Most people are afraid of “next-level” AI, where no operator is required. If trucks can be moved without anyone at the wheel, truck driving is a job that won’t exist anymore. As simple as that.
Whether trucks can be 100% autonomous remains to see.
As technology becomes more intelligent, the human comparative advantage shrinks. But what is likely to happen? Don’t look to the past. Look at the fundamentals of the system dynamics.
Consider an analogy. The differential equation that governs blackbody radiation depends on the difference between the object and the environment. Similarly, a person’s earning capability is proportional to the difference between their capability and the next best option. As the next best option gets better and better, the human differential advantage craters.
Max Tegmark uses the metaphor of a rising ocean to signify the ever greater capabilities of intelligent machines.
AI isn’t just coming for low-skilled jobs. It isn’t just supplementing knowledge work. AI will take more and more intellectually demanding jobs. This arguably doesn’t even require AGI. This just requires industry deploying capable AI’s that can do enough tasks to replace humans one industry at a time.
So people that have an ownership stake in these AIs will do fine.
My take: The others should demand a universal basic income. How do we find ways to allow people to live good lives? People that weren’t lucky enough to be born into the right places in society don’t deserve to be displaced by AI.
If you read the AI literature, I’m not alone in what I’ve said above.
Granted, I’m also not offering particular time frames and not covering all future scenarios. But what I’m saying is more considered than the usual default ‘reasoning’ you hear people say.
A truly 100% autonomous vehicle requires a much higher level of intelligence than a self driving vehicle with a driver able to take the wheel when necessary.
Take the case when some work is happening on the road and workers make signs with their hands to tell you to go this or that way. But on the same road there is also someone who is dressed up as a policeman cause it’s Halloween, and he’s waving at some friends.
It's possible someone might figure out a way to create a training loop using a multi-modal LLM to generate synthetic training data based on the situation you just explained and then updating the driving model by training on this new data until its performance improves on the task.
I imagine we will reach a place where fully autonomous vehicles will pull off to the side of the road in certain weather. Which I wish we could force for humans, but seems infeasible to implement.
Seems likely to me; we built EV's before we built the infrastructure to support them and conversely we created in infrastructure for petroleum vehicles before they entered mass production at the largest of scale in the period (1950's) after the fact.
I don't see how autonomous vehicles are not going to become a reality. Perhaps not in my lifetime, but, absolutely likely and possible.
I don't doubt that, but the timeframe is unknown. 5 years? 10 years? Within our lifetime?
So what we have is the ability to input data, but not yet a delivery system and retrieval system that can fit on say, a small chip, or light array, or other small systems.
It's a giant part of reason we'll see diminishing returns with data being applied in classical material approaches. New materials (currently being workd on, like graphite and others) will be needed to harness the compute power to enable large scale data capabilities at increasingly smaller and smaller levels (already a well known issue to be resolved).
Similar in physical approach but different in application would be TinyLM.
Timeline? Not sure but we created ION drives over 30 years ago. Seems to me we're limited not by the science, but the material needs to continue technological advancement. Seems to me autonomous driving is within reality in under 25 years. If I had to put a guestimate on it.
A lot of this breathless talk surrounding this turn of AI is so uncomfortably reminiscent of what I’ve seen before in the mainstream the last turns around the 1970’s and 1980’s, and the potential failure mode might not be so different: solving the last 5-10% is tantalizingly close but remains stubbornly out of reach of calls for the “more cowbell” of each era or call to action by the sales legions (currently cowbells look like NVIDIA boards and various counts of AI models be it tokens or what have you), and the last 5-10% is the necessary advance to cross the chasm.
I love and use the tech myself every hour, but it has deep gaps I don’t see being resolved even incrementally between versions or competitors.