Learning Asimovian Wisdom - its the law, doncha know?

laws, as practised by people, aren't the same as laws of physics - well, at least if you have a naive, high school level of physics (and people).

laws are approximate, because they are continually being re-interpreted. this is intentional - it keeps lawyers in employment. but it also allows for futures where circumstances arise that were'nt predicted by the law makers 

so maybe consider the landscape of law as evolutionary - developing in reaction to the environment.

and not being optimal, but just fitting, as best it can, to the current world (with some time lag)

so its some kind of re-enforcement learning system.

so asimov suggested 3 (4 later) laws of robotics, and he laid down the law - he wrote down what he (at least initially) believed was a good enough set that it covered all future situations (until the 4th or zeroth law) - it was likely based on his learned reading of scripture (think, ten commandments, redux - I suppose robots didn't worship any god or own any good, so a couple of the commandments were immediately unnecessary - more fool him:-)

[most of the stories in the first I Robot collection, and indeed in the robot novels like caves of steel etc, are basically about debugging]

but what if the laws hadn't been handed down written in stone (or silicon, or positronic hard-wired pathways)? what if we (oops, sorry, not we - the robots, we robots) just acquired the laws by learning them through a system of punishment and reward? what could possibly go wrong?

well, obviously, intially, robots would have to make mistakes - after all, don't we learn from our mistakes, so why shouldn't they? That begs a question - why should a robot care about "punishment" or "reward" ? animals have pain and pleasure - re-enforcement is in terms of an increase or decrease in one or the other (or both). 

so maybe robots need to be hardwired with pain and pleasure centers? and one law, which is to pay attention to those centers and update all the other laws accordingly.

or maybe we should just turn them off.

The Old Diary Farmer

 Recently, I've taken to reading diaries - mainly because I've run out of Science Fiction, but partly also out of interest for this genre - 

Dove right in at the deep end, with Pepys and Casanova - quite long, unexpurgated works of relentless detail, which is no doubt fascinating, but it is hard to see the wood for the trees - in Pepys case, there's an online service that will be deliver you a "pepys of the day" quote, presumably apposite to the calendard and selected carefully from amongst the very freshest products - which made my think about how this could be generalised as a useful service - back in the day, we had a unix thing called qotd (quote of the day) which could be used to select from some curated database (also known as a bunch of geeks or crowdsourced) amusing stuff, like Frank Zappa on Human Stupidity or Groucho Marx on Clubs, or Elvis Costello on Music ("dancing about architecture"). Indeed, in less ancient times (but still a while back, thix could just be an RSS feed...

Anyhow, I think we need to revisit this properly with Diary Farms, and Diary Herds and therapy for people who are in diary need of Condensed Diary products, or, indeed, Plant Based Diary, skimmed Diary, Pro-Biotic Diary and all the rest...

I've made a note in my journal to revisit this a year from now to see if we've made any progress.

From AI to BI and back again....

I think this was roughly the title of Andy Warhol's autobiography, but here I'm refering to Artifical Intelligence and (for want of a better word) Bullshit Intelligence  For useful background on BS, Frankfurt's book is excellent, with regards to the output from language "models", but also see David Graeber's excellent book - especially if you are considering the future of work.

We need to chart an exit strategy from today's cul-de-sac, and restore the optimism, but also intensely practical landscape of machine learning that has an honest history of 50 years (or even more if you go back to Turing), and a track record of delivering stuff (from signal processing, through medical image processing to protein folding) ....

AGI: just say no. Honest-to-god machine learning, sure - bring it on.