https://themarkup.org/hello-world/2023/07/08/ai-environmental-equity-its-not-easy-being-green

A few months ago, I spoke with Shaolei Ren, as associate professor of computer science at University of California, Riverside, and his team about their research into the secret water footprint of AI. Recently, Ren and his team studied how AI’s environmental costs are often disproportionately higher in some regions than others, so I spoke with him again to dig into those findings.

His team, which includes UC Riverside Ph.D. candidates Pengfei Li and Jianyi Yang, and Adam Wierman, a professor in the Department of Computing and Mathematical Sciences (CMS) at the California Institute of Technology, looked into a path toward more equitable AI through what they call “geographical load balancing.” Specifically, this approach attempts to “explicitly address AI’s environmental impacts on the most disadvantaged regions.”

Ren and I talked about why it’s not easy being green and what tangible steps cloud service providers and app developers could take to reduce their environmental footprint.

I've recently noticed that some particular users, who post/comment quite often, frequently contribute with high quality content across an impressive spectrum of themes. These posts/comments usually go into great detail, an approach suitable of a "semi-expert", that honestly baffles me.

This lead me to think that Tildes users might be using AI language models to aid in the writing of their posts/comments. This possibility is quite disheartening since I found Tildes to be an extraordinarily engaging community, with a distinct human element that I haven't seen in other bigger forums. The possibility of a significant portion of Tildes content being generated by AI makes this sentiment a mere illusion.

So here are my questions:

1. Is there any reasonable way of determining which posts were written with the aid of AI?
2. Is the Tildes community okay with the content being generated partly, or entirely, by AI?

Please forgive me if I'm being a cynic, I've spent too much time in forums with significantly lower quality content than Tildes.

Edit 1: Just wanted to cite this paper to somehow justify my uneasiness when it comes to writing texts with AI in command. As I've commented below, LLMs might get in the way of learning the person's true idea/opinion.

Edit 2: As I've said in the comments below, I had no intention of accusing any particular user with this post. Clearly, I've failed to convey my intent so I'll try to clarify my original idea a bit more:

Because I'm new to Tildes and haven't got any other forum experience besides Reddit, I was amazed with how much and how fast some users post extremely high quality content, since this was certainly not the norm in Reddit. As with many other online platforms, spammers armed with AI can be a difficult problem to deal with, so I've made this post with the intent of starting a discussion about this matter and to understand the Tildes community stance. My relatively negative view of the use of AI is due in part to my experience with such spammers in the past. Forgive me if I've assumed the worst...

From the little time I've spent in Tildes, I'm quite sure the community grew to know each other and I'm hopeful that my post will not cause undue witch hunts. And I've also learned that because people got to know each user's stylistic voice, it's fairly easy to spot a possible AI spammer.

Thank you for the insightful discussion so far !

Hello,
I'm looking for resources on how to develop AI, aimed at people who already have experience with programming.

They don't have to be free, I would just like to aggregate different type of resources to pick from.

Thanks!

AI Virtual Assistants are on the rise, and logically it seems like I could use one to support productivity, small business, neurodivergent accomodations, etc., BUT, when reviewing what's out there they don't seem super useful.

Otter seems the most useful because it can attend web meetings and record, contextualize screenshares, and sift the transcripts into action items, but it cant go to all webinar services and I'm not sure I can log into this in a corporate platform. Others seem to be able to check a calendar or make a reminder, but nothing I would pay for.

Some use cases might be gathering basic info from clients, scheduling meetings (calendly can handle this), blocking time for my task lists, writing basic email drafts, adding up expenses each month, sending reminders for customers, etc.

All of this could happen with various tools, but seem like good territory for an AI Virtual Assistant.

So, have you found any AI VAs that would be worth paying for? Anything that saves time or makes life easier?

I will be participating in a panel discussion about the intersection of art and Artificial intelligence next week, and I am curious how fellow creatives feel about Artificial intelligence.

Have you used AI before in the creative process? If so, what services have you used/prefer?

What do you think the role of AI is in the creative process?

Does AI enhance creativity or limit originality?

What are the ethical implications of using AI to create art?

This will be a place that I use to gather all the cool AI music videos I can find. Everyone are welcome to join!

To limit the scope somewhat, only videos where the visual is AI generated are included (so AI generated music isn't necessarily included). Please also keep each video in a separate comment so they can be voted on individually. Maybe by the end we will have a kind of Tildes best-of list.

Feel free to talk about each video in their replies or talk about general stuff here. This will be a work-in-progress so stuff may be a bit all over the place. Let me know if you have any suggestion.

This thread will likely be very spammy so please use the IGNORE button. On the other hand, if you think you may want to contribute some of your own finds in future dates, you can also 'Bookmark' (Fun fact: you can use both at the same time).

I've been a computer scientist in the field of AI for almost 15 years. Much of my time has been devoted to classical AI; things like planning, reasoning, clustering, induction, logic, etc. This has included (but had rarely been my focus) machine learning tasks (lots of Case-Based Reasoning). For whatever reason though, the deep learning trend never really interested me until recently. It really just felt like they were claiming huge AI advancements when all they really found was an impressive way to store learned data (I know this is an understatement).

Over time my opinion on that has changed slightly, and I have been blown away with the boom that is happening with transformers (GPTs specifically) and large language models. Open source projects are creating models comparable to OpenAIs behemoths with far less training and parameters which is making me take another look into GPTs.

What I find surprising though is that they seem to have only experimented with language. As far as I understand the inputs/outputs, the language is tokenized into bytes before prediction anyway. Why does it seem like (or rather the community act like) the technology can only be used for LLMs?

For example, what about a planning domain? You can specify actions in a domain in such a manner that tokenization would be trivial, and have far fewer tokens then raw text. Similarly you could generate a near infinite amount of training data if you wanted via other planning algorithms or simulations. Is there some obvious flaw I'm not seeing? Other examples might include behavior and/or state prediction.

I'm not saying that out of the box a standard GPT architecture is a guaranteed success for plan learning/planning... But it seems like it should be viable and no one is trying?