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Category: robotics/AI – Page 417

Generative AI has emerged as the next wave of innovation amidst the ongoing evolution of the technological landscape, attracting the attention of both researchers and investors.

Even as vector databases and Retrieval-Augmented Generation models become mainstream, offering innovative ways to handle and process data, traditional ETL processes retain their importance in the data management ecosystem. Traditional ETL is fundamental for preparing and structuring data from diverse sources into a coherent, standardized format, making it accessible and usable for various applications. This structured data is crucial for maintaining the accuracy and reliability of information within vector databases, which excel at handling similarity searches and complex queries by converting data into vector space.

Similarly, RAG models, which leverage vast databases to augment content generation with relevant information retrieval, depend on well-organized, high-quality data to enhance their output’s relevance and accuracy. By ensuring data is accurately extracted, cleaned and loaded into databases, traditional ETL processes complement the capabilities of vector databases and RAG models, providing a solid foundation of quality data that enhances their performance and utility. This symbiotic relationship underscores the continuing value of traditional ETL in the age of AI-driven data management, ensuring that advancements in data processing technologies are grounded in reliable and well-structured data sources.

The rise of generative AI has indeed shifted the technological focus, overshadowing some of the core technologies that have been instrumental in our digital progress.

Continue reading “Is Generative AI Overshadowing The Proven Workhorses Of Modern Tech?” | >

There’s an episode of the show “Black Mirror” where a woman, trapped by grief, starts a relationship with an AI trained on her dead boyfriend’s data.

“You’re not enough of him,” she eventually decides. “You’re nothing.”

But even an empty happily-ever-after is tantalizing in the bleakness of 2024. AI platforms like ChatGPT claim to offer infinite solutions to infinite problems, from parking tickets to homework — and apparently now heartbreak as well. That’s right: if you’re still hung up after a breakup, now you can plug your ex’s emails and texts into a large language model, and date the simulacrum instead of moving on.

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New research on the continuity illusion uncovers how the brain perceives smooth motion, emphasizing the superior colliculus’s importance and suggesting new approaches for neuroscience research and clinical practice.

A study by a team at the Champalimaud Foundation (CF) has cast a new light on the superior colliculus (SC), a deep-seated brain structure often overshadowed by its more prominent cortical neighbor. Their discovery uncovers how the SC may play a pivotal role in how animals see the world in motion, and sheds light on the “continuity illusion,” an essential perceptual process integral to many of our daily activities, from driving vehicles to watching movies.

Understanding the Continuity Illusion.

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We tend to separate the brain and muscle – the brain does the thinking; the muscle does the doing. The brain takes in complex information about the world, makes decisions, while muscle merely executes. This distinction extends to our understanding of cellular processes, where certain molecules within cells are perceived as the ‘thinkers’, processing information from the chemical environment to determine necessary actions for survival, while others are viewed as the ‘muscle’, constructing the essential structures for the cell’s survival.

But a new study shows how the molecules that build structures, i.e, the muscle, can themselves do both the thinking and the doing. The study, by scientists at Maynooth University, the University of Chicago, and California Institute of Technology was published in the journal Nature.

“We show that a natural molecular process – nucleation – that has been studied as a ‘muscle’ for a long time can do complex calculations that rival a simple neural network,” said University of Chicago Associate Professor Arvind Murugan, one of the two senior co-authors on the paper. “It’s an ability hidden in plain sight that evolution can exploit in cells to do more with less; the ‘doing’ molecules can also do the ‘thinking.’”

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The Game Changer: How AI Is Transforming The World Of Sports Gambling

Posted by Gemechu Taye in entertainment, information science, robotics/AI

In the adrenaline-fueled arena of sports gambling, a revolution is unfolding — one powered by artificial intelligence (AI). This technological marvel is transforming the art of sports betting from a game of chance into a symphony of data-driven precision. Let us explore the burgeoning world where AI intersects with sports gambling, turning bettors from mere spectators into strategic players in a game where data, algorithms, and probabilities redefine the odds.

Sports gambling, a realm where intuition, experience, and sometimes sheer luck have traditionally dictated the rules, is undergoing a transformative shift. AI, with its unparalleled ability to analyze vast datasets and discern patterns beyond human capability, is emerging as the new MVP in this field. This transition from gut-driven bets to AI-powered predictions is not just about increasing the odds of winning; it’s about elevating sports gambling to an art of calculated strategies.

At the heart of AI’s influence in sports gambling lies predictive analytics. Companies like Stratagem and Stats Perform are harnessing the power of AI to analyze historical data, player statistics, and even weather conditions to predict game outcomes with astonishing accuracy. For instance, Stratagem uses advanced machine learning algorithms to turn data from thousands of past games into insightful betting strategies, offering gamblers an edge that was unimaginable a few years ago.

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UK’s AI Safety Institute ‘needs to set standards rather than do testing’

Posted by Gemechu Taye in government, robotics/AI

He added, however, that “the technology is moving fast as well”. He said the institute should put in place standards that other governments and companies can follow, such as “red teaming”, where specialists simulate misuse of an AI model, rather than take on all the work itself.

Warner said the government could find itself in a situation where it was “red teaming everything” and that a backlog could build up “where they don’t have the bandwidth to get to all the models fast enough”

Referring to the institute’s potential as an international standard setter, he said: “They can set really brilliant standards such that other governments, other companies … can red team to those standards. So it’s a much more scalable, long-term vision for how to keep these things safe.”

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