Ask the CEO: What's Behind the AI Revolution?

Ask the CEO: What's Behind the AI Revolution?



Think about all the data we've been collecting. That has been a phenomenal wealth that we did not know how to use in the past.

Today, we are actually so much better at understanding data, processing it properly, and feature engineering it to extract meaningful insights.

In addition to the data revolution, of course, there was the hardware expansion — the new abilities we're seeing with better hardware everyday, and the better algorithms that are being developed every day.

But if you think about this: the cloud made something phenomenal happen. The cloud made everyone able to take that data with a credit card and process massive jobs of training and learning from that data with just a little bit of money. And that really helped a whole new breed of startups that use that those new AI technologies.

In addition to all that, the community developed this new concept over time called open source, and open source made things so much better.

We started building incrementally on top of the shoulders of giants so we learned from the past. We open source our products. We open source our code. We open source our technologies and somebody else improves a little bit on top of it and makes it a little bit better. And someone else makes that little bit better — incremental improvement. We are progressing so much faster than the past, almost exponentially.

And finally I think what's enabling this really is all the great work that is available for free by trainers that are helping millions of people learn the technology and get passionate about it and understand it better. A new generation is starting to learn about things that are significantly complex.



50 AI Secrets: How Every Fortune 50 Company is Using AI Right Now

Get notified when we publish a new story.

Our Most Recent Articles

Tutorial: Building Your First Kubeflow Pipelines Workflow (Part 2)
Data science workflows on Kubernetes with Kubeflow Pipelines (Part 1)
A Tale of Two Companies
The Ideal Phases of Machine Learning Projects