X.ai Announces Grok 1.5: A Look at the Improved Reasoning and Long Context Capabilities
X.ai has announced the release of Grok-1.5, an advanced version of the Grok-1 AI model with improved reasoning and a context length of 128,000 tokens. Here’s a quick breakdown of…
SambaNova Systems Sets New Artificial Intelligence AI Efficiency Record with Samba-CoE v0.2 and Upcoming Samba-CoE v0.3: Beating Databricks DBRX
In the rapidly evolving landscape of artificial intelligence, a new milestone has been achieved by AI chip-maker SambaNova Systems with its groundbreaking Samba-CoE v0.2 Large Language Model (LLM). This model…
Efficiency Breakthroughs in LLMs: Combining Quantization, LoRA, and Pruning for Scaled-down Inference and Pre-training
In recent years, LLMs have transitioned from research tools to practical applications, largely due to their increased scale during training. However, as most of their computational resources are consumed during…
FedFixer: A Machine Learning Algorithm with the Dual Model Structure to Mitigate the Impact of Heterogeneous Noisy Label Samples in Federated Learning
In today’s world, where data is distributed across various locations and privacy is paramount, Federated Learning (FL) has emerged as a game-changing solution. It enables multiple parties to train machine…
Researchers at the University of Maryland Propose a Unified Machine Learning Framework for Continual Learning (CL)
Continual Learning (CL) is a method that focuses on gaining knowledge from dynamically changing data distributions. This technique mimics real-world scenarios and helps improve the performance of a model as…
This AI Paper Explores the Impact of Model Compression on Subgroup Robustness in BERT Language Models
The significant computational demands of large language models (LLMs) have hindered their adoption across various sectors. This hindrance has shifted attention towards compression techniques designed to reduce the model size…
OpenAI Enhances Language Models with Fill-in-the-Middle Training: A Path to Advanced Infilling Capabilities
Transformer-based language models, like BERT and T5, are adept at various tasks but struggle with infilling—generating text within a specific location while considering both preceding and succeeding contexts. Though encoder-decoder…
AI21 Labs Breaks New Ground with ‘Jamba’: The Pioneering Hybrid SSM-Transformer Large Language Model
In an era where the demand for smarter, faster, and more efficient artificial intelligence (AI) solutions is continuously on the rise, AI21 Labs’ unveiling of Jamba marks a significant leap…
Do LLM Agents Have Regret? This Machine Learning Research from MIT and the University of Maryland Presents a Case Study on Online Learning and Games
Large Language Models (LLMs) have been increasingly employed for (interactive) decision-making through the model development of LLM-based agents. LLMs have shown remarkable successes in embodied AI, natural science, and social…
AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks
Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about…