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The Consumerization of Machine Intelligence – Our Decade-Long Journey

When I joined the LEF as research director in 2004, I naturally reviewed the projects then under way. These included Dynamic Outsourcing, Innovation and the CIO, Portfolio Management, and more. However, one topic clearly stood out: Consumerization. It was a term I didn’t know, and that the LEF had recently coined. 

Consumerization of Machine Intelligence

Intriguing examples of how the consumer market was becoming the main driver of IT innovation were popping up all around us.

Intriguing examples of how the consumer market was becoming the main driver of IT innovation were popping up all around us. The retail PCs that we bought for ourselves were cheaper, more functional, lighter and better looking than the ones that most organizations provided. Many of us found it more productive to work at home or in a coffee shop, or anywhere there was WiFi. Most importantly, consumer email, instant messaging, file sharing and other free services were often demonstrably more capable and easier to use than the services most large companies provided. 

The result was our April 2004 Position Paper, The Consumerization of Information Technology, which projected these trends into an increasingly disruptive future. Many of our Enterprise IT clients – especially the CIOs – were sceptical: “What about reliability, control and security?” they asked. 

Of course, since then consumerization has flourished. The tipping point came in 2007 with the arrival of the iPhone, which did three main things. It established consumer mobility as the new IT industry centre of gravity; it made the Blackberry (then the overwhelming enterprise standard) obsolete; and it demolished the boundaries between work and personal computing. The disruption was under way. 

Today, the giants of our industry are all rooted in various consumer marketplaces.

Today, the giants of our industry – Google, Apple, Facebook, Amazon, Microsoft, Samsung, Alibaba – are all rooted in various consumer marketplaces, and consumerization has become the conventional industry wisdom. But this doesn’t mean that consumerization has matured, or even that it is fully understood. Consumerization is still gaining momentum, and is expanding into areas that few of us imagined in 2004. The most obvious of these are wearable technologies and the burgeoning quantified self movement, but the most important new area is not sufficiently recognized: the consumerization of machine intelligence (MI), shown in the figure below. 

Diagram: The Consumerization of Machine Intelligence

From top-down to bottom-up intelligence 

Developing advanced MI capabilities has historically been a top-down process. (We prefer machine intelligence to artificial intelligence because it is more accurate.) Many large commercial and government organizations have built proprietary expert systems based on their internal data, software and know-how. These systems play an essential role in all manner of logistical, investment, pricing, command/control and other mission-critical applications. This important work continues to progress.

Consumerization is now transforming MI into a bottom-up dynamic.

But just as we saw with PCs, networks, online services and smart phones, consumerization is now transforming MI into a bottom-up dynamic. This time, the driving force is not technology; it’s Big Data. Many of the most important machine intelligence initiatives today – such as language translation and image, facial, activity and emotion recognition – are based on predictive analytics that get more accurate as the data sets get richer, and consumer markets are where the biggest and best data resides. 

An excellent example of this is the image and activity recognition project, ImageNet, which is based on over 1 billion downloaded internet images and the work of 50,000 people (using Amazon’s Mechanical Turk) to do the necessary cleaning, sorting and labelling. (There is an excellent TED talk by Stanford’s Fei-Fei Li on this effort.) But while ImageNet required significant new human labour, Facebook has a huge head start in facial recognition because it already knows our names and faces. Similarly, Google is a leader in machine translation largely because it has aggregated the best set of multilingual documents. 

The speed of these advances has stunned many AI/MI professionals in what was once a relatively slow-moving field. The latest deep learning surprise was the four-games-to-one triumph of the AlphaGo program (developed by DeepMind, now part of Google) over Lee Sedol, arguably the world’s best human Go player. The number of possible moves in Go is orders of magnitude greater than in chess, and few MI experts thought that machines would defeat the top human players so soon. The February announcement of a $5million XPrize to be awarded in 2020 for the best TED talk by a machine is further evidence of the MI field’s growing confidence and ambition. Of course, the whole Microsoft Tay fiasco shows there is still a long way to go.

In short, machine intelligence innovation today is increasingly dependent upon the exabytes of data created by billions of online consumers, and this powerful bottom-up dynamic may well determine the business models of the future. From an Enterprise IT perspective, change is once again coming from the outside in, and we encourage clients to get past the inevitable scepticism and embrace the exciting MI innovations now under way. 

LEF changes and the road ahead

Just as 12 years ago, the LEF asked me to take a fresh look at its research, so has that time rightly come again. I am happy to report that Dave Aron has assumed the role of LEF Research Director, and will be writing these commentaries in the months and years ahead. I will take on a more individual role as an LEF Research Fellow, focused on the industry disruption, digital business and machine intelligence developments that I have been writing about in recent years. The potential of our industry has never been greater, and I look forward to thinking about these issues full time.

I would like to thank all of the readers of these monthly commentaries over the last five years.

The LEF community continues to expand and strengthen, as we seek to provide our clients with the ideas, advice, experiences and decision-making frameworks needed to keep pace with today’s rapidly advancing digital business agenda. I can only hope that being the LEF research director will be as rewarding for Dave A as it has been for Dave M. While I will continue to write for the LEF in a variety of ways, I would like to thank all of the readers of these monthly commentaries over the last five years. Some day, a computer that has read every book and every article, and heard every conference presentation, will probably be able to write these memos too, but we are at least a few years from there.


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