Kenjutsu, Ohno and Polanyi:

ken

Taiichi Ohno, the father of Toyota Production System, has a way with his words. I was rereading his great book, “Toyota Production System – Beyond Large-Scale Production”, and I came across the section where he talked about “In an art form, action is requried.” [1]

In the section Ohno talked about the progression of swordsmanship from “gekken”, to “kenjutsu” to “kendo”. Ohno wrote that during the era of brute force fighting, “gekken” was about having the strongest swordsman winning over the weaker opponent. As time progressed, it was recognized that there is a structure to the swordsmanship, and “kenjustu” was developed. Kenjutsu is translated as “art of sword”. With this, a weaker opponent could strike down the physically stronger opponent. As time went on, there was no longer a need to carry sword around, and “kendo” was developed in its place. Kendo means “the way of the sword”. The etymology is similar to “judo” which means “the gentle way”. The “-do” stands for “the way of”. “Ken” stands for “sword”. Thus, kendo stands for “the way of the sword”. Kendo utilizes a bamboo sword called a “shinai”. Kendo is a martial art and has become very well known in Japan and outside Japan.

Ohno went on to state that he believed that swordsmanship advanced the most during the era of kenjustsu. The “jutsu” part stands for “the art of”. Ohno points out that “jutsu” is created by inserting the character “require” into the character “action”. Thus, kenjutsu advanced swordsmanship the most because it required action. Ohno continues to state that “real action is what counts”. Talking about technology and actually practicing it are two different things.

This is a great lesson from Ohno and I was reminded of tacit knowledge when he talked about “requiring action”. Tacit knowledge is the brain child of Michael Polanyi, a Hungarian-British polymath [2]. Tacit knowledge may be loosely described as the knowledge that is hard to codify and part of which cannot be codified. Polanyi stated that “we know more than we can tell”. “Tacit knowledge” is generally contrasted against “Explicit knowledge”. Explicit knowledge is the knowledge that is present in the codified form like written procedures, manuals etc. However, it is wrong to state that Tacit and Explicit knowledge are mutually exclusive and that all Tacit knowledge can be transformed to Explicit knowledge.

Polanyi believed that all knowledge is either tacit or rooted in tacit knowledge, including explicit knowledge. In Polanyi’s words;

                “While tacit knowledge can be possessed by itself, explicit knowledge must rely on being tacitly understood and applied. Hence all knowledge is either tacit or rooted in tacit knowledge. A wholly explicit knowledge is unthinkable.”

While it might be possible to codify some parts of tacit knowledge, not all tacit knowledge can be codified. Some of the examples that Polanyi gave were riding a bicycle and facial recognition. It is not easy to explain in written form how to ride a bicycle or how to recognize a person through facial recognition. With the advancement in Machine Learning, both these activities can now be performed by AI (Artificial Intelligence). However, even the AI has to perform the action and learn from errors to be somewhat successful in it. The tacit portion of the knowledge still requires action. One of the ways to teach facial recognition to AI is to give a large amount of pictures with proper identification to allow the AI to learn from the correct data first. Based on this, the AI will start performing facial recognition tasks, and every wrong answer gets corrected which adds to the learning. Once the supervised learning is complete, a new dataset with unidentified pictures are given, and the accuracy rate determined. Every attempt at recognizing a picture is a lesson that reinforces the facial recognition knowledge.

Polanyi’s theory of knowledge was based on his objections against the prevalent “objectivism” in the scientific method. Objectivism is the belief that all knowledge is posteriori (after the fact) and is derived only based on the perception of the results with senses. Thus, the knowledge is based on quantitative measures using only perception. Polanyi’s objection to this was objectivism ignored the role of the observer or the experimenter. Polanyi thought that discovery must be arrived at by the tacit powers of the mind and its content. The role of the knower is very important in the formation of knowledge. Polanyi’s ideas of tacit knowing were derived from Gestalt psychology and the part-whole perception model which requires coherence between focal and subsidiary awareness. A face is able to be recognized because of all the particularities of the face (relative position of nose, lips, eyes etc, size of the eyes, color of the eyes etc.) combined into a coherent image through subsidiary and focal awareness. There is lot more to tacit knowledge that cannot be contained in this post. I encourage the readers to read upon Michael Polanyi for more. There is a lot more to tacit knowledge than what can be written down here (no pun intended).

The tacit knowledge can only be acquired by carefully observing the expert, and performing the functions under his or her watchful eyes. In other words, tacit knowledge requires action. Even the expert may not be aware of all parts of the tacit knowledge. The tacit knowledge can be acquired only through “close interaction and buildup of shared understanding and trust”. Polanyi has said that “all knowing is personal knowing”. Explicit knowledge can be stored in hardware (computer, books discs etc.) Explicit knowledge can be thus “transferred”. This is not possible for tacit knowledge. Some Knowledge Management practitioners have argued that all tacit knowledge can be transformed to explicit knowledge. An example is the SECI model by Nonaka and Takeuchi [3]. I do not believe this is possible since I believe that tacit knowledge can be acquired only through action and personal interaction with the experts.

I will finish off with a story I read from Harry Collins’ book, Tacit and Explicit Knowledge [4].

A guy walked into a pub that he has never been to before and sat down for a few drinks. He was puzzled by the action of the locals at the bar. Every now and then one of them would shout out a number and everybody would break out into laughter. This continued for a while, and the guy was very curious about it. He went to the pub owner and quizzed him about the strange actions. The pub owner explained to him that the locals have been coming here for so long and that they have been telling the same jokes over and over that they started assigning them numbers. So now, all they have to do is just call out the number and everybody would know the joke. Armed with this information, the new guy started calling out numbers and each time he was met with silence. The pub owner felt sorry for him, and explained to him “It’s not the joke my friend, it’s how you tell it.

Always keep on learning…

In case you missed it, my last post was Shisa Kanko, a Different Kind of Checklist:

[1] https://www.amazon.com/Toyota-Production-System-Beyond-Large-Scale/dp/0915299143

[2] https://en.wikipedia.org/wiki/Michael_Polanyi

[3] https://en.wikipedia.org/wiki/SECI_model_of_knowledge_dimensions

[4] https://www.amazon.com/Tacit-Explicit-Knowledge-Harry-Collins/dp/022600421X/ref=mt_paperback?_encoding=UTF8&me=

Advertisements

Machine – Let your imagination run wild!

main

On June 17, 2015, Mordvintsev, Tyka et al posted a blog on the Google Research Blog. The title of the blog was interestingly “Inceptionism: Going Deeper into Neural Networks”. This post discussed using the Machine Learning algorithm, Neural Networks to identify objects in any picture. This was referred to as “Deep Dreaming”. Since then, the concept of machines dreaming has gone wild!

Neural Network is a Machine Learning algorithm used for fields like speech recognition, character recognition etc. The algorithm requires supervised learning at first. At this stage, the algorithm is trained using known samples. The strength of the internal networks gets stronger as the algorithm gets more answers correct. This weakens the pathways leading to incorrect answers, and strengthens the pathways leading to correct answers. Once the “training session” is over, the algorithm will be used on new samples, where the answer is not provided to the algorithm.

Google has created a program aptly named “DeepDream”. In their own words;

Google has spent the last few years teaching computers how to see, understand, and appreciate our world. It’s an important goal that the search giant hopes will allow programs to classify images just by “looking” at them.

And this is where Google’s deep dream ideas originate. With simple words you give to an AI program a couple of images and let it know what those images contain ( what objects – dogs, cats, mountains, bicycles, … ) and give it a random image and ask it what objects it can find in this image. Then the program start transforming the image till it can find something similar to what it already knows and thus you see strange artifacts morphing in the dreamed image (like eyes or human faces morphing in image of a pyramid).

In other words, we are encouraging the machine to run their imaginations wild with pareidolia, the phenomenon where one sees a shape or a form on an unrelated object, like seeing a face on the surface of the moon. The algorithm will try to find objects in a picture, and any resemblance is made stronger and stronger until it creates a familiar object. Of course, I wanted to test this out. You can see the deep dreaming effects on my current LinkedIn profile picture.

FullSizeRender

IMG_2492

You can see that my black hair was used to weave in imaginary eyes. Interestingly, my beard became the face of a dog. The algorithm uses the pictures database it knows (over 1 million pictures). Thus most of the dreamed pictures are common animals and objects.

I thought I would then test the algorithm with Card III from Rorschach Inkblot test. This card is seen to have two humans or ape like shapes interacting with each other.

IMG_2494

IMG_2496

The algorithm was able to identify two dogs with weird limbs, another dog in the middle, and two containers of some sort.

Practical Uses:

This program is very entertaining and provides insights into how the machine actually sees or dreams, as Google likes to put it. Other than the entertaining and deep philosophical aspects of a machine dreaming, there are practical uses for this.

What if the machine can imagine a picture through the eyes of a famous artist? Using a similar algorithm, we can have a “new” painting from Vincent van Gogh of the Eiffel Tower. I have taken the following example from here. You can see that the clouds were enhanced with the strong Van Gogh style strokes.

eiffel-tower-1 starry_small eiffel-tower-1_as_starry_small

More examples were found here. The following is the Starry Night version of San Francisco Golden Bridge.

golden_gate golden_gate_starry_scale1

We can apparently go one step further, and imagine what a painting of Golden Bridge would look like if it was part Vincent Van Gogh’s Starry Night and part Edvard Munch’s The Scream.

The_Scream golden_gate_starry_scream_5_5

I am blown away by this!

Can we go from Stills to Animation?

Apparently, this does not stop here. Mbartoli has created an algorithm that converted a gif file of a scene from 2001: A Space Odyssey in the artistic style of Edvard Munch’s The Scream.

original processed

My Favorite Example of Machine Learning in a Movie or a TV Show:

I have been enjoying learning Machine Learning. I always keep my eyes open for movies or TV shows that feature some form of Machine Learning. Recently, I came across “Be Right Back“, the first episode in season 2 of the British TV show, Black Mirror. If you have not seen the show, I highly recommend it. I will be revealing the main plot of the episode here, so if you have not seen it, you may want to stop reading now.

The premise of the episode is that the main character, Martha, loses her boyfriend, Ash, after he dies tragically in a car accident. Ash was a heavy social media addict. The brilliant notion that the episode put forth is this – can a computer be trained with all the social media left behind by the boyfriend in the form of tweets, blog posts, videos etc. and create an AI (Artificial Intelligence) boyfriend? The AI boyfriend looks and sounds like Ash. It also makes jokes as Ash would. Maybe, this is the final frontier? Man living on through Artificial Intelligence!

What the future holds – DeepDreamFlix?

In the future, you can turn the DeepDreamFlix app on, and type in “Alfred Hitchcock + Jackie Chan + Western” and the app will create a brand new Western movie directed in the style of Hitchcock starring Jackie Chan. 🙂

This post was written to introduce the cool concept of Neural Network, and to encourage the reader to get interested in Machine Learning. There are many free and cost effective courses online. I highly recommend Udacity, and Coursera.

Edit on 9-12-2017 – Another good resource that was brought to my attention is Springboard.

Always keep on learning…