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

testing&certification

5 Compelling Reasons to Outsource Your AI

5 Compelling Reasons to Outsource Your AI

The global AI market is expected to reach $126 billion by 2025 — however building an in-house AI team can be costly, with an average salary for AI specialists at $150,000 annually. Outsourcing your AI development offers significant advantages, including cost savings, access to expertise, faster implementation, scalability, and the ability to focus on core competencies. Companies can save up to 60% in operational costs and leverage the latest AI technologies without the burden of maintaining a large in-house team, making outsourcing a strategic solution for many businesses.

Securing the Conversational Frontier: Advanced Red Team Testing Techniques for Chatbots

Securing the Conversational Frontier: Advanced Red Team Testing Techniques for Chatbots

Chatbots, now omnipresent, face a crisis of accuracy and security highlighted by recent public blunders at Air Canada and Chevrolet, where bots made unintended promises. Air Canada's attempt to deflect blame onto its bot was rejected by authorities, underscoring a harsh reality: companies are indeed responsible for their bots' actions. Despite the prowess of language models like ChatGPT, their inherent nature to occasionally fabricate with confidence poses unique challenges. Drawing lessons from cybersecurity, this article explores four advanced red team testing strategies aimed at reining in bot misstatements and significantly bolstering chatbot security.

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:

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.