I suppose ill add that I think theres a good chance that they are somewhat intentionally trying to "draw the foul" to get the referees to intervene although thats creeping slightly into conspiracy territory
My theory for why OpenAI wants to be regulated is because Sam Altman wants to avoid having to be more responsible and self regulate internally so they can preserve the role and identity of 'move fast and break things' and outsource the more grown up boring stuff to someone externally so that when things go wrong you can point to a government organisation and say hey look were not liable thats their job
So far, yeah. It doesn't eliminate the possibility over the next 5-10 years of the AI race that we wont encounter a scenario that results in a well positioned lab making a clean break
Very cool. I'd love to see someone with access to plenty of token$ make something similar for the "Browser Desktop OS" test. That seems like a pretty comprehensive test thats also fun to test just like this!
I keep wondering why a robot-vacuum-charging-dock style solution hasn't materialized. We've seen to robotic arm demos but those aren't around because they are overkill for the task. But something that the car autonomously and accurately docks into and attaches from either the front fascia or underneath. Same charging standard but with a more forgiving connector design that guides and locks in.
On the flip side, its important to know just how much we would be sacrificing if big frontier gets their way in convincing the courts that distillation is a bad thing
Also perhaps AI agents are now capable enough to run these apps the way the user would and recognize these dark patterns. Flag those and feed it back to a warning at the point of sale that users can upvote there to signal their disapproval and a threshold score that risks removal of the app from the store. Because bad behaviour continues to make business sense if the rules allow it. Moreover, it penalizes and puts pressure on the good actors as a "missed business opportunity".
Need to rethink the system that allows for (and encourages) this kind of plausible deniability. From "Oh we need this permission for [non essential feature] and you need to accept it if you want the app at all" -> to giving the user ultimate control over what happens on their personal device. Virtualize what the app can see and use fake data/identifiers/devices if necessary to get it to do what its supposed to. If the App isn't going to act in good faith why should the user? Fine grained permissions don't really work in practice because the app can keep annoying the user until they give in and hit Allow.
Between humans, I feel that what we like to call a "good communicator" as opposed to someone who just rattles off facts or prepared statements comes down to the "theory of mind" skill and how advanced that is. The presenter knows what they have to say but beyond that they maintain a real time internal representation of the state of mind of the listener and continuously update their delivery based on that. LLMs today seem to achieve this to some degree(?) but its interesting to think of how far you could advance that skill. I think great human communicators develop a sense of different ways that people think over time and quickly get a sense of someones signature thinking patterns when communicating with someone new for the first time
In thinking of directions where LLM's could develop from here, I cant help but think that a models ability to self introspect would immensely improve their utility. The R&D on how to achieve that is beyond me though. How do you train someone how to introspect? Also would it require a continuous learning architecture that doesn't separate training and inference?
Elon may have been technically correct about 'if humans can do it with vision only then theres no reason why vision-only cannot work' but turned out to be the wrong strategy for learning how to get there. lidar gives you a constant stream of data to check your vision yourself against and interatively learn. Once trained you turn it off. whereas teslas strategy ended up having to rely on human drivers for error feedback which made the learning loop way longer.
Its about closing the gap. its the gap over everyone else that will give one country leverage over everyone else in the AI age. Makes me wonder what the world would look like if a country or group of countries did this during the industrial revolution.
Data centers should at the very least build their own renewable energy generation. It would set the right incentives in place and encourage investment in clean energy generation solutions. It would also present a very compelling problem to direct all that new AI compute towards solving.
the ignorance is a consequence of our societies system of incentives. everything keeps pointing back towards that each time theres an event like this. But if we aren't going to attach an economic price for emissions then it remains an externality and not a recognized reality to the economy. The current US administration is playing a leading role in keeping it that way. And voters keep voting for it. Incentives.