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Microsoft’s TAG-LLM: An AI Weapon for Decoding Complex Protein Structures and Chemical Compounds!

Marktechpost

The seamless integration of Large Language Models (LLMs) into the fabric of specialized scientific research represents a pivotal shift in the landscape of computational biology, chemistry, and beyond. Addressing this challenge, a groundbreaking framework developed at Microsoft Research, TAG-LLM, emerges.

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Do Language Models Know When They Are Hallucinating? This AI Research from Microsoft and Columbia University Explores Detecting Hallucinations with the Creation of Probes

Marktechpost

A dataset with more than 15,000 utterances has been produced that have been tagged for hallucinations in both natural and artificial output texts. The main focus has been on using the model’s internal representations for the detection and a dataset with annotations for both synthetic and biological hallucinations.

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Improving Neural Networks with Neuroscience

Mlearning.ai

Dr. Lambos and I had a very extensive conversation about how biological needs influence intelligence, misconceptions about our brain’s biology that have led to worse ANN design, and how we can use the recent developments in neuroscience to improve neural networks. The following article has been written by Dr. William Lambos. Lambos , M.S.,

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Meta’s Next-Generation Image Segmentation: Paving the Way for Advanced Applications

Mlearning.ai

Output using Meta’s SAM for tagging boxes Quality Management in manufacturing Image Segmentation can be used for quality management in manufacturing by identifying defective items. Additionally, SAM can be used to identify misplaced products and help workers locate specific items more quickly, reducing the time spent searching for products.

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Application of Large Language Models in Biotechnology and Pharmaceutical Research

Marktechpost

ProGen was trained on 280M protein sequences from more than 19,000 families, and the model is augmented with control tags specifying the property of the protein. ProGen can be fine-tuned to create more accurate protein sequences using specific sequences and tags.

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Building better datasets with Snorkel Flow error analysis

Snorkel AI

Streamlined tagging workflows. Improved tagging analysis. Auto-generated tag-based LFs. I see that the model confuses software engineering managers with software engineers, and biologists with biology teachers. We use the search functionality to find similar examples and use the filter-to-tag option to tag them.

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Building better datasets with Snorkel Flow error analysis

Snorkel AI

Streamlined tagging workflows. Improved tagging analysis. Auto-generated tag-based LFs. I see that the model confuses software engineering managers with software engineers, and biologists with biology teachers. We use the search functionality to find similar examples and use the filter-to-tag option to tag them.