Hey Nathan, thanks for your comments. A few quick responses:
On Taiwan Supply Chain:
Agreed that US fabs don’t become a huge factor for a few years, even if everything “goes right” in their scale-up.
Important to note that even as the US fabs develop, other jurisdictions won’t pause their progress. Even with lots to be determined re: future innovation, lots has to “go right” to displace Taiwan from the pole position.
On R&D Penalty:
The “hive mind”/“One Giant Researcher” model might smooth out the inefficiency of communicating findings within research teams. However, this doesn’t solve the problem of different R&D teams working toward the same goals, thus “duplicating” their work. (Microsoft and Google won’t unite their “AI hive minds.” Nor will Apple and Huawei.)
Giving every researcher a super-smart assistant might help individual researcher productivity, but it doesn’t stop them from pursuing the same goals as their counterparts at other firms. It might accelerate progress without changing the parallelization penalty.
Concerns about private markets investment inefficiency still also contribute to a high parallelization penalty.
On Data and Reasoning:
“I actually think we are already in a data and compute overhang, and the thing holding us back is algorithmic development. I don’t think we are likely to get to AGI by scaling existing LLMs.”
If new breakthroughs in algorithm design solve the abstract reasoning challenge, then I agree! Models will need less data and compute to do more. I just think we’re major breakthrough or two away from that.
Davidson’s initial report builds off of a compute-centric model where “2020-era algorithms are powerful enough to reach AGI, if only provided enough compute.”
If you think we’re unlikely to get to AGI—or just solve the common sense problem—by scaling existing LLMs, then we will probably need more than just additional compute.
(I’d also push back on the idea that we’re already in a “data overhang” in many contexts. Both (1) robotics and (2) teaching specialized knowledge come to mind as domains where a shortage of quality data limits progress. But given our agreement above, that concern is downstream.)
Hey Nathan, thanks for your comments. A few quick responses:
On Taiwan Supply Chain:
Agreed that US fabs don’t become a huge factor for a few years, even if everything “goes right” in their scale-up.
Important to note that even as the US fabs develop, other jurisdictions won’t pause their progress. Even with lots to be determined re: future innovation, lots has to “go right” to displace Taiwan from the pole position.
On R&D Penalty:
The “hive mind”/“One Giant Researcher” model might smooth out the inefficiency of communicating findings within research teams. However, this doesn’t solve the problem of different R&D teams working toward the same goals, thus “duplicating” their work. (Microsoft and Google won’t unite their “AI hive minds.” Nor will Apple and Huawei.)
Giving every researcher a super-smart assistant might help individual researcher productivity, but it doesn’t stop them from pursuing the same goals as their counterparts at other firms. It might accelerate progress without changing the parallelization penalty.
Concerns about private markets investment inefficiency still also contribute to a high parallelization penalty.
On Data and Reasoning:
If new breakthroughs in algorithm design solve the abstract reasoning challenge, then I agree! Models will need less data and compute to do more. I just think we’re major breakthrough or two away from that.
Davidson’s initial report builds off of a compute-centric model where “2020-era algorithms are powerful enough to reach AGI, if only provided enough compute.”
If you think we’re unlikely to get to AGI—or just solve the common sense problem—by scaling existing LLMs, then we will probably need more than just additional compute.
(I’d also push back on the idea that we’re already in a “data overhang” in many contexts. Both (1) robotics and (2) teaching specialized knowledge come to mind as domains where a shortage of quality data limits progress. But given our agreement above, that concern is downstream.)