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ResembleAI allows you to clone ANY voice without verification using on 5-10 seconds of audio, and dominates on paralinguistic tags for human-like expressions.
Chatterbox Turbo delivers:
• <150ms time-to-first-sound
• State-of-the-art quality that beats larger proprietary models
• Natural, programmable expressions
• Zero-shot voice cloning with just 5 seconds of audio
• PerTh watermarking for authenticated and verifiable audio
• Open source, full transparency, no black boxes
Try it on HuggingFace: https://huggingface.co/spaces/ResembleAI/chatterbox-turbo-demo
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He says it would take a human over 500,000 years to read that much. But a 4-year-old child sees just as much visual data in their first few years of life. This shows how much richer and more complex real-world experience is compared to reading text. Training on the web is huge but it still doesn’t match what a child learns just by living.
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"Researchers use AI and Reverse Vaccinology 2.0 on human blood samples to identify a potential new mpox (a viral infectious disease caused by the mpox virus) target, and use the target in a vaccine that elicited mpox-neutralizing antibodies in mice"
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AI Post — Artificial Intelligence
OpenAI Introduces ChatGPT Images, powered by our flagship new image generation model. - Stronger instruction following - Precise editing - Detail preservation - 4x faster than before Rolling out today in ChatGPT for all users, and in the API as GPT Image…
What’s new
• OpenAI added a dedicated Images section inside ChatGPT.
• It works as a standalone photo editor directly within the AI interface.
• Powered by a new image model that runs ~4× faster than before.
Key capabilities
• Remembers how a person or object looked in previous frames, keeping visual consistency.
• Accurately follows prompts instead of distorting the whole image.
• Allows targeted edits: pose, lighting, clothing, background without breaking the rest of the frame.
Why it matters
• Image editing shifts from “prompt-and-hope” to controlled, granular changes.
• Makes AI image tools practical for real workflows, not just experimentation.
• Already live for all ChatGPT users.
This is a quiet but meaningful step toward ChatGPT becoming a full creative workspace, not just a text-first AI.
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AI Post — Artificial Intelligence
Unsupervised Tesla Robotaxis are driving on the streets of Austin, TX! There is no safety monitor inside the vehicle. Tesla has solved self-driving. AI Post ⚪️ | Our X 🏴
Tesla said it has begun testing driverless robotaxis in Texas, marking another step in the company’s autonomous driving efforts. Investors reacted quickly to the announcement, sending Tesla shares to a new all-time high.
Attention is now turning to where Tesla might expand next, with Elon Musk known for eyeing unconventional and potentially high-growth markets.
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Attention is now turning to where Tesla might expand next, with Elon Musk known for eyeing unconventional and potentially high-growth markets.
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That means our entire "information diet" what we read, see, and hear, could be controlled by just a few powerful tech companies. He compares this to the media: just like we need diverse press, we need diverse AI systems. If we don’t build open platforms, a few companies could control global information flow.
This is his biggest fear. Not AI going rogue, but AI being monopolized.
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Almost $1 Trillion has been invested into AI so far and that may just be the start
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Disney has launched DisneyGPT, a proprietary neural network for internal use. The tool is designed to help studio employees work faster and improve content quality.
What DisneyGPT does
• Provides verified information on films and animated projects, including canon details.
• Answers character-related questions, such as backstories, motivations, and relationships.
• Helps maintain the “Disney spirit” by referencing Walt Disney’s quotes and creative philosophy.
• Supports creative workflows with idea generation, fact-checking, and noscript validation.
How it’s positioned
• Functions as a hybrid of an internal encyclopedia, a noscript assistant, and a reference guide.
• Built specifically around Disney’s intellectual property and internal knowledge base.
Does building closed, internal AI tools give studios a long-term advantage over more open, third-party models?
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AI Post — Artificial Intelligence
Additional information has surfaced about the partnership between OpenAI and Disney, clarifying the scope and limitations of the agreement.
While the deal is structured as a three-year partnership, it reportedly grants OpenAI access to Disney’s iconic characters for use in its Sora AI model for only the first year. After that initial period, Disney is free to enter into similar agreements with other companies.
According to Disney, the collaboration is intended as a trial rather than a long-term commitment. The company views the partnership as an experiment to assess whether participation in AI-driven creative projects makes strategic sense. OpenAI, in this context, was simply the first partner selected to test the model.
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It's been a meme for a long time that google translate, translates but maybe just decent. Now its powered by Gemini, clearly demonstrate that the future has arrived, talk to anyone live in your native language
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In early 2025, AI systems crossed a symbolic threshold: large language models began outperforming human doctors in diagnostic accuracy. The expectation was clear, better outcomes, faster care, less burnout. Reality turned out messier.
Doctors actively use AI and see real clinical value. Yet they are deeply unhappy with how AI is being introduced into medicine.
What doctors actually think
• AI improves decision-making and efficiency.
• It makes them better clinicians.
• But control over AI is drifting away from physicians and toward administrators, vendors, and bureaucratic processes.
Why dissatisfaction is so high
1. Doctors are excluded from decisions
• AI policy, procurement, and deployment are discussed without the people who treat patients.
2. Bottom-up adoption, top-down control
• Many doctors pay for AI tools out of pocket because hospitals lag behind.
• When institutions act, they often impose tools doctors didn’t choose.
Key statistics
• 67% use AI daily in clinical work
• 84% say AI makes them better doctors
• 81% are dissatisfied with their employer’s AI implementation pace
• 89% believe dedicated funding for AI tools is necessary
• 71% have little or no influence over which AI tools are adopted
• 48% say employer communication about AI is poor
What’s really worrying
• AI in medicine is advancing faster than governance.
• The people closest to patients trust the technology but not the system managing it.
The risk isn’t AI replacing doctors, it is powerful medical AI being shaped by service providers and bureaucracy instead of clinicians and patients paying the price.
Source: The 2025 Physicians AI Report. Survey of 1,000+ doctors across 106 specialties
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A new model, SAM Audio, can now separate individual sounds from complicated audio mixes using text prompts, visual cues, or span selection. This allows creators to extract instruments, voices, or sound effects with unprecedented precision.
The release includes a perception encoder model, benchmarks, and research papers, giving the community the tools to explore, experiment, and build innovative audio applications.
With SAM Audio, previously impossible tasks from advanced remixing to immersive sound design are now within reach, opening new frontiers in audio creativity.
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He argues that humans are highly specialized, good at navigating the physical and social world, but weak at many other tasks. We seem general only because we can imagine the problems we are good at. For LeCun, AGI mistakes human limits for universal laws of intelligence.
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Ant Group, the parent company of Alipay, also builds humanoids, and it seems they are focusing particularly on humanoid chefs.
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OpenAI partnered with Red Queen Bio to test GPT-5 inside a real biological laboratory. The model was embedded in a closed-loop wet lab workflow, not a simulation.
How the experiment worked
• GPT-5 proposed hypotheses and step-by-step lab protocols
• Human researchers or lab robots executed the instructions exactly
• Experimental results were fed back to the model
• GPT-5 analyzed failures and successes, then iterated
• The loop repeated over multiple rounds
The task
• Optimize Gibson Assembly, a standard DNA cloning technique
• Metric: number of successful bacterial colonies
• Context: a mature, well-studied protocol where typical gains are only 2–3×
The result
• GPT-5 achieved a 79× improvement over the baseline method
• The outcome was stable and reproducible across repeated experiments
What changed
• The model suggested adding two known proteins: • RecA • gp32
• Both proteins are individually well understood
• Their combined use in this cloning context had not been explored before
This is not a scientific breakthrough on its own. The performance is comparable to a strong PhD student in a narrow domain . The real signal is role evolution: AI moving from text and simulations into direct participation in physical scientific processes
GPT-5 didn’t invent new biology, it systematically explored the lab space faster and deeper than humans typically can.
Source.
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