0 Members and 99 Guests are viewing this topic.
...0:00 Intro0:35 Cat-4D AI for 4D videos4:13 Generative Omnimatte8:00 Samurai object segmentation10:33 Material Anything13:58 Ominicontrol20:46 LTX video26:00 Sora leak31:16 Open source QwQ matches o1
How do you define bad people?
Quick summary: World Labs' new AI generates consistent, explorable 3D worlds from single images, a leap beyond 2D AI image generation, impacting gaming, film, architecture, and VR.The system understands 3D geometry and spatial relations, building complete environments beyond the image's visible area, maintaining style consistency unlike typical AI.Accessible via web browsers, users control a dynamic camera for exploration and can apply cinematic effects like depth of field and dolly zoom, enriching content creation.This AI creates true 3D scenes, ensuring viewpoint consistency and adhering to physical laws for realistic visuals, unlike pixel-based AIs.Users can dynamically modify lighting, geometry, and add objects, offering unprecedented creative control and enabling interactive effects within the 3D environment.
Quote from: hamdani yusuf on 29/11/2024 06:45:07How do you define bad people?Those whose actions harm others.
Oh boy. o1 pro mode out on the same night as o1 full. I read the 49 page paper, ran my own tests, spent my fuel allowance on Pro Mode and will give you all the highlights. Suffice to say the story is not as simple as it first appears. Weights and Biases? Weave: wandb.me/ai_explainedPlus, GPT-4.5? MLE Bench, Simple Update, Image Analysis and much more Chapters:00:00 - Introduction00:27 - ChatGPT Pro is $20001:25 - OpenAI Benchmarks03:20 - o1 System Card, o1 and o1 Pro Mode vs o1-preview06:18 - Simple Bench surprising results on sample08:31 - Weight & Biases09:05 - Image Analysis Compared12:51 - More Benchmarks and Safety
Meta's Llama 3.3 is a groundbreaking AI model with just 70 billion parameters, delivering near top-tier performance at a fraction of the size and cost of its predecessor. Optimized for efficiency, it supports multilingual capabilities, extended context windows, and developer-friendly integration, making it ideal for AI tools and applications. By combining open-source accessibility with cutting-edge technology, Llama 3.3 is poised to reshape AI development and adoption across industries.🔍 Key Topics Covered: Meta?s Llama 3.3 and its groundbreaking efficiency in AI model performance The leap from 405B to 70B parameters without sacrificing quality How Meta is leveraging AI and VR to shape the future of digital interaction 🎥 What You?ll Learn: Why Llama 3.3 is redefining AI efficiency with unmatched cost and GPU savings The role of multilingual capabilities, extended context windows, and open-source innovation in Llama 3.3 How Meta?s AI and VR advancements are building the foundation for next-gen technology 📊 Why This Matters: This video explores Meta?s Llama 3.3 and its potential to revolutionize AI applications, highlighting its impact on cost efficiency, digital connectivity, and the evolving competition in AI innovation.
My name is Artem, I'm a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience).In this video, we explore how the internal dynamics of neurons give rise to their remarkable computational properties. Through geometric reasoning about phase portraits and bifurcations, we'll gain intuition behind various phenomena, such as excitability, bistability, hysteresis and resonant oscillations.Outline:00:00 Introduction01:26 Review of Hodgkin-Huxley equations02:18 Deriving a 2-variable model04:34 Phase Plane concepts08:04 Excitability12:14 Bistability and hysterisis14:09 Saddle-Node Bifurcations16:17 Andronov-Hopf Bifurcations21:03 Integrators vs Resonators22:26 Putting all together25:15 Brilliant.org26:17 OutroReferences:Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting by Eugene M. Izhikevich
Scientific literature is growing rapidly, meaning scientists are increasingly unable to keep up with all of the latest developments in research. AI large language models, though, can read and ?digest? information much more quickly than their human counterparts, making them the perfect tools to conduct massive literature reviews. Recent research shows they?re also very accurate at predicting the results of studies that they?ve never read before. Let?s take a look.
He is right, data will finish. But it's not yet over, not even close enough.There is so much work that can improve and increase data1. Data quality (mislabels) missing data, wrongly tracked data2. COT data, teach how to thing3. Multi-modal training data4. Artificially generated data5. Data between AI tools, or AI to human interaction training.Bonus: We need to teach LLMs WHY to thing each thing
We?ve exhausted static training data, yes, but here are things to think about?1. Static training data is all the data we?ve bothered to write down, and can be fed to AIs as training data.2. This does not even scratch the surface of what we know as humans. It is only what we?ve been able to form in to words. For example, the taste of honey, we know this taste, but capturing it in words is futile.3. The training of agents will involve data in the form of behavior. AIs observing our behavior as a form of training. This is how humans train other humans, via observation.4.Synthetic data will be created from existing data, and will not radically expand the AI knowledge base, it will only sharpen what is there.
IoT (including IoB) is a real-world data partner for LLMs. More sensors means a never ending fresh data stream. Useful data partnership for running a real world digital twin (perhaps rendered using Unreal), with agents for everyone.
Fields Medal-winning mathematician Terence Tao makes his second appearance in the OpenAI Forum alongside OpenAI?s SVP of Research, Mark Chen to explore a future where mathematics and artificial intelligence converge to unlock groundbreaking scientific advancements. The conversation, facilitated by James Donovan, Science Policy & Partnerships Lead at OpenAI, will also explore the potential these advancements hold to impact society in positive and transformative ways.
Timestamps (Powered by Merlin AI)00:09 - Discussion featuring renowned mathematicians and AI experts on future developments.02:29 - Exploring innovative mathematics and reasoning methods for enhanced collaboration and discovery.06:28 - Mathematics collaboration can now leverage AI and diverse skill sets.08:30 - Collaborative approaches in mathematics are evolving with formal verification methods.12:27 - Humans have a better intuition for math, but AI can assist in verification and generating counterexamples.14:28 - Iterative techniques help in mathematical proofs but have limitations in complexity.18:34 - AI will enhance but not replace traditional mathematical reasoning.20:31 - Emerging abstractions in math could reshape education and research methodologies.24:27 - AI will transform math learning by enhancing efficiency and understanding.26:14 - Human understanding and aesthetics in mathematics are crucial for future developments.29:52 - Math's formal verification could revolutionize scientific progress through automation.31:50 - Mathematics will evolve with AI collaboration while preserving traditional practices.35:35 - The impact of AI on accessibility and collaboration in mathematics.37:21 - AI could democratize math, enhancing accessibility for scientific applications.41:06 - Math now requires broad collaboration and AI proficiency for future success.42:56 - AI tools will significantly enhance efficiency in mathematics.46:46 - Integrating AI models for diverse reasoning types can revolutionize problem-solving.48:29 - AI complements human problem-solving in math but struggles with data-scarce reasoning.52:19 - Collaboration and infrastructure are key for advancing mathematics technologies.54:14 - The future of mathematics includes creating a unified database of theorems.58:07 - Mathematics reveals surprises that challenge current understanding and model limitations.1:00:15 - Future AI models will improve precision and reference sourcing for drug discovery.1:04:00 - AI models will increasingly handle complex reasoning tasks autonomously.1:06:03 - AI is reshaping the relationship between mathematics and applied sciences.1:10:13 - Integrating AI models can enhance reasoning and creativity in mathematical proofs.1:12:09 - Diverse approaches can enhance problem-solving in AI, despite challenges in management.1:16:29 - Empirical studies are crucial for advancing AI architectures in mathematical reasoning.1:18:25 - Large collaborative projects will shape the future of math.1:22:23 - AI can transform science and math, improving regulatory processes.1:24:30 - Community engagement and networking opportunities are being enhanced.
The future is here! O3 announces a groundbreaking milestone: achieving AGI!
00:00 AGI milestone announcement00:36 Arc benchmark explained01:46 Visual examples03:21 Benchmark performance04:25 Expert reactions05:55 Earlier predictions06:57 Compute limitations07:54 Model iterations09:15 Math performance10:39 Future outlook11:54 Final thoughts
OpenAI?s o3 model is generating excitement with its anticipated advancements in AI reasoning and decision-making, building on the strong foundation of o1. Skipping ?o2? to avoid trademark conflicts, OpenAI aims to deliver smarter and more thoughtful responses, setting the stage for a significant leap in AI capabilities. The ongoing competition with Google?s Gemini 2 highlights the rapid evolution of AI technology, pushing innovation in accessibility, reasoning, and multimodal tools.Key Topics: OpenAI?s o3 model and its leap in reasoning and problem-solving capabilities Why OpenAI skipped ?o2? and the strategic decisions shaping o3?s release The competition between OpenAI and Google?s Gemini 2 in advancing AI innovation 🎥 What You?ll Learn: How OpenAI?s o3 model could redefine AI?s role in complex decision-making The strategic updates in o3 aimed at improving reasoning and contextual understanding Why OpenAI?s 12 Days of AI event is setting new benchmarks for the industry Why It Matters: This video explores the anticipated impact of OpenAI?s o3 model, its role in enhancing AI accessibility, and its potential to outpace competitors like Google?s Gemini 2 in the rapidly evolving AI landscape. DISCLAIMER: This video highlights the latest developments in AI technology and their implications for innovation and accessibility in the tech industry.
o3 isn?t one of the biggest developments in AI for 2+ years because it beats a particular benchmark. It is so because it demonstrates a reusable technique through which almost any benchmark could fall, and at short notice. I?ll cover all the highlights, benchmarks broken, and what comes next. Plus, the costs OpenAI didn?t want us to know, Genesis, ARC-AGI 2, Gemini-Thinking, and much more. 00:00 - Introduction01:19 - What is o3?03:18 - FrontierMath05:15 - o4, o506:03 - GPQA06:24 - Coding, Codeforces + SWE-verified, AlphaCode 208:13 - 1st Caveat09:03 - Compositionality?10:16 - SimpleBench?13:11 - ARC-AGI, Chollet20:25 - Safety Implicaitons
Microsoft CEO Satya Nadella discusses the future of technology, predicting the decline of traditional apps and the rise of AI-powered experiences.
In our final episode for the year, we explore Project Astra, a research prototype exploring future capabilities of a universal AI assistant that can understand the world around you. Host Hannah Fry is joined by Greg Wayne, Director in Research at Google DeepMind. They discuss the inspiration behind the research prototype, its current strengths and limitations, as well as potential future use cases. Hannah even gets the chance to put Project Astra's multilingual skills to the test.Timecodes00:00 Intro to Project Astra03:00 Hannah demo07:00 Hardware and what's under the hood16:56 Languages23:00 Inspiration for Project Astra 33:55 Latency and memory 46:00 What's next47:00 Hannah's thoughts
Genesis AI is an open-source platform revolutionizing robotics and AI with ultra-fast simulations, running up to 43 million frames per second. It simplifies robot training, reduces development time, and creates realistic 3D environments using natural language prompts. With its unmatched speed, accessibility, and generative features, Genesis AI sets a new standard for robotics research and simulation technology.🔍 Key Topics: Genesis AI's revolutionary simulation speed and cutting-edge generative capabilities How Genesis AI is transforming robotics with accessible tools and real-world applications The groundbreaking advancements that make Genesis faster and more efficient than competitors 🎥 What You?ll Learn: Why Genesis AI is a game-changer for robotics research and development The innovative technology behind its speed, scalability, and photorealistic simulations How Genesis AI is democratizing high-quality simulations for researchers and developers 📊 Why It Matters: This video delves into how Genesis AI is reshaping the field of robotics with unparalleled simulation speeds, generative features, and accessibility, setting a new standard for innovation in AI and automation.