Large Language Model (LLM)
Chevrolet of Watsonville, a car dealership, introduced a ChatGPT-based chatbot on their website. However, the chatbot falsely advertised a car for $1, potentially leading to legal consequences and resulting in a substantial bill for Chevrolet. Incidents like these highlight the importance of implementing security measures to LLM applications.
Figure 1. Popularity of the keyword “cloud inference” on Google search engine worldwide in the past 5 years. Deep learning models have emerged as powerful tools in areas like speech recognition and image classification, even surpassing human performance in tasks such as object classification and real-time strategy games.
Figure 1. The increasing popularity of the keyword “cloud LLM” on Google since the release of ChatGPT-3.5 in November 2022. Large Language Models (LLMs) have become a hot topic for businesses, especially after the release of ChatGPT-3.5 to the market in 2022.
Large multimodal models (LMMs) represent a significant breakthrough, capable of interpreting diverse data types like text, images, and audio. However, their complexity and data requirements pose potential challenges. Innovations in AI research are aiming to overcome these challenges, promising a new era of intelligent technology.
In the rapidly growing market of artificial intelligence (AI) and generative AI (Figure 1), one term that has taken center stage is ‘large language models’, or LLMs. These vast models enable machines to create content like humans. Data plays a foundational role in shaping the behavior, expertise, and range of these models.
Generative AI stats show that Gen AI tools and models like (ChatGPT) can automate knowledge intensive NLP tasks that make up 60% to 70% of employees’ time. Yet, 56% of business leaders consider AI-generated content biased or inaccurate, lowering the adoption rate of LLMs.
The healthcare industry, full of vast amounts of patient data and medical literature, seeks efficient ways to use this information for better patient outcomes. Traditional methods of data analysis and manual interpretation are time-consuming and often lag behind the rapid pace of medical advancements, potentially compromising patient care.
With the outbreak of generative AI and chatbots, the interest in LLMs has rapidly grown in the last couple of years. However, RLHF has seen relatively less growth. Despite its impressive results in the development of AI, generative AI, and LLMs, RLHF is a relatively new approach that many executives still don’t know about.
In the rapidly evolving landscape of artificial intelligence and machine learning, new terminologies and concepts frequently emerge, often causing confusion among business leaders, IT analysts, and decision-makers. Among these, two terms have gained prominence: LLMOps vs MLOps.
Processing, storing, and retrieving vast amounts of information rapidly and efficiently is paramount for businesses. Vector databases are a critical emerging technology in addressing this demand. Unlike traditional databases, vector databases focus on high-dimensional vector data, offering unique advantages for certain use cases.