Convergence - of Science, Technology and Humanities - A survival instinct

When the verb "converge" was coined in 1691, it was used to describe the tendency of two lines to approach one another, as tributaries of a river flowing together. Convergence is in Nature – from Wave-Particle duality to Fractals – its visible and has always been nurturing and catalysing the curious mind towards the beauty and the fundamentalism of this. I was doing keynote talk in a conference over the weekend on the topic of Convergence of Science, Technology and Humanities for the Future of Technology and it made me think on the basics. Its about convergence of Knowledge, and about thoughts, skills and activities.

Why do we even need to talk about convergence? If it is a fundamental facet of nature then it should be inherent in everything. Why did we have the divergence in the first place – especially when we look into something as fundamental as knowledge?

We have always been keen on talking about Industry 4.0, but why not Renaissance 2.0?

Thinking about knowledge and our heroes of knowledge from the past, here are some which came to mind:

How do we recognise them? We recognise them as our knowledge heroes – without diverging into a segregation pattern to identify as physicist or novelist or artist. Knowledge was converged.

This is one of most critical knowledge-base in the history of science – giving light to Newton’s laws, fundamentals of Physics and still it is called “Natural Philosophy” – pronouncing convergence in knowledge.

Then I started to think – what has changed now? With the categorisation of knowledge into Science, Technology and Humanities – have we started to see a different world? Or this is an illusion? 

As I did earlier – I started to think of the heroes of our current digital world and here are few of them:

Steve Jobs studied Philosophy, Psychology and Calligraphy

Susan Wojcicki studied History and Literature

Ben Silbermann studied Political Science

Stewart Butterfield studied Philosophy

Cofounder Parker Harris studied English Literature

Interestingly, none of them are primarily Engineers and pronounces loudly the convergence of knowledge which we have seen in ancient times.

The quest for knowledge and innovation is driven by needs. As Abraham Maslow proposed in his 1943 paper “A Theory of Human Motivation” – the Maslow’s Hierarchy of Needs:

These needs have been more or less consistent since humans started to evolve almost 100,000 years back, and have been the motivation for the growth fuelled by knowledge and innovation.

Lets glance at the latest Gartner Hype Cycle (July 2020):

Almost half of the emerging technologies in the Gartner Hype Cycle are about Artificial Network/Machine Learning – from GAN (Generative Adversarial Networks) to Explainable AI, and that’s not surprising as that’s the fuel for the next level of innovations and every aspect of our life is getting touched by it – driven by the needs.

Its interesting to think that AI and Machine Learning and the advancement of that in the current time is one of the biggest examples of convergence. AI and Machine Learning algorithms and the mathematical basis for those has been there since more than a century – Bayes’s Theorem from Thomas Bayes’s work on An Essay towards solving a Problem in the Doctrine of Chances has been there since 1763 (published 2 years after his death), even the first Neural Network has been there since 1951 – SNARC (Stochastic Neural Analog Reinforcement Calculator) built by Marvin Lee Minsky and Dean Edmonds. Still – the advancements and all the innovations around has been accelerated and caught the attention of the wider Industry in the last few years. One of the major catalysts being the Machine Vision. The areas where machine vision has touched our lives is just unimaginable and we are still counting:

From autonomous driving to industrial automation, from enabling remote surgery to crime prevention – every aspect of our daily lives are been benefited. It has been a story of convergence which made it possible in the way we are using it now. 

David Hunter Hubel – a Canadian American Neurophysiologist and Torsten Nils Wiesel – a Swedish Neurophysiologist showed in 1950/1960 that the visual cortexes in cats and monkeys contain neurons that individually respond to small regions of the visual field. They received the Nobel Prize for Medicine in 1981.

 Inspired by the work of Hubel and Wiesel, Kunihiko Fukishima – a Japanese Computer Scientist, introduced the “Neocognitron” – the original deep CNN (Convolutional Neural Network) architecture in 1980.

 CNN is the basis for image recognition – the key enabler for machine vision, even if the first deep CNN was built in 1980 – it was not noticed widely for mass adoptions and the computing world kept focusing on algorithms and models, until a Chinese-born American AI researcher – Fei-Fei Li thought differently – and looked into the AI evolution from the point of view of data rather than just algorithms and started on the idea of “ImageNet” in 2006, she discussed with Christiane Fellbaum a Princeton Professor and linguist born in Germany – one of the creators of WordNet project to expand on the idea. The ImageNet is project is a large visual database with around 14million images of around 20,000 categories and the images have been hand-annotated using Amazon’s Mechanical Turk.

The importance of the ImageNet in image recognition can be realised by looking into how our visual system works. A normal human eye can process images at around 10fps (frames per second), so in an hour – there will be 36000 images processed, 864000 in a day and 315360000 in a year !! So for a human baby starting to see and learn about the world around will have a training data set of more than 300million labelled data. This is what was missing from the AI world and the ImageNet project filled that gap – through the out-of-the-box thinking and the AI world saw the acceleration.

This has been a story of super convergence, a multi-dimensional convergence across disciplines and even geography:

From looking into nature to Neurophysiology, and from computer science to linguistics and many unnamed individuals within the Amazon’s Mechanical Turk to photographers – all converging into an absolute knowledge and the scale of the innovation is changing the world and we have just scratched the surface at the moment.

I have a simplistic proposal for re-establishing convergence of absolute knowledge into our system and pronouncing the stronger seamlessness between Science, Technology, liberal Arts and Humanities:

Its to THINK:

T – Transparent

H – Homegeneous

I – Ingenious

N – Nature Bound (Learn From)

K – Kalon : Beauty (physical and moral)

Transparent: Transparency has multiple facets – for a seamless convergence, it needs transparency between knowledge streams without creating boundaries around them. At the same time – transparency has an element of “invisibility” – all the boundaries need to be invisible, so that they all converge into the absolute purpose of knowledge – to make humankind evolve into the next level, to make our lives better day by day and to make a society which flourishes.

Homogeneous: Homogeneity is about equalising, its about inclusivity of all the Knowledge streams towards that singularity.

Ingenious: If we do what we did – we will get what we got. The many stories of ingenious thinking have showed us the importance in the context of convergence. The super-convergence story of Machine Vision and thinking ingeniously has showed the impact in the form of advancement of AI and Machine Learning.

 Nature Bound (Learn From): Convergence is an important nature of Nature – learning from Nature has always been shown acceleration in our knowledge for betterment of humankind. As discussed earlier – learning from Nature that how visual cortex works has led to the development of CNN (Convolutional Neural Network) which then injected the acceleration in the AI/ML mass market adoption.

Kalon: Kalon is a word used in Greek Philosophy and it means physical and moral beauty – its one of the most important characteristics of enabling the convergence of Science, Technology and Humanities. As Steve Jobs said “Technology should either be beautiful or should be invisible”. Technology and Science needs to be usable and beauty makes the big leap towards the usability. It’s important to highlight the aspect of moral beauty in the current accelerated world where the fourth Industrial revolution is fuelling machines learn on their own and they will serve us based on what we let them learn – humanity, morality, empathy are the needs of the hour.

 Quoting Steve Jobs: “It’s technology married with liberal arts, married with the humanities, that yields the results that make our hearts sing”

 Let our heart sing.