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Data & Tech Stuff, Vol. 2

February 19, 2020

Phew -- what a month!data tech stuff logo

I've only made one single 2020/Hindsight joke this year so far, which, in hindsight...

OK let's get to it!

As always, email me any thoughts you have or links you want thrown in here. And please feel free to forward this newsletter to anyone you think might be interested.

I, for one, welcome the AI Winter...

There have been more recent reports (BBC, Forbes, HackerNews) that we may be on the cusp of an “AI Winter,” where the pace of innovation, advancement, and implementation of artificial intelligence (AI) slow down compared with the “Renaissance we've seen in recent years.

I'm a little of the mindset that all of this talk is relative -- because what are we even talking about when talking about AI, anyway? It's not like we're up to the level of artificial general intelligence quite yet -- so what is slowing down? Essentially, there's a recognition by folks far and wide (investment to government to public interest) that the enthusiasm and supposed benefits of an AI world aren't all they're cracked up to be. The algorithms are biased, not transparent, and not ready for adoption in prime time. This isn't just an academic point--three cities have already banned facial recognition and surveillance implementations, a technology that relies heavily on AI.

I think one of the challenges is that the term “AI” has become a placeholder for technological magic – in much the same way that big data or business intelligence or mobile once were. We should update Arthur C. Clarke's Third Law to say, "Any sufficiently advanced technology is indistinguishable from AI."

I'm not an AI hater, it should be noted. I should probably disclose here that I'm someone who spends a fair amount of time in the workplace trying to figure out when/where to apply new and emerging technologies for use in projects — solving tough challenges with the right mix of people, information, and technology.

This is all to say that not only am I not worried about an AI winter, but I actively welcome it. Let me explain why.

My modified Gartner Hype Cycle
My modified Gartner Hype Cycle

I keep coming back to that now-daily mantra of "AI in the pitch deck, machine learning (ML) in the project plan, and linear regression in the execution." (if Python is more your speed) I'm not saying that everything we see around AI is lipstick on a pig -- just that there is a LOT of hype out there, and for every one legitimate innovation with AI/ML, there are about 100 of "probably just analytics on a spreadsheet" under the hood. Conservatively.

Too much time seems to be spent on knocking down false claims about what's possible with current constraints in ML, where it would be best to apply particular frameworks and novel technological solutions over traditional methods, and where we should be prioritizing our limited resources as organizations working in the research-meets-social-impact-meets-technical-assistance-meets-sustainable-development arena.

This isn't to say we are slowing down our interest in what's on the horizon, by any means. My team is particularly excited about a current project focused on crafting a real-world standards checklist for ethical AI in our programs, for example (and building off what we're seeing from the White House on AI), and taking some of our recent lessons and packaging them into a more universal and accessible how-to guide for AI projects in social impact settings more generally.

We don't always need an AI Renaissance to be driving full-tilt — sometimes it's nice to have an AI Winter while we have a Sustainability Spring or an Implementation Summer (™, obviously).

A “Technology for Good” Roundup


Thirsty for more? Check out what else we are doing and talking about when it comes to Digital Transformations.


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