Apparently the Jeopardy victory that thrust Watson into the limelight was five years ago this month. Since then, AI has captured public and industry attention.VC investment in AI reached $8.5 billion last year according to Quid [but no mention of what that figure includes].
[bctt tweet=”‘Modern AI enabled by vast new data sources + ML software + cloud.'”]
Backers need to remember that progress will come slowly: “people routinely overestimate what can be done with new technology in three years, yet underestimate what can be done in 10 years.” Deep learning is one area seeing rapid gains over the last year or two, allowing improved software for “the problems of perception, seeing, hearing and language.”
University of Texas MD Anderson Cancer Center in Houston is building software to help manage diabetes using Watson. “It was a lot harder than we thought,” said Dr. Lynda Chin (@lyndachin), the chief innovation officer for the university health system. “But our experience has convinced me that we can build an A.I. engine that improves care.” (Tweet this)
[bctt tweet=”‘We’ll look back on AI revolution & compare impact to steam engine or electricity.’ @erikbryn”]
- We’re no doubt in the midst of an AI renaissance. In fact, AI is standing on the shoulders of it’s geekier and less-screenplay-ready cousin, machine learning.
- The “small steps” theme in this article is an important one for businesses. Most enterprises should should get started with classic use predictive analytics use cases like personalization, revenue optimization, lead scoring, churn, and fraud.
- Cloud-based machine learning and AI APIs offered by companies like IBM, Amazon and Microsoft lower the barrier of entry, raise the level of abstraction, and are the way most enterprises will deliver machine learning in the future. Identify the APIs available via your cloud platform of choice and get to know them, then build something.
Image: via randychiu on Flickr