It's also likely that AlphaGo is reasonably efficient, given that it used custom ASICs.
Low power is also still important due to issues of heat and energy availability. Low power also implies high efficiency which is important for several reasons.
The human brain is estimated at 20 watts (when people talk about computing systems they tend to not include the power needed for all the auxiliary infrastructure needed to keep it networked and cooled); it's also estimated that beyond 4 hours a day, learning effectiveness drops precipitously.
If we take the case of Go, you can take a 4 year old human and have a professional player by 13. This is about 950 megajoules spent by the brain while learning Go. For the machine, if you look at the learning part (self play, value and policy on 50 GPUs for several weeks) the estimate on energy spend is about 30,000 megajoules. The policy network is itself ~20,000 MJ, while the full AlphaGo system playing on a single GPU and 48 CPUs is just a strong amateur.
But this is not even an apples to apples comparison since the brain is not spending all of its energy on learning Go. In fact, learning how to play Go is very far from the most difficult thing the brain is learning how to do.
To say AlphaGo or any RL system is learning from self-play is not in the typical understanding of the phrase. It's more akin to evolving with competitions against previous versions of itself, which should count as different instances. As stated on page 38 of 1604.00289.pdf
Between the publication of Silver et al. (2016) and before facing world champion Lee Sedol, AlphaGo was iteratively retrained several times in this way; the basic system always learned from 30 million games, but it played against successively stronger versions of itself, effectively learning from 100 million or more games altogether (Silver, 2016).
In comparison, from that same paper, it was estimated that Sedol could not have played much more than 50,000 games. My own estimate is about 40,000 games.
As for work required to learn, it's irrelevant to point out that one can learn from others. Learning, whether from play, books or study still requires energy spend and work by the learner. Most of the extra work is from study, occasional review with a tutor and discussions with peers--the last more of a meta-step: learning to learn. Accounting for books and some time with tutors will not, I argue, shift the budget much. Especially if you include that any machine playing Go requires overhead of power infrastructure, energy, cooling, networking equipment and occasional maintenance staff. And learning, improvement in architecture, requires searching through and discarding many changes and playing through a cumulative hundreds of millions of games.
The fact that humans have other available highly efficient means of learning is a boon and not a downfall. That's the whole point of getting to AGI. Learning from books and others is akin to learning by Program Synthesis from specifications.
The reason I noted the requirement of other trained professionals for training a human is that those other humans can distill what they have learned over years of play into simple rules. The machine can also use those rules, but the particular machine you are comparing to was specifically trained without any such rules and was required to synthesize them from scratch from historical games and self-play.
The computer can train itself with just records of past games and self-play. The world champion level human cannot. You must account for the difference in training.
Visual slam is still linear-algebra/geometric/keyframe based traditional computer vision (including variants that incorporate GPS/accelerometer info). I think the state of the art is stereo LSD-SLAM, but I could be wrong.
I hear this comment a lot when defending Tesla's choices, and it's a red herring. The fact that humans only rely on two cameras means nothing. Repeating old comments of mine:
You also don't "need" megawatts of power to play top-level Go: humans do it with 100 watts of energy. Yet Google needed who knows how many megwatts of energy to train and run AlphaGo on their massive server farm.
Imagine two companies competing to win at Go, and one company had the attitude that megawatts of energy was not necessary for training and prediction, and another company threw the biggest GPU farm they could. The second company just played top-level Go this year. The first company is ~10 years away from a low-energy elite Go computer.
Humans implicitly perform SLAM (simulataneous localization and mapping). What do I mean? Look around your room. Close your eyes. Visualize the room. As a human, you've built a rough 3D model of the room. And if you keep your eyes open and walk through the room, that map is pretty fine-grained/detailed too and humans can keep track of where they are in the map.
Doing this accurately in moving environments (especially with lots of pure forward motion) with just two cameras is still a wide open research problem.
Doing this with LIDAR/GPS/IMU/recorded maps is solved. That's why people use LIDAR.
Matching the abilities of human perception is an insanely hard problem. Don't let cute problems like image classification fool you. Why make the problem even harder?