Imagine a computer following its programming and its programmers getting away with calling unwanted outputs a ”hallucination”.
And then other people start calling anything they didn’t want or expect from the computer a hallucination. As if the programmers are completely unaccountable for their programming.
It’s happening every single day and its like an adventure in Wonderland.
Tagged: Akkoma
@alexanderdyas Exactly, it’s the Ouroboros.
If our goal is a world reduced to stereotypes, generative AI is certainly an answer.
Since nobody seems tuned into what generative AI is for, and the actual purpose it is being tested for is still unknown, perhaps we need to start figuring out what the question was.
”Hey, computer, can you help me accelerate the reduction of all humans to neat little boxes based on stereotypical traits?”
…
https://restofworld.org/2023/ai-image-stereotypes/
Gotta consider how you may be training AI tools for your job just by performing your everyday work.
That is: Your employer forces you to use AI tools. Said AI tools are simultaneously trained on your work as you are using them. At some point the tool simply replaces you.
"Enter corporate spyware, invasive monitoring apps that allow bosses to keep close tabs on everything their employees are doing—collecting reams of data that could come into play here in interesting ways. Corporations, which are monitoring their employees on a large scale, are now having workers utilize AI tools more frequently, and many questions remain regarding how the many AI tools that are currently being developed are being trained.
Put all of this together and there’s the potential that companies could use data they’ve harvested from workers—by monitoring them and having them interact with AI that can learn from them—to develop new AI programs that could actually replace them. If your boss can figure out exactly how you do your job, and an AI program is learning from the data you’re producing, then eventually your boss might be able to just have the program do the job instead."
https://www.wired.com/story/corporate-surveillance-train-ai/
Don't s**t where you eat.
The one adage they forgot to "teach" AI.
https://www.wired.com/story/fast-forward-chatbot-hallucinations-are-poisoning-web-search/
@danhon
Sorry. 😅
Although I’m so on board with that statement…
@danhon
You’re likely looking for something like Renderform.
https://renderform.io/
You can likely also do this with a PHP script if it’s always the same image :)
@Beantin
I think that they are by some people intended to be two different aspects of controlling and allowing what goes into the machine instruction. Like this:
1. Prompt design is about writing prompts to get the desired output.
2. Prompt engineering is about the operational management of controlling what prompts are possible, i.e. restricting certain prompts to specific roles within a company in order to maintain data integrity and security, or controlling output length to manage computational costs.
Distinguishing between these two can be useful and set the right expectations, but I do see that prompt engineering is being used as a catch-all phrase. In that sense I would call prompt engineering the umbrella term for both the design/wording of prompts and the construction of rules for prompting.
But given that language is shaped by the people who use it we can only wait and see where these definitions end up. OpenAI themselves seem to use them interchangeably.
Sound reflections from an illustrator on the topic of generative AI.
"AI is existing as it's supposed to exist," says McKernan. "I think it has had a lot of potential to make our lives easier, to make workflows more effective. My issue is that the implementation of it, especially with AI art, hasn't been ethical, in my opinion because of the way it is built off a massive data set with 5.1 billion images, and taxpayers' data, all of which was culled from the internet without consent."
https://www.creativebloq.com/features/ai-art-the-impact-of-generative-AI
@Uva_Be I have experimented with most tools to get an idea of their workings. Both to test usefulness and reliability. For generating templates and structuring content I have seen benefits. And I’m very impressed by the programming capabilities of some language models.
Before these popular current tools I was of course also using AI, for example for speech-to-text and vice versa, which is useful and benevolent in the right settings.
In the end there is always this question that remains… are these benefits (for a subset of wealthy people) worth the cost? To judge that we need to understand the costs, and they are unfortunately often kept hidden or obscure.
Becoming reliant on many of these tools also means I become dependent on someone else to select and filter what content is deemed valuable for modelling, which is another aspect that worries me.
@starfrost Rather than generally biased (which I agree can be a bit of a misnomer as most content can be deemed biased in some way) I would be looking at specific aspects such as racist, medically harmful, misogynist, ableist, abusive etceteras.
Many general tools struggle because they are general, and without a clearly defined purpose and use-case. That makes it hard to gauge success but also hard to gauge appropriateness for anything.
When services/products have more clearly defined goals and purposes there are models for impact assessment that can be made use of as part of a development process.
I outline a couple of them at the end of this post: https://axbom.com/digital-compassion-human-act/
@starfrost For me it's a design decision to make use of biased data and unsupervised training in your manufacturing process. Of course these companies are aware that vast amounts of their data is prejudiced.
The Google photos incident where a black person was tagged as a gorilla happened in 2015 and 8 years later they haven't fixed this, just blocked the ability to search for gorilla. These companies have had time to become aware of the bias problem.
There are definitely many useful applications for AI and it's possible to work around these issues and mitigate impact to some extent before harm happens. Many companies however choose not to. To work around these moral dilemmas I believe you need to be open and honest about their presence and how you are addressing them.
If you aren't actively looking for them as a user, prejudices will also make their way into everyday use of generative models.
@karlemilnikka Gissar att det är Committee on Civil Liberties, Justice and Home Affairs om man ska följa sändningen?
https://multimedia.europarl.europa.eu/en/webstreaming/committee-on-civil-liberties-justice-and-home-affairs_20231025-1430-COMMITTEE-LIBE
@pawsplay I'm not sure I understand your question. The poster suggests what a hammer would be if it was like AI, not what a hammer is.
So if a hammer was like AI, it would have been trained on someone else's data. It's a reflection of the ethical dilemmas of AI.
But you may be getting caught on the word "constructions" which in my mind refers to structures and buildings, not the composition of hammers.
Hence, a language model does not copy the composition of other language models, it is "trained" with copies of other people's content.
I hope this helps.
@fasterandworse I'm thinking now how it will become more relevant in the future to have a policy in place with regards to what tools a company can force its employees to use.
I'm lucky to be self-employed, because there are many tools I would never want to touch but an employment may force me into.
@fasterandworse Not at all! I'm 100% onboard with the lack of purpose being one of the most maddening aspects of this whole problem space.
You added a dimension where my point became even more interesting. They're not just releasing crap and fixing it, they're releasing crap without a purpose and fixing it. 😅
@fasterandworse
I agree, but what I am seeing is that a "fixed" ChatGPT is responding to things like takedown requests from JK Rowling (by making it harder to get responses about the books), blocking explicit racism from responses and generally just addressing random garbage that pops up and makes it into the news cycle.
@tante
@tante
In my head at the time of writing was ChatGPT. :)
I've written about Zoom before, and for me it fits the bill to some extent.
https://axbom.com/avoid-zoom/
For me it's a component of the whole "ship first, fix later" or "release now, fix later" which the gaming industry appears to suffer from as well. I suppose this model naturally became normalized soon after it was possible to ship updates over the net. Many people appear to appreciate early access over fully functional.
A strategy for ethically dubious products is to release something with a set of really poor features, and then improve it significantly within a short period of time.
1. The company will be applauded, and get good press, for quickly fixing something there was already a fix for at the time of release. (Nobody would have applauded the company for releasing something that worked well enough from the start.)
2. People will now more readily accept the updated version because it is "fixed". Chances are that the updated version, had it been released from the start, would have been criticised for being inferior. Now, it can be presented as the much improved version.
3. People will tend to forget about all the other ways the product is inferior, because the company has shown that they are making efforts to improve.
Releasing crap with a quick fix makes people lower their standards.
#DigitalEthics