Category: Artificial Intelligence

In today’s fast-paced digital world, streamlining workflows and boosting productivity are paramount. That’s why we’re thrilled to share an exciting integration that will take your team’s collaboration to new heights. Get ready to unlock the power of generative artificial intelligence (AI) and bring it directly into your Slack workspace. Imagine the possibilities: Quick and efficient […]

This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. The solution harnesses the capabilities of generative AI, specifically Large Language Models (LLMs), to address the challenges posed by diverse sensor data and automatically generate Python functions based on various data formats. The fundamental objective is to build a manufacturer-agnostic database, leveraging generative AI’s ability to standardize sensor outputs, synchronize data, and facilitate precise corrections.

Name entity recognition (NER) is the process of extracting information of interest, called entities, from structured or unstructured text. Manually identifying all mentions of specific types of information in documents is extremely time-consuming and labor-intensive. Some examples include extracting players and positions in an NFL game summary, products mentioned in an AWS keynote transcript, or […]

In this post, we explore a comprehensive solution for addressing the challenges of securing a virtual travel agent powered by generative AI. We provide an end-to-end example and its accompanying code to demonstrate how to implement prompt engineering techniques, content moderation, and various guardrails to make sure the assistant operates within predefined boundaries by relying on Amazon Bedrock Guardrails. Additionally, we delve into monitoring strategies to track the activation of these safeguards, enabling proactive identification and mitigation of potential issues.

This post explains a generative artificial intelligence (AI) technique to extract insights from business emails and attachments. It examines how AI can optimize financial workflow processes by automatically summarizing documents, extracting data, and categorizing information from email attachments. This enables companies to serve more clients, direct employees to higher-value tasks, speed up processes, lower expenses, enhance data accuracy, and increase efficiency.

This post is co-written with Shamik Ray, Srivyshnav K S, Jagmohan Dhiman and Soumya Kundu from Twilio. Today’s leading companies trust Twilio’s Customer Engagement Platform (CEP) to build direct, personalized relationships with their customers everywhere in the world. Twilio enables companies to use communications and data to add intelligence and security to every step of […]

In large language model (LLM) training, effective orchestration and compute resource management poses a significant challenge. Automation of resource provisioning, scaling, and workflow management is vital for optimizing resource usage and streamlining complex workflows, thereby achieving efficient deep learning training processes. Simplified orchestration enables researchers and practitioners to focus more on model experimentation, hyperparameter tuning, […]

Enable branded user experiences with specialized features like feedback handling and seamless conversation flows personalized for your use case and business needs.

In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. However, traditional document processing workflows often involve complex and time-consuming manual tasks, hindering productivity and scalability. In this post, we discuss an approach that uses the […]

Photo by Zbynek Burival on Unsplash Time series forecasting is a specific machine learning (ML) discipline that enables organizations to make informed planning decisions. The main idea is to supply historic data to an ML algorithm that can identify patterns from the past and then use those patterns to estimate likely values about unseen periods […]