AI (or "Artificial Intelligence") is an emerging industry that's led to the creation of tools like ChatGPT, Midjourney, and Adobe Firefly. It's the study and development of computer models that can perform automated tasks and simulate human intelligence.
But how?
AI self-learns by consuming large datasets that teach and train over time on how to perform tasks or respond to a message. It's the process of "machine learning," where AI is given a set of data relevant to a task and attempts to replicate the data over and over. With each repetition, the machine compares its result to the desired outcome and then optimizes the process to reduce the number of errors.
For example, a complex machine learning model like ChatGPT requires massive amounts of data, training, and hours of refining through feedback by human developers. This helps the machine categorize when a task is performed correctly or not.
If you ask ChatGPT which Pokémon can evolve from a Thunder Stone, its goal is to reply with an answer close to what a human would say.
For a human to answer that question, they have to perform tasks like:
- Understanding the question (what are Pokémon evolutions and a Thunder Stone?)
- Finding a list of all existing Pokémon
- Researching each Pokémon's unique evolution requirements
- Compiling a list of which ones can use a Thunder Stone to evolve
Because ChatGPT is trained on a massive dataset derived from internet sources and text, all it needs to do to answer the question is:
- Select a reply that's statistically likely to answer the question based on its data and training (e.g. internet sources that include Pokémon evolution data)
- Receive and interpret any feedback from the user (if the response is correct/incorrect), then amend its answer if needed
ChatGPT can often answer questions correctly thanks to the combination of its model training and the depth of the dataset.
If this sounds impressive, that's because it is. But fear not—AI models are designed to simulate human intelligence, but can't replicate it. Machine learning models require massive amounts of data, training, and feedback by humans to simulate human behaviors like conversation.
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