IEEE Systems, Man and Cybernetics Magazine - January 2020 - 26

which makes me upset when my win rate decreases.
Moreover, it seems to me that the robot started playing
some irrational moves under a very unfavorable situation. If the robot can improve this point, a human will
have better cooperation with the robot.
Kaneko (1p) appreciated the learning opportunity:
I was very happy to play with a robot with a high-level
Go AI engine. The robot provided me with some
unexpected moves, so I learned a lot. Playing with
strong computer Go programs has become professional Go players' daily training since the high-level
Go AI engine appeared. Learning with humans is

(b)

(a)

(c)

Figure 2. The Human and Smart Machine Co-Learning
@ IEEE SMC 2018 event: (a) a poster of the special
event, (b) the involved Go players' signatures, and
(c) blessings for the event.

absolutely useful for strengthening Go players' skill.
However, machines will be more able to indicate key
points when learning with strong machines. Anyway,
AI has broadened my vision.
Chang (6d) also observed:
The human Go player, no matter professional or amateur, has been inspired by AI and has abandoned the
tradition practiced from thousands of years. Now,
they can fly freely upon the board of Go. But, first of
all, a human should open their mind, put down their
dignity, and get used to being humiliated by machines.
The strong gets stronger, and the weak gets worse.
Maybe this is a philosophical dilemma that is always
caused due to technological advancements. And we
hope that the human and machine co-learning can
improve the relationship between them. Anyway, I
congratulate human beings and congratulate machines.
This is a successful demonstration of AI self-consciousness co-worked with human beings.
Figure 4(a) depicts Game 5-1 (Reference Go game), where YiHsiu Lee (8p) and the robot (ELF OpenGo) formed the white
team and ELF OpenGo formed the black team. Figure 4(b) displays the win rate curve for Game 5-1, indicating a win rate of
42% for the white team at move 170. In this game, the black team
won. Lee (8p) commented as follows:
The situation had turned favorable for the white team
until the middle-late game. After move 90, many of my
subjective thoughts were different from the robot's top
suggested moves. There were two points that impressed
me in this game. 1) The robot recommended an aggressive move for move 100 that I had never thought about
before. However, no variations in the following moves
were provided by the robot, so I could not judge whether this suggestion was bad or good. After some hesitation, I chose a safer move instead of the robot-suggested

Attention
Left-Brain
Activation Level

Right-Brain
Activation Level

Stress
(a)

Fatigue
(b)

Figure 3. (a) (From left) Chang-Shing Lee, Li-Wei Ko, Bo-Yu Tsai, and Go player Shen-Su Chang (6d). Chang

wears a BCI system to play with Dynamic DarkForest. (b) Chang's real-time physiological states based on his
measured brainwaves.

26

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 2020



IEEE Systems, Man and Cybernetics Magazine - January 2020

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