Category: Neural Basics

All you need to know about symbolic artificial intelligence


Today, artificial intelligence is mostly about artificial neural networks and deep learning. But this is not how it always was. In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as “classical AI,” “rule-based AI,” and “good old-fashioned AI.” Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. The role of symbols in artificial…

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Machine learning: What’s the difference between supervised and unsupervised?


Machine learning, the subset of artificial intelligence that teaches computers to perform tasks through examples and experience, is a hot area of research and development. Many of the applications we use daily use machine learning algorithms, including AI assistants, web search and machine translation. Your social media news feed is powered by a machine learning algorithm. The recommended videos you see on YouTube and Netflix are the result of a machine learning model. And Spotify’s Discover Weekly draws on the power of machine learning algorithms to create a list of songs that conform to your preferences. But machine learning comes…

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Everything you need to know about artificial neural networks


Welcome to Neural Basics, a collection of guides and explainers to help demystify the world of artificial intelligence. One of the most influential technologies of the past decade is artificial neural networks, the fundamental piece of deep learning algorithms, the bleeding edge of artificial intelligence. You can thank neural networks for many of applications you use every day, such as Google’s translation service, Apple’s Face ID iPhone lock and Amazon’s Alexa AI-powered assistant. Neural networks are also behind some of the important artificial intelligence breakthroughs in other fields, such as diagnosing skin and breast cancer, and giving eyes to self-driving cars. The concept…

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Everything you need to know about neuromorphic computing


In July, a group of artificial intelligence researchers showcased a self-driving bicycle that could navigate around obstacles, follow a person, and respond to voice commands. While the self-driving bike itself was of little use, the AI technology behind it was remarkable. Powering the bicycle was a neuromorphic chip, a special kind of AI computer. Neuromorphic computing is not new. In fact, it was first proposed in the 1980s. But recent developments in the artificial intelligence industry have renewed interest in neuromorphic computers. The growing popularity of deep learning and neural networks has spurred a race to develop AI hardware specialized for neural…

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How machines see: everything you need to know about computer vision


Welcome to Neural Basics, a collection of guides and explainers to help demystify the world of artificial intelligence. If I asked you to name the objects in the picture below, you would probably come up with a list of words such as “tablecloth, basket, grass, boy, girl, man, woman, orange juice bottle, tomatoes, lettuce, disposable plates…” without thinking twice. Now, if I told you to describe the picture below, you would probably say, “It’s the picture of a family picnic” again without giving it a second thought. Those are two very easy tasks that any person with below-average intelligence and…

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Everything you need to know about artificial general intelligence


Welcome to Neural Basics, a collection of guides and explainers to help demystify the world of artificial intelligence. From ancient mythology to modern science fiction, humans have been dreaming of creating artificial intelligence for millennia. But the endeavor of synthesizing intelligence only began in earnest in the late 1950s, when a dozen scientists gathered in Dartmouth College, NH, for a two-month workshop to create machines that could “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” The workshop marked the official beginning of AI history. But as the two-month effort—and many others that followed—only…

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