[Lex Computer & Tech Group/LCTG] From an online Technology Review Service I get

Sat Jun 24 13:48:45 PDT 2023

I find myself using either MS Chat or Google Bard as they offer new content.

Still wouldn't write a paper with them. An outline, yes.

Command line syntax questions in Windows/Linux have been very accurate 
and helpful in Chat.

MS is rapidly integrating AI into Windows 11, and Office. It will soon 
be a piece of cake for somebody who does not know how to generate charts 
and graphs in Microsoft Excel to do so easily.

Some companies are beginning to ban chat GPT for their employees. I 
think they believe it's no different than using Facebook while at work. 
Programmers that work for these companies are frustrated because they've 
been using ai to check their code and run tests, and now their bosses 
are telling them they're not allowed to.

On 6/24/2023 12:07 PM, Robert Primak wrote:
> The paradigm of an "AI model they’d trained themselves that looks for 
> telltale signals of ChatGPT output" has been proven time and time 
> again to spew out false-positives. College professors have lost their 
> jobs for making unsubstantiated accusations and penalizing students 
> for allegedly using AI or ChatGPT when in fact the students came back 
> with notes and drafts proving definitively that their work was 
> original and not generated by AI. An "accuracy rate" which allows for 
> up to 40% false-positives does not encourage placing any faith in AI 
> models which claim to be able to detect AI produced content. The Swiss 
> team was either lying, or else they were deceiving themselves. Put 
> succinctly, AI can never definitively detect AI.
>
> Nevertheless, there is a very real possibility that at least some LLMs 
> are reusing data sets which were supplied with the help of AI models. 
> The models might very well be feeding upon their own errors. And this 
> does pose a real threat to the integrity of the information presented 
> as output by all LLMs. How LLMs and other AI are trained does 
> influence their behaviors when they are put into use.
>
> Outsourcing any part of AI training does run the risk that the gig 
> workers involved will take every shortcut they can to generate the 
> illusion that they are producing more finished work in a shorter time 
> interval. Gig workers are notorious for doing everything they can to 
> avoid doing any real work.
>
> The obvious solution is this: Pay qualified people to do the training 
> work under the watchful eyes of in-house research staff. It's a lot 
> more expensive, but this is the only way to make sure no one is taking 
> thee easy way out and endangering the integrity of the entire project. 
> When you take shortcuts in technology development, it is your 
> end-users who invariably pay the price.
>
> -- Bob Primak
>
>
>
>
>
>
>
> On Saturday, June 24, 2023 at 11:43:34 AM EDT, jjrudy1 at comcast.net 
> <jjrudy1 at comcast.net> wrote:
>
>
> *ARTIFICIALThis is scary INTELLIGENCE* 
> <https://www.technologyreview.com/topic/artificial-intelligence/>
>
>
>   paid to train AI are outsourcing their work… to AI
>
> *It’s a practice that could introduce further errors into already 
> error-prone models.*
>
> *By ***
>
>   * *Rhiannon Williamsarchive page
>     <https://www.technologyreview.com/author/rhiannon-williams/>*
>
> June 22, 2023
>
> anonymous workers at a conveyor belt and strings of words flow out one end
>
> STEPHANIE ARNETT/MITTR | GETTY
>
> A significant proportion of people paid to train AI models may be 
> themselves outsourcing that work to AI, a new study has found.
>
> It takes an incredible amount of data to train AI systems to perform 
> specific tasks accurately and reliably. Many companies pay gig workers 
> on platforms like Mechanical Turk 
> <https://www.technologyreview.com/2020/12/11/1014081/ai-machine-learning-crowd-gig-worker-problem-amazon-mechanical-turk/> to 
> complete tasks that are typically hard to automate, such as solving 
> CAPTCHAs, labeling data and annotating text. This data is then fed 
> into AI models to train them. The workers are poorly paid and are 
> often expected to complete lots of tasks very quickly.
>
>
>     Related Story
>
> "" 
> <https://www.technologyreview.com/2023/04/04/1070938/we-are-hurtling-toward-a-glitchy-spammy-scammy-ai-powered-internet/>
>
> ** 
> <https://www.technologyreview.com/2023/04/04/1070938/we-are-hurtling-toward-a-glitchy-spammy-scammy-ai-powered-internet/>
>
> *_We are hurtling toward a glitchy, spammy, scammy, AI-powered 
> internet<https://www.technologyreview.com/2023/04/04/1070938/we-are-hurtling-toward-a-glitchy-spammy-scammy-ai-powered-internet/>_*
>
> Large language models are full of security vulnerabilities, yet 
> they’re being embedded into tech products on a vast scale.
>
> No wonder some of them may be turning to tools like ChatGPT to 
> maximize their earning potential. But how many? To find out, a team of 
> researchers from the Swiss Federal Institute of Technology (EPFL) 
> hired 44 people on the gig work platform Amazon Mechanical Turk to 
> summarize 16 extracts from medical research papers. Then they analyzed 
> their responses using an AI model they’d trained themselves that looks 
> for telltale signals of ChatGPT output, such as lack of variety in 
> choice of words. They also extracted the workers’ keystrokes in a bid 
> to work out whether they’d copied and pasted their answers, an 
> indicator that they’d generated their responses elsewhere.
>
> They estimated that somewhere between 33% and 46% of the workers had 
> used AI models like OpenAI’s ChatGPT. It’s a percentage that’s likely 
> to grow even higher as ChatGPT and other AI systems become more 
> powerful and easily accessible, according to the authors of the study, 
> which has been shared on arXiv 
> <https://arxiv.org/pdf/2306.07899.pdf> and is yet to be peer-reviewed.
>
> “I don’t think it’s the end of crowdsourcing platforms. It just 
> changes the dynamics,” says Robert West, an assistant professor at 
> EPFL, who coauthored the study.
>
> Using AI-generated data to train AI could introduce further errors 
> into already error-prone models. Large language models regularly 
> present false information as fact. If they generate incorrect output 
> that is itself used to train other AI models, the errors can be 
> absorbed by those models and amplified over time, making it more and 
> more difficult to work out their origins, says Ilia Shumailov, a 
> junior research fellow in computer science at Oxford University, who 
> was not involved in the project.
>
> Even worse, there’s no simple fix. “The problem is, when you’re using 
> artificial data, you acquire the errors from the misunderstandings of 
> the models and statistical errors,” he says. “You need to make sure 
> that your errors are not biasing the output of other models, and 
> there’s no simple way to do that.”
>
> The study highlights the need for new ways to check whether data has 
> been produced by humans or AI. It also highlights one of the problems 
> with tech companies’ tendency to rely on gig workers to do the vital 
> work of tidying up the data fed to AI systems.
>
> “I don’t think everything will collapse,” says West. “But I think the 
> AI community will have to investigate closely which tasks are most 
> prone to being automated and to work on ways to prevent this.”
>
> hide
>
>
>       by Rhiannon Williams
>       <https://www.technologyreview.com/author/rhiannon-williams/>
>
> John Rudy
>
> 781-861-0402
>
> 781-718-8334  cell
>
> 13 Hawthorne Lane
>
> Bedford MA
>
> jjrudy1 at comcast.net
>
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