It will work - you can see it well with a Chain of Thought (CoT) model: it will keep asking itself: "am I hallucinating? let's double check" and then will self-reject thoughts if it can't find a proper grounding. In fact, this is the best part of CoT model, that you can see where it goes off rails and can add a message to fix it in the prompt.
For example, there is this common challenge, "count how many r letters in strawberry", and you can see the issue is not counting, but that model does not know if "rr" should be treated as single "r" because it is not sure if you are counting r "letters" or r "sounds" and when you sound out the word, there is a single "r" sound where it is spelled with double "r". so if you tell the model, double "r" stands for 2 letters, it will get it right.
I don't know about this specific technique, but I have found it useful to add a line like 'it's OK if you don't know or this isn't possible' at the end of queries. Otherwise LLMs have a tendency to tilt at whatever windmill you give them. Managing tone and expectations with them is a subtle but important art.
I would argue it has more to do with conventional styling and what that signals rather than some wider acceptance.
The Google Glass look tended to draw attention in a negative way, such god awful styling that narrow minded people might conclude you were an asshole for wearing one.
The form factor for these improves acceptance but no doubt there has been a societal shift, so much more of our lives are captured, shared, communicated and entertained through individuals filming. If not your peers, the generation below you is a ‘video and image as communication’ generation.
This gets my enthusiastic upvote. Reason: author bothered to do work to research his answer.
In contrast, a shockingly large amount of content is extremely lazy, gut-feeling reaction. Avoiding the work and disappointment of finding out that your hot take is wrong.
The middle ages comparison makes no sense. Some hate their jobs so much that they'll put it in balance with a 30yo life expectancy and a (short) life of hard backbreaking manual labour riddled with health hazards. There is really no point in comparing the number of working hours then and now.
Thanks, I was going to comment here about that. From your link:
>As the years passed, pinball machines continued to pop up around the country in various forms as the furor against them lessened and the laws and bans became more lax. Yet it was not until 1976 that the New York pinball ban was actually lifted.
>explains the otherwise perplexing fact that laborsaving machinery fails to benefit workers
I disagree, the reason why workers don’t benefit is because they are mostly paid to put hours in. Owners claim the gains of better machinery because they reason it is a capital investment at the business level.
Really I don’t see why see why this is perplexing. What is really perplexing is that some economists thought that productivity gains would somehow accrue gains for workers.
This is probably the best layman’s approach to Noether, her impact, and how she probably didn’t think much about the theorem later because she wasn’t interested in physics and abstract mathematics was her consuming passion.
>pot into which foodstuffs are placed and cooked, continuously. The pot is never or rarely emptied all the way, and ingredients and liquid are replenished as necessary.[1][3] Such foods can continue cooking for decades or longer if properly maintained.
So, basically, the code base I’ve been working on is a perpetual stew, maintained for decades
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