Parse notes
The rise of LLaMA.cpp was surprising to many. Why can an ordinary MacBook run state-of-the-art models only a bit slower than a mighty A100? The answer is that when you're only doing a single batch, one token at a time, memory bandwidth is the bottleneck and the gap between the M2's memory bandwidth and the A100's is not that large.
Google is rolling out a few new updates to its Search Generative Experience, the company’s AI-powered Search. These updates include automatic term definitions, programming assistance, and AI-generated summaries.
IBM and Hugging Face are releasing IBM's geospatial foundation model, crafted using NASA satellite data, to the public. Built through a collaboration with NASA, this model aims to democratize AI's role in climate science and Earth studies, addressing the challenges of handling large environmental datasets. The model has already shown a 15% improvement in satellite data analysis and can be adapted for various tasks like tracking deforestation and monitoring greenhouse gasses.
Unifying the modeling of discrete or continuous data is a challenge. More so when attempting to maintain a probabilistic likelihood interpretation of the output. This new paper showcases a new class of generative models with a pile of interesting properties such as no forward pass, gradient-based sample guidance for discrete data, and competitive language modeling likelihood.
Most code models are instruction tuned on GPT-4 distillation data. This new work shows how to avoid that, introduces an extended human eval dataset, and gets 46.7 on the original human eval. While this is impressive, it showcases the extreme gap between closed and open code models.
Virtual try-on tasks, where clothes from one image are transferred to another, are challenging, especially at higher resolutions. Many methods use Generative Adversarial Networks. The solution presented in this paper, DCI-VTON, uses a diffusion model combined with an exemplar-based inpainting and a warping module. This combination ensures that clothing details are preserved and produces realistic virtual try-on images.
While the Segment Anything Model (SAM) excels in segmentation tasks across various datasets, it misses specific class details for masks. FoodSAM overcomes this. It combines SAM's strengths with new features to segment food images more effectively, even identifying individual food items and other objects in scenes.
OpenDream brings layering, non-destructive editing, portability, and easy-to-write extensions to your Stable Diffusion workflow.
This article outlines ten key design principles for effective AI developer tools, drawing from GitHub’s experience designing, launching, and extending Copilot. They encompass leveraging natural language, addressing developer pain points, gathering meaningful feedback, acknowledging biases, and prioritizing customization, accessibility, performance, and continuous improvement in AI models.
ChatGPT creator OpenAI has been using its most advanced large language model to enforce the company’s content policies.
More marketers and content producers are turning to AI for efficiency. Over time, this will lead to a level of genericism in the market. The antidote will be authentic human production.
Kneron, an AI edge company, announced that it will ship its new neural processing unit chips by the end of the year. The KL730 chips are specifically designed for machine learning and AI applications and they are said to be 3 to 4 times more energy efficient than previous chips.
Quick read on why AI is on track to disrupt. A little cheeky too.