Civil Society Signal and Noise?

I’ve noticed an uptick in my email of research reports from nonprofits and advocacy groups. I suppose this makes sense in a time of continued pressures on journalism and the swamp of bad information that is the internet. How should we know to trust these reports?

AI won’t make artists redundant – thanks to information theory – Piotr Migdał via Hacker News and read the comments.

Would be easy to create conceptually and aesthetically new works using prompts? Think about “addiction to social media” or even a broader idea “a current societal problem”. The question of “how?” turns quickly into “what do we want to do in the first place?”. Sure, you can create a stock image of someone looking at their phone. But if you want to create something genuinely new, you will need to expand your prompt a lot.

Fan Vote 2023

The top five artists, as selected by the public, will comprise a “Fans’ Ballot” that will be tallied along with the other ballots to choose the 2023 inductees.

Roots of Rock & Roll | Country, Folk, and Bluegrass

Playlist from they Rock & Roll Hall of Fame

#Links #CivilSociety #AI #Music

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CS182: Ethics, Public Policy, and Technological Change

Our goal is to explore the ethical and social dimensions of technological innovation. Stanford has a special responsibility to address these topics in light of its role as a seedbed of Silicon Valley. By integrating perspectives from computer science, philosophy, and social science, the course will provide learning experiences that robustly and holistically examine the impact of technology on humans and societies.

What Is Pleroma? | Lainblog

Pleroma is a microblogging server software that can federate (= exchange messages with) other servers that support the same federation standards (OStatus and ActivityPub). What that means is that you can host a server for yourself or your friends and stay in control of your online identity, but still exchange messages with people on larger servers. Pleroma will federate with all servers that implement either OStatus or ActivityPub, like GNU Social, Friendica, Hubzilla and Mastodon.

Comment by nicbou on I prefer semi-automation | Hacker News

Automation is not just about saving time. It’s also about saving a “recipe” for a task, about avoiding human error, and about staying in the flow.

It’s our industry’s equivalent to mise en place. Deal with the drudgery in advance to focus on your work when it matters.

Things they didn’t teach you about Software Engineering by Vadim Kravcenko via Hacker News

Although it may sound surprising, the primary focus of a software engineer’s job is not writing code but rather creating value through the use of software that was written. Code is simply a tool to achieve this end goal. Code -> Software -> Value.

How They Must Write: Saving the Five-Paragraph Essay and Other Contingencies by Thomas Basbøll

The five-paragraph essay forces students to be both knowledgeable and articulate about an assigned topic “on the spot” and therefore gives them a straighforward opportunity demonstrate to us whether they have learned what we have tried to teach them. If they know they actually have to a write a good one to a prompt they don’t know in advance ((but of course on a topic that is relevant to the class they are taking) they have a reason, not just to learn the material, but to keep their thinking and prose in good shape.

Why cassettes? by Dominique Cyprès via Hacker News

The way I relate to a recording when I carry it around physically in my pocket—not just my phone, which can provide access to that recording and many others, but a tape dedicated to containing just that recording and no other—just feels different. The tape becomes a little talisman that turns an intangible recording into a very tangible object, something I can see and turn over in my hands and physically give to another person.

GitHub is Sued, and We May Learn Something About Creative Commons Licensing by Roy Kaufman

Plaintiffs allege that OpenAI and GitHub assembled and distributed a commercial product called Copilot to create generative code using publicly accessible code originally made available under various “open source”-style licenses, many of which include an attribution requirement. As GitHub states, “…[t]rained on billions of lines of code, GitHub Copilot turns natural language prompts into coding suggestions across dozens of languages.” The resulting product allegedly omitted any credit to the original creators.

#AI #copyright #ethics #automation #writing #links

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Image Credit: A DALL-E 2 generated image of a person standing in a field, painted in an abstract style

In ChatGPT and the Imagenet Moment Benedict Evans writes:

… a generative ML system could make lots more ‘disco’ music, and it could make punk if you described it specifically enough (again, prompt engineering), but it wouldn’t know it was time for a change and it wouldn’t know that punk would express that need. When can you ask for ‘something raw, fresh and angry that’s a radical change from prog rock?’ And when can a system know people might want that? There is some originality in creating new stuff that looks like the patterns we already have, but the originality that matters is in breaking the pattern. Can you score that?

As AI gets better and better at repeating patterns that exist. As we get better at producing prompts – what Evans calls ‘prompt engineering’. As AI starts getting integrated in tools in ways we don’t even notice. As all that happens, we and how do we identify the places we need human intervention? And not just to retain unique value or to increase the amount of interesting hard work we do, but so that we are not building a future based on our past. Because, you know what? That past is full of exploitation, extraction, and oppression. Many would argue the very AI works is an example. It’s intelligence is built off the work of others in a mass consumption way.

So, what does this mean for civil society? How do we take advantage of the technology, influence the system, and judge the output?

1. Algorithm detectives. We need an independent body who reviews the tools – algorithms, the training of AI – so that we can be clear about the patterns we are learning from and the ways we promote the responses.
2. Use AI to identify patterns of injustice. This will require excellent prompt engineers who are asking and asking and asking and then sharing the results.
3. Illuminate what is missing. If AI is trained on available massive data, we need to show what and who is missing from that data. And we have to find ways to include it. Sometimes that will come from technical means – refining, adding, training – and sometimes it will come from advocacy. It must be intentional.
4. Use the tools. We can’t just opt out. Usage shapes the tools. We have to use them and aggregate our learnings with the goal of improving our own efficiency and shaping the tools themselves. And let’s normalize it. Don’t hide that the you turned the grant question into an AI prompt.
5. Build context. That’s what struck me about the quote above: context is something’s humans have in a hyper local way. We can adjust, disregard, slow down or speed up what AI generates for us based on the context. We can identify places where the pattern is wrong for reasons of justice and equity. We have the context and the experience.

#areas #AI

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