Today's #AIIsGoingGreat brought to you by #NYC, who deployed spicy autocomplete to provide advice "on topics such as compliance with codes and regulations, available business incentives, and best practices to avoid violations and fines"
(spoiler: one great way to avoid violations and fines is to not get your legal advice from spicy autocomplete)
https://themarkup.org/news/2024/03/29/nycs-ai-chatbot-tells-businesses-to-break-the-law
Today's #AIIsGoingGreat (HT @pluralistic https://mastodon.social/@pluralistic@mamot.fr/112196496077034192)
Tired: Typo squatting
Wired: Hallucination squatting
https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/
Seems like you could put your thumb on scale for which (non existent) libraries show up with #LLM training set poisoning attacks (previously https://mastodon.social/@reedmideke/110850376856613599)
Set up a site that, when it detects known AI scrapers, serves up code or documentation that references a non-existent library, along text associating with whatever kind of code and industry you want to target
OTOH, this would leave much more of trail than just observing bogus ones that show up naturally
"If you think about the major journeys within a [fast food] restaurant that can be AI-powered, we believe it’s endless"
Sir this a fucking Wendy's and people come here to buy a fucking burger, not "take major journeys" https://arstechnica.com/information-technology/2024/04/ai-hype-invades-taco-bell-and-pizza-hut/
Today's #AIIsGoingGreat brought to you by #Ivanti: 'Among the details is the company's promise to improve search abilities in Ivanti's security resources and documentation portal, "powered by AI," and an "Interactive Voice Response system" … also "AI-powered"'
Ah yes, hard to think of any better way to fix a pattern of catastrophic security failures than *checks notes* filtering highly technical, security critical information through a hyper-confident BS machine
Here's a helpful #AI chatbot to assist you with thing that requires domain specific knowledge and has significant real-world consequences for errors… oh, by the way, you'll need to already have that same domain specific knowledge to confirm whether the answers are correct or complete BS
Who thinks this is a good idea?🤔
OpenAI argues that “factual accuracy in large language models remains an area of active research”
…in the sense that Bigfoot and Nessie remain areas of active research?
https://noyb.eu/en/chatgpt-provides-false-information-about-people-and-openai-cant-correct-it
A+ BLUF from @benjedwards: "Air-gapping GPT-4 model on secure network won't prevent it from potentially making things up"
https://arstechnica.com/information-technology/2024/05/microsoft-launches-ai-chatbot-for-spies/
Google's current #AIIsGoingGreat moment really checks all the bad #AI boxes. Starting with the dismissive "examples we've seen are generally very uncommon queries and aren’t representative of most people’s experiences" - Sure *sometimes* the answers are complete BS and possibly dangerous, but what about the times they aren't? Checkmate, Luddites!
Straight from Google CEO Sundar Pichai's mouth: 'these "hallucinations" are an "inherent feature" of AI large language models (LLM), which is what drives AI Overviews, and this feature "is still an unsolved problem"'
but they're gonna keep band-aiding until it's good, promise! ""Are we making progress? Yes, we are … We have definitely made progress when we look at metrics on factuality year on year. We are all making it better, but it’s not solved""
Today's #AIIsGoingGreat - Meta's chatbot helpfully "confirms" a scammers number is a legitimate facebook support number
(of course, #LLMs just predict likely sequences of text, and for a question like this, "yes" is one of the high probability answers. There's no indication any of the companies hyping LLMs as a source of information have any serious solution for this kind of thing)
https://www.cbc.ca/news/canada/manitoba/facebook-customer-support-scam-1.7219581
Kyle Orland hammers on my off-repeated complaint (https://mastodon.social/@reedmideke/110063208987793683) that filtering your information through an #LLM *removes* useful context: "When Google's AI Overview synthesizes a new summary of the web's top results, on the other hand, all of this personal reliability and relevance context is lost. The Reddit troll gets mixed in with the serious cooking expert"
https://arstechnica.com/ai/2024/06/googles-ai-overviews-misunderstand-why-people-use-google/
Today's #AIIsGoingGreat features Zoom CEO Eric Yuan blazed out of his mind on his own supply: "Today for this session, ideally, I do not need to join. I can send a digital version of myself to join so I can go to the beach. Or I do not need to check my emails; the digital version of myself can read most of the emails. Maybe one or two emails will tell me, “Eric, it’s hard for the digital version to reply. Can you do that?”"
"I truly hate reading email every morning, and ideally, my AI version for myself reads most of the emails. We are not there yet"
OK, points for recognizing we're "not there yet", in roughly the same sense the legend of Icarus foresaw intercontinental jet travel but was "not there yet"
That "#ChatGPT is bullshit" paper I boosted earlier does a nice job of laying out why the "hallucination" terminology is harmful "what occurs in the case of an #LLM delivering false utterances is not an unusual or deviant form of the process it usually goes through… The very same process occurs when its outputs happen to be true"
https://link.springer.com/article/10.1007/s10676-024-09775-5
Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.
Reed Mideke
Tanith the Gay