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Category: robotics/AI – Page 1,490

Machine learning paves the way for smarter particle accelerators

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.

Daniele Filippetto and colleagues at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) developed the setup to automatically compensate for real-time changes to accelerator beams and other components, such as magnets. Their machine learning approach is also better than contemporary beam control systems at both understanding why things fail, and then using physics to formulate a response. A paper describing the research was published late last year in Nature Scientific Reports.

“We are trying to teach physics to a chip, while at the same time providing it with the wisdom and experience of a senior scientist operating the machine,” said Filippetto, a staff scientist at the Accelerator Technology & Applied Physics Division (ATAP) at Berkeley Lab and deputy director of the Berkeley Accelerator Controls and Instrumentation Program (BACI) program.

Open source platform enables research on privacy-preserving machine learning

The biggest benchmarking data set to date for a machine learning technique designed with data privacy in mind has been released open source by researchers at the University of Michigan.

Called federated learning, the approach trains learning models on end-user devices, like smartphones and laptops, rather than requiring the transfer of private data to central servers.

“By training in-situ on data where it is generated, we can train on larger real-world data,” explained Fan Lai, U-M doctoral student in computer science and engineering, who presents the FedScale training environment at the International Conference on Machine Learning this week.

Energy Harvester Produces Power from Local Environment, Eliminating Batteries in Wireless Sensors

Advances in low power technology are making it easier to create wireless sensor networks in a wide range of applications, from remote sensing to HVAC monitoring, asset tracking and industrial automation. The problem is that even wireless sensors require batteries that must be regularly replaced—a costly and cumbersome maintenance project. A better wireless power solution would be to harvest ambient mechanical, thermal or electromagnetic energy from the sensor’s local environment.

Typically, harvestable ambient power is on the order of tens of microwatts, so energy harvesting requires careful power management in order to successfully capture microwatts of ambient power and store it in a useable energy reservoir. One common form of ambient energy is mechanical vibration energy, which can be caused by motors running in a factory, airflow across a fan blade or even by a moving vehicle. A piezoelectric transducer can be used to convert these forms of vibration energy into electrical energy, which in turn can be used to power circuitry.

Else Labs Announces Pro Kitchen Focused Oliver Fleet As It Pauses Rollout of Home Cooking Robot

Else Labs, the company behind the countertop home cooking robot called Oliver, announced today the launch of Oliver Fleet, a commercial kitchen reimagining of its original core product.

The new Fleet solution is a respin of its original standalone Oliver home cooking robot into a solution that allows multiple units to be used and managed simultaneously in professional kitchen environments to automate cooking tasks. According to company CEO Khalid Aboujassoum, while the Oliver Fleet units look the same from the outside as the original consumer unit, they’ve been built to withstand the more rugged requirements of the professional kitchen.

“It might look like the household unit from the outside, but the guts of the Oliver Fleet are different,” Aboujassoum said. “The Fleet units are designed for back-to-back cooking, for that harsh environment in the commercial kitchen compared to the household.”

Google fires engineer who claimed that company’s AI has become sentient

Last month, there were a lot of waves in the AI community when a senior Google engineer, Blake Lemoine, alleged that the company’s AI has become sentient. The claim was made about Google’s Language Model for Dialogue Applications (LaMDA) that can engage in a free-flowing way about a seemingly endless number of topics, an ability that unlocks more natural ways of interacting with technology.

Initially, Lemoine was put on paid administrative leave, but it appears that Google has now fired him.

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