“if you can reduce decision making to an algorithm or a formula or a process or a procedure you avoid the risk of blame” because it wasn’t an open ended question where you had to exercise choice. It was an optimization problem that could be solved by simple maths. And when we can reduce any decision to a question about optimization rather than choice we can more easily win arguments and leave responsibility to the computer instead of ourselves. — Rory Sutherland , Nudgestock 2024
This is a similar argument to the one made by economist Ha-Joon Chang in his critical discussion with Gary Stevenson about the state of economics and economics education around the world: “Math becomes a substitute for thinking”
As the world turns to Artificial Intelligence to solve all its problems, we are also giving up on a lot of things.
We lazily ask computers to write our copy, code, stories (“AI is culture” — Carole Cadwalladr”). We ask it to make sense of reports, research, contexts (sense making). We ask it to take over our relationships (write our emails and respond to our customers). And as the joy of efficiency outweigh the joy of serendipity, discovery, curiosity, we seem to revel at our ability to produce more outputs instead of getting to better outcomes (ugh).
Artificial Intelligence does what we tell it*. It is a predictability machine built on the past. If we want AI to make the world better that is on us, not the machine. Which means straining ourselves through the marshmallow test, sticking to what makes us human and taking the lead.
This article is a collection of insights, quotes and articles inspiring us to take back that lead.
ON THINKING:
“Maybe the real risk isn’t AI itself, but how small we’re letting our thinking become because of it?” — Maria Figenschau
https://www.linkedin.com/feed/update/urn:li:ugcPost:730619083754408345
“Thinking is a premium, and yet it is also the very thing that is most at risk.” — Stephanie Bown
https://www.fastcompany.com/91313811/ai-advances-mean-we-need-to-double-down-on-this-one-skill
“The act of writing helps us discover and refine our thoughts. When I sit down to write, the cadence and structure required to build a narrative helps surface ideas that exist somewhere between my conscious and unconscious mind.” — Igor Schwarzmann
https://thenew.igorschwarzmann.com/p/writing-is-thinking-but-does-it-need
[responding to a comment that chatGPT now can create MindMaps] “Thus defeating a key purpose of creating mind maps — engaging critical thinking to visually map out your thoughts and make complex topics easier to understand.”, Jonathan Boymal
EFFICIENCY IS FOR ROBOTS:
“We’re really good at things that are inefficient. Science is inherently inefficient. It runs on that fact that you have one failure after another. It runs on the fact that you make tests and experiments that don’t work, otherwise you’re not learning. It runs on the fact that there is not a lot of efficiency in it. Innovation by definition is inefficient, because you make prototypes, because you try stuff that fails, that doesn’t work. Exploration is inherently inefficiency. Art is not efficient. Human relationships are not efficient. These are all the kinds of things we’re going to gravitate to, because they’re not efficient. Efficiency is for robots.” — Kevin Kelly
https://www.ted.com/talks/kevin_kelly_how_ai_can_bring_on_a_second_industrial_revolution/transcript
“Efficiency alone doesn’t create knowledge. It doesn’t foster insight. And it certainly doesn’t lead to wisdom. Nor does it build resilience — because resilience isn’t the product of perfect systems. It comes from the ability to adapt when those systems inevitably fail. And that’s precisely what automation threatens to erode. The real challenge isn’t just how to use AI. It’s how to harness it without surrendering the very skills that make us adaptable in the first place.” — Eric Markowitz
ON INSIGHTS
Are we confusing scale, quantity and data visualization with value?
“This leads to the dangerous idea that statistically normalized and standardized data is more useful and objective than qualitative data” — Tricia Wang
https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7
“Scale [of data] is often valued over story” — Mikkel Krenchel and Christian Madsbjerg
ON THE PROCESS OF DESIGN:
Design is not the finished artifact, it is the process of “kicking and screaming”, “enthusiastically discovering and embracing all constraints” in order to get to that artifact”. With too much AI we can loose this process.
“Design is the sum of all constraints. Constraints of size, price, attention, motivation, distribution, time… The design process is defined by its ability to recognise all constraints, and its willingness and enthusiasm to work within them.” — Charles Eames
“Those artifacts are end result of design, those are the outcomes of design. Design was everything, the thinking, the hard work, the yelling and the screaming that went into trying to come to the decisions that made that artifact” — Miroslav Azis
https://mediacenter.ibm.com/media/Designthinking/1_rr228ajg
“In other words, they ran usability tests not to uncover problems or generate insight, but to justify decisions that have already been made. In that kind of environment, outcomes take a back seat to optics. We stopped asking the hard questions. Even when we wanted to, we didn’t have the time, the budget, or the air cover. Better to push pixels and pray.” — Dan Maccarone
https://www.linkedin.com/pulse/we-built-ux-broke-now-have-fix-dan-maccarone-miwpe/
