Tag: algogens

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Bookmarked ChatGPT sees Tweets: A Double-Edged Sword by Henk van Ess

Bing Chat is connected to the internet, allowing internet searches when you ask the chatbot something. This includes Twitter. It then weaves those online finds into the texts it puts together off your prompt. Henk van Ess shows how quickly the content from a Twitter message gets incorporated (and changed if additional messages are available). With just three tweets he influenced Bing Chat output. This also opens a pathway for influence and dissemination of mis-info, especially since the recent quality changes over at Twitter. The feedback loop this creates (internet texts get generated based on existing internet texts, etc.) will easily result in a vicious circle (In her recent talk Maggie Appleton listed this as one of her possible futures, using a metaphor I can’t unsee, but which does describe it effectively: Human Centipede Epistemology)

Bing/ChatGPT’s rapid response to tweets has a double-edged sword. Bing quickly corrects itself based on tweets … But those with specific agendas or biases may attempt to abuse the system … We’ve seen it all before. This is similar to Google Bombing…

Henk van Ess

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Bookmarked Project Tailwind by Steven Johnson

Author Steven Johnson has been working with Google and developed a prototype for Tailwind. Tailwind, an ‘AI first notebook’, is intended to bring an LLM to your own source material, and then you can use it to ask questions of the sources you give it. You point it to a set of resources in your Google Drive and what Tailwind generates will be based just on those resources. It shows you the specific source of the things it generates as well. Johnson explicitly places it in the Tools for Thought category. You can join a waiting list if you’re in the USA, and a beta should be available in the summer. Is the USA limit intended to reduce the number of applicants I wonder, or a sign that they’re still figuring things like GDPR for this tool? Tailwind is prototyped on PaLM API though, which is now generally available.

This, from its description, gets to where it becomes much more interesting to use LLM and GPT tools. A localised (not local though, it lives in your Google footprint) tool, where the user defines the corpus of sources used, and traceable results. As the quote below suggests a personal research assistant. Not just for my entire corpus of notes as I describe in that linked blogpost, but also on a subset of notes for a single topic or project. I think there will be more tools like these coming in the next months, some of which likely will be truly local and personal.

On the Tailwind team we’ve been referring to our general approach as source-grounded AI. Tailwind allows you to define a set of documents as trusted sources …, shaping all of the model’s interactions with you. … other types of sources as well, such as your research materials for a book or blog post. The idea here is to craft a role for the LLM that is … something closer to an efficient research assistant, helping you explore the information that matters most to you.

Steven Johnson

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On the internet nobody knows you’re a dog.

Peter Steiner, 1993

It seems after years of trollbots and content farms, with generative algorithms we are more rapidly moving past the point where the basic assumption on the web still can be that an (anonymous) author is human until it becomes clear it’s otherwise. Improving our crap detection skills from now on means a different default:

On the internet nobody believes you’re human.

until proven otherwise.