I’d like to see the strong case for autonomous driving being the harbinger of generalized AI laid out. Specialized AI, with varying hardware support, has yet to solve the last-mile of 5-10% of autonomous driving in favorable conditions. In snow, sleet, hail, dusty, smoky, foggy or some rainy conditions, progress has not been commensurate. Yet somehow this is the success template for generalized AI applications. I’m missing the chain of logic here.
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.
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.