About Actadium

How an AI Agent Works?

An AI agent refers to a program or system designed to perform tasks that would typically require human intelligence. The workings of an AI agent can vary depending on its type and purpose, but here is a general overview:

• Data Input
  

  AI agents begin by receiving input data. This can come from various sources such as sensors, cameras, text input, databases, or other digital sources.

• Data Processing
  

  The input data is processed using algorithms and models. These algorithms are sets of rules and instructions that the AI follows to analyze and understand the input.

• Feature Extraction
  

  In many cases, AI agents extract relevant features from the input data. Features are specific characteristics or patterns that the AI uses to make predictions or decisions.

• Model Training (for machine learning-based agents)
  

  If the AI agent is based on machine learning, it undergoes a training phase where it learns from labeled or historical data. During this phase, the AI adjusts its internal parameters to improve its performance on the given task.

• Decision Making or Output Generation
  

  Based on the processed data and learned patterns, the AI agent makes decisions or generates output. This could involve classifying objects, predicting future events, generating text, or taking specific actions.

• Feedback Loop (for learning agents)
  

  In the case of machine learning-based agents, there may be a feedback loop where the agent receives feedback on its performance and adjusts its model accordingly to improve over time.

• Execution of Actions
  

  The AI agent may interact with the environment or perform specific actions based on its decisions. This could include controlling robotic systems, generating recommendations, or automating tasks.

• Monitoring and Adaptation
  

  AI agents often have mechanisms to monitor their own performance and adapt to changes in the environment. This adaptability allows them to handle dynamic and evolving situations.

There are different types of AI agents, including rule-based systems, expert systems, machine learning models, and more. The specific architecture and mechanisms can vary based on the application and the goals of the AI system. It's important to note that while some AI agents operate based on predefined rules, others, especially those using machine learning, can exhibit a level of autonomy and learn from experience.

AI/ML Models We Use

We are hosting various models on our own infrastructure. Besides that, we also support Generative AI Model providers like OpenAI, Google Cloud or Microsoft Azure to achieve our customer's goals.

• Mixtral
  

  Mixtral is a state-of-the-art large language model (LLM) that leverages a Mixture of Experts (MoE) architecture. This means it consists of multiple 'expert' models working together, each specializing in different tasks. This allows Mixtral to excel in various domains, including text generation, translation, question answering, and code writing. Compared to similar LLMs like GPT-3.5, Mixtral demonstrates superior performance on several benchmarks, making it a powerful tool for various applications.

• LLaMA2
  

  LLaMA2 is a large language model developed by Meta. While details about its specific capabilities are limited, it is likely an improvement upon its predecessor, LLaMA, which excelled in tasks like text summarization, question answering, and generating different creative text formats. LLaMA2 is expected to push the boundaries of LLM capabilities further, but more information is needed for a comprehensive description.

• Mistral
  

  Mistral refers to the previous version of Mixtral, also a large language model but lacking the Mixture of Experts architecture. It still showcases impressive capabilities in text generation, translation, and code writing, laying the foundation for the advancements seen in Mixtral.

• Falcon
  

  Falcon is a family of high-performing large language models model built by the Technology Innovation Institute (TII), a research center part of Abu Dhabi government’s advanced technology research council overseeing technology research.

• ChatGPT4
  

  ChatGPT4 is an upcoming large language model from OpenAI, expected to succeed the popular ChatGPT3. While specific details are unavailable, it is anticipated to surpass its predecessor in capabilities like conversation, writing different kinds of creative content, and answering your questions in an informative way.

• Stable Diffusion XL
  

  Stable Diffusion XL is a text-to-image AI model, capable of generating high-quality images based on textual descriptions. It builds upon the Stable Diffusion model, known for its photorealistic and diverse outputs. XL stands for 'extended latent space,' suggesting potentially wider creative possibilities compared to the original model.

• DALL-E
  

  DALL-E is another powerful text-to-image AI model developed by OpenAI. It allows users to create images from natural language descriptions and has generated impressive results in terms of realism and creativity. While different from Stable Diffusion XL, both models contribute significantly to the field of image generation using AI.

• Midjourney
  

  Midjourney is an AI art platform that utilizes a text-to-image model to create unique and artistic visuals based on user descriptions. It focuses on artistic expression and allows for detailed control over the generated images, making it popular among artists and creative professionals.