31/01/2024
AI BUZZWORLD EXPLAINED: WHAT’S THE DIFFERENCE BETWEEN AI, MACHINE LEARNING AND DEEP LEARNING?
Confused by the interchangeable use of AI, Machine Learning, and Deep Learning? This post is your decoder ring 👇
1. Artificial Intelligence (AI): is broadly defined as machine systems that aim to simulate human intelligence. It's not one technology but rather an umbrella term, including 3 categories:
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
ANI is considered a “weak” AI, focusing on a specific task. Stronger forms of AI, like AGI and ASI, incorporate human behaviours more prominently, such as the ability to interpret tone and emotion. Neither form of Strong AI exists yet, but research in this field is ongoing.
2. Machine Learning (ML): a subset of AI that allows for optimization, helping you make predictions that minimize the errors that arise from merely guessing. For example, Amazon uses ML to recommend products to a specific customer based on their browsing and purchase history.
There are 3 types of ML:
- Supervised: Use labelled data to train machine learning models. Its top applications include weather prediction, sales forecasting, and stock price analysis.
- Unsupervised: Use unlabeled data to train machines. Unlabeled data doesn’t have a fixed output variable. The model learns from the data, discovers the patterns and features in the data, and returns the output. One of the applications of unsupervised learning is customer segmentation. Based on customer behaviour, likes, dislikes, and interests, you can segment and cluster similar customers into a group
- Reinforcement Learning: where a computer learns by interacting with its surroundings and getting feedback (rewards or penalties) for its actions, widely used in the gaming industry, to train robots to do human tasks.
3. Deep Learning: a subset of machine learning, which is essentially a neural network with three or more layers.
These neural networks attempt to simulate the behaviour of the human brain - albeit far from matching its ability- allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.
Deep learning is applied in customer service through chatbots and virtual assistants, providing personalized engagement and efficient responses to user queries. In healthcare, it also aids medical imaging specialists in analyzing and assessing a large volume of images quickly and accurately.
🤝Hope this post helped crack the AI buzzword code! Eager to delve further? Let's embark on a journey together and unlock the power of AI at Ai Lab!
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