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Garden of Forking Paths - Jorge Luis Borges

Artificial intelligence is bound to alter just about every industry in the year to come. Improving data technologies, faster computing and more receptive public will give way to new system models and tools we cannot yet imagine.

That said, we have some things we could do to clear the foggy mist over the current hype.

How do machines work?

Ever since the invention of the ENIAC (1946) we have been programming machines to perform different tasks.

Prior to that point tools, such as hammers, or cranes, could only do one thing. When a multipurpose machine was made available we could all of sudden write instructions for a machine to follow.

It was magical, and must have felt very much like we got a new collaborator in the office – one is that far more efficient, and never complains.

It was that awe, combined with our innate psychological biases to assign human-like attributes to machines (think of the last time you spoke to your car) that inspired our early dreams of thinking machines.

This pursuit kept generation of thinkers trying to untangle the ways in which humans think, as the precursor to program machines who can do the same.

Further reading under this school of thought includes – but not be limited to – Alan Turing, Marvin Minsky (below), John McCarthy and more.

Marvin Minsky – Machine Dreams Excerpt

The field carried on this trajectory through various dropouts in budget, and interest – commonly known as AI winters. But I want to get off this road for a moment, and focus on the work of a different group of technologist.

IA (intelligence augmentation) looked at ways in which we could celebrate human intelligence as oppose to try and replace it.

How can we focus on a user, as extended by a machine - and not as an obstacle to its math.

There has some groundbreaking work done at Manlo Park (Engelbart) and Xerox Parc (Kay). The logic these teams have written is in the very core of personal computing today. To the point of this piece I want to land on the Dynabook.

The Dynabook was a hybrid of a laptop and an iPad. Meant to be used as an early education device, it needed to be user friendly and intuitive - both novel, and non–existent ideas at the time.

To tackle that Alan Kay invited Trygve Reenskaug to join his lab in California, and together with Adele Goldberg they conceived of Model View Controller.

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Image by author - based on a graphic from MVC, Xerox Parc 1978-1979

Its original incarnation was genius. Mapping computer models to users’ mental models. The idea of motivational frameworks was mostly reserved to psychologists and philosophers during that time, but those programmers and designers had the foresight to incorporate them into their products. And in a sense, inventing the science of user interface (for more proof of that, look no further than User Interface is Theatre).



In its later, more industrial version Model View Controller lost a bit of its magic and became more of an efficient, Taylorist equation.

industrial_MVC_narrow

Model View Controller was very well suited for the internet, in its ability to break down information efficiently, hold together tidy database and build businesses that can monetize this eco system (more in Gated Products).

In an MVC system, the database is stationary, and the interface is proprietary.

What I mean by that is that your DB could be beyond cohmphernsion in its size and complexity, but when you’re not moving your data, it is as stationary as books on shelves, or bottles in a bar. It is stand–still model, data moved in and out by controllers.

The interface is proprietary, because it lives in a domain (digital or physical) nurtured and controlled by a business. An app is groomed and maintained, optimized for every click and user action.

Now comes the punchline.

MVC is de-facto the only server architecture we use. There is nothing else. Everything we do is based on stationary DB, and proprietary interface points.

This is important because it can help us to unfold opportunities for true innovation, and map current ones to this architecture.


Bots

Once we understand this underlying structure we can easily demystify bots. Those hyped–up voice controlled interfaces, are nothing more than interfaces.

What I mean by that is that really, when it comes to down to it, controllers are what does the work, the “magic”.

A controller will run that calculation for you, find a face of a friend in a photo, or reduce the speed of your self driving car as you approach a turn.

The view is important, but it is a window – a relay point. Bots are nothing more than radio dials, that use text.

Both text, and voice interfaces are welcomed advancements in the field of human machine interactions – but they’re not AI.


Machine Learning

In the world of computing, and more narrowly of databases, structure is crucial part of operating a program. You can’t run multiplications on a set of numbers and then find a letter. Your system will fail. In the linage between neat tables, and a mishmash of words – there are various gradients of data structure.

It is machine learning that can classify and organize this data into computer friendly form.

  • Picture > faces, places, dates
  • Medical journals > concepts, doctors, citations, hypotheses, diagnosis
  • Road > lanes, cars, people in cars, speed of cards

Back to MVC. We can think of machine learning (ML) as a patch to our database, taking in messy data and structuring it for better system performance, and enablement of new features. No magic involved, just statistics.


I am excited about the industry starting a conversation about common terms, users’ mental models and nuanced understanding of such a huge disruptor, and opportunity.

Citing Samuel Arbesman, we should not approach these systems with awe, nor fear.

We should know what we know, and question what we don’t.