In this blog post, Joseph Carlsmith gives a summary of his longer report estimating the number of floating point operations per second (FLOP/s) which would be sufficient to perform any cognitive task that the human brain can perform. He considers four different methods of estimation.
Using the mechanistic method, he estimates the FLOP/s required to model the brain’s low-level mechanisms at a level of detail adequate to replicate human task-performance. He does this by estimating that ~1e13 − 1e17 FLOP/s is enough to replicate what he calls “standard neuron signaling” — neurons signaling to each other via using electrical impulses (at chemical synapses) — and learning in the brain, and arguing that including the brain’s other signaling processes would not meaningfully increase these numbers. He also suggests that various considerations point weakly to the adequacy of smaller budgets.
Using the functional method, he identifies a portion of the brain whose function we can approximate with computers, and then scales up to FLOP/s estimates for the entire brain. One way to do this is by scaling up models of the human retina: Hans Moravec’s estimates for the FLOP/s of the human retina imply 1e12 − 1e15 FLOP/s for the entire brain, while recent deep neural networks that predict retina cell firing patterns imply 1e16 − 1e20 FLOP/s.
Another way to use the functional method is to assume that current image classification networks with known FLOP/s requirements do some fraction of the computation of the human visual cortex, adjusting for the increase in FLOP/s necessary to reach robust human-level classification performance. Assuming somewhat arbitrarily that 0.3% to 10% of what the visual cortex does is image classification, and that the EfficientNet-B2 image classifier would require a 10x to 1000x increase in frequency to reach fully human-level image classification, he gets 1e13 − 3e17 implied FLOP/s to run the entire brain. Joseph holds the estimates from this method very lightly, though he thinks that they weakly suggest that the 1e13 − 1e17 FLOP/s estimates from the mechanistic method are not radically too low.
Using the limit method, Joseph uses the brain’s energy budget, together with physical limits set by Landauer’s principle, which specifies the minimum energy cost of erasing bits, to upper-bound required FLOP/s to ~7e21. He notes that this relies on arguments about how many bits the brain erases per FLOP, which he and various experts agree is very likely to be > 1 based on arguments about algorithmic bit erasures and the brain’s energy dissipation.
Lastly, Joseph briefly describes the communication method, which uses the communication bandwidth in the brain as evidence about its computational capacity. Joseph thinks this method faces a number of issues, but some extremely preliminary estimates suggest 1e14 FLOP/s based on comparing the brain to a V100 GPU, and 1e16 − 3e17 FLOP/s based on estimating the communication capabilities of brains in traversed edges per second (TEPS), a metric normally used for computers, and then converting to FLOP/s using the TEPS to FLOP/s ratio in supercomputers.
Overall, Joseph thinks it is more likely than not that 1e15 FLOP/s is enough to perform tasks as well as the human brain (given the right software, which may be very hard to create). And he thinks it’s unlikely (<10%) that more than 1e21 FLOP/s is required. For reference, an NVIDIA V100 GPU performs up to 1e14 FLOP/s (although FLOP/s is not the only metric which differentiates two computational systems.)
Planned summary for the Alignment Newsletter:
Planned opinion: