Killer whales – also known as orcas – have complex social structures including close'friendships', a new study reveals. Scientists at the University of Exeter used drones to film the animals – one of the world's most powerful predators – in the Pacific Ocean. The team found killer whales (Orcinus orca) spend more time interacting with certain individuals in their pod, and tend to favour those of the same sex and similar age. Results from the new study are based on 651 minutes of video filmed over 10 days. Orcas are the largest member of the dolphin family.

A new study led by the University of Exeter and the Center for Whale Research suggests killer whales may socialise with each other based on age and gender, with younger whales and females more sociable than other groups. The research used drone cameras to study one pod of southern resident killer whales off the US coast of Washington State, in the Pacific Ocean. Around 10 hours of footage was captured over 10 days.

Brussels – NATO leaders warned Monday that China's military ambitions pose "systemic challenges" to their alliance, and agreed to enhance ties with Japan and other Asia-Pacific nations to back the rules-based international order. The tough line against Beijing, taken in a communique released after the NATO summit, came as U.S. President Joe Biden rallies allies to counter what he calls autocracies like China and Russia that are challenging an open international order. "China's stated ambitions and assertive behavior present systemic challenges to the rules-based international order and to areas relevant to alliance security," said the communique from the 30-member organization that brings together North American and European countries. The leaders also expressed concerns over what they called China's coercive policies, while pointing out the country's rapid expansion of its nuclear arsenal and criticizing the opaqueness of its military modernization. The communique, meanwhile, named Australia, Japan, New Zealand and South Korea as countries with which NATO plans to strengthen its "political dialogue and practical cooperation" in a bid to promote cooperative security and support the rules-based international order.

The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.

Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements in anomaly detection in high-dimensional datasets; however, existing methods do not explicitly learn the structure of existing relationships between variables, or use them to predict the expected behavior of time series. Our approach combines a structure learning approach with graph neural networks, additionally using attention weights to provide explainability for the detected anomalies. Experiments on two real-world sensor datasets with ground truth anomalies show that our method detects anomalies more accurately than baseline approaches, accurately captures correlations between sensors, and allows users to deduce the root cause of a detected anomaly.

Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods. This is a much harder setting than for continuous tasks, which enjoy gradient flows from discriminators to generators, usually leading to dramatic learning instabilities. However, we claim that this can be solved by making discriminator and generator networks cooperate to produce output sequences during training. These cooperative outputs, inherently built to obtain higher discrimination scores, not only provide denser rewards for training, but also form a more compact artificial set for discriminator training, hence improving its accuracy and stability. In this paper, we show that our SelfGAN framework, built on this cooperative principle, outperforms Teacher Forcing and obtains state-of-the-art results on two challenging tasks, Summarization and Question Generation.

Veeva [NYSE: VEEV] is the leader in cloud-based software for the global life sciences industry. Committed to innovation, product excellence, and customer success, our customers range from the world's largest pharmaceutical companies to emerging biotechs. Veeva's software helps our customers bring medicines and therapies to patients faster. We are the first public company to become a Public Benefit Corporation. As a PBC, we are committed to making the industries we serve more productive, and we are committed to creating high-quality employment opportunities.

One afternoon in late November of last year, Timnit Gebru was sitting on the couch in her San Francisco Bay Area home, crying. Gebru, a researcher at Google, had just clicked out of a last-minute video meeting with an executive named Megan Kacholia, who had issued a jarring command. Gebru was the coleader of a group at the company that studies the social and ethical ramifications of artificial intelligence, and Kacholia had ordered Gebru to retract her latest research paper--or else remove her name from its list of authors, along with those of several other members of her team. The paper in question was, in Gebru's mind, pretty unobjectionable. It surveyed the known pitfalls of so-called large language models, a type of AI software--most famously exemplified by a system called GPT-3--that was stoking excitement in the tech industry.

Eggs littered the sand, but there was no sign of life around or in them. The seabirds that should have been keeping watch had taken off, terrified by a drone that crash-landed into their nesting grounds on an island at the Bolsa Chica Ecological Reserve. "We've never seen such devastation here," said Melissa Loebl, an environmental scientist who manages the Huntington Beach reserve. "This has been really hard for me as a manager." Some 3,000 elegant terns fled the reserve after the drone crashed May 12, leaving behind 1,500 to 2,000 eggs, none of them viable.

As the Pentagon prepares its report into UFOs, due later this month, Chinese military researchers have turned to artificial intelligence to track and analyse the increasing number of unknown objects in China's airspace. To the People's Liberation Army, they are "unidentified air conditions" – a phrase which echoes the US military's "unidentified aerial phenomena" – but to the public they are better known as unidentified flying objects, or UFOs. According to Wuhan-based researcher Chen Li from the Air Force Early Warning Academy, human analysts have been overwhelmed in recent years by the rapidly mounting sighting reports from a wide range of military and civilian sources across the country. "The frequent occurrence of unidentified air conditions in recent years … brings severe challenges to air defence security of our country." The PLA's task force dedicated to the unknown objects increasingly relies on AI technology to analyse its data, according to Chen's report, which is in line with several other military studies published in domestic journals, most recently in August last year.