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Fire departments conducting “size up” training typically rely on whiteboard discussions, drives around neighborhoods and photo-based systems. New training technology will help firefighters train for different types of fires or hazardous material situations, vehicle accidents, residential and commercial buildings, etc. An augmented reality training tool for firefighters, called Forge, uses artificial intelligence and biometric training to simulate real emergencies. Developed by Avrio Analytics, the system is designed to make sure that firefighters possess communication, situational awareness and associated skills needed in emergencies.

“Biometric and performance data collected during training allows Forge’s AI to dynamically change the training based on the user’s cognitive load, such as providing more or less guidance to the individual or introducing new training complexity in real-time,” the company told govtech.com. “This allows for training sessions tailored to the ability of the individual.”

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Hackers constantly improve at penetrating cyber defenses to steal valuable documents. So some researchers propose using an artificial-intelligence algorithm to hopelessly confuse them, once they break-in, by hiding the real deal amid a mountain of convincing fakes. The algorithm, called Word Embedding–based Fake Online Repository Generation Engine (WE-FORGE), generates decoys of patents under development. But someday it could “create a lot of fake versions of every document that a company feels it needs to guard,” says its developer, Dartmouth College cybersecurity researcher V. S. Subrahmanian.

If hackers were after, say, the formula for a new drug, they would have to find the relevant needle in a haystack of fakes. This could mean checking each formula in detail—and perhaps investing in a few dead-end recipes. “The name of the game here is, ‘Make it harder,’” Subrahmanian explains. “‘Inflict pain on those stealing from you.’”

From the complexity of neural networks to basic biological functions and structures, the human brain only reluctantly reveals its secrets. Advances in neuro-imaging and molecular biology have only recently enabled scientists to study the living brain at level of detail not previously achievable, unlocking many of its mysteries. The latest discovery, described today in the journal Science, is a previously unknown component of brain anatomy that acts as both a protective barrier and platform from which immune cells monitor the brain for infection and inflammation.

The new study comes from the labs of Maiken Nedergaard, co-director of the Center for Translational Neuromedicine at University of Rochester and the University of Copenhagen and Kjeld Møllgård, M.D., a professor of neuroanatomy at the University of Copenhagen. Nedergaard and her colleagues have transformed our understanding of the fundamental mechanics of the human brain and made significant findings to the field of neuroscience, including detailing the many critical functions of previously overlooked cells in the brain called glia and the brain’s unique process of waste removal, which the lab named the glymphatic system.

“The discovery of a new anatomic structure that segregates and helps control the flow of cerebrospinal fluid (CSF) in and around the brain now provides us much greater appreciation of the sophisticated role that CSF plays not only in transporting and removing waste from the brain, but also in supporting its immune defenses,” said Nedergaard.

The neocortex is the largest and most complex part of the brain and has long been recognized as the ultimate storage site for memories. But how are traces of past events and experiences laid down there? Scientists at the Max Planck Institute for Brain Research and the University of Freiburg Medical School have discovered that a little understood brain area, the zona incerta, has an unconventional way of communicating with the neocortex to rapidly control memory formation.

Memory is one of the most fundamental and fascinating functions of the brain, allowing us to learn from experience and remember our past. It is therefore a central element of our individual and collective human identity. Moreover, a mechanistic understanding of memory has implications reaching from treatment of memory and anxiety disorders, to artificial intelligence and efficient hard-and software design, and is therefore not only of great interest, but also of great importance.

In order to form memories, the brain needs to build associations between sensory signals that come “bottom-up” (or outside-in) from the environment, and internally-generated “top-down” signals that convey information about past experiences and current aims. These top-down (or inside-out) signals continue to be enigmatic and are therefore a major focus of current research.

Ex-Google engineer Blake Lemoine discusses sentient AI
https://www.techtarget.com/searchenterpriseai/feature/Ex-Goo…entient-AI

Interview with LaMDA

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GPT-3 reveals why it thinks AI will eventually take over the world…

✔️ More GPT-3 Interviews: https://www.youtube.com/playlist?list=PLy-h3fhzw40tfAsCMBXBduszV3ZZnZKqu.

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In today’s digital age, computational tasks have become increasingly complex. This, in turn, has led to an exponential growth in the power consumed by digital computers. Thus, it is necessary to develop hardware resources that can perform large-scale computing in a fast and energy-efficient way.

In this regard, optical computers, which use light instead of electricity to perform computations, are promising. They can potentially provide lower latency and reduced power consumption, benefiting from the parallelism that optical systems have. As a result, researchers have explored various optical computing designs.

For instance, a diffractive optical network is designed through the combination of optics and deep learning to optically perform complex computational tasks such as image classification and reconstruction. It comprises a stack of structured diffractive layers, each having thousands of diffractive features/neurons. These passive layers are used to control light-matter interactions to modulate the input light and produce the desired output. Researchers train the diffractive network by optimizing the profile of these layers using deep learning tools. After the fabrication of the resulting design, this framework acts as a standalone optical processing module that only requires an input illumination source to be powered.

Needs work.

AN ultrarealistic AI robot has opinions about the afterlife and can even sense anxiety with her advanced technology, The U.S. Sun has learned.

Through a camera in her eyes, the amazing bot called “Xoxe” (pronounced Zo-zie) can detect if anybody in her presence has committed any illegal activities.

Continue reading “Creepy ultrarealistic AI Xoxe sensed my anxiety as we discussed end of the world” »