aAs you may have noticed, we’re in the midst of a craze about something called generative AI. Many hitherto ordinary people, and economists alike, are riding a wave of irrational enthusiasm about the potential for change. It’s the newest new thing.
Two antidotes are recommended for people suffering from fever. The first one,
Hype Cycle Monitor created by consultant Gartner
This indicates that the technology is currently at the “peak of inflated expectations” before plummeting into the “trough of disillusionment”. the other one is,
hofstadter’s law
describes the difficulty of estimating the time required for difficult tasks: “Even when Hofstadter’s law is taken into account, it always takes longer than expected.” Just because a powerful industry and its media patrons are losing their marbles about something doesn’t mean it’s going to wash over society as a whole like a tsunami. Reality moves at a much slower pace.
In the Christmas issue,
economist We published an instructive article titled “
Tractor history in English
” (itself a low-key homage to Marina Levicka’s hilarious 2005 novel).
History of Ukrainian tractors
of
)This article aims to explain “What tractors and horses can tell us about generative AI.” The lesson is that tractors have a long history, but they took a long time to transform agriculture. He has three reasons for this. Early versions were not as useful as backers thought. Introducing these required changes in the labor market. And farms had to reinvent themselves to use them.
So history suggests that whatever transformations AI hypemongers predict, they will materialize more slowly than expected.
However, there is one exception to this rule. It’s computer programming, or the business of creating software. Ever since digital computers were invented, humans have had to tell machines what they want them to do. Because machines could not speak English, machine code and programming languages such as Fortran, Algol, Pascal, C, C++, Haskell, and Python evolved over generations. So if you wanted to communicate with a machine, you had to learn to speak Fortran. , C++ or whatever, is a tedious process for many people. And as the title the great Donald Knuth gave to the first book of his seminal five-volume guide suggests, programming has become something of an esoteric craft.
the art of computer programming
. As the world went digital, this craft became industrialized and rebranded as “software engineering” to downplay its artisanal origins. But mastering it remained an esoteric and valuable skill.
Then along came ChatGPT and the amazing discovery that not only could you create apparently clear sentences, but you could also create software. What’s even more remarkable is that when you outline a task with a plain English prompt, the machine writes the Python code needed to accomplish that task. Often the code is not perfect, but can be debugged by further interaction with the machine. And suddenly, a whole new perspective opened up. Even non-programmers can tell a computer to do something without having to learn computer conversation.
inside
new yorker Programmer James Summers recently wrote the following:
Lamentation essay What are the implications of this development? “A range of knowledge and skills that previously took a lifetime to acquire are being swallowed up all at once,” he said. “For me, coding has always felt like an endlessly deep and rich field. Now, I want to write a memorial to it. I’ve been thinking about Lee Sedol. Sedol is the world One of the best Go players and a national hero in South Korea, he is now best known for losing to a computer program called AlphaGo in 2016.”
That seems a little strange to me. The evidence we have suggests that programmers are embracing AI assistance like ducks to water.a
recent research
For example, 70% of software developers are using or plan to use AI tools in their work this year, and 77% of them have a “favorable or very favorable” opinion of these tools. I found out that They see them as a way to increase your productivity as a programmer, speed up your learning, and even “improve accuracy” when writing computer code.
This doesn’t seem like defeatism to me, but the attitude of experts who see this technology as “power steering for the mind,” as the saying goes. In any case, they don’t sound like horses.
economist's story. But just as tractors ultimately transformed agriculture, this technology will ultimately transform the way software is developed. In that case, software engineers will need to be more like engineers than craftsmen. It’s almost time (says this engineer and columnist).
what i was reading
Smart move?
Great quote from Gary Marcus on his Substack blog.
AI companies will be exempted from lobbying activities Not responsible for copyright infringement.
control mechanism
A very thoughtful article by Diana Enríquez on the Tech Policy Press website about what it means to be.
“managed” by algorithms.
Get out of your head
a
nice post Margaret Atwood’s Substack on films about the French Revolution, including Ridley Scott’s works
napoleon.
