Unveiling Challenges in Language Model Performance: A Study of Saturation and Representation Degeneration
Language Models (LMs) face challenges in self-supervised learning due to representation degeneration. LMs like BERT or GPT-2 LMs have low angular variability and outlier dimensions on a small scale, comprised…
MIT Researchers Use Deep Learning to Get a Better Picture of the Atmospheric Layer Closest to Earth’s Surface: Improving Weather and Drought Prediction
MIT researchers proposed working with deep learning to address the challenges of understanding and accurately modeling the planetary boundary layer (PBL) to improve weather forecasting and climate projections and deal…
6 Free Artificial Intelligence AI Courses from Google
The following six free AI courses offer a structured pathway for beginners to start their journey into the world of artificial intelligence. Each course is designed to introduce fundamental concepts…
‘Inheritune’ by UT Austin Assists Efficient Language Model Training: Leveraging Inheritance and Reduced Data for Comparable Performance
Scaling up LLMs presents significant challenges due to the immense computational resources needed and the need for high-quality datasets. Typically, the pre-training process involves utilizing models with billions of parameters…
Researchers at Stanford University Explore Direct Preference Optimization (DPO): A New Frontier in Machine Learning and Human Feedback
Exploring the synergy between reinforcement learning (RL) and large language models (LLMs) reveals a vibrant area of computational linguistics. These models, primarily enhanced through human feedback, demonstrate remarkable ability in…
3 Ways to Run Llama 3 on Your PC or Mac
Running Llama 3 locally on your PC or Mac has become more accessible thanks to various tools that leverage this powerful language model’s open-source capabilities. Below are three effective methods…
Formal Interaction Model (FIM): A Mathematics-based Machine Learning Model that Formalizes How AI and Users Shape One Another
Machine learning has become an important domain that has contributed to developing platforms and products that are data-driven, adaptive, and intelligent. The AI systems help to shape the users, and…
Understanding Causal AI: Bridging the Gap Between Correlation and Causation
Artificial Intelligence (AI) has traditionally been driven by statistical learning methods that excel in identifying patterns from large datasets. These methods, however, predominantly capture correlations rather than causations. This distinction…
This AI Paper from MLCommons AI Safety Working Group Introduces v0.5 of the Groundbreaking AI Safety Benchmark
MLCommons, a collaborative effort of industry and academia, focuses on enhancing AI safety, efficiency, and accountability through rigorous measurement standards like MLPerf. Its AI Safety Working Group, established in late…
Researchers at CMU Introduce TriForce: A Hierarchical Speculative Decoding AI System that is Scalable to Long Sequence Generation
With the widespread deployment of large language models (LLMs) for long content generation, there’s a growing need for efficient long-sequence inference support. However, the key-value (KV) cache, crucial for avoiding…