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Replit has its own coding assistant that runs across its products. It is quite powerful, trained with Mosaic, and free for all user tiers.
Google Cloud introduced AI-enhanced search tools to streamline access to diverse medical records for healthcare professionals. While the technology aims to reduce administrative work, its integration is undertaken with caution to fit seamlessly into existing workflows and ensure HIPAA compliance.
OpenAI's Residency is an initiative to empower exceptional researchers and engineers from diverse fields to transition into the world of AI and machine learning. It is especially beneficial for researchers specializing in fields outside of deep learning and welcomes software engineers seeking to pivot into AI research roles. Residents will tackle real AI challenges while receiving a full salary.
LLMs have been trained on massive amounts of data, much of which is copyright protected. Removing or unlearning such data is a challenge. This work uses a proxy model as a teacher to simulate a clean model and further fine-tunes the base model, which reduces knowledge of undesirable information while maintaining benchmark performance.
Researchers present a solution, called Multi-Head Self-Attention Convolution (MSA-Conv), which allows Vision Transformers (ViTs) to handle images of different sizes without needing changes in their design or the need to resize images.
This study introduces a novel Transformer model that is tailored to blind super-resolution, a task that deals with images with unknown noise. The method outshines current approaches through its blend of traditional CNNs and a new Transformer design attuned to degradation patterns.
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Attention is useful but computationally expensive. However, once trained, with a bit of fine-tuning compute you can drop in SRF attention and remove the dependence on sequence length, speeding things up considerably.
A Pythonic framework to simplify AI service building.
This study delves into how different architectural elements in machine learning models affect their performance in federated learning, especially when handling varied data sources. Through in-depth analysis, researchers found that with certain tweaks, Convolutional Neural Networks (CNNs) can perform as well or even better than Vision Transformers in such settings.
There is a heavy shortage of GPUs for the current AI demand. Many services have long queue times or are restricting access as a result. If other chip makers can overcome some of their software deficiencies, maybe via recent Pytorch improvements, they may be able to compete for some of this demand.
AI is often misunderstood as a one-size-fits-all solution, but businesses need to recognize that it's merely a tool, and not every business requires it. Identifying genuine problems and needs should precede technology adoption, rather than trying to retrofit AI solutions where they may not be needed or beneficial.
Great read on the tricky parts of creating value around generative AI for startups. While the incumbents are favored, finding your path and creating real value early is where startups can have a fighting chance.
ChatGPT's mobile apps saw a record 15.6 million downloads and $4.6 million in September, but growth is starting to slow down.
Meta is paying creators up to $5 million for just six hours of studio time to use their likeness.