#AI#GenerativeAI#LLMs#Chatbots#RAG: "There are two reasons why using a publicly available LLM such as ChatGPT might not be appropriate for processing internal documents. Confidentiality is the first and obvious one. But the second reason, also important, is that the training data of a public LLM did not include your internal company information. Hence that LLM is unlikely to give useful answers when asked about that information.
Enter retrieval-augmented generation, or RAG. RAG is a technique used to augment an LLM with external data, such as your company documents, that provide the model with the knowledge and context it needs to produce accurate and useful output for your specific use case. RAG is a pragmatic and effective approach to using LLMs in the enterprise.
years ago, the “language of machine learning” was split between #R and #python but it’s been steadily shifting toward python. At this point, after all the #LLM developments, i think it’s clearly python. i don’t see much R in the LLM world at all. And increasingly, i’m seeing #rust being the “systems language of #ML” #rustlang#LLMs
A. Journalists should stop speaking about AI models as if they have personalities, and they are sentient. That is really harmful because it changes the conversation from something that we as humans control to a peer-to-peer relationship. We built these tools and we can make them do what we want.
Another thing I would recommend is talking about AI specifically. Which AI model are we talking about? And how does that compare to the other AI models? Because they are not all the same. We also need to talk about AI in a way that’s domain-specific. There’s a lot of talk about what AI will do to jobs. But that is too big a question. We have to talk about this in each field.
#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.
Asked if a restaurant could serve cheese nibbled on by a rodent, the Microsoft / New York City government official AI chatbot replied:
“Yes, you can still serve the cheese to customers if it has rat bites,” before adding that it was important to assess the “the extent of the damage caused by the rat” and to “inform customers about the situation.”
AI is spewing out this sort of surreal garbage all over the world right now. AI is a monumental grift.
@ikt@gerrymcgovern This is a misunderstanding. #LLMs have no semantic layer; consequently they have no concept of truth and falsity. All they know is whether there is a statistical probability that words will fit together in a particular order.
No LLM can ever be 'right', except by accident (which, statistically, will sometimes happen).
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.