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DDN + You + Cybertruck 😎 🧡 Come check it out: #AI #ArtificialIntelligence #ML #MachineLearning #LLMs #tech #data #DataStorage #DataCenters #DataAnalytics #innovation #SC24 SC26

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🏆 The New Year 2025 arrived with another big recognition for me – I was named among the Top 10 Tech Influencers to Follow in 2025 by @insightdottech❕ 🔝🔟👥 Thank you very much, @insightdottech! You can find more information in my #blog post: 👇🏽 👏🏽 Congratulations to the fellow tech influencers in the list Top 10 Tech Influencers to Follow in 2025: Antonio Grasso, Glen Gilmore, Harold Sinnott #MWC26, Kevin L. Jackson, Kirk Borne, Linda Grasso, Dr. Monika Sonu I Founder Healthinnovationtoolbox, Peggy Smedley and Ronald van Loon 🙌🏽 Looking forward to new exciting partnerships in 2025! 🤝🏽💼 📌 Read the full list Top 10 Tech Influencers to Follow in 2025 at: 🔗➡️ ℹ️ Sponsored by Intel, @insightdottech is a global publication that showcases members of the #Intel® Partner Alliance ecosystem. #Technology #Tech #TechTrends #TechTrends2025 #EmergingTech #FutureTech #TechInfluencer #Influencers #ThoughtLeadership #Leadership #InfluencerMarketing #Marketing #SocialMedia #Innovation #AI #ArtificialIntelligence #GenAI #ML #IoT #IIoT #CyberSecurity #Cloud #FutureofWork #DigitalTransformation #Industry40 #RPA #Robotics #Robots #Web3 #5G #AR #VR #DataScience #BigData #Analytics #CX #Business #TechForGood #Sustainability #WomenInTech #WomenInSTEM
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Elitsa Krumova

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Birdsong is not just sound. It is data made physical. If you could see the air at the exact moment a hemp bunting sings, you would not see empty space. You would see a structured three-dimensional data array. What we hear as a soft “chirp” can be mapped as frequencies, rhythms, amplitudes, and harmonic relationships. A scatter plot turns a fleeting song into a topographic map of sound. And the technical beauty is remarkable: → Sound is a mechanical wave, built from compressions and rarefactions in the air. → The bird controls it through the syrinx, a vocal organ capable of generating two frequencies at once. → Frequency shapes pitch. → Amplitude shapes volume and cluster density. → Timbre creates the unique waveform, the texture of each “sound island.” → Each cluster shows the acoustic proximity of syllables and motifs. What looks like chaos is not chaos. It is a bioengineered signal. Territory. Genetic profile. Hormonal state. Aggression level. Mating fitness. All encoded into patterns of pitch, timing, volume, and timbre. This is why I find it so fascinating. A bird is not simply singing into the air. It is organizing the air. It is carving space into sectors of influence using sound pressure. It is making a physical claim to territory. For some, birdsong is peaceful background music. For others, it is a complex mathematical model calibrated by millions of years of evolution for survival. And here is the urgent lesson for the AI age: We often mistake invisible systems for simplicity. A bird sings, and we hear romance. An AI responds, and we see magic. But underneath both are signals, compression, feedback loops, optimization, and information architecture. The future belongs to those who can read what others dismiss as noise. So I’ll ask you: When you hear birdsong, do you hear music, data, or both? #AI #ArtificialIntelligence #Bioacoustics #Nature #Technology #Data #MachineLearning #Innovation #FutureOfWork #Signals #Evolution
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Birdsong is not just sound. It is data made physical. If you could see the air at the exact moment a hemp bunting sings, you would not see empty space. You would see a structured three-dimensional data array. What we hear as a soft “chirp” can be mapped as frequencies, rhythms, amplitudes, and harmonic relationships. A scatter plot turns a fleeting song into a topographic map of sound. And the technical beauty is remarkable: → Sound is a mechanical wave, built from compressions and rarefactions in the air. → The bird controls it through the syrinx, a vocal organ capable of generating two frequencies at once. → Frequency shapes pitch. → Amplitude shapes volume and cluster density. → Timbre creates the unique waveform, the texture of each “sound island.” → Each cluster shows the acoustic proximity of syllables and motifs. What looks like chaos is not chaos. It is a bioengineered signal. Territory. Genetic profile. Hormonal state. Aggression level. Mating fitness. All encoded into patterns of pitch, timing, volume, and timbre. This is why I find it so fascinating. A bird is not simply singing into the air. It is organizing the air. It is carving space into sectors of influence using sound pressure. It is making a physical claim to territory. For some, birdsong is peaceful background music. For others, it is a complex mathematical model calibrated by millions of years of evolution for survival. And here is the urgent lesson for the AI age: We often mistake invisible systems for simplicity. A bird sings, and we hear romance. An AI responds, and we see magic. But underneath both are signals, compression, feedback loops, optimization, and information architecture. The future belongs to those who can read what others dismiss as noise. So I’ll ask you: When you hear birdsong, do you hear music, data, or both? #AI #ArtificialIntelligence #Bioacoustics #Nature #Technology #Data #MachineLearning #Innovation #FutureOfWork #Signals #Evolution

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Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email [email protected]. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!

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We are excited to announce the first revolutionary decentralised crypto market making on Telegram Messenger . Volumint Volume Bot for MEXC is now accessible on Telegram marking a substantial leap forward in our journey of development and innovation. Specifically designed for crypto projects, our advanced Volume bot can help you in generating organic trading volume for your token across multiple pairs. Subscribe now and upgrade your token's chart directly from Telegram! 🔗Check it out: $vMINT #VoluMint #AI #marketmaking #Telegram

VoluMint Labs.

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I AM LIVID. Let me tell you what just happened. Last week I set up my AI agent. First thing I did: review my website security. What it found made my blood BOILLLL. Over 50 invisible links to gambling websites were BURIED in the source code of …..they were hidden from anyone visiting the site BUT fully visible to Google. Pointing to casinos like Pin-Up, Mostbet, 1Win, and Parimatch…. I didn’t know it was a thing until I found out it’s actually called SEO spam injection. Hackers break into your website and hide their casino links in your code. Google crawls your site and thinks YOU are vouching for their sketchy gambling platforms. It tanks your SEO and boosts theirs…ALL while you have absolutely no idea it's happening, you think your website sucks, and people don’t even bother to look at it. To the gambling sites tanking my SEO: I don't care what you do with your life. But hijacking small businesses to do it? That's just pathetic. It’s low. And it’s plain sad. The wild part: I never would have caught this without my AI agent. Shoutout to Clawbot for literally saving my website. AI out here protecting businesses while we sleep. If you have a website, please, go check your source code. You might be surprised what's hiding in there. If you also want to support small businesses come check us out, we are hosting a Bitcoin 101 retreat April 9-12.
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I AM LIVID. Let me tell you what just happened. Last week I set up my AI agent. First thing I did: review my website security. What it found made my blood BOILLLL. Over 50 invisible links to gambling websites were BURIED in the source code of …..they were hidden from anyone visiting the site BUT fully visible to Google. Pointing to casinos like Pin-Up, Mostbet, 1Win, and Parimatch…. I didn’t know it was a thing until I found out it’s actually called SEO spam injection. Hackers break into your website and hide their casino links in your code. Google crawls your site and thinks YOU are vouching for their sketchy gambling platforms. It tanks your SEO and boosts theirs…ALL while you have absolutely no idea it's happening, you think your website sucks, and people don’t even bother to look at it. To the gambling sites tanking my SEO: I don't care what you do with your life. But hijacking small businesses to do it? That's just pathetic. It’s low. And it’s plain sad. The wild part: I never would have caught this without my AI agent. Shoutout to Clawbot for literally saving my website. AI out here protecting businesses while we sleep. If you have a website, please, go check your source code. You might be surprised what's hiding in there. If you also want to support small businesses come check us out, we are hosting a Bitcoin 101 retreat April 9-12.

isa⚡️

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🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.
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🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

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We've Joined the Loaded Lions Family! 🦁⚔️ These Lions aren't just assets ⏭️ they're keepers! We are not chasing trends. We believe in the tech and innovation behind it. 👑 VIP: Owning one will unlock the best experiences and perks, making you part of an elite group of ≈4500 holders out of millions of Cronos users worldwide. Since day one, we've tapped into the Crypto.com ecosystem, Cosmos SDK, and IBC for our core development, aligning with our missions. Note: This is NOT a paid promotion, neither are any of our posts! Just pure genuine engagement and belief in projects we talk about. Our GOAL? ⚽️ To connect with the best, innovate together, and unlock value beyond price tags. Happy to share our tech insights and opinions, stay tuned for more to come. Let's make history together! 👊 #NFTCommunity #LoadedLions #FFTB #crofam #BornBrave

Coldspace

10,968 görüntüleme • 1 yıl önce

Remember when we as football fans had to rely solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)
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Remember when we as football fans had to rely solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)

Andy Jassy

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As always everyone is blind staring at the progress of LLMs for coding and chat But meanwhile the new SOTA video model Seedance 2.5 has been slowly rolling out and it's really quite exceptional It's made by ByteDance (TikTok) who of course have lots of training data With just a few reference pics, it can get quite close to how you look IRL and you can do quite professional video shots with just a prompt I'd say it's the first video model that's now at the level of image models with the level of character likeness, cracking that in image models also took about 3 years (2022-2025) Generating 15 seconds takes about 4 minutes I put it live now on Photo AI, you can use it under [ Make video ] from the sidebar with just a prompt and your model selected! So you don't need to take an AI photo first and then turn that into a video! Saves lots of time :D It's more expensive than but I kept the credits the same (30 for 1 video) It also works inside the new video editor and you can make changes in your video with [ Magic edit ] in both the main app and the video editor Also a message for my server guy Daniel Lockyer (it can do voice too and you can even submit a voice sample of yourself, but I didn't here)
0:15

As always everyone is blind staring at the progress of LLMs for coding and chat But meanwhile the new SOTA video model Seedance 2.5 has been slowly rolling out and it's really quite exceptional It's made by ByteDance (TikTok) who of course have lots of training data With just a few reference pics, it can get quite close to how you look IRL and you can do quite professional video shots with just a prompt I'd say it's the first video model that's now at the level of image models with the level of character likeness, cracking that in image models also took about 3 years (2022-2025) Generating 15 seconds takes about 4 minutes I put it live now on Photo AI, you can use it under [ Make video ] from the sidebar with just a prompt and your model selected! So you don't need to take an AI photo first and then turn that into a video! Saves lots of time :D It's more expensive than but I kept the credits the same (30 for 1 video) It also works inside the new video editor and you can make changes in your video with [ Magic edit ] in both the main app and the video editor Also a message for my server guy Daniel Lockyer (it can do voice too and you can even submit a voice sample of yourself, but I didn't here)

@levelsio

669,462 görüntüleme • 1 gün önce

🚨 THE ESX BLOCKCHAIN EXPLORER IS NOW LIVE! 🚨 Hey EstateX Family! 👋 Big news from the tech front! After some serious work by our incredible dev team (shoutout to our superstar CTO Graham and our design and dev team 💪), we’re thrilled to announce the launch of the initial version of the ESX Testnet Block Explorer! 🔍 What can you do with it? - Easily track transactions - Watch the ESX token price and movements in real time. - Monitor block production live - Dive into transaction data from the team and our testing partners - ⁠And much more This is just the beginning! Over the coming weeks, we’ll be rolling out even more features, making the explorer smarter, faster, and packed with insights. Oh, and stay tuned... we’ll soon be revealing the first wave of projects building on the ESX chain 👀 👉 Check it out here: Very exciting time, family!
0:18

🚨 THE ESX BLOCKCHAIN EXPLORER IS NOW LIVE! 🚨 Hey EstateX Family! 👋 Big news from the tech front! After some serious work by our incredible dev team (shoutout to our superstar CTO Graham and our design and dev team 💪), we’re thrilled to announce the launch of the initial version of the ESX Testnet Block Explorer! 🔍 What can you do with it? - Easily track transactions - Watch the ESX token price and movements in real time. - Monitor block production live - Dive into transaction data from the team and our testing partners - ⁠And much more This is just the beginning! Over the coming weeks, we’ll be rolling out even more features, making the explorer smarter, faster, and packed with insights. Oh, and stay tuned... we’ll soon be revealing the first wave of projects building on the ESX chain 👀 👉 Check it out here: Very exciting time, family!

EstateX

124,350 görüntüleme • 1 yıl önce

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️
0:12

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️

Bilawal Sidhu

68,515 görüntüleme • 1 yıl önce

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:
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BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

Brian Roemmele

37,827 görüntüleme • 7 ay önce

✨Introducing YesCheff is a web app that helps you learn, cook, and enjoy your favorite recipes through step-by-step cooking guides built from real YouTube content. You can even use your own YouTube links. A small but special project where I brought all my capabilities together, from design (brand & product) to animation, and development. This project came from exploring how Al models parse data into frontend components and the development of generative interfaces. I designed and developed the structure through React components (HTML, CSS, JS) for the frontend and Node.js for the backend. This is a testament of how one could develop digital products in a less frustrating and more flexible way with the use of Al, particularly when it comes to getting the styling and functionality right. Now which foods are you eager to learn? Check it out at #YesCheff #WebApp #AI #Design #FrontendDevelopment #ReactJS #NodeJS #ProductDesign
0:18

✨Introducing YesCheff is a web app that helps you learn, cook, and enjoy your favorite recipes through step-by-step cooking guides built from real YouTube content. You can even use your own YouTube links. A small but special project where I brought all my capabilities together, from design (brand & product) to animation, and development. This project came from exploring how Al models parse data into frontend components and the development of generative interfaces. I designed and developed the structure through React components (HTML, CSS, JS) for the frontend and Node.js for the backend. This is a testament of how one could develop digital products in a less frustrating and more flexible way with the use of Al, particularly when it comes to getting the styling and functionality right. Now which foods are you eager to learn? Check it out at #YesCheff #WebApp #AI #Design #FrontendDevelopment #ReactJS #NodeJS #ProductDesign

JEHTOMS 5.0

85,213 görüntüleme • 10 ay önce