Link tags: model

Automate the CEOs - by Hamilton Nolan - How Things Work

Let’s be rational here. If I were to imagine a job that was a perfect candidate for replacement by AI, it would be one that consists of measurable tasks that can be learned—allocation of capital, creation and execution of market strategy, selection of candidates for top roles—and one that costs the company a shitload of money. In other words: executives.

The logic is sound. However…

The CEOs will be spared from automation not because they should be, but because they are making the decisions about who is spared from automation.

“Artificial Intelligence & Humanity,” an article by Dan Mall

AI is great anything quantity-related and bad and anything quality-related.

Sensible thinking from Dan here, that mirrors what we’re thinking at Clearleft.

In other words, it leans heavily on averages; the closer the training data matches an average, the higher degree of confidence that the result is more “correct,” or at least desirable.

The problem is that this is the polar opposite of what we consider creativity to be. Creativity isn’t about averages. It’s about the outliers, sometimes the one thing that’s different than all the rest.

Generative AI: What You Need To Know

Generative AI: What You Need To Know is a free resource that will help you develop an AI-bullshit detector.

You can read all the cards on one page, print them out, or print to PDF.

ChatGPT is not ‘artificial intelligence.’ It’s theft. | America Magazine

But in calling these programs “artificial intelligence” we grant them a claim to authorship that is simply untrue. Each of those tokens used by programs like ChatGPT—the “language” in their “large language model”—represents a tiny, tiny piece of material that someone else created. And those authors are not credited for it, paid for it or asked permission for its use. In a sense, these machine-learning bots are actually the most advanced form of a chop shop: They steal material from creators (that is, they use it without permission), cut that material into parts so small that no one can trace them and then repurpose them to form new products.

How you want me to cover artificial intelligence

Seven principles for journalism in the age of AI

1. Be rigorous with your definitions.
2. Predict less, explain more.
3. Don’t hype things up.
4. Focus on the people building AI systems — and the people affected by its release.
5. Offer strategic takes on products.
6. Emphasize the tradeoffs involved.
7. Remember that nothing is inevitable.

To have “true AI,” we need much more than ChatGPT - Big Think

LLMs have never experienced anything. They are just programs that have ingested unimaginable amounts of text. LLMs might do a great job at describing the sensation of being drunk, but this is only because they have read a lot of descriptions of being drunk. They have not, and cannot, experience it themselves. They have no purpose other than to produce the best response to the prompt you give them.

This doesn’t mean they aren’t impressive (they are) or that they can’t be useful (they are). And I truly believe we are at a watershed moment in technology. But let’s not confuse these genuine achievements with “true AI.”

Google’s AI Hype Circle

Google has a serious AI problem. That problem isn’t “how to integrate AI into Google products?” That problem is “how to exclude AI-generated nonsense from Google products?”

AI isn’t the app, it’s the UI - Stack Overflow Blog

In some ways, the fervor around AI is reminiscent of blockchain hype, which has steadily cooled since its 2021 peak. In almost all cases, blockchain technology serves no purpose but to make software slower, more difficult to fix, and a bigger target for scammers. AI isn’t nearly as frivolous—it has several novel use cases—but many are rightly wary of the resemblance. And there are concerns to be had; AI bears the deceptive appearance of a free lunch and, predictably, has non-obvious downsides that some founders and VCs will insist on learning the hard way.

This is a good level-headed overview of how generative language model tools work.

If something can be reduced to patterns, however elaborate they may be, AI can probably mimic it. That’s what AI does. That’s the whole story.

There’s very practical advice on deciding where and when these tools make sense:

The sweet spot for AI is a context where its choices are limited, transparent, and safe. We should be giving it an API, not an output box.

Artificial intelligence: who owns the future? - ethical.net

Whether consciously or not, AI manufacturers have decided to prioritise plausibility over accuracy. It means AI systems are impressive, but in a world plagued by conspiracy and disinformation this decision only deepens the problem.

Why Chatbots Are Not the Future by Amelia Wattenberger

Of course, users can learn over time what prompts work well and which don’t, but the burden to learn what works still lies with every single user. When it could instead be baked into the interface.

Talk: The Expanding Dark Forest and Generative AI

Maggie Appleton:

An exploration of the problems and possible futures of flooding the web with generative AI content.

“the secret list of websites” - Chris Coyier

Google is a portal to the web. Google is an amazing tool for finding relevant websites to go to. That was useful when it was made, and it’s nothing but grown in usefulness. Google should be encouraging and fighting for the open web. But now they’re like, actually we’re just going to suck up your website, put it in a blender with all other websites, and spit out word smoothies for people instead of sending them to your website. Instead.

I concur with Chris’s assessment:

I just think it’s fuckin’ rude.

The one about AI - macwright.com

Writing, both code and prose, for me, is both an end product and an end in itself. I don’t want to automate away the things that give me joy.

And that is something that I’m more and more aware of as I get older – sources of joy. It’s good to diversify them, to keep track of them, because it’s way too easy to run out. Or to end up with just one, and then lose it.

The thing about luddites is that they make good punchlines, but they were all people.

The Technium: Dreams are the Default for Intelligence

I feel like there’s a connection here between what Kevin Kelly is describing and what I wrote about guessing (though I think he might be conflating consciousness with intelligence).

This, by the way, is also true of immersive “virtual reality” environments. Instead of trying to accurately recreate real-world places like meeting rooms, we should be leaning into the hallucinatory power of a technology that can generate dream-like situations where the pleasure comes from relinquishing control.

The Intelligence Illusion

Baldur has new book coming out:

The Intelligence Illusion is an exhaustively researched guide to the business risks of Generative AI.

LukeW | Ask LukeW: New Ways into Web Content

I like how Luke is using a large language model to make a chat interface for his own content.

This is the exact opposite of how grifters are selling the benefits of machine learning (“Generate copious amounts of new content instantly!”) and instead builds on over twenty years of thoughtful human-made writing.

Welcome to the Artificial Intelligence Incident Database

The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems.

We need to tell people ChatGPT will lie to them, not debate linguistics

There’s a time for linguistics, and there’s a time for grabbing the general public by the shoulders and shouting “It lies! The computer lies to you! Don’t trust anything it says!”

Stochastic Parrots Day Tickets, Fri, Mar 17, 2023 at 8:00 AM | Eventbrite

This free event is running online from 3pm to 7pm UK time this Friday. The line-up features Emily Bender, Safiya Noble, Timnit Gebru and more.

Since the publication of On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜 two years ago, many of the harms the paper has warned about and more, have unfortunately occurred. From exploited workers filtering hateful content, to an engineer claiming that chatbots are sentient, the harms are only accelerating.

Join the co-authors of the paper and various guests to reflect on what has happened in the last two years, what the large language model landscape currently look like, and where we are headed vs where we should be headed.

ChatGPT Is a Blurry JPEG of the Web | The New Yorker

A very astute framing by Ted Chiang—large language models as a form of lossy compression for text.

When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression.

A lot of uses have been proposed for large language models. Thinking about them as blurry JPEGs offers a way to evaluate what they might or might not be well suited for.