Contemporary large language models process diverse data similarly to the human brain's semantic hub, MIT researchers find. Insights could lead to improved future models for handling various languages and tasks.
Honesty in probabilistic predictions is key to avoiding biased forecasts. Linear scoring rules can incentivize dishonesty, leading to poorly calibrated machine forecasts. David Spiegelhalter's book highlights the importance of penalizing confident but wrong convictions for unbiased assessments.
Elon Musk's xAI unveils Grok-3 chatbot to rival DeepSeek, OpenAI, and Google Gemini in AI arms race. Musk's 'maximally truth-seeking' bot aims to compete with industry giants amid widespread adoption challenges.
Formula 1® (F1) partners with Amazon Web Services (AWS) to develop AI-driven solution for faster issue resolution during live races, reducing triage time by up to 86%. The purpose-built root cause analysis (RCA) assistant empowers engineers to troubleshoot and resolve critical issues within 3 days, enhancing operational efficiency.
Kaya Scodelario shines in Beau Willimon's AI-themed play at Hampstead theatre, London. Despite the gripping plot, Ellen McDougall's production lacks tension and falls flat.
Summary: Learn how Large Language Models (LLMs) are built and trained, demystifying the process. Explore pre-training, tokenization, and neural network training in GPT4.
Learn how to use AI prompts and LLMs to perform semantic clustering of user forum messages faster and with less effort. Inspired by Clio, this tutorial uses publicly available Discord messages to analyze tech help conversations.
Cycling safety is a growing concern due to dangerous encounters with vehicles. A machine learning solution using Amazon Rekognition helps cyclists identify close calls and promote road safety.
Poisson regression predicts numeric values for count data using specialized techniques and mathematical assumptions. A demo using C# generated synthetic Poisson data and achieved high accuracy with a single constant and coefficients.
Tech giants like Microsoft, Alphabet, Amazon, and Meta are heavily investing in AI, reminiscent of 'plastics' in The Graduate. The pursuit of human-level intelligence is questioned for more practical achievements.
Binary classification problems can be tricky to interpret due to ambiguity in the confusion matrix, where definitions of TP, TN, FP, and FN can vary. Understanding these terms is crucial for accurate analysis. Be cautious when interpreting confusion matrices to avoid confusion in machine learning outcomes.
Experts are divided on future tech threats vs present dangers. Maria Ressa warns of big tech's negative impacts on society.
Share your AI job impact experiences to explore the current and future effects of technology on work. Contribute to understanding AI's positive, negative, or mixed influence on job roles.
Data science advancements like Transformer, ChatGPT, and RAG are reshaping tech. Understanding NLP evolution is key for aspiring data scientists.
Causal reasoning can unveil relationships in data, avoiding misinterpretation. Understanding the story behind the data is crucial for better analyses.