January 21st, 2020
Last time we covered the definition of AI. You’ll probably have heard the terms AI and ML being used interchangeably in the mainstream press or at events, but they are actually quite different topics.
Machine learning is a sub-category of AI. Machine learning is the scientific field concerned with algorithms and statistical models which allow computers to complete a specific task without explicit instructions, relying on patterns and inference instead.
To carry on with the chat bot example from earlier, the ML version is called Natural Language Processing (NLP). Whereas the above chat bot used a decision tree to pick the correct response to a question from a set of pre-written answers; NLP applies algorithms to identify and extract natural language rules from data of an entire language and then computes them in a more conversational way. Alexa is a perfect example of NLP; she is able to understand the underlying patterns that structure the English language to understand and respond to requests in a fluid way. She requires a lot more data of conversations than a simple chat bot, who only requires a script of questions and answers.
Machine learning goes a level of complexity deeper than AI when referring to types of AI systems: advancing from pure logic (if input x, then output y in conditions z and w) to the ‘understanding’ and inference of patterns in data (breaking a sentence down into syntax and semantics categories to allow a computer to ‘understand’ its meaning).