Scientists are urging people who live in southcentral Alaska to begin preparing for a possible eruption of the Mount Spurr volcano.
The Alaska Volcano Observatory said now is a good time for Alaskans to “familiarize themselves with the possible hazards of a Spurr eruption” following last week’s announcement that the likelihood of an eruption has increased.
“The major hazards to Alaska residents from Spurr would be from ash risk to aviation and possible ashfall,” the observatory said in a Wednesday post on X.
To understand exactly what’s going on, we need to back up a bit. Roughly put, building a machine-learning model involves training it on a large number of examples and then testing it on a bunch of similar examples that it has not yet seen. When the model passes the test, you’re done.
What the Google researchers point out is that this bar is too low. The training process can produce many different models that all pass the test but—and this is the crucial part—these models will differ in small, arbitrary ways, depending on things like the random values given to the nodes in a neural network before training starts, the way training data is selected or represented, the number of training runs, and so on. These small, often random, differences are typically overlooked if they don’t affect how a model does on the test. But it turns out they can lead to huge variation in performance in the real world.
In other words, the process used to build most machine-learning models today cannot tell which models will work in the real world and which ones won’t.
Mark Rober’s Tesla crash story and video on self-driving cars face significant scrutiny for authenticity, bias, and misleading claims, raising doubts about his testing methods and the reliability of the technology he promotes.
Questions to inspire discussion.
Tesla Autopilot and Testing 🚗 Q: What was the main criticism of Mark Rober’s Tesla crash video? A: The video was criticized for failing to use full self-driving mode despite it being shown in the thumbnail and capable of being activated the same way as autopilot. 🔍 Q: How did Mark Rober respond to the criticism about not using full self-driving mode? A: Mark claimed it was a distinction without a difference and was confident the results would be the same if he reran the experiment in full self-driving mode. 🛑 Q: What might have caused the autopilot to disengage during the test?
NVIDIA has just unveiled the Isaac GR00T N1, a foundation model that is revolutionizing humanoid robotics. This AI-driven system can learn tasks, make decisions, and adapt like never before!
At GTC 2025, NVIDIA CEO Jensen Huang revealed the Isaac GR00T N1, a next-generation AI model designed to train humanoid robots with unprecedented efficiency. It uses a dual-system approach—one for instant reactions and another for strategic thinking. NVIDIA also introduced Newton, a physics engine developed in collaboration with Google DeepMind and Disney, aiming to enhance robotic motion.
Additionally, NVIDIA’s Isaac GR00T Blueprint enables large-scale training with synthetic data. In just 11 hours, the system generated over 780,000 training examples, drastically improving robot accuracy. These advancements could reshape industries by making humanoid robots more intelligent and useful in real-world applications.
What do you think of NVIDIA’s latest robotics breakthrough? Let us know in the comments! Do not forget to like, subscribe, and turn on notifications for more updates on AI and robotics.
Adopting liquid cooling technology could significantly reduce electricity costs across the data center.
Many Porsche “purists” reflect forlornly upon the 1997, 5th generation, 996 version of the iconic 911 sports car. It was the first year of the water-cooled engine versions of the 911, which had previously been based on air-cooled engines since their entry into the market in 1964. The 911 was also the successor to the popular air-cooled 356. For over three decades, Porsche’s flagship 911 was built around an air-cooled engine. The two main reasons often provided for the shift away from air-cooled to water-cooled engines were 1) environmental (emission standards) and 2) performance (in part cylinder head cooling). The writing was on the wall: If Porsche was going to remain competitive in the sports car market and racing world, the move to water-cooled engines was unavoidable.
Fast forward to current data centers trying to meet the demands for AI computing. For similar reasons, we’re seeing a shift towards liquid cooling. Machines relying on something other than air for cooling date back at least to the Cray-1 supercomputer which used a freon-based system and the Cray-2 which used Fluorinert, a non-conductive liquid in which boards were immersed. The Cray-1 was rated at about 115kW and the Cray-2 at 195kW, both a far cry from the 10’s of MWs used by today’s most powerful supercomputers. Another distinguishing feature here is that these are “supercomputers” and not just data center servers. Data centers have largely run on air-cooled processors, but with the incredible demand for computing created by the explosive increase in AI applications, data centers are being called on to provide supercomputing-like capabilities.
Hydrogen is often seen as the fuel of the future on account of its zero-emission and high gravimetric energy density, meaning it stores more energy per unit of mass compared to gasoline. Its low volumetric density, however, means it takes up a large amount of space, posing challenges for efficient storage and transport.
In order to address these deficiencies, hydrogen must be compressed in tanks to 700-bar pressure, which is extremely high. This situation not only incurs high costs but also raises safety concerns.
For hydrogen-powered fuel-cell vehicles (FCVs) to become widespread, the US Department of Energy (DOE) has set specific targets for hydrogen storage systems: 6.5% of the storage material’s weight should be hydrogen (gravimetric storage capacity of 6.5 wt%), and one liter of storage material should hold 50 grams of hydrogen (a volumetric storage capacity of 50 g L‒1). These targets ensure that vehicles can travel reasonable distances without excessive fuel.
Unsubstituted π-electronic systems with expanded π-planes are highly desirable for improving charge-carrier transport in organic semiconductors. However, their poor solubility and high crystallinity pose major challenges in processing and assembly, despite their favorable electronic properties. The strategic arrangement of these molecular structures is crucial for achieving high-performance organic semiconductive materials.
In a significant breakthrough, a research team led by Professor Hiromitsu Maeda from Ritsumeikan University, including Associate Professor Yohei Haketa from Ritsumeikan University, Professor Shu Seki from Kyoto University, and Professor Go Watanabe from Kitasato University, has synthesized a novel organic electronic system incorporating gold (AuIII) and benzoporphyrin molecules, enabling enhanced solubility and conductivity.
The findings of the study were published online in Chemical Science.
We often never hear of many inventions, which is why Lifeboat is good at informing people.
Gregorio Zara (March 8, 1902–October 15, 1978) was a Filipino scientist best known as the inventor of the videophone, the first two-way electronic video communicator, in 1955. All told, he patented 30 devices. His other inventions ranged from an alcohol-powered airplane engine to a solar-powered water heater and stove.
Filipino scientist Gregorio Zara won 30 patents for his inventions, which included the first videophone and many breakthroughs in aeronautics.
