Front-end developer, designer, writer, and avid user of the superpowered information superhighway.
I just saw the term 'Synthetic Intelligence' thrown forward, which I quite like.
Many people agree that 'artificial intelligence' is a poor term that is vague and has existing connotations. People use it to refer to a whole range of different technologies.
However, I struggle to come up with any better terminology. If not 'artificial intelligence', what term would be ideal for describing the capabilities of multi-modal tools like Claude, Gemini, and ChatGPT?
We talk and think a lot about echo chambers with social media. People view what they're aligned with, which snowballs as algorithms feed them more content of that type, which pushes their views to the extreme.
I wonder how tailor-made AI-generated content will feed into that. It's my thinking and worry that AI systems can produce content perfectly aligned with a user in all ways, creating a flawless self-feeding ideological silo.
I was thinking a little bit about the bystander effect in the context of AI safety, alignment, and regulation.
With many independent actors working on and around AI – each operating with safety intentions regarding their own project – is there worrying potential for a collective bystander effect to emerge? Each regulatory body might assume that AI companies, or other regulatory bodies, or the wider AI safety community are sufficiently addressing the overall problems and ensuring collective safety.
This could lead to a situation where no single entity feels the full weight of responsibility for the holistic safety of the global AI ecosystem, resulting in an overall landscape that is flawed, unsafe, and/or dangerous.
Taking time away from something and then returning to it later often reveals flaws otherwise unseen. I've been thinking about how to gain the same benefit without needing to take time away.
Changing perspective is the obvious approach.
In art and design, flipping a canvas often forces a reevaluation and reveals much that the eye has grown blind to. Inverting colours, switching to greyscale, obscuring, etc, can have a similar effect.
When writing, speaking written words aloud often helps in identifying flaws.
Similarly, explaining why you've done something – à la rubber duck debugging – can weed out things that don't make sense.
I don't necessarily believe or disbelieve in the final 1% taking the longest in this case – there are too many variables to make a confident prediction. However, it does tend to be a common occurrence.
It could very well be that the 1% before the final 1% takes the longest. Based on the past few years, progress in the AI space has been made fairly steadily, so it could also be that it continues at just this pace until that last 1% is hit, and then exponential takeoff occurs.
You could also have a takeoff event that carries from now till 99%, which is then followed by the final 1% taking a long period.
A typical exponential takeoff is, of course, very possible as well.
A great collection of posts there. Plenty of useful stuff.
This prompted me to write down and keep track of my own usage:
https://vale.rocks/posts/ai-usage
To me, this sounds like you're simply pushing the problem a little bit downstream without actually addressing it. You're still not verifying the facts; you're just getting another system with similar flaws to the first (you). You aren't actually fact checking at any point.