Large Language Models (LLMs)

Overview How are LLM's built? Large Language Models Uses Use Existing LLMs or Build New LLM Benefits of Using Existing LLMs Benefits of Creating a New LLM Risks with Existing LLMs Risks of New LLMs Task Complexity - Existing LLMs Vs New LLM Data Availability - Existing LLMs vs New LLM Budget and Resources - Existing LLMs vs New LLM Expertise Requirement - Existing LLMs vs new LLM AI Services that are not LLM based

AI Services that are not LLM based

LLMs are a type of AI that are specifically designed to generate and understand text. They are trained on massive amounts of text data, and they can be used for a variety of tasks, such as writing different kinds of creative content, translating languages, and answering your questions in an informative way. However, LLMs are not well-suited for all AI tasks. For example, they are not good at identifying objects in images, analyzing medical data, or detecting fraud.

Here is a list of AI service types that do not use LLM:

Computer vision: AI services that can identify and classify objects in images and videos. Examples include Amazon Rekognition, Google Cloud Vision, and Microsoft Azure Cognitive Services Computer Vision.

Natural language processing (NLP): AI services that can understand and process human language. Examples include Amazon Comprehend, Google Cloud Natural Language, and Microsoft Azure Cognitive Services Text Analytics.

Machine learning: AI services that can learn from data and make predictions. Examples include Amazon SageMaker, Google Cloud AutoML, and Microsoft Azure Machine Learning.

Robotics: AI services that can control and power robots. Examples include Amazon Robotics, Google Cloud Robotics, and Microsoft Azure Robotics.

Decision support: AI services that can help businesses make better decisions. Examples include H2O.ai, IBM Watson Analytics, and SAS Visual Analytics.

AI-powered facial recognition: This type of AI service can identify and verify people in images and videos. It is not LLM based because it does not use natural language processing. Instead, it uses computer vision to analyze facial features and match them to known databases.

AI-powered chatbots: These chatbots can interact with customers and answer their questions in a human-like way. However, they are not LLM based because they do not generate text from scratch. Instead, they use a variety of techniques to select and combine pre-written responses.

AI-powered medical diagnosis tools: These tools can help doctors diagnose diseases and recommend treatments. They are not LLM based because they do not use natural language processing. Instead, they use machine learning to analyze medical data, such as patient symptoms, lab results, and imaging scans.

AI-powered fraud detection tools: These tools can help businesses identify and prevent fraudulent transactions. They are not LLM based because they do not use natural language processing. Instead, they use machine learning to analyze financial data and identify patterns that are indicative of fraud.

AI-powered predictive maintenance tools: These tools can help businesses predict when machines are likely to fail, so that they can be repaired or replaced before they cause downtime. They are not LLM based because they do not use natural language processing. Instead, they use machine learning to analyze data from sensors on machines to identify patterns that are indicative of impending failure.