By Peter Bailis - January 15, 2020
Original article: https://www.rtinsights.com/3-inconvenient-truths-about-ai-and-ml/
To bridge the gap between the data we’re collecting and the way organizations interface with it, we need to address some uncomfortable realities.
As we step into the next decade, there’s a growing sense – almost an inevitable momentum – that we’re headed towards a golden age of AI. Over the past year, we’ve witnessed incredible advances in applying artificial intelligence techniques to image recognition, language processing, planning, and information retrieval. We’re seeing practical applications of machine learning, improving everyday activities. There are more amusing applications, too, including one team teaching AI how to craft puns.
This is a future I’ve been researching and investing in for the past five years, starting at Berkeley and continuing through our work at the Stanford DAWN project. Our goal is to democratize AI by making it dramatically easier to design, build, deploy, and manage AI- and ML-powered applications. And we’ve seen huge success in a few very targeted domains.
However – particularly in the world of business – it feels like we’re “not quite there yet” when it comes to finding meaningful enterprise ML and AI applications. There’s a growing sentiment that many applications of enterprise ML are too bespoke, require extensive consulting investment, and are at risk for never showing a positive ROI. If we’re going to bridge the gap between the kinds of data we’re collecting at scale and the way analysts, business leaders, and organizations interface with it, we need to address some uncomfortable realities.