There is now a formula for machine intelligence innovation.

Author:Moschella, David

A new report by the Leading Edge Forum (LEF), announced at their US Executive Forum in Washington DC, explains why Machine Intelligence (MI) is taking off today.

While the field of machine intelligence has been around for decades, it is now nearing its tipping point due to three developments, which taken together define the formula for Machine Intelligence (MI) innovation:

1) Big Data. It has now been convincingly demonstrated that large unstructured data sets can be used to develop powerful machine intelligence capabilities, without specific subject matter expertise, or even human intervention. Many of the most important MI 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 behind them gets richer, and the internet is making larger and more relevant data sets more available than ever before. In these MI applications, human subject matter experts such as professional translators and psychologists are not only not necessary, they often get in the way of purely algorithmic approaches.

An influential example of this has been the image recognition project, ImageNet, which has created a database of some 14 million labelled images that can now be used to train machines to recognize just about any thing. In 2015, Microsoft demonstrated how deep learning could be used to enable computers to recognize these images as well as or better than humans. Similarly, Facebook enjoys a huge head start in facial recognition because it can already match our names and faces, just as Google has important advantages in machine translation because it has aggregated the best set of multilingual documents.

Looking ahead, new and established MI companies will use millions of internet images, videos and podcasts of people smiling, laughing, frowning, talking, arguing, holding hands, walking, playing football and so on as the basis for unprecedented Emotion and Activity Recognition capabilities. MI is now clearly among the most important Big Data applications.

2) Software and hardware advances. For decades, machine intelligence researchers have predicted that neural networks and parallel processing would be important MI development tools because they more closely resemble the way the human brain works and because they enable machines to learn. However, until the last few years, progress in both areas was slower than in many computer science fields. Happily, this is now changing, with the emergence of new software and hardware architectures that are particularly good in MI applications.

While deep learning is one of today's hottest IT buzzwords, its meaning is often poorly understood. Deep learning is basically the latest generation of neural network design. It is called deep because there are more layers of processing than in the past. Although this is a highly technical and mathematical field, the basic idea is that the use of additional layers of abstraction enables tasks to be broken down more finely, and this enables a greater capacity for detailed analysis and self-improvement. This multi-layer approach was used in the recent triumph of Google's AlphaGo program over Lee Sedol, one of the world's best Go players.

Both neural networks and deep learning are computation-intensive, and real MI applications can overwhelm traditional systems. Fortunately, new hardware designs have emerged at both the individual system and cloud level. Many MI developers now use hardware that includes Nvidia's GPUs (Graphical Processing Units) which can greatly accelerate neural processing speeds. And when even more computational capacity is required, the almost unlimited cloud resources of Amazon, Microsoft, Google and others are available at affordable prices. Taken together, deep learning software and parallel processing hardware now provide a powerful MI platform.

3) Cloud business models. The ability to leverage Big Data and the availability of much more capable hardware and software mark major steps forward in the MI journey. But as important as these computer science advances are, the emergence of powerful MI business models is arguably the single biggest reason that the MI field is so energized today.

We are essentially seeing the merger of machine intelligence with cloud economics. This merger will prove fundamental to the innovations of the future, but it is still not sufficiently recognized. Before the cloud, most Al work was isolated and relatively high cost However, as shown in the figure below, MI advances can now take advantage of the full panoply of cloud capability--including 24x7 availability, rapid global deployment, variable costs, continuous improvement, real-time data, effectively zero marginal cost, easy integration with supporting web services, venture capital funding, and winner-take-all market tendencies.

This means that MI capabilities such as recognizing faces or translating languages will soon be no different from everyday web services such as using Shazam to identify a song or Googling a search term (both of which are actually highly complex MI activities). As MI enthusiasts have long observed, once an advanced new application becomes ubiquitous, people no longer see it as MI--it becomes just another cool service. Consider the way speech translation capabilities are now being bundled into Skype.

Looking ahead, virtually every capability listed in Figure I (and many more) has the potential to be used by billions of people and thus may well be worth billions of dollars. It is this realization that is triggering both the explosion of highly specialized MI start-ups as well as the major MI pushes at Google, Facebook, Microsoft, Apple, IBM and their various global rivals. Arguably for the first time, MI is seen as a potentially huge business opportunity. Microsoft paid $250 million for the tiny UK keystroke anticipation firm SwiftKey because every little piece of the MI/cloud can be extremely valuable; and there are hundreds, perhaps thousands, of such pieces.


Taken together, these three developments--ever-richer data sets/algorithms, improved MI computing platforms, and powerful cloud-based economics--have triggered a new Silicon Valley gold rush. While the timing is still uncertain, the formula for rapid innovation and deployment is now in place. Providing clients with a first-hand sense of these developments is the goal of this year's LEF Machine Intelligence Study Tour (26-30 September 2016).

"In the past, many MI efforts were stuck in their own silos, noted LEF Research Fellow, David Moschella. But today, they have access to the full panoply of cloud economics. Virtually every capability listed in the figure below (and many more) has the potential to be used by billions of people and thus may well be worth billions of dollars," Moschella added. (Terminology note: we prefer the term 'machine intelligence' to 'artificial intelligence' because there is nothing artificial about it.)

As MI innovation migrates to the cloud, the pace of market innovation will outstrip what most firms can do for themselves. But importantly, the next generation of advanced MI systems will likely consist of a mix of traditional Expert Systems and modern cloud-based services. Driverless cars, robo-investing, digital health care, and advanced robotics are all likely to require this type of hybrid innovation.

"We are particularly interested in and will be researching, MI's effect on the professions--doctors, lawyers, professors, accountants, architects--as well as the new forms of insurance/assurance that autonomous machine operations will require," added Richard Davies, Managing Director of the Leading Edge Forum.

But while the overall formula for MI innovation appears to have been cracked, the market is not yet at its tipping point. Given this, the report includes a ten-point agenda that companies can use to prepare for the challenges ahead. "While there are many uncertainties, about the only thing you can be sure of is if your firm does not keep up with machine intelligence innovation, a future competitor will," advised Moschella.

The paper, There is Now a Formula for Machine Intelligence Innovation, can be found at: mula-for-machine-intelligence-innovation/

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