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.

A robot army is beginning its march across rural America, promising to transform the future of food. Twenty-five intelligent machines were dispatched last month to the Midwest and the Mississippi Delta, where they will advance over newly planted fields at 12 miles an hour, annihilating baby weeds. Produced by John Deere and created by the startup Blue River Technology, these robotic weeders look much like standard industrial sprayers at first glance, but each is rigged with an intricate system of 36 cameras and a mass of tiny hoses. They use computer vision to distinguish between crops and weeds and then deploy with sniper-like precision tiny jets of herbicide onto the weeds -- sparing the crop and ending the common practice of broadcast-spraying chemicals across billions of acres.

A team of researchers at Carnegie Mellon University's Robotics Institute (RI) has developed an algorithmic planner that can help delegate tasks to humans and robots. The planner is called "Act, Delegate or Learn" (ADL), and it considers a list of tasks before deciding the best way to assign them.

A cockroach-inspired robot can handle complicated and bumpy terrain. It could be used for surveying earthquake rubble or even other planets. The robot, called Omni-Roach, has a rounded body, wings that can open and close, curved legs and a tail that can move left to right and up and down. It is around 20 centimetre long and 10 centimetres tall. Chen Li at Johns Hopkins University in Maryland and his team tested the robot in an obstacle course that mimicked the forest floor.