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A trio of researchers, two with Princeton University, the other the Max Planck Institute for Biological Cybernetics, has developed a reinforcement learning–based simulation that shows the human desire always to want more may have evolved as a way to speed up learning. In their paper posted in the open-access PLOS Computational Biology, Rachit Dubey, Thomas Griffiths and Peter Dayan describe the factors that went into their simulations.

Researchers studying human behavior have often been puzzled by people’s seemingly contradictory desires. Many people have an unceasing desire for more of certain things, even though they know that meeting those desires may not result in the desired outcome. Many people want more and more money, for example, with the idea that more money would make life easier, which should make them happier. But a host of studies has shown that making more money rarely makes people happier (with the exception of those starting from a very low income level). In this new effort, the researchers sought to better understand why people would have evolved this way. To that end, they built a simulation to mimic the way humans respond emotionally to stimuli, such as achieving goals. And to better understand why people might feel the way they do, they added checkpoints that could be used as a happiness barometer.

The simulation was based on reinforcement learning, in which people (or a machine) continue doing things that offer a positive reward and cease doing things that offer no reward or a negative reward. The researchers also added simulated emotional reactions to the known negative impacts of habituation and comparison, whereby people become less happy over time as they get used to something new and become less happy when seeing that someone else has more of something they want.

Summary: Researchers have identified a new type of retinal ganglion cells.

Source: Northwestern University.

Northwestern Medicine investigators have identified a new type of retinal ganglion cell, the neurons in the retina that encode the visual environment and transmit information back to the brain, according to a study published in Neuron.

Meanwhile, Taiwan’s Presidential Palace said cyberattack traffic on its website spiked by 200 times hours before Nancy Pelosi’s arrival in Taipei.

Bill Gates-founded Breakthrough Energy Ventures co-led a $44 million funding round for a startup that aims to accelerate solar far construction.

Bill Gates-founded Breakthrough Energy Ventures co-led a $44 million funding round for a startup that aims to accelerate solar far construction.

Breakthrough Energy Ventures, a climate change solution-focused VC firm backed by the likes of Bill Gates, has joined a $44 million backing of solar startup Terabase Energy, a press statement reveals.

Continue reading “Bill Gates-backed startup is using robots to build enormous solar farms” »

(Reuters) — An artificial intelligence system cannot be an inventor under United States patent law, a U.S. appeals court affirmed Friday.

The Patent Act requires an “inventor” to be a natural person, the U.S. Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler’s bid for patents on two inventions he said his DABUS system created.

Thaler said in an email Friday that DABUS, which stands for “Device for the Autonomous Bootstrapping of Unified Sentience,” is “natural and sentient.”

The stellar smash produced a short gamma-ray burst that could provide important context for understanding similar blasts.

Studio’s state-of-the-art MCRT renderer, used on the upcoming ‘Puss in Boots: The Last Wish’ and previous films like ‘The Bad Guys’ and ‘Croods: A New Age,’ includes a USD Hydra render delegate and multi-machine / cloud rendering via Arras.

Another version of the PCP theorem, not yet proved, specifically deals with the quantum case. Computer scientists suspect that the quantum PCP conjecture is true, and proving it would change our understanding of the complexity of quantum problems. It’s considered arguably the most important open problem in quantum computational complexity theory. But so far, it’s remained unreachable.

Nine years ago, two researchers identified an intermediate goal to help us get there. They came up with a simpler hypothesis, known as the “no low-energy trivial state” (NLTS) conjecture, which would have to be true if the quantum PCP conjecture is true. Proving it wouldn’t necessarily make it any easier to prove the quantum PCP conjecture, but it would resolve some of its most intriguing questions.

Then in June of 2022, in a paper posted to the scientific preprint site arxiv.org, three computer scientists proved the NLTS conjecture. The result has striking implications for computer science and quantum physics.

One of the cornerstones of the implementation of quantum technology is the creation and manipulation of the shape of external fields that can optimize the performance of quantum devices. Known as quantum optimal control, this set of methods comprises a field that has rapidly evolved and expanded over recent years.

A new review paper published in EPJ Quantum Technology and authored by Christiane P. Koch, Dahlem Center for Complex Quantum Systems and Fachbereich Physik, Freie Universität Berlin along with colleagues from across Europe assesses recent progress in the understanding of the controllability of quantum systems as well as the application of quantum control to quantum technologies. As such, it lays out a potential roadmap for future technology.

While quantum optimal control builds on conventional control theory encompassing the interface of applied mathematics, engineering, and physics, it must also factor in the quirks and counter-intuitive nature of quantum physics.