Lifeboat Foundation

Human Brain Project researchers have trained a large-scale model of the primary visual cortex of the mouse to solve visual tasks in a highly robust way. The model provides the basis for a new generation of neural network models. Due to their versatility and energy-efficient processing, these models can contribute to advances in neuromorphic computing.

Modeling the brain can have a massive impact on artificial intelligence (AI): Since the brain processes images in a much more energy-efficient way than artificial networks, scientists take inspiration from neuroscience to create neural networks that function similarly to the biological ones to significantly save energy.

In that sense, brain-inspired neural networks are likely to have an impact on future technology, by serving as blueprints for visual processing in more energy-efficient neuromorphic hardware. Now, a study by Human Brain Project (HBP) researchers from the Graz University of Technology (Austria) showed how a large data-based model can reproduce a number of the brain’s visual processing capabilities in a versatile and accurate way. The results were published in the journal Science Advances.

With mathematical modeling, a research team has now succeeded in better understanding how the optimal working state of the human brain, called criticality, is achieved. Their results mean an important step toward biologically-inspired information processing and new, highly efficient computer technologies and have been published in Scientific Reports.

“In particular tasks, supercomputers are better than humans, for example in the field of artificial intelligence. But they can’t manage the variety of tasks in everyday life —driving a car first, then making music and telling a story at a get-together in the evening,” explains Hermann Kohlstedt, professor of nanoelectronics. Moreover, today’s computers and smartphones still consume an enormous amount of energy.

“These are no sustainable technologies—while our brain consumes just 25 watts in everyday life,” Kohlstedt continues. The aim of their interdisciplinary research network, “Neurotronics: Bio-inspired Information Pathways,” is therefore to develop new electronic components for more energy-efficient computer architectures. For this purpose, the alliance of engineering, life and natural sciences investigates how the human brain is working and how that has developed.

Artificial intelligence has long been a hot topic: a computer algorithm “learns” by being taught by examples: What is “right” and what is “wrong.” Unlike a computer algorithm, the human brain works with neurons—cells of the brain. These are trained and pass on signals to other neurons. This complex network of neurons and the connecting pathways, the synapses, controls our thoughts and actions.

Biological signals are much more diverse when compared with those in conventional computers. For instance, neurons in a biological neural network communicate with ions, biomolecules and neurotransmitters. More specifically, neurons communicate either chemically—by emitting the messenger substances such as neurotransmitters—or via electrical impulses, so-called “action potentials” or “spikes”.

Artificial neurons are a current area of research. Here, the efficient communication between the biology and electronics requires the realization of artificial neurons that emulate realistically the function of their biological counterparts. This means artificial neurons capable of processing the diversity of signals that exist in biology. Until now, most artificial neurons only emulate their biological counterparts electrically, without taking into account the wet biological environment that consists of ions, biomolecules and neurotransmitters.

A new type of material can learn and improve its ability to deal with unexpected forces thanks to a unique lattice structure with connections of variable stiffness, as described in a new paper by my colleagues and me.

The new material is a type of architected material, which gets its properties mainly from the geometry and specific traits of its design rather than what it is made out of. Take hook-and-loop fabric closures like Velcro, for example. It doesn’t matter whether it is made from cotton, plastic or any other substance. As long as one side is a fabric with stiff hooks and the other side has fluffy loops, the material will have the sticky properties of Velcro.

My colleagues and I based our new material’s architecture on that of an artificial neural network—layers of interconnected nodes that can learn to do tasks by changing how much importance, or weight, they place on each connection. We hypothesized that a mechanical lattice with physical nodes could be trained to take on certain mechanical properties by adjusting each connection’s rigidity.

A molecular ratchet, in which a crown ether is pumped from solution onto an encoded molecular strand by a pulse of chemical fuel, opens the way for the reading of information along molecular tapes.

Scientists have found new similarities between Human Brains and current Artificial Intelligence models which clearly show that in general, there’s not a lot of things needed except for more and better hardware, and some more improvements in efficiency for Artificial Intelligence to beat Humans in nearly every imaginable field.
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TIMESTAMPS:
00:00 More Similar than not… 01:37 How AI and us perceive Time 04:19 What is Artificial General Intelligence 05:57 When can we expect AGI? 07:12 Last Words — #ai #agi #humans …
01:37 How AI and us perceive Time.
04:19 What is Artificial General Intelligence.
05:57 When can we expect AGI?
07:12 Last Words.
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#ai #agi #humans

This time I come to talk about a new concept in this Age of Artificial Intelligence and the already insipid world of Social Networks. Initially, quite a few years ago, I named it “Counterpart” (long before the TV series “Counterpart” and “Black Mirror”, or even the movie “Transcendence”).

It was the essence of the ETER9 Project that was taking shape in my head.

Over the years and also with the evolution of technologies — and of the human being himself —, the concept “Counterpart” has been getting better and, with each passing day, it makes more sense!

Imagine a purely digital receptacle with the basics inside, like that Intermediate Software (BIOS⁽¹⁾) that computers have between the Hardware and the Operating System. That receptacle waits for you. One way or another, it waits patiently for you, as if waiting for a Soul to come alive in the ether of digital existence.

Continue reading “Digital Doubles and Second Selves” »

With her eerily realistic facial expressions and movements, Ameca has been billed as the world’s most advanced humanoid robot.

But if that wasn’t impressive enough, soon she could be walking around too.

Continue reading “‘World’s most advanced’ robot will have working legs within a year” »

A new global agreement has been established by eight worldwide universities to commit to the development of human-centered approaches to artificial intelligence (AI). The newest university to join the agreement, which could impact people all across the globe, was the University of Florida (UF).

The Global University Summit was held back on October 27 at Notre Dame University. Joseph Glover, UF provost and senior vice president of academic affairs, signed The Rome Call for AI Ethics on behalf of the University of Florida. He also served as a panelist for the two-day summit, which was attended by 36 universities from around the world.

Ensuring Human-Centered Principles