[This is the 2nd in a series of three articles exploring new concepts in edge related user-centered Artificial Intelligence, or ‘Extended Intelligence’]

Can we “Be the Ball”? ~ An analogy for the ‘Extended Intelligence’ model of people centered AI

7 min readMay 5, 2019

In the 1998 animated film Antz, the main character ‘Zee’ makes light of his supervisor’s command to “Be the Ball” (link to video). The script cleverly couched this as mocking compliance, but it was also a tacit admission that to get big stuff done a collective approach is required. The message was clear: Working alone we are not strong, working together, we can be powerful.

The aim of this article is to explore how and where new non conventional collaboration models may arise.

Through the ages the way we work collectively has changed a lot, however the reasons why we work collectively and for who we work, have not changed a great deal. With some exceptions, collaborative teams are typically employees working in a firm, not working for themselves collectively.

Technology has enabled spectacular efficiencies and business has taken advantage of these very effectively. However, the idea of collective enterprise for the common good has rarely been successfully implemented at scale.

The Age of Artificial Intelligence..

We are now living on the cusp of the age of Artificial Intelligence, and the clear popular perception has been that AI is encroaching relentlessly into our world, taking over jobs, transforming industries and leading us to question what the future of human work will be.

Nikola Danaylov, writing on the Singularity Weblog states:

Every time we’ve had a technological change we’ve had both social and political change. Karl Marx was but one of a few who has pointed out that our socio-economic system, and therefore our politics, is determined by and derived from the mode of production. That was the case with the Industrial Revolution when we replaced muscle power with artificial power. And it will be the case with the AI Revolution when we replace human intelligence with artificial intelligence.

Note: Its probably a good moment to point out that the word ‘artificial’ actually means: ‘made or produced by human beings’.

Herein is the conundrum. AI is apparently either going to replace or enhance human intelligence.. Logically, it cannot do both.

There are a range of different variations in this field from the purist concept of AI centered on making machines more intelligent, to research in the area of Intelligence Augmentation (IA) to ‘bootstrap’ the human intelligence of small groups of scientists [1], to Extended Intelligence (EI) that recognizes intelligence as a distributed phenomenon beyond any one human being, and seeks ways to manifest a powerful decentralized human intelligence.

The Director of the MIT Media Lab Joi Ito, in his article Extended Intelligence writes:

At the individual level, in the future we may look less like terminators and more like cyborgs; less like isolated individuals, and more like a vast network of humans and machines creating an ever-more-powerful EI. Every elements at every scale connected through an increasingly distributed variety of interfaces. Each actor doing what it does best — bits, atoms, cells and circuits — each one fungible in many ways, but tightly integrated and part of a complex whole.

Extended Intelligence necessarily occurs at the edges of the network

To understand the nature of the EI model one must understand the concept of ‘Core vs Edge’. This binary concept has been around for many years, and is associated with the idea of edge computing. ‘The target of edge computing is any application or general functionality needing to be closer to the source of the action where distributed systems technology interacts with the physical world’.[2]

However the edge concept goes beyond computing, as illustrated in the Harvard Business Review in 2009 by John Hagel et al, in their publication ‘How to Bring the Core to the Edge’..

The “edge” takes many forms. Generally speaking, edges are peripheral areas with high growth potential. For example, emerging economies form a geographic edge. New generations of people form demographic edges. Technology edges take shape as technological innovations begin offering new capabilities..
..Why are edges so important? They represent fertile seedbeds for innovation as unmet needs and unexploited capabilities tend to surface first on the edge. Edges also tend to be filled with people who are risk takers. Edge participants tend to connect more readily with each other because they all confront significant challenges in addressing the growth opportunities. Since there is so much growth potential for everyone, they are more willing to share insights and learning. Edges also have limited inertia since most of the large institutions, installed base and current sources of profitability are in the core.

Will EI produce a rare Black Swan event?

A Black Swan event deviates beyond what is normally expected of a situation and is extremely difficult to predict. Black swan events are typically random and unexpected. [3]

If we are to work collectively and ‘Be the Ball‘, outside the confines of the Firm, it will very likely manifest as a new kind of EI model rising from the edge.. and it is likely to be quite without precedent.

What radical new ways can large groups of people work collectively to create new forms of value? If there is to be a sector where radical new innovations might appear, what sector might they appear in and what user problem would they address?

One edge-related project, that is a potential candidate is the ‘Hub of All Things’ or HAT system.

The aim of the HAT system is to allow end users to harvest their personal data that is currently stored centrally by companies like Facebook, Spotify etc (words, photos, music, locations, financial transactions) and allow this data to be controlled by users on their own device.

The HAT microserver (installed on the users device, or in the cloud with “trust anchor”[4] ) pulls data about the user from Facebook (for instance) and allows each user to then offer that data to other 3rd parties for benefits, that might be personalisation, recommendation or even a fee.

Thus the HAT system starts to address some of the problematic data privacy issues that plague the web, but does not yet fulfill the potential of a pure EI edge solution, of processing user-generated data that is generated at the edge, independent of the core, although this is apparently in their sights.

In the article ‘Decentralised AI has the potential to upend the online economy (co written with Hamed Haddad) HAT Director Irene Ng writes:

In 2019, we will see an alternative to these practices emerging in the form of AI at the edge — machine learning that will take place “near” the user, on their device or home hub, or at a local data-aggregation point. This will take different forms, including local learning (where the model is trained locally); distributed or federated learning approaches (where a globally trained model is optimised and retrained locally without transferring data back to the cloud); or co-operative learning approaches (where local data contributes to a global model on an ongoing basis). [5]

I asked Irene Ng (here on Medium) about the issue of data processing at the edge, as opposed to simply moving it from the core..

Simon Edhouse: “..taking a data-dump from centralized 3rd party repositories like FB and hosting that data at the edge is not really edge-processing in terms of making use of the proximity between the user and their device. I humbly suggest that processing unique user data that is not derived from 3rd parties (who must be negotiated with and can pull the plug), is the sweet spot. :)

Irene Ng: I absolutely agree on the sweet spot but if you are like me who remember 1978 when I looked at an Apple IIe with a VisiCalc thinking “so what do I do with this next” you would probably understand why the HAT way of thinking is that how we start is not how we grow/evolve. We start by getting the data that we already have into our HATs. This gives people something to look at. Something that makes an abstract into a concrete. Something that, in doing, we learn, because the actual interacting with data brings a host of issues in all its concrete-ness that the abstract will never get.
We evolve by getting more original data into HATs (hence HAT value proposition is backend-as-a-service for apps. See our developers portal).

[EDIT]

Now we can go to government and say that individuals can give data in real time like they can give money in real time. Until this is possible, the centralised systems will have to prevail, because coming down too hard on them is seen to stifle the market. But showing that an alternative exists ala “here’s a black swan” (sorry NT) is where we want to get to for policy. That changes the game.

In the 3rd and final article, I will map out the desired characteristics of a hypothetical ‘Pure EI edge solution’, and discuss its implementation issues.

See also, article one in this series: ‘Putting the Art into Artificial Intelligence’.