Matt White

🔥 Build advanced AI agents - LangGraph & Cerebras tutorial for Beginners 🔥 Check out our new YouTube tutorial on freeCodeCamp, created in partnership with Cerebras Systems. In this session, our own Lance Martin walks you through building multi-agent workflows using LangGraph and Cerebras. This tutorial is great for beginners who want to level up quickly. Watch the tutorial and get building ➡️ https://lnkd.in/gYyBuMWs

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gLSTM extends xLSTM to a graph neural network architecture: https://lnkd.in/da3at-Y9 "gLSTM mitigates sensitivity over-squashing and capacity over-squashing." "gLSTM achieves comfortably state of the art results on the Diameter and Eccentricity Graph Property Prediction tasks, and very strong performance on SSSP"

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It was Databricks that first made Ion Stoica (and at least two of his Berkeley cofounders) billionaires. In 2013, Stoica set out to discover how to process huge amounts of data more effectively with Ali Ghodsi, a visiting scholar from the KTH Royal Institute of Technology in Stockholm, and five PhD students. Together, they developed Spark, a piece of predictive software that serves as a powerful data-processing tool. Stoica wanted to turn Spark into a startup to encourage users to take the lab’s research more seriously, according to Matei Zaharia, one of the PhD students who cofounded Databricks and is now an associate professor at Berkeley. Plus, he wanted to help smaller companies without sophisticated infrastructure manage and analyze massive amounts of data for both business insights and building AI tools. The company projected that annualized revenue would hit $3.7 billion by this July, and has been rumored to be eyeing the public market for years. FULL STORY: https://trib.al/CTVoZ30 (Photo: Timothy Archibald for Forbes)

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Understanding the qubit is fundamental to quantum computing. Charina Chou, COO of Quantum AI, explains how superposition allows a qubit to exist in a linear combination of 0 and 1 simultaneously. This key difference from classical bits enables potential computational advantages → https://goo.gle/3H8H14N

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One of the biggest challenges in radiological AI research is the lack of large datasets to train AI models, Akshay Chaudhari explains. "If we have small data sets, how can we still build high-quality AI models?" he asks. The RoentGen open AI model, developed by AIMI Center Associate Director Dr. Akshay Chaudhari, AIMI Center Director Curtis Langlotz, AIMI faculty Sergios Gatidis and colleagues, seeks to address this by producing realistic synthetic X-rays from medical descriptions. "The purpose of RoentGen would be to produce additional data that would be used to train an AI software tool to provide additional accuracy for radiologists who are working in the clinic, helping them identify disease sooner and identify disease that they might miss, and then extend it to other areas like CTs and MRIs and ultrasounds," Dr. Langlotz says. The model could also help fill other critical gaps. "RoentGen could be used to reduce bias, and we implement algorithms by creating synthetic data for some subgroups where we don't have enough training data," Dr. Langlotz continues. "That means better privacy for patients. It means more accurate AI models and it means more responsible implementation of AI algorithms." See more in this video from Stanford University School of Medicine spotlighting the team's development of RoentGen: https://bit.ly/467QGBi Read the article: https://stan.md/4fT6Tyy #StanfordAIMI #AIMICenter #RoentGen #AIinMedicine

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Examining how managers should deploy AI in sequential team workflows finds that optimal AI deployment is stochastic, replaces early and late positions in the workflow, and results in lower wage inequality, from Xienan Cheng, Mustafa Dogan, and Pinar Yildirim https://lnkd.in/ewSrbPmH

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Automate detection of unreliable LLM outputs by combining MLflow tracing with Cleanlab’s Trustworthy Language Models (TLM). 🚀 This blog post covers: ✅ Setting up MLflow to capture complete LLM interactions, including system prompts. ✅ Retrieving traces and efficiently batch-evaluating them with TLM to get trustworthiness scores and explanations—without ground truth labels. ✅ Tagging each MLflow trace with its trust score and explanation for ongoing review. ✅ Using MLflow Evaluation with a custom TLM metric to systematically compare model outputs. For the full guide and code samples, read more ➡️ https://lnkd.in/eHNWQrrK cc Chris Mauck, Data Scientist at Cleanlab #opensource #llm #oss #mlflow #cleanlab

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