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2026-02-28 23:00:00

The post This Week’s Awesome Tech Stories From Around the Web (Through February 28) appeared first on SingularityHub.

2026-02-27 23:00:00

Modern AI chatbots can do amazing things, from writing research papers to composing Shakespearian sonnets about your cat. But amid the sparks of genius, there are flashes of idiocy. Time and again, the large language models, or LLMs, behind today’s generative AI tools make basic errors—from failing to solve basic high school math problems to stumbling over the rules of Connect Four.

This instability has been called “jagged intelligence” in tech circles, and it isn’t just a quirk—it’s a critical failing and part of the reason many experts believe we’re in an AI bubble. You wouldn’t hire a doctor or lawyer who, despite giving sound medical or legal advice, sometimes acts like they are clueless about how the world works. Enterprises seem to feel the same way about putting “jagged” AI in charge of supply chains, HR processes, or financial operations.

To solve the jagged intelligence problem, we must give our AI models access to a more powerful, more structured, and ultimately far more human stock of knowledge. Having engineered a range of AI systems over 30 years, I have found such knowledge to be an indispensable component of any reliable system.

This is because the technological innovations that launched the AI era aren’t capable of smoothing out these jagged edges. Current AI models don’t possess clear rules about how the world works; instead, they infer things from vast pools of data. In other words, they don’t know things, so they’re forced to guess—and when they guess wrong, the results range from the comical to the catastrophic.

Think about how humans learn. Born into “blooming, buzzing confusion,” babies spot patterns in the world around them: Faces are fun to look at, mom smells great, the cat scratches if you yank its tail. But pattern recognition is soon supplemented by clearly articulated knowledge: rules we’re taught, rather than things we absorb. From ABCs to arithmetic to how to load a dishwasher or drive a car, we use codified knowledge to learn efficiently—and avoid idiotic or dangerous mistakes along the way.

Current AI models don’t possess clear rules about how the world works; instead, they infer things from vast pools of data.

Frontier AI labs are already dabbling in this approach. Early LLMs struggled with grade-school math, so researchers bolted on actual mathematical knowledge—not hazy inferences, but explicit rules about how math works. The result: Google’s latest models can now reliably solve math Olympiad problems.

Adding more data of different types—for example video data, being advocated by AI luminaries such as Yann LeCun—won’t overcome the fundamental challenge of jagged intelligence. Even with extra data, it’s mathematically certain that the models will keep making mistakes—because that’s how probabilistic, data-driven AI works. Instead, we need to give models knowledge—rigidly described concepts and constraints, rules and relationships—that anchor their behavior to the realities of our world.

To give AI models a human stock of knowledge, we need to rapidly build a public database of formal knowledge spanning a range of disciplines. Of course, the rules of math are clear; the workings of other fields—health care, law, economics, or education, say—are, in some ways, vastly more complex. This challenge is now within our reach, as the growth of companies such as Scale AI, which provides high-quality data for training AI models, points to the emergence of a new profession—one that translates human expertise into machine-readable form and, in doing so, shapes not just what AI can do, but what it comes to treat as true.

This knowledge base could be accessed on demand by developers (or even AI agents) to provide verifiable insights covering everything from loading a dishwasher to the intricacies of the tax code. AI models would make fewer absurd mistakes, because they wouldn’t need to deduce everything from first principles. (Some research also suggests that such models would require far less data and energy, though these claims have yet to be proven.)

Unlike today’s opaque AI models, whose knowledge emerges from pattern recognition and is spread across billions of parameters, a formally distilled body of human knowledge could be directly examined, understood, and controlled. Regulators could verify a model’s knowledge, and users could ensure that tools were mathematically guaranteed not to make idiotic mistakes.

We need to give models knowledge—rigidly described concepts and constraints, rules and relationships—that anchors their behavior to the realities of our world.

The ambition to create such a knowledge resource is nothing new in AI. Even though previous efforts produced inconclusive results, it’s time to make a fresh start. Much as biologists use algorithms to speedrun the once-laborious process of modeling proteins, AI researchers could leverage generative AI to aid knowledge modeling.

It’s clear that current AI models are getting smarter and will get better by using different data. And yet, to overcome the challenge of jagged intelligence—and turn AI models into trusted partners and true drivers of value—we need to redefine the way models relate to and learn about the world. Data-driven algorithms allowed us to start talking to machines. But knowledge, not data, is the key to sustaining the future of AI past the potential bubble.

This article was originally published on Undark. Read the original article.

The post Sparks of Genius to Flashes of Idiocy: How to Solve AI’s ‘Jagged Intelligence’ Problem appeared first on SingularityHub.

2026-02-27 07:28:40

Booming energy demand is driving a scramble to set up new generating capacity, and one technology is proving to be the clear winner. Newly released federal data shows that solar power grew by more than 35 percent year-over-year in 2025, outpacing all other forms of generation.

After decades of relatively flat electricity use, US power demand is rising again. A new report from the US Energy Information Administration shows that consumption jumped 2.8 percent in 2025, thanks to rising industrial activity and the rapid expansion of energy-hungry AI data centers.

While increased fossil-fuel generation met much of that additional demand—a revival in coal, in particular—solar power posted the fastest growth of any major source. According to an analysis by Ars Technica, the Energy Information Administration data shows US solar generation increased by more than 35 percent year-over-year, driven by 27 gigawatts of newly installed capacity.

The surge pushed total solar output above hydroelectric power for the first time in terms of total annual generation. Hydropower output itself was relatively stable compared with the prior year, while solar continued its recent breakneck expansion, with capacity increasing rapidly across multiple regions.

Solar’s surge, which added about 85 terawatt-hours of generation, met about two-thirds of the increased energy demand. This number rose to 73 percent when combined with wind power, which grew by a more modest 2.8 percent.

But the data’s not-so-silver lining is that the remaining demand was met primarily by 13 percent growth in coal power. That bucks the recent trend of coal’s diminishing importance in the US power supply and was driven by a complex confluence of changes in the US energy system.

In previous years, natural gas has been the go-to fossil fuel due to abundant domestic supply and the ability to rapidly ramp power up and down. However, the Trump administration’s tariff policies have made it more difficult and expensive to source the equipment for gas power plants, and rapid expansion in gas exports means domestic utilities are competing with foreign buyers for fuel.

Altogether, this made gas generation a less reliable bet, and generation actually shrank 3.3 percent last year. That made coal a more attractive option, and its position in the US energy mix grew.

Still, the outlook remains bright for renewables, and solar in particular. A recent Energy Information Administration analysis showed roughly 43 gigawatts of utility-scale solar capacity is planned or under construction for 2026, potentially making it an even bigger year for solar than 2025. More than half of this new capacity is in just four states: Texas, Arizona, California, and Michigan.

And wind power’s growth could more than double this year with planned additions of 11.8 gigawatts, 60 percent of which are in New Mexico, Texas, Illinois, and Wyoming.

Growth in renewables will also be supported by another record-breaking year for added battery storage. Last year, the industry added a record 15 gigawatts of capacity to the grid. Planned projects would grow capacity by another 24 gigawatts in 2026, 80 percent of which will be in Texas, California, and Arizona.

It’ll be a while yet before green energy overtakes fossil fuels, which still accounted for 58 percent of total generation in 2025. But the data suggests the energy transition is well underway. If solar and wind continue to meet growing energy demand, the US energy system could soon look very different.

The post US Solar Surged 35% in 2025, Overtaking Hydro for the First Time appeared first on SingularityHub.

2026-02-25 05:50:18

In the early morning of April 2, 2024, the sky over southern California lit up with flashes of blazing light. Residents were bewildered. Were they missiles? A crashing plane? The unusual activity confused even experts—until they realized it was a disposable part of China’s Shenzhou-15 spacecraft burning up in the atmosphere as it returned to Earth.

Scientists knew the event was on the horizon and had mapped out a potential entry point over the northern Atlantic Ocean, thousands of miles from metropolitan Los Angeles. Luckily, no one was hurt as the module broke apart over the city.

But the incident underlined an uncomfortable truth. We’re nowhere near being able to accurately predict the path of space debris as it rains down. As more spacecraft are launched and reenter the atmosphere, damage to infrastructure and Earthlings is only a matter of time.

Researchers are looking into a solution from an unexpected source: sensors that measure earthquakes. As space debris plummets to the ground at hypersonic speeds, it generates a sonic boom. This causes a slight tremor in the ground that the sensors readily register.

Using data from a network of these sensors, Benjamin Fernando at Johns Hopkins University and Constantinos Charalambous at Imperial College London developed a system that can reconstruct the path of space debris with unprecedented accuracy. They used the system to map Shenzhou-15’s speed, altitude, gradual disintegration, and final destination.

To be clear, this isn’t an early warning system. Because sonic booms lag behind the objects causing them, the method is like a forensic reconstruction of space debris’ final journey. Still, it can quickly identify potential fall-out zones for faster retrieval and cleanup, which is especially important if the junk is toxic or radioactive.

The work is “a crucial step toward near-real-time monitoring of natural and anthropogenic objects entering from space,” wrote Chris Carr at the Los Alamos National Laboratory, who was not involved in the work.

An Embarrassment of Riches

Launching satellites was once a colossal undertaking. But thanks to innovations by SpaceX and national space agencies across the world, it’s becoming far more routine.

These spacecraft have already changed life on Earth. Thousands of Starlink satellites beam the internet to previous dead zones and disaster areas. Miniature satellites are now an affordable research platform scientists use to profile weather, measure solar winds, and track the effects of microgravity and radiation on living cells. And a new space race will only grow the fleets of spacecraft already blanketing the Earth.

“The big change that we’ve seen since 2020 is the rise of satellite mega-constellations…companies not putting up a dozen spacecraft, but maybe a thousand or ten thousand over the course of a few years,” Fernando told Science.

Mega-constellations have already caused problems for scientists by polluting astronomical images with  bright streaks. They may also increase the rate at which space debris rains down. In a paper describing their system, Fernando and Charalambous write that in 2025 there were roughly four to five re-entries a day, and the numbers are likely to rapidly grow.

We already monitor spacecraft in orbit. Telescopes bring real-time visuals. Radar tracks location and speed. But these tools struggle as a spacecraft drifts into the Earth’s upper atmosphere.

The interaction between fragments and air becomes “really chaotic,” said Fernando. “We can no longer predict with particularly good accuracy exactly where [and when] a piece of re-entering space debris is going to enter the atmosphere.”

Radar can track spacecraft parts as they return to Earth, but the technology is limited to small regions of the world and barely covers the oceans. Even when we know the final fate of a piece of debris, it’s often difficult to reconstruct its full trajectory.

Supersonic Waves

The new work was inspired by the way scientists track meteoroids using a dense network of earthquake sensors to detect tiny vibrations in the ground.

The Shenzhou-15 capsule entered the atmosphere going roughly 25 to 30 times the speed of sound. Like a fighter jet, it triggered a powerful sonic boom roughly 80 kilometers (50 miles) above the ground. The boom traveled to Earth’s surface where seismic sensors detected it.

It’s like picking up an earthquake, only “in this case the waves are coming from up versus with earthquakes they tend to come from down,” said Fernando.

Southern California is heavily dotted with seismic sensors, each measuring activity in a small area. To model the spacecraft’s path and speed, the team compiled the largest sonic boom each sensor registered and its arrival time and compiled the data into a map.

The map captured where, when, and how the capsule broke down as it hurtled through the atmosphere. Earlier on, the sensors recorded large, discrete signals. These later became more scattered and complex, suggesting the capsule gradually disintegrated rather than blowing up all at once.

The results are “consistent with on-ground observations, including videos and witness reports of multiple fireballs flying across the sky,” wrote Carr. After more deeply combing through the data, the team showed it could also be used to measure the size of each piece of decaying debris.

The spacecraft’s sonic signature differed from those generated by meteorites, making it possible to tease apart human-made objects and those of natural origins.

Differentiating the two categories is key. Meteorites pose “kinetic risk” as chunks slam into the ground, damaging cars, houses, and other infrastructure. Human space debris, however, could also contain metals, toxic or flammable material, or in rare cases, radioactive components. The model also reconstructed how different parts of the spacecraft disintegrated, potentially making it easier to predict whether chunks have burned up completely in the atmosphere or have reached the ground, making it useful for recovery or clean-up missions.

Crash-and-burn isn’t a spacecraft’s only destiny. Engineers are also working to move defunct satellites into higher orbits that would be stable for “thousands of years” according to Fernando, though this doesn’t solve the space junk problem. Other researchers are exploring ways to design spacecraft such that they completely burn up both safely and predictably.

For now, the technology works best in places with lots of seismic sensors, which are rare. But there’s a push to add sensors in places that are vulnerable due to sensitive ecology or geology at prices far lower than building radar systems to track re-entry, said Fernando.

The post More Space Junk Is Plummeting to Earth. Earthquake Sensors Can Track It by the Sonic Booms. appeared first on SingularityHub.

2026-02-24 06:35:51

The inner workings of large AI systems remain largely opaque, raising significant safety and trust issues. Researchers have now developed a technique to extract and manipulate the internal concepts governing model behavior, providing a new way to understand and steer their activity.

Modern AI models are marvels of engineering, but even their creators remain in the dark about how they represent knowledge internally. This is why subtle shifts in prompting can produce surprisingly different outputs. Simply asking a model to show its work before answering often improves accuracy, while certain deliberately malicious prompts can override built-in safety features.

This has motivated significant research aimed at teasing out the patterns of activity in these models’ neural networks that correspond to specific concepts. Investigators hope to use these methods to better understand why models behave certain ways and potentially modify their behavior on the fly.

Now researchers have unveiled an efficient new way of extracting concepts from models that works across language, reasoning, and vision algorithms. In a paper in Science, the researchers used these concepts to both monitor and effectively steer model behavior.

“Our results illustrate the power of internal representations for advancing AI safety and model capabilities,” the authors write. “We showed how these representations enabled model steering, through which we exposed vulnerabilities and improved model capabilities.”

Key to the team’s approach is a new algorithm called the Recursive Feature Machine (RFM). They trained the algorithm on pairs of prompts—some containing a concept of interest, others not—and then identified patterns of activity in the model’s neural network tracking each concept.

This allows the algorithm to learn “concept vectors”—essentially patterns of activity that nudge the model in the direction of a specific concept. The vectors can be used to modify the model’s internal processes when it’s generating an output to steer it toward or away from specific concepts or behaviors.

To test the approach, the researchers asked GPT-4o to produce 512 concepts across five concept classes and generate training data on each. They extracted concept vectors from the data and used the vectors to steer the behavior of several large AI models.

The approach worked well across a broad range of model types, including large language models, vision-language models, and reasoning models. Surprisingly, they found newer, larger, and better-performing models were actually more steerable than some smaller ones.

Crucially, the team showed they could use the technique to expose and address serious vulnerabilities in the models. In one test, they created a vector for the concept of “anti-refusal,” which allowed them to bypass built-in safety features in vision-language models to prevent them from giving advice on how take drugs. But they also learned a vector for “anti-deception,” which they successfully used to steer a model away from giving misleading answers.

One of the study’s more interesting findings was that the extracted features were transferable across languages. A concept vector learned with English training data could be used to alter outputs in other languages. The researchers also found they could combine multiple concept vectors to manipulate model behavior in more sophisticated ways.

But the new technique’s real power is in its efficiency. It took fewer than 500 training samples and less than a minute of processing time on a single Nvidia A100 GPU to identify activity patterns associated with a concept and steer towards it.

The researchers say this could not only make it possible to systematically map concepts within large AI models, but it could also lead to more efficient ways of tweaking model behavior after training compared to existing methods.

The approach is still a long way from delivering complete model transparency. But it’s a useful addition in the growing arsenal of model analysis tools that will become increasingly important as AI pushes deeper into all of our lives.

The post Researchers Break Open AI’s Black Box—and Use What They Find Inside to Control It appeared first on SingularityHub.

2026-02-21 23:00:00

The post This Week’s Awesome Tech Stories From Around the Web (Through February 21) appeared first on SingularityHub.