Start with the problem, not the solution. Avoid forcing chatbot solutions onto problems, focus on business processes first.
Open Food Facts uses Machine Learning to enhance its food database by reducing unrecognized ingredients, improving data accuracy. The project showcases the success of creating a custom model, outperforming existing solutions by 11%.
Multimodal embeddings merge text and image data into a single model, enabling cross-modal applications like image captioning and content moderation. CLIP aligns text and image representations for 0-shot image classification, showcasing the power of shared embedding spaces.
Dogs prefer to poop facing North-South. Learn how to measure this at home using a compass app and Bayesian statistics. Researcher replicates study with own dog, capturing over 150 "alignment sessions."
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.
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.
Datadog's integration with AWS Neuron optimizes ML workloads on Trainium and Inferentia instances, ensuring high performance and real-time monitoring. The Neuron SDK integration offers deep observability into model execution, latency, and resource utilization, empowering efficient training and inference.
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.
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.
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.
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.