Namaste Yogis. Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered, mostly correct! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question was submitted by Alberto from Montebello, CA, and he wants to know the 10 terms in artificial intelligence (AI) that he should know.
Alberto, you came to the right place. Great question. Learning the vocabulary is an intelligent way to master the topic. Let’s roll it in alphabetical order.
Algorithm: I’m sure you have heard the term algorithm tossed about like a basketball. Let’s settle what it means; it’s not complicated. Algorithms are the step-by-step set of instructions followed by computers to perform a specific task. That’s it. Nothing more.
Artificial Intelligence (AI): is essentially teaching machines to learn from data and derive a variety of useful insights. AI is commonly defined as a simulation of human intelligence in machines that are programmed to think and learn like humans. Basically, AI enables computers to mimic human intelligence (hopefully a smart human) using logic.
Data Mining is all about discovery. In data mining patterns of knowledge from large data sets are discovered. Data discoveries are designed to assist with decision making, predictions, etc. Put on your computer hardhat and start digging!
Deep Learning (not to be confused with the Deep State, ha ha) is a specialized form of machine learning (as if machine learning is not sufficiently specialized!). Deep learning involves many layers of neural networks; hence, the label “deep”. (See below for neural networks.).
Machine Learning (ML): is a subset of AI that provide systems the ability to automatically learn and improve from the experience without being explicitly programmed. The key is not explicitly programmed.
Natural Language Processing (NLP): is the ability of a computer program to understand spoken and written human language. Holy linguist! However, human language is more than just sounds and we often have misunderstandings. Hence, it might take more time for software program to truly understand human language.
Neural Networks: a neural network is a computer system based on the human brain and nervous system. Which human I don’t know and afraid to ask! Ha, ha.
Reinforced learning is focused on finding a balance. A balance between what? A balance between exploration of new ground and exploitation of what is already known. Think of it as the yin/yang of machine learning.
Supervised Learning is a form of machine learning (see above). In supervised learning the system uses algorithm (see above) to identify patterns in the data and then “learn” from the patterns. Computer swamis call it supervised because the program is being directed by the instructions coded in the software. A good example of supervised learning is your email spam detector. Thank the universe for the spam detector, otherwise I may have fallen for a love letter from a 20-year-old Russian beauty queen! Ha, ha.
Unsupervised Learning is where the AI tells the boss to get loss! Ha, ha. Just kidding. In unsupervised learning the machine uses algorithms (see above) to analyze and cluster unlabeled data. In other words, in unsupervised learning the machine learns to uncover patterns without direct human direction. Transcribing audio is a good example.
A special acknowledgement to the Cardano Foundation Emurgo Academy, and these two books: AI Made Simple and Artificial Intelligence 2023.
Alberto, please remember those terms and the proverb from Suriname, where they say: You cannot fly with wings made of butter.
Until next time.
Yogi Nelson
Blockchain & AI Forum 