Over the last several weeks, I have been on the road a lot for various engagements with customers as well as for our research activities. In virtually every meeting, artificial intelligence, especially machine (ML) and deep learning (DL), comes up as a discussion topic. The AI/ML/DL area is incredibly exciting and I am both surprised and impressed by the powerful applications and examples that make the headlines on a regular basis.
At the same time, it is clear from all the discussions that AI is at the top of the hype cycle, which is concerning in that the expectations on what AI will deliver is potentially inflated. This is most evident by those who are not as well-versed on the topic or underlying technology and who often expound on the fabulous opportunities that their products and services can offer if sprinkled with a bit of AI dust over them.
Even worse are the cases where individuals talk about their expectations that, in no uncertain terms, would require “general AI” rather than the “narrow AI” technologies available today. Inflated expectations are not helping anyone and will only lead to disappointment.
AI is at the top of the hype cycle, which is concerning in that the expectations on what AI will deliver are potentially inflated
The challenge is that we are at a stage in society, as discussed in my previous post, where we’re moving toward the “post-intelligent design” era. In ML/DL, as humans, we are building systems that are building (or rather training) systems that accomplish incredible feats, but we don’t actually know how these systems work. This is a major departure from the predominant engineering approaches over the last decades and even the last centuries.
Recently, I have been writing about the software engineering challenges associated with building AI systems, but the key message that I am looking to get across is broader than what I have communicated to date. The core idea is that AI is not a silver bullet, and is not going to magically solve problems without any significant investments from our end.
For a machine or deep learning model to work well, we need accurate, clean and typically labeled training and validation data; a well-designed model; iterations with several alternative designs to figure out which model performs best; reliable data pipelines to hook the model to; monitoring and logging to track model performance during operation; continuous remodeling and retraining to support continuous deployment; and more.
As I’ve outlined in previous posts in this series, building production-quality ML/DL systems requires a solid engineering approach. In that sense, these technologies are tools in our toolbox rather than silver bullets that magically solve global warming, poverty, inequality and everything else that ails the world. This also goes the other way: it is counterintuitive to blame AI for everything that goes bad in the world.
Despite the hopes and expectations of the people that I meet, artificial intelligence, machine learning and deep learning are no silver bullets. They are novel technologies in our toolbox that help us solve problems that we were unable to solve before, or at least solve as well. But, as the saying goes, there is no free lunch. Achieving success using AI/ML/DL requires engineering, discipline and operationalization. And although any advanced technology may seem indistinguishable from magic when looking at it from the outside, as famous science fiction writer Arthur C. Clarke once quipped, on the inside, it’s typically heavyweight engineering. So, apply AI to your heart’s content, but remember that, as Thomas Edison advised, it comes dressed in overalls.
This article was originally published on janbosch.com