Why Is the Human Brain So Efficient? (2018)

I agree. I think its very widely known that our ANN’s are only very rough approximations of how the brain actually works, I think the people who say its a computer implementation of the brain are either laypeople who don’t know much about machine learning or the brain, are people marketing the hype for personal gain or people without neuroscience knowledge who have bought into the hype.

I also recently heard an argument for why our ANN models won’t spontaneously become sentient: human brains don’t learn from just observation, but also interaction. A young child doesn’t learn abouthow blocks are stacked by looking at images of stacked boxes, they learn through experimentation, by stacking boxes and seeinghow their actions affect the world around them. For an AI, that means we either need to also work on robotics so the AI can interact with its environment, not just sense it, or we need to simulate an interactive virtual environment. Some people are working on this and making great strides, but your average toy ANN won’t exhibit human intelligence in isolation, in my opinion.

Combine those two things and we’re still quite a ways away from human-like intelligence or implementing a human (or animal)-like brain.

You might be right about brains being better at certain kinds of tasks, but I don't think it's right to think of them as having only one processing mode.

Someone else mentioned "Thinking, Fast and Slow", and I find it fascinating how closely the two thinking modes in that book seem to map to CPU (mostly serial) and GPU (parallel) processing. It also claims that people have natural preferences for each mode of thinking, which is super interesting as it suggests that the tasks that brains are best at performing will vary from person to person (I guess this is obvious, but perhaps gets lost when we start comparing to computers).

I'd bet on brains getting a lot of their efficiency from tight integration of CPU-like, GPU-like, and ASIC-like, and full on analog components. We'd probably have to apply deep-learning like approaches to the hardware design itself to get close.

Thanks for this. Much better than what I did although I don't think 58 is a third of 126, more like a half.

The article’s articulation of what goes into returning a serve is a bit simplistic, but the underlying idea is not crazy.

* When you return a serve in tennis, you are doing so from only one side of the court. The opponent’s serve can only land in a service box that provides 13 feet of lateral space.

* Practically, there are relatively few spots in the service box that can be reached by a serve. Because of human physiology (the length of our arms, joints in the arms etc.), it would be extremely painful to try to hit a fast serve to certain parts of the service box. Either that, or the server would have to stand in atypical positions on the service line (i.e. not at the center tick) that would be a dead giveaway of where the server was trying to hit to.

* So, in simplistic terms, most tennis players are choosing between more-or-less staying in place (to return a body serve), or leaping to their left or right. The serve must bounce before you hit it, and it will be bouncing “towards you” vertically. The returner thus is very rarely going to move vertically. This usually only happens when you are moving in to pummel a slow and short serve.

* At the highest levels of tennis, the vast majority (60%+) of serves are going out wide, or down the middle (https://www.atptour.com/en/news/berrettini-infosys-serve-loc...). Mind you, these are also the same player who would have the physical conditioning and athleticism to actually be able to hit these blazing fast serves.

* Additional information for the returner is conveyed by the serve toss. Almost all players are giving away tells here. For example, if I’m a right hander serving from the deuce court, and I toss my ball to the left (the “11 o’clock position”), it’s high unlikely that I’m hitting the ball down the middle. Doing so would require one of those aforementioned contortions in my arms and legs, and I would then be unlikely to generate the power needed to strike the ball in a way that leads to a super fast serve.

* So in reality, by the time that the server is making contact between their racket and the ball, the returner will have a general idea of the direction that the ball is going in.

* The article does conflate getting your racquet on the ball, and making a successful return. Just as with any other tennis shot, there is not guarantee that your return does not go into the net, or go flying out. I think it’s a far more plausible claim that professional tennis players can get their ball on the racquet vs claiming that they can cleanly/successfully return these super fast serves.

Some other thoughts:

* Placement is just as important as speed in determining how returnable a serve is.

* For example, there are plenty of examples of top tennis players returning extremely fast serves. Federer against Isner (140 mph): https://youtu.be/5gcvLbtaNxM, Murray against Raonic (147 mph): https://youtu.be/8GYX4ZIPJsg

* The commonality between these successful returns is that the serves themselves were fast, but poorly placed. By serving right down the middle, the servers allowed Federer and Murray to take one small step, and then make good contact with the serves for an “easy return”.

* One small quibble with the “world record tennis serve” you cite. It’s not 144 mph, but rather 157.2 mph (hit by John Isner). If anything though, this is helps your argument.

* The unofficial record is 160+ MPH (hit by Sam Groth), but this was at a second tier tournament with a questionable radar gun (https://youtu.be/uKeL-W7xft0). Notice how even with this serve, the returner correctly guesses where the serve is headed, and even looks to have gotten a racquet on it.

* It’s a bit of a chicken and an egg problem as well. There is a very tiny sliver of people in the world who are physically fit enough and who possess the natural physical traits (like height and broad shoulders) necessary to hit serves in the 140+ MPH range. These people are likely playing on the ATP against the players in the world best equipped (mentally and physically) to return their serves.

* So all this is to say, returning serves in that 140-160 MPH range is a low probability proposition. Heck, a perfectly placed and well disguised serve even in the 110 MPH range can be unreturnable (as seen in two decades of Federer highlights). But, humans are indeed “capable” of returning serves in that speed range.

Is it though?

I think having a good metric is really hard.

For example i can have a neural net running on my smartphone doing recognition tasks.

A task the brain is typically good at due to its neural net structure while the computer basically has to simulate the net.

But still my smartphone can mark all the faces in a crowd multiple times over in a time i can not recognize even a single person.

And that with a camera way beyond the capabilities of the human eye.

Modern smartphone processors draw around 1 or 2 watt max. So is my phone more efficient at doing this?

One could argue that my brain does other stuff at the same time like controlling heartbeat and what not but my phone has to keep the wifi, clock and so on too.

The truly impressive part is the ability of the brain to do completely generic problem solving for basically everything; while running on 10 watt. With the added ability to learn a few activities to a really high level.

Its is not efficient at doing a singular thing it is efficient doing everything at once.