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Category: robotics/AI – Page 1,183
Forebrain Engraftment
Posted in robotics/AI
Human astrocytes are larger and more complex than those of infraprimate mammals, suggesting that their role in neural processing has expanded with evolution. To assess the cell-autonomous and species-selective properties of human glia, we engrafted human glial progenitor cells (GPCs) into neonatal immunodeficient mice. Upon maturation, the recipient brains exhibited large numbers and high proportions of both human glial progenitors and astrocytes. The engrafted human glia were gap-junction-coupled to host astroglia, yet retained the size and pleomorphism of hominid astroglia, and propagated Ca2+ signals 3-fold faster than their hosts. Long-term potentiation (LTP) was sharply enhanced in the human glial chimeric mice, as was their learning, as assessed by Barnes maze navigation, object-location memory, and both contextual and tone fear conditioning. Mice allografted with murine GPCs showed no enhancement of either LTP or learning. These findings indicate that human glia differentially enhance both activity-dependent plasticity and learning in mice.
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Have you ever used Alexa to help you decide what movie you should watch? Maybe you asked Siri for restaurant recommendations. Artificial intelligence and virtual assistants are constantly being refined, and may soon be making appointments for you, offering medical advice, or trying to sell you a bottle of wine.
Although AI technology has miles to go to develop social skills on par with ours, some AI has shown impressive language understanding and can complete relatively complex interactive tasks.
“All things are numbers,” avowed Pythagoras. Today, 25 centuries later, algebra and mathematics are everywhere in our lives, whether we see them or not. The Cambrian-like explosion of artificial intelligence (AI) brought numbers even closer to us all, since technological evolution allows for parallel processing of a vast amounts of operations.
Progressively, operations between scalars (numbers) were parallelized into operations between vectors, and subsequently, matrices. Multiplication between matrices now trends as the most time-and energy-demanding operation of contemporary AI computational systems. A technique called “tiled matrix multiplication” (TMM) helps to speed computation by decomposing matrix operations into smaller tiles to be computed by the same system in consecutive time slots. But modern electronic AI engines, employing transistors, are approaching their intrinsic limits and can hardly compute at clock-frequencies higher than ~2 GHz.
The compelling credentials of light—ultrahigh speeds and significant energy and footprint savings—offer a solution. Recently a team of photonic researchers of the WinPhos Research group, led by Prof. Nikos Pleros from the Aristotle University of Thessaloniki, harnessed the power of light to develop a compact silicon photonic computer engine capable of computing TMMs at a record-high 50 GHz clock frequency.
Summary: Artificial neural networks based on human brain dynamics can outperform current deep learning models in learning capabilities.
Source: Bar-Ilan University.
Traditionally, artificial intelligence stems from human brain dynamics. However, brain learning is restricted in a number of significant aspects compared to deep learning (DL).
The race of the AI labs heats up
Posted in robotics/AI
An “epic race for AI supremacy” is underway, reports The Economist. To find out who is leading, the publication ran Google’s yet-to-be launched chatbox against ChatGPT. The results are mixed.
Google is testing new artificial intelligence-powered chat products that are likely to influence a future public product launch. They include a new chatbot and a potential way to integrate it into a search engine.
The Alphabet company is working on a project under its cloud unit called “Atlas,” which is a “code red” effort to respond to ChatGPT, the large language chatbot that took the public by storm when it launched late last year.
Kevin Systrom and Mike Krieger, the co-founders of Instagram, have launched a brand new app called Artifact. This revolutionary software uses artificial intelligence (AI) technology to provide users with a personalized news feed. Currently, an invitation code is required to access the app; however, individuals can join a waitlist in order to gain early access. Systrom and Krieger left Instagram back in 2018 after working for eight years at the Facebook-owned company. Although they publicly stated that their departure was due to their desire to explore creativity and curiosity again, tensions between the two co-founders and CEO Mark Zuckerberg were also reported. Last year, the duo debuted a website dedicated to tracking the spread of COVID-19.
Current diffusion-based AI models can and will reproduce copyrighted images from its data set, though rarely. It will get worse the bigger these systems become.