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Discover the latest in artificial intelligence and machine learning.
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.
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.
In today’s blog, we’re going to unpack the first core step to solving a business problem with machine learning. Contrary to popular belief, ML is not magic, which is to say it’s not well suited to all problems. Here are the top things to look for to ensure your foray into ML is successful.
This blog is the first in a series designed to help enterprise leaders capture the promise of machine learning—pragmatically. That means operationalizing ML to drive revenue and business results.
When it comes to privacy, best intentions often fall short. Today, we’re going to look at how companies like Apple and Google are adapting privacy techniques, and how this all fits into the bigger puzzle of the roles that trust and responsibility play in artificial intelligence. Along the way, we’ll take a closer look at a technical approach called differential privacy, which is reshaping how companies analyze and share user data.
Getting at a simple definition for artificial intelligence isn’t easy. After all, it’s a complicated topic. But understanding AI and what it could mean for your business is critical. For one, AI is transforming how consumer enterprises deliver value to their customers. More than that, it’s empowering businesses to derive value from every customer interaction.