Originally published on Towards AI.
One of the major challenges in using LLMs in business is that LLMs hallucinate. How can you entrust your clients to a chatbot that can go mad and tell them something inappropriate at any moment? Or how can you trust your corporate AI assistant if it makes things up randomly?
That’s a problem, especially given that an LLM can’t be fired or held accountable.
PlantRNA-FM, an AI model trained on RNA data from over 1,100 plants, decodes genetic patterns to advance plant science, improve crops, and tackle global agricultural challenges.
A groundbreaking Artificial Intelligence (AI) model designed to decode the sequences and structural patterns that form the genetic “language” of plants has been launched by a research collaboration.
Named Plant RNA-FM, this innovative model is the first of its kind and was developed by a partnership between plant researchers at the John Innes Centre and computer scientists at the University of Exeter.
Built to full scale, it mimics our anatomy in an incredibly precise way. But it is not yet fully functional.
A muscuskeletal designed for the home.
DeepMind’s Vice President of Drastic Research and Gemini co-tech lead Oriol Vinyals describes how artificial intelligence is moving from narrowly focused systems toward autonomous agents, and what challenges remain ahead.
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According to Vinyals, AI is going through a fundamental transformation away from highly specialized systems and toward autonomous agents. Speaking on a company podcast, he explained that early AI systems like AlphaStar, which focused on playing StarCraft, were just the beginning of this development.
Learn about the benefits of robotic surgery, how surgeons perform it and what surgeries you can get done with a da Vinci robot.
Futurism, transhumanism, bioethics, ethics, science, philosophy, artificial intelligence, personhood.
The technique, called “universal transformer memory,” uses special neural networks to optimize LLMs to keep bits of information that matter and discard redundant details from their context.
I have liked kdnuggets for a while now. I used it for information on Tensorflow. This is cool though:
Neural networks are the building blocks behind every advanced AI system nowadays: from computer vision solutions to generative AI solutions and language models, most real-world solutions that involve some degree of AI have intricate neural network architectures at their core. But, what are neural networks and how do they perform surprisingly well in intelligently solving challenging tasks? To satisfy your curiosity at no cost, this post lists five resources to help you understand the mechanisms behind neural networks.
Here are five free resources in diverse formats and difficulty levels to acquaint with deep learning models at no cost.
Researchers at UCLA have developed a new AI model that can expertly analyze 3D medical images of diseases in a fraction of the time it would otherwise take a human clinical specialist.
The deep-learning framework, named SLIViT (SLice Integration by Vision Transformer), analyzes images from different imagery modalities, including retinal scans, ultrasound videos, CTs, MRIs, and others, identifying potential disease-risk biomarkers.
Dr. Eran Halperin, a computational medicine expert and professor at UCLA who led the study, said the model is highly accurate across a wide variety of diseases, outperforming many existing, disease-specific foundation models. It uses a novel pre-training and fine-tuning method that relies on large, accessible public data sets. As a result, Halperin believes that the model can be deployed—at relatively low costs—to identify different disease biomarkers, democratizing expert-level medical imaging analysis.
