Machine Learning

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Machine learning is an application of artificial intelligence that enables computer programs to learn and improve automatically as they are exposed to new data, without being explicitly programmed.

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) is the ability of a computer to mimic human cognitive skills such as learning and understanding. Machine learning refers to the ability of AI systems to take in data through observations and interactions and adapt accordingly. Essentially, machine learning models program themselves. Futurists predict that every facet of our lives and every line of business will eventually be transformed by machine learning.

By 2020, Gartner predicts that customers will manage 85% of the relationship with an enterprise without interacting with a human. This is becoming possible because of advances in machine learning. There are three main factors influencing this fast-moving space:

  • A wealth of computing resources and large data samples have pushed AI further and faster than we ever expected.
  • Development trends have moved away from monolithic applications and toward the adoption of APIs and microservices.
  • Business-to-consumer communication has created new business cases for messaging bots, which is driving even greater adoption of this technology.

Machine Learning and Twilio

Machine learning algorithms are at the core of Twilio's most advanced communication deployments. From individual developers to major enterprises around the globe, Twilio powers the natural language understanding and dialog management of their entire customer experience.

Natural Language Understanding (NLU)

NLU is branch of natural language processing (NLP), which helps computers understand and interpret human language by breaking down the elemental pieces of speech. Speech recognition and text analytics are powered by statistical machine learning methods which add numeric structure to large datasets.

Of course, understanding language requires more than finding statistical patterns in numbers. That’s where machine learning helps NLU models improve over time as they learn to recognize syntax, context, language patterns, unique definitions, sentiment, and intent.

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