US regulators expanded a probe into Tesla's "Autopilot" system, moving the investigation closer to a potential recall of a controversial feature in Elon Musk's electric vehicles. The National Highway Traffic Safety Administration is investigating whether "Autopilot and associated Tesla systems may exacerbate human factors or behavioral safety risks by undermining the effectiveness of the driver's supervision," according to a summary statement. The agency now considers the probe an "engineering analysis" -- which in NHTSA parlance upgrades the status from a "preliminary evaluation" -- to determine "whether a safety recall should be initiated or the investigation should be closed." Tesla did not immediately respond to a request for comment. NHTSA opened the probe in August 2021 after identifying 11 crashes involving a first responder vehicle and a Tesla in which Autopilot or Traffic Aware Cruise Control was engaged, and five additional cases were later found that fit into this group.

With the intensification of ecosystem damage, birds have become the symbolic species of the ecosystem. Ornithology with interdisciplinary technical research plays a great significance for protecting birds and evaluating ecosystem quality. Deep learning shows great progress for birdsongs recognition. However, as the number of network layers increases in traditional CNN, semantic information gradually becomes richer and detailed information disappears. Secondly, the global information carried by the entire input may be lost in convolution, pooling, or other operations, and these problems will weaken the performance of classification. In order to solve such problems, based on the feature spectrogram from the wavelet transform for the birdsongs, this paper explored the multi-scale convolution neural network (MSCNN) and proposed an ensemble multi-scale convolution neural network (EMSCNN) classification framework. The experiments compared the MSCNN and EMSCNN models with other CNN models including LeNet, VGG16, ResNet101, MobileNetV2, EfficientNetB7, Darknet53 and SPP-net. The results showed that the MSCNN model achieved an accuracy of 89.61%, and EMSCNN achieved an accuracy of 91.49%. In the experiments on the recognition of 30 species of birds, our models effectively improved the classification effect with high stability and efficiency, indicating that the models have better generalization ability and are suitable for birdsongs species recognition. It provides methodological and technical scheme reference for bird classification research.

When Bradford Newman began advocating for more artificial intelligence expertise in the C-suite in 2015, "people were laughing at me," he said. Newman, who leads global law firm Baker McKenzie's machine learning and AI practice in its Palo Alto office, added that when he mentioned the need for companies to appoint a chief AI officer, people typically responded, "What's that?" But as the use of artificial intelligence proliferates across the enterprise, and as issues around AI ethics, bias, risk, regulation and legislation currently swirl throughout the business landscape, the importance of appointing a chief AI officer is clearer than ever, he said. This recognition led to a new Baker McKenzie report, released in March, called "Risky Business: Identifying Blind Spots in Corporate Oversight of Artificial Intelligence." The report surveyed 500 US-based, C-level executives who self-identified as part of the decision-making team responsible for their organization's adoption, use and management of AI-enabled tools. In a press release upon the survey's release, Newman said: "Given the increase in state legislation and regulatory enforcement, companies need to step up their game when it comes to AI oversight and governance to ensure their AI is ethical and protect themselves from liability by managing their exposure to risk accordingly."

A flying robot inspired by a male peregrine falcon can scare away flocks of birds in fields within 5 minutes of flying over and keep them away for up to four hours, on average. Birds can eat crops on farmland or damage aircraft at airports if they collide with them by accident. As a result, several methods have been developed to deter them from congregating at these sites. These include traditional scarecrows, recordings of bird distress calls or lethal approaches involving guns or trained birds of prey.

The robot ship designed by IBM was meant to dock in Massachusetts, but mechanical trouble forced it to dock in Halifax, Canada. The ship didn't have a crew.

Artificial general intelligence (AGI) is back in the news thanks to the recent introduction of Gato from DeepMind. As much as anything, AGI invokes images of the Skynet (of Terminator lore) that was originally designed as threat analysis software for the military, but it quickly came to see humanity as the enemy. While fictional, this should give us pause, especially as militaries around the world are pursuing AI-based weapons. However, Gato does not appear to raise any of these concerns. The deep learning transformer model is described as a "generalist agent" and purports to perform 604 distinct and mostly mundane tasks with varying modalities, observations and action specifications.

A Cruise AV, General Motor's autonomous electric Bolt EV is displayed in Detroit on Jan. 16, 2019. California regulators on Thursday gave Cruise's robotic taxi service the green light to begin charging passengers for driverless rides in San Francisco. A Cruise AV, General Motor's autonomous electric Bolt EV is displayed in Detroit on Jan. 16, 2019. California regulators on Thursday gave Cruise's robotic taxi service the green light to begin charging passengers for driverless rides in San Francisco. California regulators on Thursday gave a robotic taxi service the green light to begin charging passengers for driverless rides in San Francisco, a first in a state where dozens of companies have been trying to train vehicles to steer themselves on increasingly congested roads.

A new study finds that utilizing artificial intelligence could allow scientists to detect arthritis earlier. Researchers have been able to teach artificial intelligence neural networks to distinguish between two different kinds of arthritis and healthy joints. The neural network was able to detect 82% of the healthy joints and 75% of cases of rheumatoid arthritis. When combined with the expertise of a doctor, it could lead to much more accurate diagnoses. Researchers are planning to investigate this approach further in another project. This breakthrough by a team of doctors and computer scientists has been published in the journal Frontiers in Medicine.

In light of growing concern about the energy requirements of large machine learning models, a recent study from MIT Lincoln Laboratory and Northeastern University has investigated the savings that can be made by power-capping GPUs employed in model training and inference, as well as several other techniques and methods of cutting down AI energy usage. The new work also calls for new AI papers to conclude with an'Energy Statement' (similar to the recent trend for'ethical implication' statements in papers from the machine learning research sector). The chief suggestion from the work is that power-capping (limiting the available power to the GPU that's training the model) offers worthwhile energy-saving benefits, particularly for Masked Language Modeling (MLM), and frameworks such as BERT and its derivatives. Constraining power consumption does not constrain training efficiency or accuracy on a 1-1 basis, and offers power savings that are notable at scale. For larger-scale models, which have captured attention in recent years due to hyperscale datasets and new models with billions or trillions of parameters, similar savings can be obtained as a trade-off between training time and energy usage.

Pony.ai is the latest autonomous car company to make headlines for the wrong reasons. It has just lost its permit to test its fleet of autonomous vehicles in California over concerns about the driving record of the safety drivers it employs. It's a big blow for the company, and highlights the interesting spot the autonomous car industry is in right now. After a few years of very bad publicity, a number of companies have made real progress in getting self-driving cars on the road. If you're curious about what Pony.ai and some of the other major outfits are up to, here's a handy alphabetized guide to some of the key firms working on autonomous vehicles.