#AI#GenerativeAI#LLMs#Chatbots#Hype: "...[T]he AI hype of the last year has also opened up demand for a rival perspective: a feeling that tech might be a bit disappointing. In other words, not optimism or pessimism, but scepticism. If we judge AI just by our own experiences, the future is not a done deal.
Perhaps the noisiest AI questioner is Gary Marcus, a cognitive scientist who co-founded an AI start-up and sold it to Uber in 2016. Altman once tweeted, “Give me the confidence of a mediocre deep-learning skeptic”; Marcus assumed it was a reference to him. He prefers the term “realist”.
He is not a doomster who believes AI will go rogue and turn us all into paper clips. He wants AI to succeed and believes it will. But, in its current form, he argues, it’s hitting walls.
Today’s large language models (LLMs) have learnt to recognise patterns but don’t understand the underlying concepts. They will therefore always produce silly errors, says Marcus. The idea that tech companies will produce artificial general intelligence by 2030 is “laughable”.
Generative AI is sucking up cash, electricity, water, copyrighted data. It is not sustainable. A whole new approach may be needed. Ed Zitron, a former games journalist who is now both a tech publicist and a tech critic based in Nevada, puts it more starkly: “We may be at peak AI.”" https://www.ft.com/content/648228e7-11eb-4e1a-b0d5-e65a638e6135
“AI” as currently hyped is giant billion dollar companies blatantly stealing content, disregarding licenses, deceiving about capabilities, and burning the planet in the process.
It is the largest theft of intellectual property in the history of humankind, and these companies are knowingly and willing ignoring the licenses, terms of service, and laws that us lowly individuals are beholden to.
I guess we wait this one out until the “AI” bubble bursts due to the incredible subsidization the entire industry is undergoing. It is not profitable. It is not sustainable.
It will not last—but the damage to our planet and fallout from the immense amount of wasted resources will.
Large language models can do jaw-dropping things. But nobody knows exactly why.
And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.
#AI#LLMs#Media#Journalism#GenerativeAI#News#BBC#Automation#Audiences: "The appearance of large language models (LLMs) and other forms of generative AI portend a new era of disruption and innovation for the news industry, this time focused on the production and consumption of news rather than on its distribution. Large news organizations, however, may be surprisingly well-prepared for at least some of this disruption because of earlier innovation work on automating workflows for personalized content and formats using structured techniques. This article reviews this work and uses examples from the British Broadcasting Corporation (BBC) and other large news providers to show how LLMs have recently been successfully applied to addressing significant barriers to the deployment of structured approaches in production, and how innovation using structured techniques has more generally framed significant editorial and product challenges that might now be more readily addressed using generative AI. Using the BBC's next-generation authoring and publishing stack as an example, the article also discusses how earlier innovation work has influenced the design of flexible infrastructure that can accommodate uncertainty in audience behavior and editorial workflows – capabilities that are likely to be well suited to the fast-approaching AI-mediated news ecosystem." https://onlinelibrary.wiley.com/doi/10.1002/aaai.12168
#AI#GenerativeAI#LLMs#AITraining#Hallucinations#AITraining: "Models like ChatGPT and Claude are deeply dependent on training data to improve their outputs, and their very existence is actively impeding the creation of the very thing they need to survive. While publishers like Axel Springer have cut deals to license their companies' data to ChatGPT for training purposes, this money isn't flowing to the writers that create the content that OpenAI and Anthropic need to grow their models much further. It's also worth considering that these AI companies may already have already trained on this data. The Times sued OpenAI late last year for training itself on "millions" of articles, and I'd bet money that ChatGPT was trained on multiple Axel Springer publications along with anything else it could find publicly-available on the web.
This is one of many near-impossible challenges for an AI industry that's yet to prove its necessity. While one could theoretically make bigger, more powerful chips (I'll get to that later), AI companies face a kafkaesque bind where they can't improve a tool for automating the creation of content without human beings creating more content than they've ever created before. Paying publishers to license their content doesn't actually fix the problem, because it doesn't increase the amount of content that they create, but rather helps line the pockets of executives and shareholders. Ironically, OpenAI's best hope for survival would be to fund as many news outlets as possible and directly incentivize them to do in-depth reporting, rather than proliferating a tech that unquestionably harms the media industry." https://www.wheresyoured.at/bubble-trouble/
I remember the days of email before spam, phishing or any of that.
Decades of those and we have accepted them, most never knew a time without them.
#AI isn't just going to automate spam-like activity, make better malware etc.
AI is going to be much, much worse, creating indistinguishable human like personas that control rather than leave specific traps. And they will be much harder to spot than spam.
The internet of shit right now is nothing to what's coming, and making it is legal. #LLMs
#AI#GenerativeAI#LLMs#Automation#Hallucinations: "The only reason bosses want to buy robots is to fire humans and lower their costs. That's why "AI art" is such a pisser. There are plenty of harmless ways to automate art production with software – everything from a "healing brush" in Photoshop to deepfake tools that let a video-editor alter the eye-lines of all the extras in a scene to shift the focus. A graphic novelist who models a room in The Sims and then moves the camera around to get traceable geometry for different angles is a centaur – they are genuinely offloading some finicky drudgework onto a robot that is perfectly attentive and vigilant.
But the pitch from "AI art" companies is "fire your graphic artists and replace them with botshit." They're pitching a world where the robots get to do all the creative stuff (badly) and humans have to work at a robotic pace, with robotic vigilance, in order to catch the mistakes that the robots make at superhuman speed.
#LLMs, even generative LLMs, doesn’t create new things. It just remixes at a phenomenal rate. High-output remixing tends to produce low-quality output, with occasional gems. So on net, they increase the ratio of low-quality garbage to gems. That’s all they can do. And that just gums up the works of, well, everything.
Generative AI, in its current trajectory, cannot be anything but a net-negative for society. But it is a great grift to rip off investors.
A colegue had some problems in their experiments and I found out the issue was they were using #chatGPT for unit conversions. I wonder how many people use it for doing math and trust the results
I corrected the problem and explained them why they shouldn't use it like this, but honestly I don't think they understood clearly what the problem was.
I'm not a programmer and don't understand the details in how #llms work, but I can already see what the lack of even surface understanding can do
You know all those sponsored stories at the bottom of news articles—the ones with sensationalist headlines and eye-catching photos that often have little to nothing to do with the "article" they link to? Making those articles is somebody's job.
So, if AI is going to destroy a bunch of jobs, can we start with that one?
There's a host of legal risks AI companies and companies that use generative AI are putting themselves in the path of, that we don't talk about enough:
📜 It's pretty clear Section 230, the foundational law enabling today's internet, DOES NOT protect AI-generated content like that from ChatGPT, Claude or Google's generative search experience
🚗💥🚙 Generative AI could also put companies at risk of product liability claims
@mimsical Counter: "using #AI the way most commentators expect" is already far from the most common use case today, and will be less and less in importance.
Section 230 doesn't apply to e.g. automated pipelines of internal documents and using #LLMs for it doesn't change that.
For all the media attention on content creation for public consumption, most #LLM use is very boring office work.
#AI#GenerativeAI#USA#Misinformation#ChatGPT#LLMs: "The Pew Research Center—which did similar probes during the rise of the internet, social media, and mobile devices—released a study of how ChatGPT was being used, regarded, and trusted. The sample was taken between February 7 and 11 of this year.
Some of the numbers at first seem to indicate that the LLM controversy might be a parochial disagreement that most people don’t care about. A third of Americans haven’t heard of ChatGPT. Just under a quarter have used it. Oh, and for all the panic about how AI is going to flood the public square with misinformation about the 2024 election? So far, only 2 percent of Americans have used ChatGPT to get information about the presidential election season already underway.
More broadly, though, data from the survey indicates that we’re seeing a powerful technology whose rise is just beginning. If you accept Pew’s sample as indicative of all Americans, millions of people are indeed familiar with ChatGPT. And one thing in particular stands out: While 17 percent of respondents said they have used it for entertainment and an identical number says they’ve tried it to learn something new, a full 20 percent of adults say that they have used ChatGPT for work. That’s up dramatically from the 12 percent who responded affirmatively when the same question was asked six months earlier—a rise of two-thirds." https://link.wired.com/view/5fda497df526221fe830f4d4kr4to.j4/3764bd02
This one is sooo good. I recommend this to anyone playing with #AI to understand the biases and the complexities. Oh and the discussion of alt text is amazing.
Inside LAION-5B, an AI training dataset of 5B+ images that has been unavailable for download after researchers found 3,000+ instances of #CSAM in December 2023.
I love how OpenAI's image description feature pretends to pat itself on the back when describing an image after telling me that part of an image is not clear so no text is visible. My system prompt specifically instructs it to not make things up when something isn't clear.