Lifeboat Foundation

Tapping quantum weirdness to make an ultra-accelerometer.

How much of the multiverse is TRUE?? Join us… and find out!

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Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short-and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

In this episode, I discuss how our brain and body track time and the role that neurochemicals, in particular dopamine and serotonin, but also hormones such as melatonin, allow us to orient ourselves in time. I review the three types of time perception: of the past, of the present, and the future, and how dopamine and serotonin adjust both our perception of the speed of the passage of time and our memory of how long previous experiences lasted. I also discuss circannual entrainment, which is the process by which our brain and body are matched to the seasons, and circadian (24 hours) entrainment, both of which subconsciously adjust our perceived measurement of time. I explain the mechanisms of that subconscious control. And I cover the ultradian (90 minutes) rhythms that govern our ability to focus, including how to track when these 90-minute rhythms begin and end for the sake of work and productivity. I include ten tools based on the science of time perception that you can apply to enhance productivity, creativity, and relationships in various contexts.

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The Google employee who claimed last June his company’s A.I. model could already be sentient, and was later fired by the company, is still worried about the dangers of new A.I.-powered chatbots, even if he hasn’t tested them himself yet.

Blake Lemoine was let go from Google last summer for violating the company’s confidentiality policy after he published transcripts of several conversations he had with LaMDA, the company’s large language model he helped create that forms the artificial intelligence backbone of Google’s upcoming search engine assistant, the chatbot Bard.

Lemoine told the Washington Post at the time that LaMDA resembled “a 7-year-old, 8-year-old kid that happens to know physics” and said he believed the technology was sentient, while urging Google to take care of it as it would a “sweet kid who just wants to help the world be a better place for all of us.”

Researchers have taken inspiration from geckos to develop a new robot gripper that can handle a wide range of objects — from raw eggs to an angle grinder.

The human body relies heavily on electrical charges. Lightning-like pulses of energy fly through the brain and nerves and most biological processes depend on electrical ions traveling across the membranes of each cell in our body.

These electrical signals are possible, in part, because of an imbalance in electrical charges that exists on either side of a cellular membrane. Until recently, researchers believed the membrane was an essential component to creating this imbalance. But that thought was turned on its head when researchers at Stanford University discovered that similar imbalanced electrical charges can exist between microdroplets of water and air.

Now, researchers at Duke University have discovered that these types of electric fields also exist within and around another type of cellular structure called biological condensates. Like oil droplets floating in water, these structures exist because of differences in density. They form compartments inside the cell without needing the physical boundary of a membrane.

A new planet outside the solar system was discovered using Artificial Intelligence (AI) technology, in what can be called as a major success achieved by AI, which has been making headlines these days.

The technology was put into use by the astronomers to discover the new planet, which gave a major boost to machine learning.

Researchers, working at the University of Georgia, said that the discovery of a previously unknown planet which was present outside our solar system took place using the technology.