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Discover the latest in artificial intelligence and machine learning.

 
 

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Responsible AI: a roadmap

Artificial intelligence represents an unprecedented opportunity. To realize this potential, it’s critical that stakeholders and implementation teams understand the privacy, security, and ethical issues associated with using AI. Take the first step to applying an ethical framework to existing or anticipated AI systems by downloading our guide here.

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The risks of using third-party data

In 2018, companies spent an estimated $19 billion on third-party data in the United States alone. Unfortunately, using third-party data isn’t always your best bet. In fact, it can pose a serious risk to you and your customers.

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AI privacy and customer experience: avoiding the creep factor

The creep factor is when companies get a little too personal with their customers. So personal, in fact, that it feels like a breach of privacy. Chances are, you’ve experienced this at one point or another, so you can understand just how unsettling it is. To avoid it, businesses can be more transparent about how they’re using customer data. Better yet, they can deliver experiences that don’t rely on using any personally identifiable information.

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Making AI more human, one sticker at a time

In the spirit of simplicity, our machine-learning scientists put together a list of some of the most enigmatic-sounding algorithms, problems, and theorems. Then, we created graphics to represent each of them. By putting a face to concepts that are typically considered abstract, we hope to make them easier to understand. We also hope to humanize them. 

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The AI dilemma: build versus buy

Businesses that are navigating AI for the first time often ask us the same thing: Should we build it, or buy it? It’s a pretty common question, actually. And a good one. That’s why we’re going to break down the pros and cons of both approaches. Then, we’ll give you our totally unbiased answer.

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3 reasons why you needed to adopt AI yesterday

In 2017, about 20% of businesses adopted AI. A year later, that number has more than doubled. This is all to say that artificial intelligence isn’t just a buzzword. It’s here to stay. Just ask the businesses that are already benefitting from it.

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A guide to applying machine learning in the enterprise - part 3

You’ve picked a problem or opportunity that ML is suited to address and lined up resources to work on the project. Fortunately, you don’t need to recreate the wheel to do this: existing scoping and planning frameworks provide a useful starting point. Here, we’ll draw from McKinsey’s problem definition framework.

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