Inclusive accessible spaces, community development and engagement | turns user research into things |
cycles/walks/swims | she/her/they | learning arabic | Could be called mostly Australian | Lives in Glasgow | rachelkress.com
@EdinbR As promised, you can get the slides and code from my talk here: https://github.com/mikerspencer/arrow_test
As an extra special treat, @nic_crane came along too 🤩
Large numbers of people don't understand what a billionaire is.
If you, like most of us, ONLY make $1 million a year, it would take you 1,000 years to make $1 billion.
Or, if REALLY lie most of us, you make $50,000 a year, it would take you 20,000 years.
TWENTY THOUSAND YEARS!
If you tax someone who makes a billion a year at a 90% top rate, they would "only" take home $100 million a year.
Stephen Hill, expert from experience, on being involved in health research and why people should be involved in #Research.
Sadly, the questions are asked only visually but Stephen's thoughts still tell enough.
#Engagement #Accessibility #A11y #UserResearch #PublicInvolvement
Reading about #ChatGPT plug-ins and wondering why this is framed as plug-ins for #ChatGPT (giving it "capabilities") rather than #ChatGPT as a plug-in to provide a conversational front-end to other services.
Nevermind, I know why: This is #OpenAI yet again trying to sell their text synthesis machine as "an AI".
The problem with regulating AI isn't in coming up with effective regulations. That's the easy part.
The problem is that tech and the authorities don't want effective regulation because "effective" in this context always means "less profitable for the industry".
That's why they tend to either come up with ineffective half-measures or measures that strengthen the incumbents' positions.
But, what if you didn't care about protecting Microsoft's ability to profit from AI? What would you do then?
The point isn't to create metadata that's impossible to remove. The point is to discourage the creation of products that automate deception.
All the announced products from Google, Microsoft, and AI startups are primarily for automating deception: chatbots that don't say they're AI, AI-generated email and docs, pictures that are presented as photographs.
That's their go-to-market strategy: automate and normalise deception
They should be made to understand that this is not okay
Thanks to a nudge from a colleague I've just found this wonderful research paper by Sophie Beier and Kevin Larson in 2010. It evaluates different design characteristics of commonly confused (difficult to identify) letters using multiple different identification tests. the goal being to help design more easily identifiable letters. fabulous!
https://www.researchgate.net/publication/233608849_Design_Improvements_for_Frequently_Misrecognized_Letters. It was uploaded to researchGate (Open access) by Sophie Beier. Awesome!
Mike Spencer
Dave Johnson
Prof. Emily M. Bender(she/her)
Baldur Bjarnason
Hilary Palmén