Category: genetics

While the US government was making silly Trump-Jesus images, China has been secretly creating extremely realistic AI propaganda videos and releasing them on social media. A video titled "Reassuring! The state police keeps us all safe" went viral on Chinese social media but few could tell it was actually fake. The 1-min long AI video is so well made that it's hard to tell even given the clues. Does the US have the capability of making such realistic/consistent minute long AI videos? Are the Chinese ahead in the AI race? (To save the trouble of debating whether it's fake or real, here are some strong indicators:) 1. the Chinese characters on the green road sign are typical AI gibberish 2. The zebra crossing has only three lines, it's unfinished in the middle of the road 3. At 0:06 when the guy wearing the black helmet landed his scooter, you can hear the landing sound so loud and clearly and you can see the whole screen shaked for ~0.5s. Neither makes any sense unless the scooter weighted 5 tons. 4. From 0:09 to 0:14, two items were dropped on the ground and you can hear the sound very clearly as if they are dropped in a kitchen. When the police pushed the old man, you can hear the sound of him hitting the ground too. If that weren't fake, the man would have had very serious head injuries. All impossible when recording with a phone in a noisy street. 5. Around 0:20-0:24, a guy walks into the road in the background. This guy moves his arm around 0:22-0:24, and the middle of his arm appears to glitch in and out of existence. 6. At 0:46, watch in 0.25x how the guy in red riding a scooter turned his head 90 degrees in 0s. 7. At 0:51, the guy's hand emerges from his back as a discolored bud before becoming a full hand. 8. None of the shadows under automobiles is correct 9. The way the old man bounced on the ground like rubber is typical AI submitted by /u/PotentialKlutzy9909 [link] [comments]

Hi, this has probably already been posted but I couldn't find it. But as someone who has used ChatGPT & Claude for work I have a question: which one do you prefer? For ChatGPT (although I have never automated anything with it) I did find that making reports and writing texts work better. E.g.: if I told them: less chatgpt and more human I got better results. Whereas I feel like Claude might be better for automations but I do not like the way it write, you can clearly tell it is AI and it is not nice to read, the flow is disruptive if you know what I mean. Every line. Has a stop. For extra dramatic. Effect. << Like that Am I missing something or am I doing something wrong or does anyone have the same feeling? Thank you in advance, submitted by /u/Livanie [link] [comments]

I'm trying to understand what the workflow looks like in research labs (especially PhD labs and university groups) that work on LLMs, AI agents, or applied AI. I'm planning to study in graduate school in Computer Science. There are now a lot of agent frameworks and tools available, such as: OpenAI Agents SDK LangChain / LangGraph CrewAI Google ADK AutoGen Semantic Kernel MCP PydanticAI Agno Mastra Do research groups actually use these frameworks in their projects, or do they mostly implement their own orchestration, tool calling, memory, and agent loops directly in Python? Or maybe it's a mixture of both ? I can understand why companies might use frameworks to ship products faster, but I'm curious about academia, where reproducibility and experimental control matter more. Some specific questions: If you're a PhD student or researcher, what does your codebase typically look like? Are frameworks common, or are they considered too opinionated? Which parts do you usually implement yourself (agent loop, planning, memory, RAG, evaluation, tracing, etc.)? Are there any frameworks that have become standard in research labs? If you're publishing papers, do reviewers or collaborators prefer minimal dependencies? I'd especially love to hear from PhD students, professors, or research engineers working on LLMs or AI agents. Thanks! submitted by /u/One_Fix5763 [link] [comments]

TL;DR In April, someone threw a Molotov cocktail at Sam Altman's San Francisco home, and a second attack days later added gunfire to the property. Data Center Watch found organized opposition groups roughly doubled from 396 at the end of 2025 to 833 by the end of March 2026, spanning 49 states. Opponents blocked or delayed at least 75 data-center projects worth about $130 billion in the first quarter of 2026 alone. In April, someone threw a Molotov cocktail at Sam Altman's San Francisco home, and within days a second attack put gunfire into the property. The Wall Street Journal reports that AI executives are hardening personal security as opposition to the industry moves from online posts into the physical world. Prosecutors say Daniel Moreno-Gama, the 20-year-old accused in the first attack, traveled from Texas to San Francisco intending to kill Altman, and had writings on him about AI's purported risk to humanity. He faces two counts of attempted murder and attempted arson in California state court. Two more suspects were later arrested in connection with the second incident. Around the same time, The Information described Silicon Valley leaning into a new breed of bodyguards for AI leadership. aiweekly.co/alerts/ai-executives-add-personal-security-as-backlash-turns-violent submitted by /u/Justgototheeffinmoon [link] [comments]

Alexandr Wang just did his first long interview since Meta paid a fortune to bring him in — and the most useful part wasn't the gossip, it was the mechanism. 🧠 His actual argument: a small team where everyone is genuinely elite will always move faster than a large one where responsibility gets spread across a "melange." Not a hot take — he ran an entire $14B lab rebuild on it. Worth sitting with if you've ever felt your own output get lost inside a bigger structure. The fix isn't more headcount. It's less dilution. DM for credit or removal request (no copyright intended) © All rights and credits reserved to the respective owner(s). #ScaleAI #MetaAI #CareerGrowth submitted by /u/cen6wkf [link] [comments]

TL;DR In April, someone threw a Molotov cocktail at Sam Altman's San Francisco home, and a second attack days later added gunfire to the property. Data Center Watch found organized opposition groups roughly doubled from 396 at the end of 2025 to 833 by the end of March 2026, spanning 49 states. Opponents blocked or delayed at least 75 data-center projects worth about $130 billion in the first quarter of 2026 alone. The pressure isn't only on the people at the top. According to the Data Center Watch Q1 2026 report, organized opposition groups roughly doubled from 396 at the end of last year to 833 by the end of March, spanning 49 states, and opponents blocked or delayed at least 75 projects worth about $130 billion in a single quarter. That is a very different problem from a viral tweet. It is permits denied, votes lost, sites relocated https://aiweekly.co/alerts/ai-executives-add-personal-security-as-backlash-turns-violent submitted by /u/Justgototheeffinmoon [link] [comments]

"just wondering what's on everyone's mind in this subreddit. no fluff, no trouble, just plain curious." hahahahahaha submitted by /u/PROfil_Official [link] [comments]

I keep seeing more AI consultants pop up everywhere, LinkedIn, agencies, startup circles, even local business groups. Some of them seem legit: they understand workflows, automation, LLM limitations, data privacy, integration costs, and where AI actually creates value. But others feel like they learned a few buzzwords, made a ChatGPT prompt pack, and now sell AI transformation strategy for thousands of dollars. Do AI consultants really need deep technical knowledge to be useful? Or is the real value just helping non-technical businesses understand what’s possible and where to start? Because on one hand, most businesses don’t need a machine learning researcher. They need someone who can say: This process can be automated. This shouldn’t use AI. This tool is risky for customer data. This workflow will save your team 10 hours a week. But on the other hand, if a consultant doesn’t understand the limitations, hallucinations, model differences, security issues, API costs, or implementation complexity… aren’t they just selling hype? Feels like the AI consulting space is becoming a mix of real experts, smart operators, and pure bluffers. submitted by /u/Few-Garlic2725 [link] [comments]

The amount of digital content created by humans may be enormous, but it is still finite. As AI models consume more and more of it, how will future models be trained? Will they rely mostly on synthetic data generated by other AIs? What do you think will happen in the long term? submitted by /u/Hafam_Hock [link] [comments]

submitted by /u/talkingatoms [link] [comments]