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

Bookmarked Will A.I. Become the New McKinsey? by Ted Chiang in the New Yorker

Ted Chiang realises that corporates are best positioned to leverage the affordances of algorithmic applications, and that that is where the risk of the ‘runaway AIs’ resides. I agree that they are best positioned, because corporations are AI’s non-digital twin, and have been recognised as such for a decade.

Brewster Kahle said (in 2014) that corporations should be seen as the 1st generation AIs, and Charlie Stross reinforced it (in 2017) by dubbing corporations ‘Slow AI’ as corporations are context blind, single purpose algorithms. That single purpose being shareholder value. Jeremy Lent (in 2017) made the same point when he dubbed corporations ‘socio-paths with global reach’ and said that the fear of runaway AI was focusing on the wrong thing because “humans have already created a force that is well on its way to devouring both humanity and the earth in just the way they fear. It’s called the Corporation“. Basically our AI overlords are already here: they likely employ you. Of course existing Slow AI is best positioned to adopt its faster young, digital algorithms. It as such can be seen as the first step of the feared iterative path of run-away AI.

The doomsday scenario is … A.I.-supercharged corporations destroying the environment and the working class in their pursuit of shareholder value.

Ted Chiang

I’ll repeat the image I used in my 2019 blogpost linked above:

Your Slow AI overlords looking down on you, photo Simone Brunozzi, CC-BY-SA

I have installed AutoGPT and started playing with it. AutoGPT is a locally installed and run piece of software (in a terminal window) that you theoretically can set a result to achieve and then let run to achieve it. It’s experimental so it is good advice to actually follow its steps along and approve individual actions it suggests doing.
It interacts with different generative AI tools (through your own API keys) and can initiate different actions, including online searches as well as spawning new interactions with LLM’s like GPT4 and using the results in its ongoing process. It chains these prompts and interactions together to get to a result (‘prompt chaining’).

I had to tweak some of the script a little bit (it calls python and pip but it needs to call python3 and pip3 on my machine) but then it works.

Initially I have it set up with OpenAI’s API, as the online guide I found were using that. However in the settings file I noticed I can also choose to use other LLM’s like the publicly available models through Huggingface, as well as image generating AIs.

I first attempted to let it write scripts to interact with the API. It ended up in a loop about needing to read the API documentation but not finding it. At that time I did not yet provide my own interventions (such as supplying the link to the API documentation). When I did so later it couldn’t come up with next steps, or not ingesting the full API documentation (only the first few lines) which also led to empty next steps.

Then I tried a simpler thing: give me a list of all email addresses of the people in my company.
It did a google search for my company’s website, and then looked at it. The site is in Dutch which it didn’t notice, and it concluded there wasn’t a page listing our team. I then provided it with the link to the team’s page, and it did parse that correctly ending up with a list of email addresses saved to file, while also neatly summarising what we do and what our expertise is.
While this second experiment was successfully concluded, it did require my own intervention, and the set task was relatively simple (scrape something from this here webpage). This was of limited usefulness, although it did require less time than me doing it myself. It points to the need of having a pretty clear picture of what one wants to achieve and how to achieve it, so you can provide feedback and input at the right steps in the process.

As with other generative AI tools, doing the right prompting is key, and the burden of learning effective prompting lies with the human tool user, the tool itself does not provide any guidance in this.

I appreciate it’s an early effort, but I can’t reproduce the enthusiastic results others claim. My first estimation is that those claims I’ve seen are based on hypothetical things used as prompts and then being enthusiastic about the plausible outcomes. Whereas if you try an actual issue where you know the desired result it easily falls flat. Similar to how ChatGPT can provide plausible texts except when the prompter knows what good quality output looks like for a given prompt.

It is tempting to play with this thing nevertheless, because of its positioning as a personal tool, as potential step to what I dubbed narrow band digital personal assistants earlier. I will continue to explore, first by latching onto the APIs of more open models for generative AI than OpenAI’s.

Enjoyed this one by Karl Schroeder a lot. A fun extrapolation of “not your keys-not your crypto“, set in a society in ecocollapse with AI automating most work, institutions both public and private holding on to their assets while they disappear and crumble, surveillance everywhere and everyone bumping into the demands and constraints of the planet’s carrying capacity. Will explore his other books.

Schroeder is a futurist and writes for clients as foresight consultancy.
Reading it made me ask a number of questions, around the development of AR/MR glasses, specific aspects of crypto and smart contracts (also because of its role in the book I read right before this by Suarez), reducing the cost and increasing the scale of sensors in the environment, and gaming and virtualisation. I’ve jotted those down during reading and started exploring.

Bookmarked WordPress AI: Generative Content & Blocks (by Joe Hoyle, found via Chuck Grimmett)

As many others I am fascinated by what generative algorithms like ChatGPT for texts and Stable Diffusion for images can do. Particularly I find it fascinating to explore what it might do if embedded in my own workflows, or how it might change my workflows. So the link above showing an integration of ChatGPT in WordPress’ Gutenberg block editor drew my attention.

The accompanying video shows a mix of two features. First having ChatGPT generate some text, or actually a table with specific data, and having ChatGPT in ‘co-pilot’ style generate code for Gutenberg blocks. I think the latter might be actually useful, as I’ve seen generative AI put to good use in that area. The former, having ChatGPT write part of your posting is clearly not advisable. And the video shows it too, although the authors don’t point it out or haven’t reflected on the fact that ChatGPT is not a search engine but geared to coming up with plausible stuff without being aware of its actual information (the contrast with generating code is that code is much more highly structured in itself so probabilities collapse easier to the same outcome).

The blogpost in the video is made by generating a list of lunar missions, and then turning them into a table, adding their budgets and sorting them chronologically. This looks very cool in the vid, but some things jump out as not ok. Results jump around the table for instance: Apollo 13 moves from 1970 to 2013 and changes budget. See image below. None of the listed budgets for Apollo missions, nor their total, match up with the detailed costs overview of Apollo missions (GoogleDocs spreadsheet). The budget column being imaginary and the table rows jumping around makes the result entirely unfit for usage of course. It also isn’t a useful prompt: needing to fact check every table field is likely more effort and less motivating than researching the table yourself from actual online resources directly.

It looks incredibly cool ‘see me writing a blogpost by merely typing in my wishes, and the work being done instantly’, and there are definitely times I’d wish that to be possible. To translate a mere idea or thought into some output directly however means I’d skip confronting such an idea with reality, with counter arguments etc. Most of my ideas only look cool inside my head, and need serious change to be sensibly made manifest in the world outside my head. This video is a bit like that, an idea that looks cool in one’s head but is great rubbish in practice. ChatGPT is hallucinating factoids and can’t be trusted to create your output. Using it in the context of discovery (as opposed to the justification context of your output such as in this video) is possible and potentially useful. However this integration within the Gutenberg writing back-end of WordPress puts you in the output context directly so it leads you to believe the generated plausible rubbish is output and not just prompting fodder for your writing. ‘Human made’ is misleading you with this video, and I wouldn’t be surprised if they’re misleading themselves as well. A bit like staging the ‘saw someone in half and put them together again’ magician’s trick in an operating room and inviting surgeons to re-imagine their work.

Taking a native-first approach to integrating generative AI into WordPress, we’ve been experimenting with approaches to a “WordPress Copilot” that can “speak” Gutenberg / block-editor.

Copy-pasting paragraphs between ChatGPT and WordPress only goes so far, while having the tools directly embedded in the editor … open up a world of possibilities and productivity wins…

Joe Hoyle

An android robot is filling out a table listing Apollo missions on a whiteboard, generated image using Midjourney

I installed the StableDiffusion image generator locally on my Mac, using this straightforward instruction. To run the generator I need to start the tool in Terminal, and then can access its interface in the browser on
It uses the 2.1 model, which was over 5GB to download.

Running an image generator locally is slower than using Hugging Face’s online demo. As far as I can tell I need to restart if I want to run a fresh prompt, otherwise it will treat it as an iteration on the previous prompt.

I feel completely illiterate w.r.t. the interface, so there’s a lot to learn before I actually will be capable to somewhat effectively use this tool.

Below the local interface with its various settings to learn about.

A screenshot of the local Stable Diffusion interface. Click to enlarge.