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.
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.
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.
From 2023, Lesley Stahl’s report on AI, chatbots and a world of unknowns. From 2024, Stahl’s story on Kenyan workers training AI who say they’re overworked, underpaid and exploited by big American tech companies. Also from 2024, Anderson Cooper’s report on \.
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.
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? === 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.
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.
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Imagine having the computational power of a full-fledged data center sitting on your desk for just $3,000. It might sound too good to be true, but Nvidia’s Project DIGITS is making it a reality. Designed in collaboration with MediaTek, this compact machine is pushing the boundaries of what’s possible with AI development and high-performance computing, offering impressive power in a surprisingly small form factor.
Silicon Valley’s earliest stage companies are getting a major boost from artificial intelligence. Startup accelerator Y Combinator — known for backing Airbnb, Dropbox and Stripe — this week held its annual demo day in San Francisco, where founders pitched their startups to an auditorium of potential venture capital investors.
S not just the number one or two companies — the whole batch is growing 10% week on week, said Tan, who is also a Y Combinator alum. That App developers can now offload or automate more repetitive tasks, and they can generate new code using large language models. Tan called it vibe coding, a term for letting models take the wheel and generate software. In some cases, AI can code entire apps. The ability for AI to subsidize an otherwise heavy workload has allowed these companies to build with fewer people. For about a quarter of the current YC startups, 95% of their code was written by AI, Tan said.
Over the past decades, roboticists have introduced a wide range of systems with distinct body structures and varying capabilities. As the number of developed robots continuously grows, being able to easily learn about these many systems, their unique characteristics, differences and performance on specific tasks could prove highly valuable.
Researchers at Technical University of Munich (TUM) recently created the “Tree of Robots,” a new encyclopedia that could make learning about existing robotic systems and comparing them significantly easier. Their robot encyclopedia, introduced in a paper published in Nature Machine Intelligence, categorizes robots based on their performance fitness on various tasks.
“The aspiration for intelligent robots that can understand their environment as we humans do, and execute tasks independently, has existed for ages,” Robin Jeanne Kirschner, first author of the paper, told Tech Xplore.
