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Category: robotics/AI

In this insightful conversation with OpenAI’s CPO Kevin Weil, we discuss the rapid acceleration of AI and its implications. Kevin makes the shocking prediction that coding will be fully automated THIS YEAR, not by 2027, as others suggest. He explains how OpenAI’s models are already ranking among the world’s top programmers and shares his thoughts on Deep Research, GPT-4.5’s human-like qualities, the future of jobs, and the timeline for GPT-5. Don’t miss Kevin’s billion-dollar startup idea and his vision for how AI will transform education and democratize software creation.

00:00 — Summary.
01:21 — Introduction.
03:20 — Discussion on OpenAI being both a research and product company.
11:05 — Timeline for GPT-5
11:38 — AI model commoditization and maintaining competitive advantage.
15:09 — Deep Research capabilities.
24:22 — Coding automation prediction: THIS YEAR
30:05 — AI in creative work and design.
36:43 — Future of programming and engineers.
38:32 — Will AI create new job categories?
40:58 — Billion-dollar AI startup ideas.
46:27 — Voice interfaces and robotics.
49:28 — Closing thoughts.

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For decades, the Turing Test was considered the ultimate benchmark to determine whether computers could match human intelligence. Created in 1950, the “imitation game”, as Alan Turing called it, required a machine to carry out a text-based chat in a way that was indistinguishable from a human. It was thought that any machine able to pass the Turing Test would be capable of demonstrating reasoning, autonomy, and maybe even consciousness – meaning it could be considered human-level artificial intelligence, also known as artificial general intelligence (AGI).

The arrival of ChatGPT ruined this notion, as it was able to convincingly pass the test through what was essentially an advanced form of pattern recognition. It could imitate, but not replicate.

Last week, a new AI agent called Manus once again tested our understanding of AGI. The Chinese researchers behind it describe it as the “world’s first fully autonomous AI”, able to perform complex tasks like booking holidays, buying property or creating podcasts – without any human guidance. Yichao Ji, who led its development at the Wuhan-based startup Butterfly Effect, says it “bridges the gap between conception and execution” and is the “next paradigm” for AI.

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The best answer will be unsettling to both the hard skeptics of AI and the true believers.

Let’s take a step back. What exactly is reasoning, anyway?

AI companies like OpenAI are using the term reasoning to mean that their models break down a problem into smaller problems, which they tackle step by step, ultimately arriving at a better solution as a result.

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The mergers of massive black hole binaries could generate rich electromagnetic emissions, which allow us to probe the environments surrounding these massive black holes and gain deeper insights into the high energy astrophysics. However, due to the short timescale of binary mergers, it is crucial to predict the time of the merger in advance to devise detailed observational plans. The overwhelming noise and slow accumulation of the signal-to-noise ratio in the inspiral phase make this task particularly challenging. To address this issue, we propose a novel deep neural denoising network in this study, capable of denoising a 30-day inspiral phase signal. Following the denoising process, we perform the detection and merger time prediction based on the denoised signals.

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Contemplate a future where tiny, energy-efficient brain-like networks guide autonomous machines—like drones or robots—through complex environments. To make this a reality, scientists are developing ultra-compact communication systems where light, rather than electricity, carries information between nanoscale devices.

In this study, researchers achieved a breakthrough by enabling direct on-chip communication between tiny light-sensing devices called InP nanowire photodiodes on a silicon chip. This means that light can now travel efficiently from one nanoscale component to another, creating a faster and more energy-efficient network. The system proved robust, handling signals with up to 5-bit resolution, which is similar to the information-processing levels in biological neural networks. Remarkably, it operates with minimal energy—just 0.5 microwatts, which is lower than what conventional hardware needs.

S a quadrillionth of a joule!) and allow one emitter to communicate with hundreds of other nodes simultaneously. This efficient, scalable design meets the requirements for mimicking biological neural activity, especially in tasks like autonomous navigation. + In essence, this research moves us closer to creating compact, light-powered neural networks that could one day drive intelligent machines, all while saving space and energy.

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Based on how an AI model transcribes audio into text, the researchers behind the study could map brain activity that takes place during conversation more accurately than traditional models that encode specific features of language structure — such as phonemes (the simple sounds that make up words) and parts of speech (such as nouns, verbs and adjectives).

The model used in the study, called Whisper, instead takes audio files and their text transcripts, which are used as training data to map the audio to the text. It then uses the statistics of that mapping to “learn” to predict text from new audio files that it hasn’t previously heard.

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The next time you’re due for a medical exam you may get a call from someone like Ana: a friendly voice that can help you prepare for your appointment and answer any pressing questions you might have.

With her calm, warm demeanor, Ana has been trained to put patients at ease — like many nurses across the U.S. But unlike them, she is also available to chat 24–7, in multiple languages, from Hindi to Haitian Creole.

That’s because Ana isn’t human, but an artificial intelligence program created by Hippocratic AI, one of a number of new companies offering ways to automate time-consuming tasks usually performed by nurses and medical assistants.

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01:13 How Does Tesla Bot Gen 3 Handle Real-World Tasks?
06:12 How much does the Tesla Bot Gen 3 truly cost?
10:36 How is Tesla planning to sell the Bot Gen 3?
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New UPDATE! Elon Musk LEAKED Tesla Bot Gen 3 10K Mass Production & All Real-Life Tasks Testing! Recently, Elon Musk confidently announced that the Tesla Bot Optimus can navigate independently in 95% of complex environments and react in just 20 milliseconds!
With a plan to produce 10,000 Tesla Optimus Gen 3 units in 2025, Tesla is leveraging its AI infrastructure, manufacturing capabilities, and real-world testing across more than 1,000 practical tasks to prepare for mass production this year.

New UPDATE! Elon Musk LEAKED Tesla Bot Gen 3 10K Mass Production & All Real-Life Tasks Testing! In today’s episode, we have compiled evidence from official announcements, technical demonstrations to validate the feasibility of this plan and pinpoint the final timeline and pricing for the 2025 production model.
But before we dive into price analysis in Part 2 and exactly launching time in Part 3 of this episode, you should first understand what we expect from this Tesla humanoid robot—and more importantly, whether it’s truly worth the price.
How Does Tesla Bot Gen 3 Handle Real-World Tasks?

New UPDATE! Elon Musk LEAKED Tesla Bot Gen 3 10K Mass Production & All Real-Life Tasks Testing! John Kennedy, nearly seventy, lay motionless on the floor, pain radiating from his hip and spine. His phone was just a few steps away—close, yet out of reach. Then, everything went dark.
A humanoid robot detected his fall. It gently lifted him up, scanned his injuries, and instantly sent an alert to his doctor.
Then came the doctor’s words, they wanted to send him to an assisted living facility.

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#888999evs #teslacarworld #teslacar #888999 #teslabot #teslaoptimus #teslabotgen2 #teslabotgen3
subcribe: https://bit.ly/3i7gILj

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In this episode, Peter answers the hardest questions about AI, Longevity, and our future at an event in El Salvador (Padres y Hijos).

Recorded on February 2025
Views are my own thoughts; not Financial, Medical, or Legal Advice.

Chapters.

00:00 — Navigating Confusion in Leadership and Purpose.
02:00 — The Evolution of Work and Purpose.
03:50 — AI’s Role in Information Credibility.
07:17 — Sustainability and Technology’s Impact on Nature.
09:26 — Building a Future with AI and Longevity.
11:40 — The Economics of Longevity and Accessibility.
15:15 — Reimagining Education for the Future.
19:23 — Overcoming Human Obstacles to Progress.

I send weekly emails with the latest insights and trends on today’s and tomorrow’s exponential technologies. Stay ahead of the curve, and sign up now: https://www.diamandis.com/subscribe.

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Continue reading “Our Lives after the AI Revolution — Answering the Hard Questions | EP #155” | >