AI is the Big Data of 2019
I attended a Silicon Flatirons Artificial Intelligence Roundtable last week. Over the years Amy and I have sponsored a number of these and I always find the collection of people, the topics, and the conversation to be stimulating and provocative.
At the end of the two hours, I was very agitated by the discussion. The Silicon Flatirons roundtable approach is that there are several short topics presented, each followed by a longer discussion.
The topics at the AI roundtable were:
- Safety aspects of artificial general intelligence
- AI-related opportunities on the horizon
- Ethical considerations involving AI-related products and services
One powerful thing about the roundtable approach is that the topic presentation is merely a seed for a broader discussion. The topics were good ones, but the broader discussion made me bounce uncomfortably in my chair as I bit my tongue through most of the discussions.
In 2012, at the peak moment of the big data hype cycle, I gave a keynote at an Xconomy event on big data titled something like Big Data is Bullshit. My favorite quote from my rant was:
“Twenty years from now, the thing we call ‘big data’ will be tiny data. It’ll be microscopic data. The volume that we’re talking about today, in 20 years, is a speck.”
I feel that way about how the word AI is currently being used. As I listened to participants at the roundtable talk about what they were doing with AI and machine learning, I kept thinking “that has nothing to do with AI.” Then, I realized that everyone was defining AI as “narrow AI” (or, “weak AI”) which has a marvelous definition that is something like:
Narrow artificial intelligence (narrow AI) is a specific type of artificial intelligence in which a technology outperforms humans in some very narrowly defined task. Unlike general artificial intelligence, narrow artificial intelligence focuses on a single subset of cognitive abilities and advances in that spectrum.
The deep snarky cynic inside my brain, which I keep locked in a cage just next to my hypothalamus, was banging on the bars. Things like “So, is calculating 81! defined as narrow AI? How about calculating n!? Isn’t machine learning just throwing a giant data set at a procedure that then figures out how to use future inputs more accurately? Why aren’t people using the phase neural network more? Do you need big data to do machine learning? Bwahahahahahahaha.”
That part of my brain was distracting me a lot so I did some deep breathing exercises. Yes, I know that there is real stuff going on around narrow AI and machine learning, but many of the descriptions that people were using, and the inferences they were making, were extremely limited.
This isn’t a criticism of the attendees or anything they are doing. Rather, it’s a warning of the endless (or maybe recursive) buzzword labeling problem that we have in tech. In the case of a Silicon Flatirons roundtable, we have entrepreneurs, academics, and public policymakers in the room. The vagueness of the definitions and weak examples create lots of unintended consequences. And that’s what had me agitated.
At an annual Silicon Flatirons Conference many years ago, Phil Weiser (now the Attorney General of Colorado, then a CU Law Professor and Executive Director of Silicon Flatirons) said:
“The law doesn’t keep up with technology. Discuss …”
The discussion that ensued was awesome. And it reinforced my view that technology is evolving at an ever-increasing rate that our society and existing legal, corporate, and social structures have no idea how to deal with.
Having said that, I feel less agitated because it’s just additional reinforcement to me that the machines have already taken over.