Chess vs. Go: Why Business and Financial Transformation Become a Must in a Post-Covid World


In my opinion, the workers of the world performed admirably in handling the Covid crisis.  We never saw the apocalyptic loss in productivity some feared.  Companies leveraged a number of new and existing technologies and processes to remain productive despite the change and restriction caused by the pandemic.  The world rapidly moved to new methods of doing business and that cat, now out, is not likely to go back into the bag any time soon.

What does this have to do with the games of Chess and Go?  It turns out…  everything.  Covid has created recent change and provided impetus for future change.  Given this, a valid question is  ‘to what destination?’   From a technology perspective, above all others, we hear of Artificial Intelligence (AI), and how transformative it will be to businesses in the future.  However, few have an innate feel for how powerful AI really can be, nor do they necessarily know what preparations must be made to properly leverage AI in the future.  It all seems a foggy compendium of some future state that will never materialize.  However, if you peer through this haze, you start to realize that some very bright people speak of AI with awe, with glee, and even with fear.  Why?

For me, like many, the realization occurred when the Alpha Go AI engine (owned by Google of course) beat the world Go Champion, Lee Sedol, in 2016.  This led me to learn the game of Go, and finally, to a revelation.  To understand this, we have to go a little deep on the difference between Chess and Go.  It was once thought that for a machine to beat a human in Chess it was the mark of machine intelligence.  The reality, however, is this can be done with a goal-seeking exercise, albeit a big one.  You need to have a means of valuing the board with each piece in a given position in terms of likelihood of a win.  Once you have this, it becomes a reasonable matter of writing a program that knows the limited number of moves each piece can make and the resultant value of the board after that move. There are plenty of combinations that can occur, but at the end of the day, Chess is played on an 8 x 8 board of squares.   At any given time there are on average 20 individual moves that could be made.   Of course, the other player will move, but it is possible for a computer to run through each piece move, each best counter move, each retort, and so on for N levels in order to determine the single move now that will result in highest probability of success over time.  As stated, with the power of modern computers, one can imagine how the result can be achieved through brute force.

Enter Go…  Few in the West are familiar with this ancient Chinese game, but it is fascinating.  Instead of having 8 x 8 squares it has 19 x 19.  Instead of there being roughly 20 possible moves at any given time, there are on the order of 200.  Instead of having a countable set of moves and counter moves, the combination rapidly exceeds the number of atoms in the Universe.  There is no option to brute force this problem through goal-seeking.  It requires intuition, creativity and pattern recognition to play go, all thought to be uniquely human qualities.  It therefore seemed impossible to program a machine to beat a human… until it did.  Alpha Go employed a neural network that mimics the synaptic function of the human brain.  It was created by a gang of geniuses at the DeepMind company (now owned by Google) and trained by a professional Go player.  At some point this group advanced Alpha Go far enough to beat a human.  This was a tremendous feat on its own.  Then in October of 2015, it went on to beat the European top ranked player.  By March of 2016 it was up against Lee Sedol, the best player in the world.  Even at the time of the match, Lee estimated he would win either 5-0 or 4-1 in a set of five games against the computer.  He lost the first three games in a row ending at 1-4 over the 5.  Importantly, there was a ‘move 37’ in game 2 that surprised all on-lookers.   It was a move that all admitted no human would ever play, yet it became the lynchpin of a strategy that culminated in a win for Alpha Go.  After the match, Lee Sedol described the move as ‘beautifully creative’, an attribution once thought to be the sole province of the human mind.

To ram the point home, one has to consider the full implication.  Humans are capable of transmitting data through speech at 39 Kbits/sec. (Science Magazine, 9/2019).  The human mind processes data at a rate of 60 bits/sec (MIT Technology Review, 8/2019).  Compare this to machines capable of backplane data transmission speeds well into the 10’s of gigabits/sec and processors running at 3 giga-hz.  Now scale this to something the size of the Amazon EC2 cloud and you start to sense the magnitude of the possibility.  Were general AI to be achieved, and a human to have a conversation with it, the human might be able to start to respond to a question in milli-seconds.  But, to the AI eons would have passed since it asked the question.  It likely would have performed a tremendous number of activities while waiting over the span of this protracted period of time.

What does this mean to finance and business of today?  Artificial Intelligence may seem a thing of the future, but it is actually not.  It is here today, albeit in specific, not general, form.  It is currently at the core of operations at Google, Facebook, Twitter, Tesla and others and very likely the destination to which the rest of us are all heading.  It is safe to say, these companies would not have their monopolistic properties without it.  It will take time, probably years, to trickle through the hierarchy of companies around the world, but this will most definitely happen.  Companies that have a profound understanding of the potential of AI, that prepare and embrace the change, will thrive.  Those that turn a blind eye will not.  Will this future be in 1 year? 3? 5?  It is hard to tell, but it is definitely coming and preparations themselves could also take many years.  Today, most companies are still employing sets of manual processes in their operations.  Data, while managed, is often not in a state to support training of an AI or consumption once trained.  There are a million other small tweaks that will be required.  Per Laozi, ‘A journey of a thousand miles starts with the first step’.  The post Covid era we are entering will allow CEOs and CFOs to look at internal infrastructure and systems to determine the projects that must be done to prepare for the coming age of AI.

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