Human intelligence varies. One person may have a “knack” for communicating with others, while another person may naturally excel in athletic achievements. AI is a lot like that, while there is not a general AI that is good at everything, through machine learning AI models can be specialized to any number of specific tasks.
AI is used in two main capacities:
- Predictive – where machine learning is utilized to help an AI model identify patterns in data and predict outcomes to certain scenarios.
- Generative – the ability of AI to use data to generate new data such as images, sounds, and text response to typed inquiries.
In addition to the two different capacities of AI there are also four broad macro capabilities that can be narrowed down into specializations depending on the application of the AI model:
- Classification – Often the first step in AI modeling, classification is AI’s ability to organize data into different classes, where objects are then assigned. Data used for Classification is typically narrow and specialized based on the needs of the specific AI model’s task.
- Navigation – From autonomous cars to the humble household robotic vacuum, the navigation capability is an aspect of generative AI applied primarily to obstacle navigation in the field of robotics. The navigation capability is also used to fine-tune supply chain operations through optimizing transport of goods based on conditional data.
- Numeric predictions – Numeric predictions on the other hand are an aspect of predictive AI. Numeric predictions can forecast the price of a plane ticket based on market conditions or insurance rates based on both historic and customer data.
- Language Processing – A massive aspect of generative AI often referred to as natural language processing (NLP). Language processing capabilities allow AI to interpret written or even spoken language to then provide responses and create new data in the form of images or sound.
For AI to do anything, it needs data – a lot of data. To meet this need, both Structured and Unstructured training data is utilized.
- Structured data – comes mostly from spreadsheets and other databases. The data is predictable and organized with labels for every column and every input has an expected output.
- Unstructured data – is data that has no obvious connection from one object to the next. Through machine learning, the data is processed and organized to create actionable, usable data.
In addition to data types there is also a learning protocol dictating how AI takes in the data, these are Supervised and Unsupervised learning.
- Supervised learning – can be used with Unstructured data but used primarily with Structured data. Supervised learning ensures that the data is fully organized and predictable.
- Unsupervised learning – small algorithms interpret unstructured data to identify patterns and relationships. The algorithms then sort the data into classes. Association rule algorithms can be used to simplify the data used for classification.
The machine learning capabilities of AI provide a strong basis for growth and proliferation of AI into many different applications. Along with deep learning neural networks, AI learning models continue to foster immense changes.