I read a book last evening … “The Zen of Steve Jobs”.
It’s a nice book, and it makes you think.
There was something in it that I wanted to share with you.
It’s a concept called “Mu”.
Mu
In the book, a Zen teacher – Kobun Chino Otogawa – who is a close friend of Steve’s, asks him a series of questions:
Question 1: ‘That tree over there. Does it have Buddha nature?’
Question 2: ‘What about that flock of birds?’ (that is, does it have Buddha nature?)
Question 3: ‘What about that billboard?’ (that is, does that billboard have Buddha nature?)
Question 4: ‘Ask me an answer.’ (the question itself is meaningless)
It turns out that the answer for none of these questions is ‘yes’. BUT, the answer is not ‘no’ either.
To answer ‘yes’ or ‘no’ to any of these questions is absurd.
The right answer in such a situation, Kobun says, is ‘mu’.
Mu is a meaningless term – a word with no meaning – to be used as a way of saying that you offer no answer.
Classification
Now, this is very very interesting to a researcher working on the machine learning problem called ‘classification’.
For instance, given a question like ‘Does AI have Buddha nature’, a classifier is an AI tool that will look at the features in question, and return one of two answers – ‘Yes’ or ‘No’. A classifier trained to give just those two answers answer will give no other answer.
This poses problems when we move out of the domain that we built the AI tool for, and into another domain that the AI engine was not meant for.
A good example is a sentiment classifier. In a lot of early studies on sentiment classification, the training sets used were customer reviews. In other words, reviews were the sole domain of the classifier. Since most sentences in reviews were opinions, early sentiment classifiers could label anything as positive or negative and be mostly right about it.
In 2004, social media became big. But Twitter and Facebook were full of sentences that were not opinions. Running an old-fashioned sentiment classifier on those sentences could lead to a lot of false positives.
Take the sentence ‘Is that a good phone?’ Does this sentence contain a positive or a negative opinion? The sentence contains neither.
That’s because the sentence is not an opinion. The sentence is a question. So you can’t perform sentiment analysis on it.
So, the answer is ‘mu’.
In classification, we sometimes call this category ‘Other’.
Intentions
One day, we attempted to use intention analysis to identify when a customer might quit.
The intention categories we used to do this were: ‘criticize, complain, accuse, quit’.
If we could identify these intentions, we would have a way to identify and reengage an aggrieved customer before they gave up on the service provider forever.
To build such a system, we started looking at sentences in complaint forums.
Some of the sentences were easy to categorize.
Example 1: ‘my login id not open’ is obviously a complaint.
Example 2: ‘FRADULENT DEDUCTION OF CHARGES’ is an accusation (it implies bad faith in the service provider).
But what about Example 3: ‘NEGLIGIENT SERVICE’ ?
In Example 3, the customer is not only reporting a problem but also implying bad faith in the provider.
The interesting thing about this sentence is that it certainly belongs to one or the other of the categories we had created for it.
It could be a complaint. It could be an accusation.
Though you may not know which one of these categories it belongs to, you can be certain that it belongs to one of the two.
So, the answer in this case cannot be ‘mu’.
You need more than one answer.
Why did the Zen philosophers miss it?
What is interesting is that the Zen philosophers whom Steve Jobs so admired seem to have completely failed to notice the existence of an anti-mu (multiple viewpoints that could all be right) though they pondered “mu” over for thousands of years.
But AI researchers have run splat into all of it within just 100 years of AI research.
So, does AI have Buddha nature?
The answer could be ‘mu’ and ‘yes’ and ‘no’.
Aiaioo Labs Blog 
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