Meditation And Machine Learning: A Guide To Acceptance And Equanimity – Forbes

The events of the past few months have taught us, among other things, how little control we have over our destiny. A major crisis, such as the Covid-19 pandemic, can come unexpectedly at any point of our lives and ruin everything we have worked to achieve. But as much as it may seem unfair, the unpredictability of future events and the constant change is the only thing that is certain.

Many things could have been done better. When it comes to the world of tech and AI many businesses will learn that it is worth investing in robust and bias-free machine learning solutions. On a larger scale, hopefully the world leaders will learn to take scientific data more seriously and with a greater sense of urgency. However, the reality is that no matter how much we invest in making our predictive modelling algorithms more accurate, change will always bring unexpected events our way.

The fact that change is the only thing we can be sure of is one of the key wisdoms of Vipassana meditation practice. Both good and bad things will keep on coming our way and we have to just observe and keep calm. Easier said than done? True, but meditation can help people find their path to equanimity. If you are not too familiar with meditation but have a good understanding of the world of AI and machine learning, there is an interesting connection between the two that can help you grasp the key principles of meditation practice.

Before going any further, it is necessary to clarify that meditation focuses on the brain and you cannot really compare a machine learning model to a human brain. To simplify, it would be a bit like comparing the first basic telephone from the 19th century with the latest iPhone 11 Pro. The original telephone was only capable of performing one task at a time whereas the iPhone is capable of multitasking and has complex functionalities which are not fully understood to most users. However, it is worth observing that the overarching process which describes many commonly used machine learning systems can also be used to describe the functionality of our brains.

A machine learning system consists of an input (i.e. data), algorithms that adapt and improve the more data you feed into them and an output which is a result of that process. Similarly with the brain there is an input in a form of sensory data (i.e. our senses, such as sight and sound) and neurons transmitting electrical signals in the brain that produce an outcome, such as your thoughts and actions.

Everything we experience throughout our lifetimes can be treated as input data and contributes to the shape and functionality of the brain. What is interesting in the case of a human brain is that some of the input data is processed consciously, however the majority happens sub-consciously without individual's awareness. This sub-conscious data processing in cognitive science is often referred to as priming and is a reason for another well-known concept in machine learning, namely bias.

People as well as algorithms are prone to making biased decisions. Some famous examples include: the familiarity bias[1] liking more what you already know, symmetry bias[2] perceiving symmetric faces as more attractive, other biases related to appearance such as perceiving wider male faces as less trustworthy[3], and many more incorrect or inaccurate inferences made based on first impressions. Everything we experience in life has an impact on our brain processing and therefore our decisions.

In machine learning, data scientists spend a lot of time and effort on data pre-processing and data mining to remove bias from the data. Similarly, Vipassana meditation practice focuses on peoples data input the five senses: sight, sound, smell, taste and touch. Throughout the meditation practice students are encouraged to sit still for hours at a time without any distraction and simply be aware of and observe the sensations of the body i.e., the data input. This is what is being fed into the brain at any given time, and should therefore require at least as much attention as the data fed into a machine learning system.

The overarching process is simple: you smell a flower -> feel a pleasant sensation in the brain -> you smile. The simplicity behind this input-output scenario (as well as many neuroscientific studies[4] which show that activity in the brain starts before people consciously realise what they are about to do) can help us understand and accept that the concept of conscious free will is an illusion[5].One of the key objectives of Vipassana practice is that the scientific laws that operate one's thoughts, feelings, judgements and sensations become clear. Life becomes characterised by increased awareness, non-delusion, self-control and peace[6].

The concept of free will and attaching too much importance to the idea of the self is a common source of unhappiness. Mr. Goenka, the Burmese-Indian teacher of Vipassana meditation points out that there is a tremendous amount of attachment towards this physical structure, this mental structure, by identifying oneself as I, I, I And the result is misery[7]. This is commonly seen in our society, people often attach their self-worth to imaginary physical or mental concepts such as their background, skin colour, religion, wealth or nationality. Too much focus on self-identity results in many social problems such as racism and identity politics.

Understanding the simplicity of the input-output scenario that describes our brain functionality can help us move beyond these made-up concepts that divide cultures and societies across the globe. Instead, we should perhaps take inspiration from a simple reinforcement learning system, reward the brain with positive experiences for ourselves and others and allow it to evolve in the direction of tolerance, understanding and compassion in order to find our path to equanimity.

[1] Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2007). Straight choices: The psychology of decision making

[2] Little, A. C., Jones, B. C., Waitt, C., Tiddeman, B. P., Feinberg, D. R., Perrett, D. I., Apicella, C. L. & Marlow, F. W. (2008) Symmetry is related to sexual dimorphism in faces: data across culture and species

[3] Stirrat, M., & Perrett, D.I. (2010). Valid facial cues to cooperation and trust: Male facial width and trustworthiness.

[4] Haggard, P. (2008). Human volition: towards a neuroscience of will

[5] Wegner, D. M. (2002). The Illusion of Conscious Will. Bradford Books/MIT Press.

[6] Vipassana Meditation, As taught by S.N. Goenka in the tradition of Sayagyi U Ba Khin (https://www.dhamma.org/en/about/vipassana)

[7] Vipassana Meditation 10-day Course, S.N. Goenka

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Meditation And Machine Learning: A Guide To Acceptance And Equanimity - Forbes