As part of a series of articles on smart homes, the Verge visits an energy-efficient home in the southern California desert that's "part of California's first planned smart, solar-powered residential microgrid community." A surprisingly small number of solar panels on the roof soak up the sun in the desert landscape... funneling power into the tightly designed building envelope. Here, a 13-kilowatt hour home battery sits beside a smart load panel that controls every electrical appliance in the home, from the hybrid electric heat-pump water heater and high-efficiency heat pump HVAC system — both Wi-Fi enabled to share data — to the light switches, EnergyStar fridge, and energy-efficient induction cooktop. Using software algorithms, the Schneider load center intelligently determines where to best draw power from — the SunPower solar panels, the battery, or the grid. It then makes recommendations the Conriques can use to set automations that change power sources or reduce energy use when prices and demand spike...

The 43 new residences in KB Home-built Shadow Mountain, which launched in November 2022, and the 176 more planned as part of two communities, Durango and Oak Shade, are all-electric, solar-powered smart homes. By next year they will be connected to a 2.3 megawatt-hour community battery, sending any excess energy their panels generate to the common power source and creating a community microgrid. When the power goes down, the microgrid will kick in, isolating all 219 homes from the grid and keeping their essential functions up and running. The homes will draw first from their own battery (and potentially their EV) and then from the community battery. "When the system hits a potential steady state, they can ride a power outage for days, if not in perpetuity, with proper solar production," explains Brad Wills of Schneider Electric, manufacturers of the home's smart load panel, the community's microgrid components, and the software that runs the system...

Developed as a partnership between SunPower, KB Home, University of California, Irvine, Schneider Electric, Southern California Edison, Kia America, and the US Department of Energy, Shadow Mountain is designed to be a blueprint for how we can build better, smarter communities in the future... A recent DOE study estimated that by 2030, grid-interactive efficient buildings like those at Shadow Mountain could save up to $18 billion per year in power system costs and cut 80 million tons of carbon emissions annually.
The article describes how the community helps the larger power grid:

• They can send electricity back into the grid during periods of peak demand.

• The local power company now also has the option to "island" the entire community off the grid in times of high demand.

• The community "is also trialing high-output vehicle-to-home and vehicle-to-grid functions."

The Ecoomist asks: How can we "green" the high-temperature chemical processes in industries like steelmaking or the production of chemical or cement. "Because it is tricky or impossible to produce such temperatures for some industrial processes using electricity alone, firms rely on fossil fuels."

But a Finnish engineering firm called Coolbrook thinks they have an answer: The easiest way to think about Coolbrook's system is as a gas turbine in reverse. A conventional gas turbine — as used in power stations or jet engines — burns fossil fuel to create a hot, high-pressure gas that spins rotor blades. That rotational energy can be used to run a thrust-generating fan (as in jet aircraft) or converted to electricity in a generator (as in a power station). The new system begins instead with an electric motor. The motor spins the turbine's rotors. Gas or liquid is then fed to the turbine. Once inside, the rotors accelerate the stuff to supersonic speeds, and then rapidly slow it again. The sudden deceleration transforms the kinetic energy contained in the accelerated gas or fluid into heat. If the motor is powered by green electricity, then no carbon dioxide is produced...

Laboratory trials have shown that yields from the electrified process could be significantly higher than what can be obtained with fossil fuels. Assuming that everything goes according to plan, the firm will try producing heat for several other industrial processes... Joonas Rauramo, Coolbrook's boss, reckons his firm's technology could eliminate perhaps 30% of heavy-industrial emissions. And, he says, it can do so without needing to invent anything fundamentally new. "It is a known science," says Mr Rauramo. "It has just not been applied in exactly the way we are doing it."
The article's subheading puts it succinctly. "Running a turbine backwards can produce green heat."

Thanks to long-time Slashdot reader SpzToid for sharing the article.

"I predict that Generative Artificial Intelligence is going to go a long way toward keeping Moore's Law in force," writes long-time tech pundit Robert X. Cringely, "and the way this is going to happen says a lot about the chip business, global economics, and Artificial Intelligence, itself." The current el cheapo AI research frenzy is likely to subside as LLaMA ages into obsolescence and has to be replaced by something more expensive, putting Google, Microsoft and OpenAI back in control. Understand, too, that these big, established companies like the idea of LLMs costing so much to build because that makes it harder for startups to disrupt. It's a form of restraint of trade, though not illegal...

[T]here is an opportunity for vertical LLMs trained on different data — real data from industries like medicine and auto mechanics. Whoever owns this data will own these markets. What will make these models both better and cheaper is they can be built from a LLaMA base because most of that data doesn't have to change over time... Bloomberg has already done this for investment advice using its unique database of historical financial information. With an average of 50 billion nodes, these vertical models will cost only five percent as much to run as OpenAI's one billion node GPT-4...

[I]t ought to be pretty simple to apply AI to chip design, building custom chip design models to iterate into existing simulators and refine new designs that actually have a pretty good chance of being novel.

And who will be the first to leverage this chip AI? China... Look for fabless AI chip startups to spring-up around Chinese universities and for the Chinese Communist Party to put lots of money into this very cost-effective work. Because even if it's used just to slim-down and improve existing designs, that's another generation of chips China might otherwise not have had at all.

Quanta magazine thinks there's a better alternative to the artificial neural networks (or ANNs) powering AI systems. (Alternate URL) For one, ANNs are "super power-hungry," said Cornelia Fermüller, a computer scientist at the University of Maryland. "And the other issue is [their] lack of transparency." Such systems are so complicated that no one truly understands what they're doing, or why they work so well. This, in turn, makes it almost impossible to get them to reason by analogy, which is what humans do — using symbols for objects, ideas and the relationships between them....

Bruno Olshausen, a neuroscientist at the University of California, Berkeley, and others argue that information in the brain is represented by the activity of numerous neurons... This is the starting point for a radically different approach to computation known as hyperdimensional computing. The key is that each piece of information, such as the notion of a car, or its make, model or color, or all of it together, is represented as a single entity: a hyperdimensional vector. A vector is simply an ordered array of numbers. A 3D vector, for example, comprises three numbers: the x, y and z coordinates of a point in 3D space. A hyperdimensional vector, or hypervector, could be an array of 10,000 numbers, say, representing a point in 10,000-dimensional space. These mathematical objects and the algebra to manipulate them are flexible and powerful enough to take modern computing beyond some of its current limitations and foster a new approach to artificial intelligence...

Hyperdimensional computing tolerates errors better, because even if a hypervector suffers significant numbers of random bit flips, it is still close to the original vector. This implies that any reasoning using these vectors is not meaningfully impacted in the face of errors. The team of Xun Jiao, a computer scientist at Villanova University, has shown that these systems are at least 10 times more tolerant of hardware faults than traditional ANNs, which themselves are orders of magnitude more resilient than traditional computing architectures...

All of these benefits over traditional computing suggest that hyperdimensional computing is well suited for a new generation of extremely sturdy, low-power hardware. It's also compatible with "in-memory computing systems," which perform the computing on the same hardware that stores data (unlike existing von Neumann computers that inefficiently shuttle data between memory and the central processing unit). Some of these new devices can be analog, operating at very low voltages, making them energy-efficient but also prone to random noise.
Thanks to Slashdot reader ZipNada for sharing the article.