Is your current smart lock frustrating you endlessly, like mine is? The Yale Approach Smart Lock (8/10, WIRED Review) is currently marked down to just $164 on Amazon, a healthy 32% discount on our editors’ top pick for smart locks. This sale comes at a perfect time, because I was just complaining about the fingerprint reader on mine no longer working.

The Yale Approach uses part of your existing deadbolt, which is great news for renters who don’t want to make major changes. You’ll also get to use your existing keys to unlock the deadbolt, which can save you a trip to the locksmith. There’s also a wi-fi bridge that needs a nearby plug to provide other services, but that’s not uncommon for smart locks. Our reviewer, Nena Farrell, even said it “works perfectly,” which is great news, because I have to unplug mine and plug it back in at least once a week.

Approach isn’t just a name, as this smart deadbolt’s standout feature is auto-unlock. By setting up your location in the Yale Access App, you can set the bolt to unlock as your get close to home, which our reviewer said “worked smoothly”, as long as she got far enough away from home for it to recognize her return. There’s an auto-lock, too, using timers from 10 seconds to 30 minutes.

This version of the Yale Approach includes the touchscreen keypad, which needs its own flat space to either stick or screw to. In exchange, it lets you set codes for yourself or friends, with options for time and access limits if you need to manage entry to your home more carefully. It also gives you an easy button to press to lock the deadbolt as you leave the house, and a biometric fingerprint scanner.

No matter what smart lock you buy, there’s going to be a little bit of hassle, that just comes with the territory, unfortunately. The Yale smooths out a lot of the worst parts by adapting to your existing hardware, and mostly stays out of the way afterwards. The auto-unlock feature isn’t totally unique to the Approach, but it is currently our favorite implementation. The price is normally a bit on the high side, so the discount here makes this a very appealing pickup for anyone ready to relegate their old front door lock to the garage door, like I’m about to.

New research has found similarities in how humans and artificial intelligence integrate two types of learning, offering new insights about how people learn as well as how to develop more intuitive AI tools.

The study is published in the Proceedings of the National Academy of Sciences.

Led by Jake Russin, a postdoctoral research associate in computer science at Brown University, the study found by training an AI system that flexible and incremental learning modes interact similarly to working memory and long-term memory in humans.

“These results help explain why a human looks like a rule-based learner in some circumstances and an incremental learner in others,” Russin said. “They also suggest something about what the newest AI systems have in common with the human brain.”

Russin holds a joint appointment in the laboratories of Michael Frank, a professor of cognitive and psychological sciences and director of the Center for Computational Brain Science at Brown’s Carney Institute for Brain Science, and Ellie Pavlick, an associate professor of computer science who leads the AI Research Institute on Interaction for AI Assistants at Brown.

Depending on the task, humans acquire new information in one of two ways. For some tasks, such as learning the rules of tic-tac-toe, “in-context” learning allows people to figure out the rules quickly after a few examples. In other instances, incremental learning builds on information to improve understanding over time—such as the slow, sustained practice involved in learning to play a song on the piano.

While researchers knew that humans and AI integrate both forms of learning, it wasn’t clear how the two learning types work together. Over the course of the research team’s ongoing collaboration, Russin—whose work bridges machine learning and computational neuroscience—developed a theory that the dynamic might be similar to the interplay of human working memory and long-term memory.

To test this theory, Russin used “meta-learning”—a type of training that helps AI systems learn about the act of learning itself—to tease out key properties of the two learning types. The experiments revealed that the AI system’s ability to perform in-context learning emerged after it meta-learned through multiple examples.

One experiment, adapted from an experiment in humans, tested for in-context learning by challenging the AI to recombine similar ideas to deal with new situations. If taught about a list of colors and a list of animals, could the AI correctly identify a combination of color and animal (e.g. a green giraffe) it had not seen together previously? After the AI meta-learned by being challenged to 12,000 similar tasks, it gained the ability to successfully identify new combinations of colors and animals.

The results suggest that for both humans and AI, quicker, flexible in-context learning arises after a certain amount of incremental learning has taken place.

“At the first board game, it takes you a while to figure out how to play,” Pavlick said. “By the time you learn your hundredth board game, you can pick up the rules of play quickly, even if you’ve never seen that particular game before.”

The team also found trade-offs, including between learning retention and flexibility: Similar to humans, the harder it is for AI to correctly complete a task, the more likely it will remember how to perform it in the future. According to Frank, who has studied this paradox in humans, this is because errors cue the brain to update information stored in long-term memory, whereas error-free actions learned in context increase flexibility but don’t engage long-term memory in the same way.

For Frank, who specializes in building biologically inspired computational models to understand human learning and decision-making, the team’s work showed how analyzing strengths and weaknesses of different learning strategies in an artificial neural network can offer new insights about the human brain.

“Our results hold reliably across multiple tasks and bring together disparate aspects of human learning that neuroscientists hadn’t grouped together until now,” Frank said.

The work also suggests important considerations for developing intuitive and trustworthy AI tools, particularly in sensitive domains such as mental health.

“To have helpful and trustworthy AI assistants, human and AI cognition need to be aware of how each works and the extent that they are different and the same,” Pavlick said. “These findings are a great first step.”

In addition to expanding renewable energy generation, a successful energy transition requires stable system operation at all times. To achieve this, renewable energies and storage power plants will have to take on extensive system services and essential grid-forming properties in the future.

In the “GFM Benchmark” project, Fraunhofer ISE developed a test procedure for grid-forming inverters on behalf of the four German transmission system operators and applied it to devices from various manufacturers. On the one hand, the project provided a comprehensive overview of the market readiness of grid-forming inverters.

On the other hand, the project results provide an important practical check for new national and European testing standards.

The fully integrated grid components with grid-forming properties planned by the transmission system operators will not be able to fully meet the demand for grid-forming power. Therefore, customer systems must also contribute to stabilizing the power grid: They should behave in a grid-forming manner, i.e., contribute to providing a grid voltage with stable amplitude and frequency.

But what exactly does that mean? In recent years, many scientific studies and publications have been produced on this topic, and some countries have grid operator documents that describe grid-forming behavior. However, there is no uniform standardization or definition, which leaves room for interpretation.

Therefore, in the first step of the project, the Fraunhofer ISE team worked with the grid operators 50Hertz Transmission GmbH, Transnet BW GmbH, Amprion GmbH, and Tennet TSO GmbH to develop a measurement and evaluation procedure for the stabilizing properties of inverters, incorporating findings from both grid operation and research.

Major differences in grid-forming behavior

“We wanted to see what manufacturers understand by grid formation and how they implement this in the programming of their devices,” explains department head Dr. Sönke Rogalla from Fraunhofer ISE. “So we invited them to put their devices to the test in our laboratory.”

Seven companies responded to the call and had their storage inverters, which cover a power range from a few kilowatts to five megawatts, measured according to the new test procedure. They came from different countries and were at different technology readiness levels, from pilot to prototype to series production.

The researchers used the tests to investigate the differences between the devices in terms of grid formation by exposing them to various operating conditions in the laboratory. In addition to normal operation, critical grid situations such as rapid frequency changes, short circuits, and phase jumps were simulated.

“The devices exhibited similar behavior under clearly defined requirements. In other cases, however, there were major differences, and we were able to provide the manufacturers with suggestions for optimization for almost every device,” explains project manager Roland Singer from Fraunhofer ISE. The willingness and commitment of manufacturers to advance the development of grid-forming inverters is high.

Proven verification methods are essential for market launch

At the same time, the project provided relevant practical experience in testing grid-forming inverters and optimized the test procedures. Important findings were incorporated into the ongoing standardization work at the European level even during the project phase. The Fraunhofer ISE team contributed its expertise to the creation of the VDE FNN note “Grid-forming properties.”

The recently published document describes the requirements and verification procedures for grid-forming units. It forms the normative basis for participation in the future market for instantaneous reserve, which will start at the beginning of 2026 and represents an additional interesting remuneration path, especially for battery storage systems.

With its experience in the “GFM Benchmark” project, the team at Fraunhofer ISE is ideally positioned to support manufacturers and users of grid-forming units with certification measurements in accordance with the FNN note. Standardization work at the European level is also progressing. ENTSO-E, the network of European transmission system operators, is working on an implementation guide with comprehensive grid-forming requirements, which should facilitate the transition to national regulations.

Provided by
Fraunhofer ISE