The AI Canvas Newsletter #4
Explore AI's latest: OpenAI's versatile robotics learning, Microsoft multimodal GPT-4V model, and the expansive capabilities of LLAMA 2 Long...
The AI Canvas Newsletter #4
The AI Canvas: Your weekly palette of inspiration, insights, and innovation in the world of AI.
- 🤖 Embracing Cross-Embodiment Learning: Open X-Embodiment dataset and RT-X model
- 🔍The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
- 🧠Unleashing the Power of LLAMA 2 Long
Written by Oli Wilkins.
A Unified Frontier: Advancing Robotics Through Cross-Embodiment Learning
In a collaborative effort involving 33 academic labs, the Open X-Embodiment dataset and RT-X model have been unveiled. These resources bridge the gap between robot specialisations, enabling the training of general-purpose robots across various types and environments. This initiative promises a 50% boost in success rates for different robots and showcases significant advancements in cross-embodiment robotics research. Explore how this open-source approach is transforming the future of robotics and fostering collaboration within the scientific community.
Read more with Google DeepMind’s blog here and paper here.
The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
In Microsoft’s fascinating new paper, the capabilities of GPT-4V(ision), a cutting-edge multimodal model are explored. Their exploration showcases its remarkable ability to process a wide range of multimodal inputs, combining text and images seamlessly. Potential applications are discussed and hint at the future possibilities for even more powerful multimodal models, emphasizing the need for continuous self-evolution and incorporating additional modalities beyond text and images.
Read the paper here.
Unlocking the Power of Long-Context Language Models with LLAMA 2 Long
Delve into the world of long-context language models (LLMs) with LLAMA 2 Long, a series of models capable of handling context windows up to 32,768 tokens. Through continual pretraining and position encoding refinements, these models outperform their predecessors and even challenge gpt-3.5-turbo-16k's performance on long-context tasks, all without the need for human-annotated data. The comprehensive analysis sheds light on the key factors contributing to their success, making long-context LLMs more accessible for future advancements in the field.
Read more at _akhaliq’s Twitter post and the paper here.
Hey, Computer, Make Me a Font - Sergey Tselovalnikov
“This is a story of my journey learning to build generative ML models from scratch and teaching a computer to create fonts in the process.”
Deep Learning Systems Course - Carnegie Mellon University
Carnegie Mellon University have released the course material to their Deep Learning Systems course. Lectures range from the basics all the way up to generative models.
Causality for Machine Learning - Cloudera Fast Forward Labs Research
As machine learning continues to reshape industries and decision-making processes, it's crucial to move beyond pure prediction and embrace causality. This report delves into the intersection of causal inference and machine learning, explaining the importance of causal reasoning and its application in building more robust, adaptable, and fair machine learning systems.
LLMs are Interpretable - Tim Kellogg
“This might be a hot take but I truly believe it: LLMs are the most interpretable form of machine learning that’s come into broad usage.”
The Artificiality of Alignment - Jessica Dai
“Retrieval systems aim to identify and return the most relevant information for a given query from a large corpus of data. This could involve searching a database of documents, finding pertinent passages from a collection of texts, or retrieving the most useful knowledge from a vast knowledge graph. The challenge is doing this quickly, accurately, and at scale.”
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