Welcome to the AI Literacy Mini-Series
Whilst Artificial intelligence (AI) and Machine Learning (ML) have been actively researched for many years, the recent breakthroughs in their capabilities and adoption are poised to have profound, lasting impacts on business and the world.
As part of the ‘AI Literacy’ mini-series, we will delve into key concepts and terminology you need to know to better inform your decision making, enabling you to:
Interpret and understand the latest news and developments
Gain a deeper understanding of the core mechanics that drive AI technology
What can AI models do?
AIs have the ability to interpret and generate various data formats, reapply learnings, and generate data
Key Topic Covered
Data formats supported by AI Models
Data transformation capabilities
Capabilities that support learning and training
AIs have the ability to interpret and process a wide variety of data formats
Natural Language Processing (NLP): Subfield of AI that focuses on interprets, processes and manipulates text. This enables the AI to perform tasks such as sentiment analysis, named entity recognition, and machine translation.
Speech Recognition: Converting spoken language into written text.
Vision: Specifically refers to the ability to reference or generate images and videos to perform tasks. GenAI models perform these tasks by analysing images to identify patterns.
Multi-modal: AI models that can reference or generate different formats such as text, images, videos, music and descriptive labels to perform tasks.
AIs can transform data across various modalities
Inpainting: Ability for an AI model to alter or transform parts of an existing image or video.
Outpainting: (Opposite of inpainting) Ability for an AI model to generate content outside of the boundaries of the original image or video)
Texture Synthesis: Generating new textures based on a given texture or set of textures. Typically used in computer graphics and design.
Diffusion: Unwanted noise or artefact reduction. This is achieved by teaching an AI model on the impact of making images, videos, or songs increasingly more nosier. The AI is then asked to reverse this process.
Upscaling: The ability for an AI model to enhance the quality of image or video through the application of interpolation or extrapolation techniques to improve the resolution, size and quality.
AI has the ability to reapply learnings and generate test data for other AIs
Transfer Learning: Reapplying knowledge from one domain or task to another.
Generate Synthetic Data: AIs can be trained to generate realistic data based on pre-formed rules and patterns as a result of training and interacting with the environment.
Thank you for reading! Stay tuned for the continuation of this series. The next issue we will be exploring how to train your AI model.
Follow @ExecSumFIN on twitter or Substack Note for daily news updates and notifications.
Like what you read, refer a friend and get rewarded, or leave a like or comment.