Lily Nelson
Using AI to Fight Wildfires -- Prevention, ...

In a world overshadowed by the rapid deterioration of its climate, AI engineers are beginning to apply machine learning technology to issues of environmental justice and crisis. In California, where wildfires have ravaged over 570,000 acres of land and forced 41,000 evacuations already this summer, AI is being used to prevent and monitor forest fires throughout the state.

Using AI to Fight Wildfires -- Prevention, Detection, & Monitoring with Computer Vision

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When Is the Right Time for Your Business to Invest in Custom AI Solutions?

When Is the Right Time for Your Business to Invest in Custom AI Solutions?

80% of AI projects fail—not because the technology isn’t ready, but because businesses aren’t. Companies that thrive with AI begin by identifying clear, high-impact problems it can solve, backed by quality data and a strategic vision for success. This article explores the critical elements of AI readiness: defining your business challenges, ensuring your data infrastructure is robust, and leveraging AI to gain a competitive edge. Whether it’s automating repetitive tasks, personalizing customer experiences, or predicting trends, the key to success isn’t adopting AI early—it’s adopting it smartly. Learn how to assess your readiness and prepare for an AI-powered future.

Why a Virtual Concierge is the Key to Superior Customer Service

Why a Virtual Concierge is the Key to Superior Customer Service

Explore how AI-powered virtual concierges are transforming customer service in industries like hospitality and education. With 80% of customers likely to switch brands after two bad experiences, businesses are turning to AI to meet rising expectations. This article delves into real-world examples of AI concierges offering personalized recommendations, streamlining tasks like bookings and check-ins, and supporting students with career guidance—all while allowing human teams to focus on more complex customer needs.

From RAG to Riches: A Practical Guide to Building Semantic Search Using Embeddings and the OpenSearch Vector Database

From RAG to Riches: A Practical Guide to Building Semantic Search Using Embeddings and the OpenSearch Vector Database

In this article, we delve into the evolution of search technologies, tracing the journey from the conventional keyword-based search methods to the cutting-edge advancements in semantic search. We discuss how semantic search leverages sentence embeddings to comprehend and align with the context and intentions behind user queries, thereby elevating the accuracy and relevance of search outcomes. Through the integration of vector databases such as OpenSearch, we illustrate the development of sophisticated semantic search systems designed to navigate the complexities of modern data sets. This approach not only delivers a more refined search experience but also enhances the precision of results by accurately interpreting the intent of user inquiries, representing a notable leap forward in the progression of search technology.

AI Gone Wrong? The Critical Role of Chatbot Testing and Certification

AI Gone Wrong? The Critical Role of Chatbot Testing and Certification

AI chatbots are transforming customer service by providing 24/7 availability and interactions that resemble human conversation. It's anticipated that by 2025, 80% of customer support will utilize Generative AI to improve the customer experience and increase agent efficiency. However, the swift adoption of this promising technology has faced obstacles, particularly miscommunications that have risked brand reputations. To prevent inaccuracies it's essential to adopt thorough AI testing and certification processes. In this article, learn more about why rigorous testing and certification are critical for the successful integration of AI chatbots in customer service.

Measuring Accuracy and Trustworthiness in Large Language Models for Summarization & Other Text Generation Tasks

Measuring Accuracy and Trustworthiness in Large Language Models for Summarization & Other Text Generation Tasks

Large Language Models (LLMs) are increasingly popular due to their ability to complete a wide range of tasks. However, assessing their output quality remains a challenge, especially for complex tasks where there is no standard metric. Fine-tuning LLMs on large datasets for specific tasks may be a potential solution to improve their efficacy and accuracy. In this article, we explore the potential ways to assess LLM output quality:

Why NLP is a Game-Changer for the Insurance Industry: Implementation Benefits and Best Practices

Why NLP is a Game-Changer for the Insurance Industry: Implementation Benefits and Best Practices

The insurance industry is moving towards a more tech-driven future with the help of natural language processing (NLP). Artificial intelligence could improve productivity and save up to 40% on insurance costs by 2030, according to a 2021 McKinsey report. In this article we address how you can utilize NLP to automate customer service, streamline underwriting, and analyze social media data:

Practical Applications of AI and NLP for Automated Text Generation

Practical Applications of AI and NLP for Automated Text Generation

In this article, we explore some practical uses of AI driven automated text generation. We demonstrate how technologies like GPT-3 can be used to better your business applications by automatically generating training data which can be used to bootstrap your machine learning models. We also illustrate some example uses of language transformations like transforming english into legalese or spoken text into written.

Modern AI Text Generation: An Exploration of GPT-3, Wu Dao 2.0 & other NLP Advances

Modern AI Text Generation: An Exploration of GPT-3, Wu Dao 2.0 & other NLP Advances

Within this last year alone, there has been a paradigm shift in model development as research groups are ingesting (nearly) the entire world's worth of information on the internet to train massive deep learning models capable of performing fantastic or frightening feats, depending on your perspective. In this article, we explore an AI compositional technology, known as generative modeling, and demonstrate its ability to simulate human-realistic text.

Understanding Conversations in Depth through Synergistic Human/Machine Interaction

Every day, billions of people communicate via email, chat, text, social media, and more. Understanding the conversation begins with understanding one document. Once we can teach a machine to understand everything in a single document, we can project this understanding up to a collection, thread or larger corpus of documents to understand the broader conversation.