Lifeboat Foundation

Summary: Utilizing a classic neural network, researchers have created a new artificial intelligence model based on recent biological findings that shows improved memory performance.

Source: OIST

Computer models are an important tool for studying how the brain makes and stores memories and other types of complex information. But creating such models is a tricky business. Somehow, a symphony of signals – both biochemical and electrical – and a tangle of connections between neurons and other cell types creates the hardware for memories to take hold. Yet because neuroscientists don’t fully understand the underlying biology of the brain, encoding the process into a computer model in order to study it further has been a challenge.

Did Google get scared of AI? Is that why Microsoft is now leading? 😃

Researchers developed a powerful chatbot years before rival ChatGPT went viral. After management stalled its release, they quit.

New technologies can greatly advance research in various fields, including medicine and neuroscience. In recent years, for instance, engineers have created increasingly sophisticated devices to record brain activity and other biological signals with high precision.

A multi-disciplinary research team at University of California, Los Angeles (UCLA) and other institutes in the U.S. have recently developed the Neuro-stack, a new wearable technology that can record the activity of single neurons in the brain as a human being is walking or moving. This device, presented in a paper published in Nature Neuroscience, could help to gather valuable insight about neuronal activity during walking, while also potentially improving treatments for brain disorders.

“Our study was motivated by the need for smaller size and more flexible devices for clinical neuroscience,” Dejan Markovic, one of the researchers who carried out the study, told Medical Xpress. “Our primary objectives were to make a device that is small enough to be wearable, for mobile experiments, and to provide broadband recordings including local field potentials and single units.”

As the use of machine learning (ML) algorithms continues to grow, computer scientists worldwide are constantly trying to identify and address ways in which these algorithms could be used maliciously or inappropriately. Due to their advanced data analysis capabilities, in fact, ML approaches have the potential to enable third parties to access private data or carry out cyberattacks quickly and effectively.

Morteza Varasteh, a researcher at the University of Essex in the U.K., has recently identified new type of inference attack that could potentially compromise confidential user data and share it with other parties. This attack, which is detailed in a paper pre-published on arXiv, exploits vertical federated learning (VFL), a distributed ML scenario in which two different parties possess different information about the same individuals (clients).

“This work is based on my previous collaboration with a colleague at Nokia Bell Labs, where we introduced an approach for extracting private user information in a data center, referred to as the passive party (e.g., an insurance company),” Varasteh told Tech Xplore. “The passive party collaborates with another data center, referred to as the active party (e.g., a bank), to build an ML algorithm (e.g., a credit approval algorithm for the bank).”

Researchers are coming to understand that the best performing materials in sustainable energy applications, such as converting sunlight or waste heat to electricity, often use collective fluctuations of clusters of atoms within a much larger structure. This process is often referred to as “dynamic disorder.”

Understanding dynamic disorder in materials could lead to more energy-efficient thermoelectric devices, such as solid-state refrigerators and heat pumps, and also to better recovery of useful energy from waste heat, such as car exhausts and power station exhausts, by converting it directly to electricity. A thermoelectric device was able to take heat from radioactive plutonium and convert it to electricity to power the Mars Rover when there was not enough sunlight.

Continue reading “New ‘camera’ with shutter speed of 1 trillionth of a second sees through dynamic disorder of atoms” »

Animals, especially those on land, can’t grow indefinitely. The reason has to do with the square-cube law.

End of the universe would look like?

It’s difficult to speak of the far future of the universe with any level of precision, but we can make rough estimates. Our cosmos is currently 13.77 billion years old, and galaxies throughout the universe will continue making new stars for many years to come. But eventually—roughly one trillion years from now—the last star will be born.

That star will likely be a small red dwarf, barely a fraction of our sun’s mass. Red dwarf stars live fantastically long lives, gently sipping on hydrogen to power a slow but steady fusion reaction. But eventually, all stars, including the red dwarfs, will come to an end. In roughly 100 trillion years, the last light will go out.

To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/.
The first 200 of you will get 20% off Brilliant’s annual premium subscription.

My name is Artem, I’m a computational neuroscience student and researcher. In this video we talk about the concept of critical point – how the brain might optimize information processing by hovering near a phase transition.

Continue reading “Brain Criticality — Optimizing Neural Computations” »

In recent years, the field of artificial intelligence has made tremendous strides, but what happens when #AI systems become #selfaware? In this video, we’ll explore the concept of AI self-awareness, its #scary implications for society, and what it means for the #future of AI.

AI self-awareness is the ability of an #artificialintelligence system to recognize its own existence and understand the consequences of its actions. While there are different levels of self-awareness that an AI system could potentially exhibit, it generally involves the system being able to recognize and respond to changes in its own state.
One way that researchers are exploring AI self-awareness is by using neural networks and other machine learning algorithms. For example, researchers have created AI systems that can recognize and respond to their own errors, which is an important first step in developing higher-order self-awareness.

Continue reading “WHAT happens When AI Becomes SELF-AWARE…” »