Artificial intelligence is a large field with a multitude of techniques based of different ideas. Deep learning is just one of them, but it is the one with the most success in recognizing image content in recent years.

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There is an epidemic in machine learning due largely to a few core misconceptions. As CEO, your team could be giving you a “false confidence” that they have it under control.

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Machine learning is the secret sauce that allows us to use computers to automate tasks in powerful new ways. However, there are a lot of steps that must go right for the ML to work: ideas must be mined from huge amounts of data, clean sample data must be provided to train models, and models must be managed and

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Improve machine learning models by avoiding bias, refining training data, and seeing what’s missing.

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Machine learning and AI are providing new options for business; here's what some leading organisations make of it.

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While Arm slowly unveils its machine-learning hardware architecture, Nvidia enters the fray with its open-source NVDLA hardware.

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Why aren’t more companies embracing machine learning? Perhaps the reason is as simple as a lack of understanding, or even fear of the unknown.

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What is artificial intelligence? And how can investors profit from the innovation in computer learning?

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A company as big as Google has the resources to focus on a lot of different endeavors, but for Google, AI is quickly becoming one of the biggest. In addition to things like Search and Android, we've seen AI become a big initiative over at Google in recent years. Perhaps it shouldn't come as a surprise, then, that

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Artificial Intelligence (AI), and more pragmatically, Machine Learning (ML) have been called the new electricity that's powering the Fourth Industrial Revolution (4IR). AI is considered to be the ability for an artificial system to make cognitive decisions without human input, whereas ML is considered to be the ability for a computerized system to improve performance of an automated task over time. While AI is still futuristic, ML has been causing huge disruptions across multiple industries over the past decade. For example:

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