Stars die at different rates depending on how they kick the bucket.
Principles behind gravity-mediated entanglement were experimentally demonstrated in a simulation using photons, providing new insights into the nature of gravity.
Researchers are making significant progress in the field of quantum gravity, aiming to reconcile Einstein’s theory of gravity with quantum mechanics. Recent experiments demonstrate the principles of gravity-mediated entanglement using photons, a breakthrough in testing theories like string theory and loop quantum gravity. These experiments could transform our understanding of the universe and support future theoretical frameworks.
Quantum Gravity Research Advances
Base Power is led by Zach Dell — the son of Dell Technologies founder and CEO Michael Dell — and Justin Lopas.
The company provides a distributed battery storage to homeowners, which allows them to have a “reliable and affordable” backup source if there is an outage. The battery itself, according to Lopas, allows the grid to be more efficient.
“If the grid goes off, the battery automatically kicks in and you might not even know you’re in an outage and the battery protects you from that outage,” said Lopas.
Recent advancements in spintronics have enabled better prediction and control of spin currents by studying the magnetic properties and temperature effects on materials.
Spintronics is attracting significant interest as a promising alternative to conventional electronics, offering potential benefits such as lower power consumption, faster operation, non-volatility, and the possibility of introducing new functionalities.
Spintronics exploits the intrinsic spin of electrons, and fundamental to the field is controlling the flows of the spin degree of freedom, i.e., spin currents. Scientists are constantly looking at ways to create, remove, and control them for future applications.
Recent advances in artificial intelligence, primarily driven by foundation models, have enabled impressive progress. However, achieving artificial general intelligence, which involves reaching human-level performance across various tasks, remains a significant challenge. A critical missing component is a formal description of what it would take for an autonomous system to self-improve towards increasingly creative and diverse discoveries without end—a “Cambrian explosion” of emergent capabilities i-e the creation of open-ended, ever-self-improving AI remains elusive., behaviors, and artifacts. This open-ended invention is how humans and society accumulate new knowledge and technology, making it essential for artificial superhuman intelligence.
DeepMind researchers propose a concrete formal definition of open-endedness in AI systems from the perspective of novelty and learnability. They illustrate a path towards achieving artificial superhuman intelligence (ASI) by developing open-ended systems built upon foundation models. These open-ended systems would be capable of making robust, relevant discoveries that are understandable and beneficial to humans. The researchers argue that such open-endedness, enabled by the combination of foundation models and open-ended algorithms, is an essential property for any ASI system to continuously expand its capabilities and knowledge in a way that can be utilized by humanity.
The researchers provide a formal definition of open-endedness from the perspective of an observer. An open-ended system produces a sequence of artifacts that are both novel and learnable. Novelty is defined as artifacts becoming increasingly unpredictable to the observer’s model over time. Learnability requires that conditioning on a longer history of past artifacts makes future artifacts more predictable. The observer uses a statistical model to predict future artifacts based on the history, judging the quality of predictions using a loss metric. Interestingness is represented by the observer’s choice of loss function, capturing which features they find useful to learn about. This formal definition quantifies the key intuition that an open-ended system endlessly generates artifacts that are both novel and meaningful to the observer.
(Inside Science) – Primitive “protocells” like those that evolved into the first living cells can form in bubbles on mineral surfaces that were plentiful on the early Earth, according to new research.
The researchers created artificial protocells that they believe may be similar to the protocells that may have formed on Earth about 3.8 billion years ago. The artificial protocells can absorb other small molecules by forming a barrier membrane around them — behavior that is strikingly like that of modern living cells when they absorb cellular fuel and other essential materials while blocking off harmful substances.
And the artificial protocells also exhibit a primitive form of “division,” where the outer membrane of a protocell ruptures and leaves behind several “daughter” protocells with the same capabilities.
Nvidia was already the world’s most valuable semiconductor firm. Now, it’s become the first computer-chip company ever to hit $3 trillion in market capitalization, as.
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Nvidia Corp. was already the world’s most valuable semiconductor firm. Now, it’s become the first computer-chip company ever to hit $3 trillion in market capitalization.
With all the buzz about generative AI and a rumored Apple-OpenAI partnership announcement at WWDC, can Siri finally move from punchline to essential tool?
Japanese electronics company Toshiba has developed a new kind of cobalt-free battery that could lead to cheaper, more sustainable EVs in the future.
The challenge: Road transportation is a significant contributor to climate change, accounting for 12% of global greenhouse gas emissions, so transitioning from fossil fuel-powered cars and trucks to electric vehicles (EVs) is crucial to meeting our climate goals.
The lithium-ion batteries used in most of today’s EVs have several problems, though, and a big one is that their cathodes are made of cobalt, a rare and expensive metal often mined using child labor and environmentally destructive practices.