This content originally appeared on Ethan Marcotte’s website and was authored by Ethan Marcotte
Hey, you. I made us a playlist.
In this new era of AI, the possibilities are endless, not just for design but across the entire product development process. It will play a central role in how teams work in Figma, helping them get to a first draft faster and then design and build through to great products.
With AI Companion empowering you in Zoom, you can save time, improve the quality of your work, stay more connected with teammates no matter where or when they work, and receive insightful coaching that will help you level up skills like delivering a great presentation.
Generative AI tools have the potential to significantly increase worker productivity by automating tasks, generating content, and assisting with decision making.
…it is clear automation and AI have the potential to meaningfully improve productivity while giving employees a greater sense of purpose and well-being. Smarter tools and workflows can free employees from tedious low-level tasks and accelerate their accomplishments.
If [artificial intelligence] is successfully created, this technology could help us elevate humanity by increasing abundance, turbocharging the global economy, and aiding in the discovery of new scientific knowledge that changes the limits of possibility.
The takeaway from our qualitative investigation was that letting GitHub Copilot shoulder the boring and repetitive work of development reduced cognitive load. This makes room for developers to enjoy the more meaningful work that requires complex, critical thinking and problem solving, leading to greater happiness and satisfaction.
When you train a LLM on a design system’s codebase, conventions, syntax and documentation, you get a component boilerplate generator on steroids. Human developers can then evaluate any outputted code, refine it (with or without the help of AI), and bring it over the finish line. In this scenario, AI becomes a junior developer contributing code for review like any other developer. We guestimate this approach could help developers create components 40-90% faster than manually writing things from scratch.
GitClear analyzed approximately 153 million changed lines of code, authored between January 2020 and December 2023. […] We find disconcerting trends for maintainability. Code churn — the percentage of lines that are reverted or updated less than two weeks after being authored — is projected to double in 2024 compared to its 2021, pre-AI baseline. We further find that the percentage of “added code” and “copy/pasted code” is increasing in proportion to “updated,” “deleted,” and “moved” code. In this regard, code generated during 2023 more resembles an itinerant contributor, prone to violate the DRY-ness of the repos visited.
In a six-week pilot at Deloitte with 55 developers for 6 weeks, a majority of users rated the resulting code’s accuracy at 65% or better, with a majority of the code coming from [GPT-3’s Codex program]. Overall, the Deloitte experiment found a 20% improvement in code development speed for relevant projects. Deloitte has also used Codex to translate code from one language to another. The firm’s conclusion was that it would still need professional developers for the foreseeable future, but the increased productivity might necessitate fewer of them.
Many technological systems, when examined for context and overall design, are basically anti-people. People are seen as sources of problems while technology is seen as a source of solutions. When, in the factory, the owners feel workers are too slow or too unreliable or too demanding, they are replaced by machines.
With cybernetics fully upon us, we are faced with the “deskilling” or even elimination of segments of the workforce. “Performance accelerators” and online job aids replace cognitive decision-making, and job automation takes over lifetimes of human knowledge and experience. Although we know the computer age is here to stay, none of us wants to be replaced by a machine, even if it is only “artificial intelligence.”
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In some cases skilled workers no longer use the expertise they developed over years of experience. Instead, like their less experienced counterparts, they learn how to operate automated systems that have been modeled on their skills. In other cases, their jobs may be totally obsolete.
In 1969, researchers studied 25,000 white-collar workers in 88 major industries and confirmed a marked decline in job satisfaction. “The office today,” researchers concluded, “where work is segmented and authoritarian, is often a factory.” The introduction of computer technology and other office automation in the last 20 years has compounded this assessment.
In the making of a Watch, If one Man shall make the Wheels, another the Spring, another shall Engrave the Dial-plate, and another shall make the Cases, then the Watch will be better and cheaper, than if the whole Work be put upon any one Man.
AI does provide a powerful pretext for bosses and institutions and boards of directors to degrade the working conditions of the workers who are managing these AI systems.
But very few jobs now offer real fulfillment, professional or material. For millions, life has become precarious. Increasingly, the job as an institution is under siege, because employers — public and private — hire only on a contingent or contract basis and do not offer health care coverage, pensions, or paid holidays and vacations to a substantial portion of their workforce.
Just as Black people are overrepresented in the jobs that will succumb to automation, they are underrepresented in the jobs that will thrive in the automation economy.
Automation, of course, has been an approaching death star for generations, with major debates about technological unemployment in every modern decade. […] All evidence, however, now points to the (robo-)wolf actually at the door, especially the doors of low-income workers. The 2016 Annual Report of the Council of Economic Advisers warned that fully 83 percent of jobs paying less than $20 per hour face the threat of automation in the near future.
According to the Economic Policy Institute, productivity has gone up 64.7% over the last 40 years, yet wages have only gone up 14.8%. Over the same-ish timespan, according to Pew Research…
From 1970 to 2018, the share of aggregate income going to middle-class households fell from 62% to 43%. Over the same period, the share held by upper-income households increased from 29% to 48%.
We cannot be part of a discussion on what risks a certain technology has without asking whose risks. It makes an awful lot of difference. Assume you are talking about video display terminals, for example; the great discussion is “Are they or are they not putting the operator’s health or eyes at risk?” You don’t discuss whether there are risks; you discuss whose risks. Who is it that is at risk? It’s quite pointless to talk about risk-benefit without saying, “Are those who are at risk also getting the benefits, or are those who are getting the benefits very far removed from risk?”
[…] The questions to ask are “Whose benefits? Whose risks?” rather than “What benefits? What risks?”
This has been “Generative.” a post from Ethan’s journal.
This content originally appeared on Ethan Marcotte’s website and was authored by Ethan Marcotte
Ethan Marcotte | Sciencx (2024-02-29T05:00:00+00:00) Generative.. Retrieved from https://www.scien.cx/2024/02/29/generative/
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