Booking.com collaborated with AWS Professional Services to use Amazon SageMaker and modernize their ML infrastructure, reducing wait times for model training and experimentation, integrating essential ML capabilities, and reducing the development cycle for ML models. This improved their search experience and benefited millions of travelers worldwide.
Discover how transformers and topic modeling can help interpret and understand the semantic structures of big data. Explore the operational definitions of topics and the spatial definition of semantics, and see their practical application in a case study.
The article discusses the evolution of GPT models, specifically focusing on GPT-2's improvements over GPT-1, including its larger size and multitask learning capabilities. Understanding the concepts behind GPT-1 is crucial for recognizing the working principles of more advanced models like ChatGPT or GPT-4.
Canadian Prime Minister Justin Trudeau is cracking down on car theft by targeting the Flipper Zero, an open source hardware used to steal vehicles by copying wireless signals. The Innovation, Science and Economic Development Canada agency plans to ban the device and collaborate with law enforcement agencies to remove it from the Canadian marketplace.
Companies of all sizes are reaching levels of data scale previously reserved for giants like Netflix and Uber. Data streaming and data orchestration have become crucial aspects of building a modern data platform.
OpenAI CEO Sam Altman is seeking to raise $5 trillion to $7 trillion for AI chip manufacturing to address the scarcity of GPUs for language models like ChatGPT and Microsoft Copilot. Altman is pitching a partnership between OpenAI, investors, chip makers, and power providers to build chip foundries, with OpenAI as a significant customer.
Build a chat application using LangChain, LLMs, and Streamlit to interact with a complex SQL database. Enhance the chatbot's ability to make SQL queries and provide a user-friendly interface with memory features using Streamlit.
AI surveillance software was used to monitor thousands of people on the London Underground, looking for aggressive behavior, weapons, and unsafe situations. Transport for London tested 11 algorithms, generating over 44,000 alerts, marking the first time AI and live video footage were combined for real-time alerts to staff.
Recommender systems generate significant revenue, with Amazon and Netflix relying heavily on product recommendations. This article explores the use of controlled vocabularies and LLMs to improve similarity models in recommender systems, finding that a controlled vocabulary enhances outcomes and building a genre list using an LLM is easy but creating a detailed taxonomy is challenging.
Hector Xu, a former MIT student, founded Rotor Technologies to make helicopter flight safer by retrofitting existing helicopters with autonomous technology. Rotor's autonomous aircraft can fly faster and longer than drones and are already conducting demo flights.
Learn how to create a custom AI using OpenAI's Assistants and Fine-tuning APIs in this step-by-step guide. Build an AI assistant with knowledge retrieval capabilities, like a YouTube comment responder, using the Assistants API.
Google has renamed its AI assistant Bard to Gemini and launched its most capable AI model, Ultra 1.0, as part of a $20/month subscription feature. The nomenclature and accessing the new model can be confusing, but it offers different AI "engines" for enhanced performance.
The article "Principal Component Analysis (PCA) from Scratch Using the Classical Technique with C#" in Microsoft Visual Studio Magazine explains how PCA can reduce the number of columns in a dataset and its applications in machine learning algorithms. It also discusses the difficulty of computing eigenvalues and eigenvectors and provides a demo using a subset of the Iris dataset.
Automate mortgage document fraud detection using ML models and business-defined rules with Amazon Fraud Detector, a fully managed fraud detection service. Upload historical data, train the model, review performance, and deploy the API to make predictions for improved fraud detection and underwriting accuracy.
This article explores three key encoding techniques for machine learning: label encoding, one-hot encoding, and target encoding. It provides a beginner-friendly guide with pros, cons, and Python code examples to help data scientists understand and implement these techniques effectively.