Hailuo AI Voice Cloning 文字生成語音
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Demis Hassabis: Next for AI at DeepMind
60 Minutes Demis Hassabis- What's next for AI at DeepMind, Google's artificial intelligence lab
At Google DeepMind, researchers are chasing what’s called artificial general intelligence: a silicon intellect as versatile as a human's, but with superhuman speed and knowledge.
John Michael Jumper and Demis Hassabis are American chemist and computer scientists. They both were awarded with the 2024 Nobel Prize in Chemistry for protein structure prediction. They currently serve as director and CEO at Google DeepMind.
Jumper currently serves as director at Google DeepMind. Jumper and his colleagues created AlphaFold, artificial intelligence (AI) model to predict protein structures from their amino acid sequence with high accuracy. Jumper stated that the AlphaFold team plans to release 100 million protein structures.
The scientific journal Nature included Jumper as one of the ten "people who mattered" in science in their annual listing of Nature's 10 in 2021.
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SIMPLEST Explanation of How AI Works?
SIMPLEST Explanation of How Artificial Intelligence Works? No Jargon | What is AI?
How AI Works | A Layman’s Guide to Artificial Intelligence (2025)
Have you ever wondered if the terrifying AI from movies like Terminator or The Matrix could actually become real? Or are you just curious how tools like ChatGPT, Google Gemini, Alexa, or even your phone’s camera know exactly what to do?
In this video, we break down how Artificial Intelligence really works—without any jargon. Whether you're a total beginner or someone who's simply curious, this is the most simplified, clear, and engaging guide to understanding AI today.
We connect the dots between science fiction and science fact—from the warnings of Stephen Hawking and Elon Musk, to the algorithms running your social media feed, YouTube suggestions, and even medical tools. AI is no longer the future—it is already shaping your world.
You’ll discover:
🔹 The key difference between basic programs and real AI
🔹 How AI "learns" from data—with everyday, relatable examples
🔹 What powers modern AI: massive datasets, feedback loops, and neural networks
🔹 The mysterious black box problem—why even developers sometimes cannot explain AI decisions
🔹 The everyday AI you already use: NLP, Generative AI (like ChatGPT), Computer Vision, and more
🔹 What’s real and what’s myth about Weak AI, Strong AI (AGI), and Super AI
🔹 How AI affects jobs—and how to stay relevant in a changing tech landscape
This is not a video meant for AI experts. This is a video for you. The goal?
To help you stop fearing AI—and start understanding it.
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