s--t!, sorry--it gave up there,' the driver announces with an apologetic wave to pedestrians. Seattle YouTuber'HyperChange,' posted a video trying out v.10 of Tesla's Fully Self Driving beta.

As I prepare to embark on a new chapter in my career, pivoting to a venture investor role, I have spent some time reflecting on the evolution of technology, and what it means for our present and future worlds. Here are some of my thoughts. The Information Age is upon us in full force. Not a day goes by without AI being in the news. Or… you get the picture.

In order to settle a lawsuit brought by the US Equal Employment Opportunity Commission, Activision Blizzard has agreed to establish an $18 million fund for eligible claimants -- meaning, employees who were harmed by the company's discriminatory hiring and management practices. The EEOC lawsuit was filed Monday, and that same afternoon, Activision Blizzard announced the $18 million conclusion. Activision Blizzard is the company behind blockbuster video game franchises including Call of Duty, World of Warcraft, Diablo and Overwatch. Activision Blizzard's revenue for the year 2020 was $8.1 billion, with a profit of more than $2 billion. Today's $18 million agreement follows a three-year investigation into Activision Blizzard by the EEOC.

Robots are becoming everyday devices, increasing their interaction with humans. To make human-machine interaction more natural, cognitive features like Visual Voice Activity Detection (VVAD), which can detect whether a person is speaking or not, given visual input of a camera, need to be implemented. Neural networks are state of the art for tasks in Image Processing, Time Series Prediction, Natural Language Processing and other domains. Those Networks require large quantities of labeled data. Currently there are not many datasets for the task of VVAD. In this work we created a large scale dataset called the VVAD-LRS3 dataset, derived by automatic annotations from the LRS3 dataset. The VVAD-LRS3 dataset contains over 44K samples, over three times the next competitive dataset (WildVVAD). We evaluate different baselines on four kinds of features: facial and lip images, and facial and lip landmark features. With a Convolutional Neural Network Long Short Term Memory (CNN LSTM) on facial images an accuracy of 92% was reached on the test set. A study with humans showed that they reach an accuracy of 87.93% on the test set.

Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? Existing benchmarks are not designed to measure progress in applied settings, and so don't directly answer this question. The RAFT benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment. Baseline evaluations on RAFT reveal areas current techniques struggle with: reasoning over long texts and tasks with many classes. Human baselines show that some classification tasks are difficult for non-expert humans, reflecting that real-world value sometimes depends on domain expertise. Yet even non-expert human baseline F1 scores exceed GPT-3 by an average of 0.11. The RAFT datasets and leaderboard will track which model improvements translate into real-world benefits at https://raft.elicit.org .

Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, safety for ML should be a leading research priority. In response to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address. We present four problems ready for research, namely withstanding hazards ("Robustness"), identifying hazards ("Monitoring"), steering ML systems ("Alignment"), and reducing risks to how ML systems are handled ("External Safety"). Throughout, we clarify each problem's motivation and provide concrete research directions.

Temporal Information and Event Markup Language (TIE-ML) is a markup strategy and annotation schema to improve the productivity and accuracy of temporal and event related annotation of corpora to facilitate machine learning based model training. For the annotation of events, temporal sequencing, and durations, it is significantly simpler by providing an extremely reduced tag set for just temporal relations and event enumeration. In comparison to other standards, as for example the Time Markup Language (TimeML), it is much easier to use by dropping sophisticated formalisms, theoretical concepts, and annotation approaches. Annotations of corpora using TimeML can be mapped to TIE-ML with a loss, and TIE-ML annotations can be fully mapped to TimeML with certain under-specification.