Artificial Intelligence Wiki
A repository of machine learning, data science, and artificial intelligence (AI) terms for individuals and businesses.
Whether you’re looking to explore new concepts or brush up on your terminology, this wiki offers up-to-date information on key topics in data science, machine learning, and deep learning.
Not sure where to start? Check out our definition of MLOps to discover a modern approach to model training and deployment.
What is Artificial Intelligence (AI)?
Artificial Intelligence is an umbrella term for a range of concepts and technologies that allow machines to exhibit human-like capabilities. Some common implementations include self-driving cars, human-impersonating chatbots, and facial recognition apps. A few recent breakthroughs have led to applications that don’t just mimic human intelligence but go well above and beyond, performing tasks that are otherwise impossible for humans.
AI dates back to the 1950s and has been through several boom and bust cycles. Over the past few years, we’ve seen tremendous resurgence in investment and excitement in AI due to the culmination of three key ingredients:
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Abundant and cheap parallel computation with GPUs
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Growing data sets and collection techniques
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Advancements in underlying algorithms — especially the advent of a neural network-based approach called Deep Learning
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AI powers applications used by hundreds of millions of people every day. Businesses are using AI to perform an almost infinite number of tasks, from implementing recommender systems for e-commerce apps to diagnosing cancer.
Barriers to AI Adoption
AI is in its infancy and as an early-stage technology it is rapidly changing and challenging to implement. To gain more widespread adoption, AI needs to overcome a number of hurdles. These obstacles generally fall into two primary areas:
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A lack of best practices (MLOps) across the entire model lifecycle
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Infrastructure complexity inherent in developing and productionizing models
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Today, Data Scientists only spend around 25% of their time developing models — the other 75% of their time is spent managing tooling and infrastructure.