TheSequence - Amazing Dream Machine
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You can subscribe to The Sequence below:📝 Editorial: Amazing Dream MachineVideo is highly regarded as one of the next frontiers for generative AI. Months ago, OpenAI dazzled the world with its minute-long videos, while startups such as Runway and PikaLabs are being widely adopted across different industries. The challenges with video generation are many, ranging from physics understanding to the lack of high-quality training datasets. This week, we had a surprising new entrant in the generative video space. A few days ago, Luma AI unveiled Dream Machine, a text-to-video generation model that produces high-quality, physically accurate short videos. Luma claims that Dream Machine is based on a transformer architecture trained directly on videos and attributes this approach to the high quality of the outputs. Beyond the high quality, the most impressive aspect of Dream Machine is that it is freely available. This contrasts with other text-to-video models such as OpenAI’s Sora and Kuaishou’s Kling, which remain available only to a select group of partners. The distribution model is particularly impressive when we consider that video generation requires very high computational costs. This reminded me of the Stable Diffusion release at a time when most text-to-image models were still closed source. The generative video space is in its very early stages, and my prediction is that most of the players will be acquired by the largest generative AI providers. Mistral, Anthropic, and Cohere have all raised tremendous sums of venture capital but remain constrained to language models. In order to compete with OpenAI, the acquisition of the early innovative startups working on generative video makes a lot of sense. For now, you should definitely try Dream Machine. It makes you dream of the possibilities. 📽 Recommended webinarDo you still rely on 'vibe checks' or ChatGPT for your GenAI evaluations? See the future of instant, accurate, low-cost evals in our upcoming webinar, including:
🔎 ML ResearchLiveBenchResearchers from Abacus.AI , NVIDIA, UMD, USC and NYU published LiveBench, a new multidimensional benchmark that addresses LLM benchmark memorization. LiveBench benchmark changes regularly with new questions and objective evaluations —> Read more. HuskyResearchers from Meta AI, Allen AI and University of Washington published a paper introducing Husky, an LLM agent for multi-step reasoning. Husky is able to reason over a given action space to perform tasks that include different datasets such as numerical, tabular or knowledge-based —> Read more. Mixture of AgentsResearchers from Together.ai, Duke and Stanford University published a paper introducing mixture-of-agents(MoA), an architecture that combines different LLMs for a given task. Some of the initial results showed MoA outperforming GPT-4o across different benchmarks —> Read more. Test of TimeResearchers from Google DeepMind published a paper introducing Test of Time, a benchmark for evaluating LLMs on temporal reasoning capabilities in scenarios that require temporal logic. The benchmarks use synthetic datasets to avoid previous memorization that could produce misleading results —> Read more. TextGradResearchers from Stanford University published a paper unveiling TextGrad, an automatic differentiation technique for multi-LLM systems. TextGrad works similar to backpropagation in neural networks distributing textual feedback from LLMs to improve individual components of the system —> Read more. Transformers and Neural Algorithmic ReasonersResearchers from Google DeepMind published a paper proposing a technique that combine transformers with GNN-based neural algorithmic reasoners. The results are models that could be more effective at reasoning tasks —> Read more. 🤖 Cool AI Tech ReleasesDream MachineLuma Labs release Dream Machine, a text-to-video model with impressive high quality —> Read more. Stable Diffusion 3 MediumStability AI open sourced Stable Diffusion 3 Medium, a 2 billion parameter text-to-image model —> Read more. Unity CatalogDatabricks open sourced its Unity Catalog for datasets and AI models —> Read more. 🛠 Real World AIRed Teaming at AnthropicAnthropic shares practical advice learned from red teaming its Claude models —> Read more. Serverless Notebooks at MetaMeta engineering discusses how they use Bento and Pyodide to enable serverless interaction of Jupyter notebooks —> Read more. 📡AI Radar
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Edge 404: Learn About Meta AI's Promising Technique to Predict Multiple Tokens at the Same Time in LLMs
Thursday, June 13, 2024
The mehod addresses the limitations of the classic next token prediction method. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 403: Memory-Based Planning and Autonomous Agents
Tuesday, June 11, 2024
Supplying agents with an external memory to execute more complex plans. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore
Sunday, June 9, 2024
Two major open source datasets were released this week. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 402: UC Berkeley's Large World Model Can Understand Really Long Videos
Thursday, June 6, 2024
One of the most impressive research in generative video of the last year. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 401: Reflection and Refinement Planning Methods in Autonomous Agents
Tuesday, June 4, 2024
Can LLM agents handle planning errorts effectively? ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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