Agentic AI combines specialized agents for enhanced capabilities. Major players like Microsoft and Google are investing heavily in agentic AI research.
LLMs.txt is a new web standard optimized for reasoning engines, gaining rapid adoption thanks to Mintlify's support. Co-founder Jeremy Howard proposed LLMs.txt to help AI systems understand website content more efficiently.
Whitehall departments lack transparency in AI use. Concerns arise as AI affects millions of lives, with examples in DWP and Home Office.
Senate recommends standalone AI legislation & protections for creative workers. Amazon, Google, Meta criticized for vagueness on Australian data use in AI training.
Sophos utilizes AI and ML to protect against cyber threats, fine-tuning LLMs for cybersecurity. Amazon Bedrock enhances SOC productivity with Anthropic's Claude 3 Sonnet, tackling alert fatigue.
Rad AI's flagship product, Rad AI Impressions, uses LLMs to automate radiology reports, saving time and reducing errors. Their AI models generate impressions for millions of studies monthly, benefiting thousands of radiologists nationwide.
Implemented AdaBoost regression from scratch in C#, using k-nearest neighbors instead of decision trees. Explored original AdaBoost. R2 algorithm by Drucker, creating a unique implementation without recursion.
Hallucinations in large language models (LLMs) pose risks in production applications, but strategies like RAG and Amazon Bedrock Guardrails can enhance factual accuracy and reliability. Amazon Bedrock Agents offer dynamic hallucination detection for customizable, adaptable workflows without restructuring the entire process.
Spines startup faces backlash for using AI to edit and distribute books for $1,200-$5,000. Critics question quality and impact on traditional publishing.
AI technology like Amazon Bedrock allows for complex stock technical analysis queries to be answered efficiently, transforming natural language requests into actionable data using generative AI agents. With Amazon Bedrock, users can build and scale AI applications securely, leveraging high-performing foundation models from leading AI companies through a single API.
Quantization limits are being pushed with ft-Quantization, a new approach to address current algorithm limitations. This memory-saving technique compresses models and vectors for retrieval, popular in LLMs and vector databases.
Learn how to set up lifecycle configurations for Amazon SageMaker Studio domains to automate behaviors like preinstalling libraries and shutting down idle kernels. Amazon SageMaker Studio is the first IDE designed to accelerate end-to-end ML development, offering customizable domain user profiles and shared workspaces for efficient project management.
Optimizing LLM-based applications with a serverless read-through caching blueprint for efficient AI solutions. Utilizing Amazon OpenSearch Serverless and Amazon Bedrock to enhance response times with semantic cache for personalized prompts and reducing cache collisions.
Neuromorphic Computing reimagines AI hardware and algorithms, inspired by the brain, to reduce energy consumption and push AI to the edge. OpenAI's $51 million deal with Rain AI for neuromorphic chips signals a shift towards greener AI at data centers.
Salesforce centralizes customer data for insights. Amazon Q Business AI empowers employees with data-driven decisions and productivity.