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

#gpt3 #lamda #ai #artificialintelligence #google #alphabet #sentient

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.

Disclaimer: We spoke to GPT-3 using Emerson by QuickChat.ai then visualized the responses with Synthesia.io and are not associated with these companies in any way. All responses from the AI are genuine and are only edited for grammar and/or processing errors.

#gpt3 #artificialintelligence #takeover

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.

But for some industry leaders, chatbots and image-generators are far from the final robotic frontier. Next up? Consciousness.

“This topic was taboo,” Hod Lipson, the mechanical engineer in charge of the Creative Machines Lab at Columbia University, told The New York Times. “We were almost forbidden from talking about it — ‘Don’t talk about the c-word; you won’t get tenure’ — so in the beginning I had to disguise it, like it was something else.”

Consciousness is one of the longest standing, and most divisive, questions in the field of artificial intelligence. And while to some it’s science fiction — and indeed has been the plot of countless sci-fi books, comics, and films — to others, like Lipson, it’s a goal, one that would undoubtedly change human life as we know it for good.

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Engineering student Priyanjali Gupta does not have tall tales of the inspiration behind her AI model that translates American Sign language (ASL) into English immediately.

Instead, the driving factor was her mum, who asked her “to do something now that she’s studying engineering”, a statement echoed by most Indian mums. Gupta is a third-year computer science student specializing in data science from the Vellore Institute of Technology, Tamil Nadu.

Humanity has taken yet another step toward the inevitable war against the machines (which we will lose) with the creation of Vall-E, an AI developed by a team of researchers at Microsoft that can produce high quality human voice replications with only a few seconds of audio training.

Vall-E isn’t the first AI-powered voice tool— xVASynth (opens in new tab), for instance, has been kicking around for a couple years now—but it promises to exceed them all in terms of pure capability. In a paper available at Cornell University (opens in new tab) (via Windows Central (opens in new tab) ), the Vall-E researchers say that most current text-to-speech systems are limited by their reliance on “high-quality clean data” in order to accurately synthesize high-quality speech.