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Category: robotics/AI – Page 359
The 7 Stages of AI (And What’s Coming Next)
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Intel Introduces Hala Point, the Most Advanced Neuromorphic System
Intel utilizes neuromorphic computing, a hyper-efficient solution inspired by the human brain, to address the need for fundamentally new approaches to sustainable artificial intelligence.
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Tesla’s ‘We, Robot’ Event: Everything Revealed in 8 Minutes
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New Gmail Security Alert For Billions As 7-Day AI Hack Confirmed
Google has implemented increasingly sophisticated protections against those who would compromise your Gmail account —but hackers using AI-driven attacks are also evolving. Here’s what you need to know.
Sam Mitrovic, a Microsoft solutions consultant, has issued a warning after almost falling victim to what is described as a “super realistic AI scam call” capable of tricking even the most experienced of users.
It all started a week before Mitrovic realized the sophistication of the attack that was targeting him. “I received a notification to approve a Gmail account recovery attempt,” Mitrovic recounts in a blog post warning other Gmail users of the threat in question. The need to confirm an account recovery, or a password reset, is a notorious phishing attack methodology intended to drive the user to a fake login portal where they need to enter their credentials to report the request as not initiated by them.
Follow The Data: Why AI Demands A Data-Driven Culture
Companies that embrace a data-driven culture will be better able to thrive in not just life sciences, but every industry. By leveraging AI and strengthening the collaboration between humans and machines, organizations can achieve better quality, ensure data-driven decision-making and democratize data across their enterprise.
It’s incredible to think of where we’ll be in just a few short years. Those who can adapt to a data-first culture and not only implement AI technologies, but also understand its application and address the challenges, will be better positioned for success.
I’ll close with this—follow the data. Make decisions based on that data. That’s it. It’s that simple. In the search for truth, data-driven decisions are the way forward.
This is a Monumental Breakthrough… [Part 2]
Edward Frenkel is a renowned mathematician, professor of University of California, Berkeley, member of the American Academy of Arts and Sciences, and winner of the Hermann Weyl Prize in Mathematical Physics. In this episode, Edward Frenkel discusses the recent monumental proof in the Langlands program, explaining its significance and how it advances understanding in modern mathematics.
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Edward Frenkel’s previous lecture on TOE [Part 1]: • Revolutionary Math Proof No One Could…
Check out Edward Frenkel’s New York Times Bestselling book “Love and Math” which covers a lot of material in this video: https://amzn.to/4evbBkS
LINKS:
• Edward Frenkel’s Part 1 on TOE for the Langlands Program: • Revolutionary Math Proof No One Could…
• Edward Frenkel’s Twitter: https://twitter.com/edfrenkel.
• Edward Frenkel’s Official Website: https://edwardfrenkel.com.
• Edward Frenkel’s YouTube: / @edfrenkel.
• Edward Frenkel’s Instagram: / edfrenkel.
• Edward Frenkel’s Linkedin / edfrenkel.
• Edward Frenkel’s SoundCloud (DJ Moonstein): / moonstein.
• Edward Frenkel’s 1st TOE Episode: • Edward Frenkel: Infinity, Ai, String…
• Andre Weil’s letter on “Rosetta Stone” of Math: https://www.ams.org/notices/200503/fe…
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Overcoming ‘catastrophic forgetting’: Algorithm inspired by brain allows neural networks to retain knowledge
Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience “catastrophic forgetting” when taught additional tasks: They can successfully learn the new assignments, but “forget” how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.
Biological brains, on the other hand, are remarkably flexible. Humans and animals can easily learn how to play a new game, for instance, without having to re-learn how to walk and talk.
Inspired by the flexibility of human and animal brains, Caltech researchers have now developed a new type of algorithm that enables neural networks to be continuously updated with new data that they are able to learn from without having to start from scratch. The algorithm, called a functionally invariant path (FIP) algorithm, has wide-ranging applications from improving recommendations on online stores to fine-tuning self-driving cars.