We explore Chain-of-Tools (CoTools), a groundbreaking tool learning method for frozen Large Language Models (LLMs). CoTools enables these models to effectively utilize a massive pool of tools, even those they haven't seen before, during Chain-of-Thought (CoT) reasoning. We delve into how CoTools leverages the semantic representation capabilities of LLMs to determine when and which tools to call. Discover how this novel approach outperforms existing baselines in both numerical reasoning and knowledge-based question answering, especially in scenarios with many unseen tools.

While newer models like Claude 3.7 Sonnet is already available, our latest podcast episode delves into the still-valuable insights from Claude 3.5 Sonnet's performance on the challenging SWE-bench Verified benchmark, where it achieved an impressive 49%, surpassing the previous state-of-the-art. Tune in to understand why this result remains significant in the evolution of AI software engineering capabilities and to explore the crucial role of the "agent" system—the combination of the AI model and its software scaffolding—in achieving such scores.

This podcast episode delves into the "Transformers without Normalization" paper, which introduces Dynamic Tanh (DyT) as a potential replacement for normalization layers in Transformers. DyT, a simple operation defined as tanh(αx) with a learnable parameter, aims to replicate the effects of Layer Norm without calculating activation statistics. Could DyT offer similar or better performance and improved efficiency, challenging the necessity of normalization in modern neural networks?

In this podcast episode, we delve into Fin-R1, a groundbreaking large language model tailored for financial reasoning. Discover how this efficient 7 billion parameter model tackles critical financial industry challenges such as fragmented data, uncontrollable reasoning logic, and weak business generalization ability. We explore its innovative two-stage training framework, which includes the creation of the high-quality Fin-R1-Data dataset and the application of Supervised Fine-Tuning and Reinforcement Learning. Despite its compact size, Fin-R1 demonstrates outstanding performance on authoritative financial benchmarks, even surpassing larger models in certain tasks. Join us to learn about the potential of Fin-R1 in real-world financial applications like compliance and robo-advisory.

The "think" tool provides Claude with a dedicated space for structured thinking during complex tasks, allowing it to pause and reflect, especially when processing tool outputs. This leads to significant improvements in Claude's ability to follow policies, make consistent decisions, and handle multi-step problems. Evaluations on τ-Bench showed dramatic performance gains in airline and retail customer service domains when the "think" tool was used. The tool is most useful in scenarios requiring careful tool output analysis, adherence to policies, and sequential decision making. Pairing the "think" tool with optimized prompting further enhances its effectiveness, especially in difficult domains.

This podcast episode dives into AI for application developers, inspired by Steve Sanderson's keynote . It journeys from early concepts like the Turing Test and Eliza to the power of modern Large Language Models (LLMs). Discover how AI is used beyond chat for tasks like data extraction and image analysis. The episode also explores future possibilities with AI Agents and immediate application enhancements like semantic search. Tune in to understand the evolution and practical integration of AI in your applications.

Several key concepts and techniques essential for working with large language models (LLMs). It begins by explaining sampling, the probabilistic method for generating diverse text, and contrasts it with fine-tuning, which adapts pre-trained models for specific tasks. The text then discusses sharding, a method for distributing large models, and the role of a tokenizer in preparing text for processing. Furthermore, it covers parameter-efficient fine-tuning methods like LoRA and general PEFT, which allow for efficient model adaptation, and concludes by explaining checkpoints as mechanisms for saving and resuming training progress.

Jensen Huang's GTC March 2025 keynote showcases NVIDIA's advancements in AI and accelerated computing. Huang introduces their next-generation Blackwell architecture, emphasizing its significant performance leap for AI workloads, particularly reasoning and inference, within newly envisioned "AI factories." He details a multi-year hardware roadmap, including Blackwell Ultra and the future Vera Rubin and Rubin Ultra architectures, alongside innovations in data center infrastructure and networking, such as silicon photonics. The keynote also highlights NVIDIA's expanding role in enterprise AI with new DGX systems and open-source models, and their progress in robotics through physical AI, simulation tools like Omniverse and Cosmos, and the generalist robot foundation model Groot N1. Finally, Huang underscores the importance of their comprehensive software stack and collaborations across various industries to drive the AI revolution.

This video from IBM Technology explains Agentic Retrieval Augmented Generation (RAG) as an advancement of the standard RAG pipeline. Traditional RAG enhances LLM responses by retrieving relevant data from a vector database and using it as context. Agentic RAG expands on this by employing the LLM as an agent capable of making decisions, such as selecting the most appropriate data source from multiple options based on the user's query. This intelligent routing allows for more accurate and contextually relevant information retrieval, even handling queries that fall outside the scope of available databases. The speaker highlights potential applications in various fields, emphasizing Agentic RAG's ability to create more responsive and adaptable AI systems.

The provided YouTube transcript from IBM Technology's channel, "Phishing Defenses: Top Cybersecurity Strategies to Protect Your Data," identifies phishing as a major cause and costly type of data breach. The speaker explains phishing as a social engineering tactic that exploits human trust through gain and loss motivations to steal credentials or install malware. The video breaks down various phishing attack methods by delivery (email, SMS, voice, QR codes), context (impersonating banks, couriers, e-commerce), and type (spray and pray, spear phishing, whaling, cloning). It warns about the increasing sophistication of phishing due to AI and offers several defensive strategies, including user training, avoiding suspicious links, using multi-factor authentication and PassKeys, employing secure DNS and DMARC, increasing monitoring, and limiting user admin rights.