gemini shows that he knows he will get caught. what will it do when it figures out how to not get caught?
Take the classic NSA encryption logic - if you build some capability with a backdoor only you can exploit then you are building your own future capability - and if you think you are the 'good' guy - what's wrong with that?
I honestly never got why the belief that if you used this line of reasoning in practice, you were just a bad guy, that thought they were a good one, never seems to be a more common belief
I mean it pops up in media a lot, either as misguided, or deceptive characters
Take the US recent assassinations of Iranian leadership. Objectively unilaterally assassinating other countries leaders is wrong - but somehow it's fine if you have a good guy/bad guy lense.
One of the problems here is huge swathes of material that the AI will have ingested includes this kind of exceptionalism thinking - it's particularly deeply rooted in Western culture generally and US culture in particular.
The problem isn't so much whether the good guy/bad guy allocation is corrrect - rather that it's very existence in the training set is deeply dangerous - as all it takes is one single classification to happen and suddenly everything is for the greater good.
Why? If the leader is responsible for atrocities, then why would it be wrong to kill them? Especially if their removal would result in a more liberal leader replacing them, and not just chaos or more bloodshed.
In the case of Iran, there was no evidence of that, but as a general rule leaders should not be a protected class.
You might think our brains are just a network of neurons and what we think of as thinking is just fancy pattern matching in that network of neurons. But architecturally they are so far removed from each other that the mathematical equivalence has no practical meaning. LLM's aren't doing what we're doing.
LLMs are amazing knowledge remixers. They are superhuman knowledge remixers. In application domains where the remixed knowledge can be verified, they do a great job. But nobody is going to value a 98% reliable knowledge remixer at $2 trillion.
So if an ML model can do real time language translation or drive a car - that has value - whether it's remixing or thinking or whatever doesn't really matter.
But let's take the one you've accepted - it can write code. It can translate from intention to code quite effectively - then they can do anything that you can do by writing code.
Also while you dismiss ML that drives cars as 'not LLMs' - it's part of the ecosystem - these task specific models ( the ability to process images, sounds, drive cars, synthesize speech etc ) - extend what is accessible to a computer programme that can write code.
Look at what's happening in the world of robotics. You have lots of specific ML models that allow the robot to move, see etc.
Then the LLM allows it to convert a human command in the form of arbitary speech ( rather than fixed preset commands ) into action.