Artificial Intelligence

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Deep learning vs. machine learning: Understand the differences

Both machine learning and deep learning discover patterns in data, but they involve dramatically different techniques

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Mitigating the risks of the AI black box

If we don’t understand how machine learning works, how can we trust it? Increasing model transparency creates risks as well as rewards

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Wanted: More types of machine learning

Now that we’re big into machine learning in the cloud, perhaps we should start thinking about how to do it better

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Artificial intelligence predictions for 2020

Big changes in machine learning applications, tools, techniques, platforms, and standards are on the horizon

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The true value of cloud-based AI isn’t what you think

Along with intelligent applications, using AI to improve automation, security, and knowledge sharing will have far-reaching benefits

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Is that you or a virtual you? Are chatbots too real?

There's a fine line between AI that interacts for us and AI that interacts AS us. Or is there a line at all? Will anyone care? Questions abound.

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3 ways to kick off a devops program

Timing is everything, but so is knowing how CI/CD, IaC, and AIops can impact your business

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Will Google's AI make you artificially stupid?

The biggest threat from AI isn't software that steals your job. It's software that steals your mind.

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Why SaaS-based AI training will be a game changer

As machine learning engines multiply, we see the benefits of having them train each other, versus using limited training data we may or may not have

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6 best programming languages for AI development

Which programming language should you pick for your machine learning or deep learning project? These are your best options

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Handicapping the AI modeling horse race

AI has become a core focus for application developers everywhere. It’s as hot in the consumer space as it is in business, industry, research, and government

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What surgery taught me about the cloud

In the process of fixing my shoulder, I discovered some lessons for all enterprises regarding the effective integration of information

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AI gets real (sort of) in the enterprise

Turns out AI isn’t magic pixie dust to sprinkle over legacy processes and legacy tech, but a fundamental rethinking of how to do business

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Artificial intelligence today: What’s hype and what’s real?

Two decades into the AI revolution, deep learning is becoming a standard part of the analytics toolkit. Here’s what it means

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Inside AI ebook: Artificial intelligence in the enterprise

AI is having a profound effect on enterprises. But where do you begin? A variety of tools and techniques can help you get started on your own implementation. Download our 17-page guide on real-world artificial intelligence.

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Semi-supervised learning explained

Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always

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IBM Trusted AI toolkits for Python combat AI bias

IBM has released Python toolkits for identifying and mitigating against bias in training data and machine learning models

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PyTorch vs. TensorFlow: How to choose

If you actually need a deep learning model, PyTorch and TensorFlow are the two leading options

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Deep learning frameworks: PyTorch vs. TensorFlow

If you actually need a deep learning model, PyTorch and TensorFlow are both good choices

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Get started with AI using ML.Net and Model Builder

Microsoft’s .Net machine learning tooling makes it easy to add AI to your code

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