Artificial Intelligence: Terms 101

A friend of mine who works in higher education recently asked me to define several different AI terms for them. Given how pivotal these concepts are in our rapidly advancing technological landscape, I thought it would be beneficial to share some basic explanations with my LinkedIn network. We all learn and grow together, after all!

?? Machine Learning (ML): Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without being explicitly programmed in what they output. Think of it like training a dog: you reward it for good behavior (data), and over time, it learns to perform tricks on command (make decisions).

?? Deep Learning: Deep learning is a subset of machine learning that uses neural networks with many layers to analyze various factors of data. Imagine an orchestra: each musician (layer) plays their part to contribute to the overall performance, producing complex, harmonious music (deep analysis).

?? Neural Networks: Neural networks are computing systems inspired by the biological and electrical networks in our brains. These human and computer "neurons" process information at incredible speeds through interconnected nodes, similar to how a bee colony works: each bee (node) has a small role, but together they make complex decisions and actions through their collaboration.

?? Unsupervised Learning: Unsupervised learning involves training a model on data without pre-labeled answers, allowing it to identify patterns on its own. It’s like exploring a cave without a map; you discover hidden treasures (patterns) based on your exploration and update your knowledge based on what you find without pre-written instructions.

?? Supervised Learning: Supervised learning is training a model with labeled data, so it knows the desired outcome. It's like baking with a recipe: you follow specific instructions (labeled data) to achieve the intended dish (outcome).

?? Reinforcement Learning: Reinforcement learning is a type of machine learning where a model learns to make decisions by receiving rewards for actions. It's akin to training a pet with treats; the pet learns to repeat behaviors that lead to rewards and ignore the things that don't lead to the good stuff.

?? Data Mining: Data mining is the process of discovering patterns and knowledge from large sets of data. Think of it as mining for gold: sifting through dirt and rock (data) to find gold nuggets (insights).

??? Predictive Analytics: Predictive analytics uses statistical techniques to forecast future events based on historical data. It's like weather forecasting, where meteorologists predict the weather based on past patterns. The same can be done with student GPAs, the stock market, and more, with strong results based on the amount of data available over time.

?? Large Language Models (LLM): Large language models are advanced AI systems capable of understanding and generating human-like text. They are like libraries brimming with books (data) that can produce new stories or reports on demand, by using the books already in the library as inspiration.

?? Small Language Models: Small language models are simpler and require less data to function, suitable for specific tasks. They're like a diary, compact yet capable of capturing essential thoughts and reflections efficiently.

? Compute: In the context of tech and computing, "compute" refers to the ability of a computer system to process and execute tasks. Compute is like the horsepower of a car: just as more horsepower means a car can go faster and handle more demanding tasks, more compute power in a computer allows it to process data faster and run complex applications more efficiently.

?? Artificial General Intelligence (AGI): AGI represents a level of artificial intelligence we haven't achieved yet but most technologists and futurists believe is on the horizon. AGI is where a machine can understand, learn, and apply its intelligence to solve problems on its own, much like a human. Imagine a Swiss Army knife, a tool that can perform many functions at once. AGI (once it arrives, which some folks like the CEO of NVIDIA believe will be in 5 years or less) will have the versatility and adaptability to handle a wide range of tasks and challenges across different domains. Like a human.

?? Artificial Superintelligence (ASI): ASI refers to a hypothetical future AI that surpasses human intelligence across most or all areas, including creativity, general wisdom, and problem-solving. Think of ASI as a spacecraft capable of reaching beyond our solar system, symbolizing an intelligence that goes far beyond human capabilities, exploring and understanding the universe in ways we cannot fathom. "To boldly go where no man has gone before" in other words, due to the limitations of our biological human brains.

I hope that helps! Let me know if there are other definitions you'd like to see broken down or if you want to explore any of the concepts for education and workforce training applications.

P.S. Even though the main image has some misspellings, I chose to use it anyway to showcase the progress that ChatGPT is making. In just a few weeks or months, we will likely have advanced to ChatGPT-5, where such misspellings will probably be far less frequent, just as ChatGPT-4 has improved on earlier versions.

#AI #MachineLearning #DeepLearning #NeuralNetworks #UnsupervisedLearning #SupervisedLearning #ReinforcementLearning #DataMining #PredictiveAnalytics #LargeLanguageModels #SmallLanguageModels #TechnologyEvolution #EducationalTechnology