Reducing the cost of LLMs with quantization and efficient fine-tuning: how can businesses benefit from Generative AI with limited hardware?

Researchers from MIT and IAIFI have developed a framework called Feature Fields for Robotic Manipulation (F3RM), which addresses the challenge of enabling robots to manipulate objects in cluttered environments. F3RM leverages distilled feature fields to combine…

On day two of the AI Safety Summit, UK Prime Minister Rishi Sunak announced that industry leaders such as Meta, Google Deep Mind, and OpenAI have agreed to allow government evaluation of their AI tools before…

Together.ai has released RedPajama-V2, a dataset with 30 trillion tokens that can be used for training large language models (LLMs). RedPajama-1T, a 5TB dataset, was released earlier this year. The researchers believe that RedPajama-V2 will provide…

Cognitive science studies suggest typicality is vital for category knowledge, affecting human judgment. Machine learning methods offer assurance in predictions, but considering atypicality alongside confidence improves accuracy and uncertainty quantification. Recalibration techniques with atypicality-aware measures elevate…

Researchers at Cornell University have developed HiQA, an advanced framework for multi-document question-answering (MDQA). Traditional QA systems struggle with indistinguishable documents, impacting precision and relevance of responses. HiQA uses a novel soft partitioning approach and a…

Amazon’s AI chatbot, Amazon Q, has allegedly leaked sensitive internal information including AWS data centers and unreleased features. While Amazon denies security breaches, internal Slack communications show employee concerns. This leak is unconfirmed but follows past…

Yann LeCun, Meta AI’s chief and deep learning pioneer, has expressed skepticism about the near-term development of artificial general intelligence (AGI) and quantum computing’s role in AI. He contrasts industry leaders by downplaying imminent AGI breakthroughs…

David AI: The Data Marketplace for AI Improving AI is complicated by data, as the amount of training data required for each new model release has increased significantly. This burden is further worsened by the growing…

Practical AI Framework for Large-Scale LLM Agent Systems Revolutionizing Agent Cooperation Large Language Models (LLMs) have evolved into powerful tools for complex planning and cognitive tasks, paving the way for LLM-powered multi-agent systems (LLM-MA systems). These…

In the context of voice assistant systems, steering is when users issue follow-up commands to direct or clarify previous turns. We propose STEER, a model that detects whether a follow-up turn is the user attempting to…

Practical Solutions and Value in Drone Detection and Classification Techniques Introduction In recent years, advancements in micro uncrewed aerial vehicles (UAVs) and drones have expanded applications and technical capabilities. Comparison of Satellite, Aircraft and UAV UAVs…

The OECD has updated its definition of AI, which is expected to be included in the European Union’s AI Act. The new definition recognizes AI systems that can have emergent goals beyond their original objectives and…

A UC Berkeley research team has developed a novel LM pipeline, a retrieval-augmented language model system designed to improve forecasting accuracy. The system utilizes web-scale data and rapid parsing capabilities of language models, achieving a Brier…

Claude 3.5 Sonnet: Unveiling the Future of Artificial Intelligence AI with Revolutionary Capabilities N-Body Particle Animation: Unleashing Complex Simulations Claude 3.5 Sonnet can swiftly generate intricate n-body particle animations and simulate complex systems involving phenomena like…

Advancements in AI Debugging Tools: Microsoft’s Debug-Gym Advancements in AI Debugging Tools: Microsoft’s Debug-Gym The Challenges of Debugging in AI Coding Tools Despite notable advancements in code generation, AI coding tools still encounter significant challenges when…