Lessons from autonomous cars could drive your business


Among the more dramatic aspects of the Digital Transformation most surely be the prospect of driverless cars. It truly feels like science fiction becoming reality. It is useful to examine this phenomena as it’s emerging to see the lessons about AI that we can learn and apply to business transformations elsewhere.

LittleBlogAuthorGraphic  David Hodgson, April 10, 2016

As the prospect of the mass deployment of driverless cars comes careening towards us, are there lessons about AI that you could learn to get a competitive edge for your business? Like many other applications of AI, autonomous vehicles have taken longer to arrive than expected, and longer than predicted even a few years ago. But make no mistake, the robots are coming, and all that we have imagined about AI will probably pale into comparison with the reality we will experience when it’s widely adopted.

It seems like every car manufacturer now has plans to introduce driverless vehicles and there is continued growth in the use of software to transform the experience. Making the in-car experience a connected one is a natural extension of our lives now, and a recent Mckinsey report showed that consumers are growing in their willingness to pay more for this.

What could be

The developments and imminent delivery of highly connected, self-driving cars is very exciting to those who love technology and I am really looking forward to buying one! However, what is perhaps more interesting are the wide-ranging follow on developments and ramifications that go way broader than our immediate riding experience.

Traffic lights will eventually disappear, as will driving licenses, both being redundant. Denser parking will mean more available space, as computers become adapt at squeezing vehicles in and organizing them for access.  There will be no more tickets, traffic courts and a reduced police presence on roads. Traffic policies for special events can be piped to cars in the area programmatically and dynamically adjusted, reducing or eliminating frustrating backups.

Signposts will no longer needed although of course map data will be more important, but the cars could be the cartographers. Similarly the cars could monitor the state of repair  of roads and dispatch an autonomous truck with a mending crew of swarm bots and supplies to fix potholes. Damage from road usage might be reduced through coordination to vary the precise paths by slight offsets for more even surface wear.

Certainly there will be improved flow and throughput through reduced “noise” and disruptive movement. Connectedness could create convoys of cars going to similar destinations with individuals peeling off and joining as needed.

With reduced accidents, hospital and ER space will be freed up and there will be massive disruption to the insurance industry meaning it will probably be at the forefront of resisting adoption!  For sure there will be massive disruption to jobs wherever it is discovered that AI based robotic systems replace humans. One of the first might be the trucking industry where driverless transport convoys are already being tested in Europe.

The general point here is that the AI required to drive cars, drives a much broader impact to business and society than the specific solution area itself. The same thing will be true for changes that AI makes to your business.

The bigger picture and what it means to you

Seen in this bigger context, autonomous cars become an instructive use case of the disruptive influence of AI on the business processes for an industry and connected markets. Whether or not your business will be impacted by driverless cares, you should get ahead of how AI can be leveraged in your industry. It might be a wave that you can ride to survive and surf over your dying competitors that ignore it.

The term “cognitive business” describes a company instrumented with systems that understand data and can realize new insights on their own. This is not as far fetched as it sounds and we can see the dawning of the possibilities with IBM’s offerings that open up Watson as a cloud service through APIs.

In this scenario computers do the more significant things faster, better and more reliably than people. Not just math and report creation, as they have done traditionally, but “thinking”, predicting and decision making.  Eventually it leads to software that maintains and modifies its algorithms to better solve problems and solve new problems.

Imagine a cognitive supply chain that can quickly adapt to real-time changes in demand, differentiate between local and national trends, and accurately predict  the impact of upcoming events. Both know like social, sporting and weather events but also hidden pattern events perhaps created by competitor activity, or changes in consumer preferences. It could balance activity between on-line and bricks and mortar store fronts. It could optimize manufacturing, distribution and stock levels. And of course, given our theme, it could interact with fleets of autonomous distribution and delivery vehicles.

To achieve this and other scenarios, the AI will be integrated with huge amounts of “Big Data” but will also leverage human knowledge. Some of the most powerful solutions will be the interactions of experts with AI systems.  We have seen this already in the advanced weaponry of fighter planes and drone systems.  The medical world holds great promise for new solutions that combine expertise in this way too. There is no reason to think that advanced business systems will not be implemented in the same way.

You control your future

All this is future right now, but the sooner you get started in preparing yourself, the more likely you are to be a winner. This means experimenting with advanced analytics now, finding new uses for existing data and discovering new sources of data. And while you do that, simultaneously starting to grasp the security and compliance aspects of gathering and processing all this existing and new data in new ways.

The best time to plant a tree was 20 years ago, but assuming that your strategy planning has not been that prescient, then there is no time like the present to start planning for the future.


Image credit: CNN

Can AlphaGo Help You Stay Alpha Dog?


The recent triumph of AI program AlphaGo playing against a human, signals just how far advanced analytics has come. What lessons can you learn to get a competitive edge for your business?

LittleBlogAuthorGraphic  David Hodgson, March 15, 2016

Almost two decades ago, in 1997, IBM’s Deep Blue chess playing computer beat the reigning world champion, Garry Kasparov, in a six game match under tournament conditions. The world realized, perhaps for the first time, that HAL of “2001 A Space Odyssey” fame, was going to arrive at some point, though a few years later than cast by Kubrick.

Then, in 2011, IBM’s Watson computer stunned us by winning at Jeopardy. If you haven’t actually seen Watson playing Jeopardy click on that YouTube link; it’s truly awesome. The feeling of invasion is greater seeing Watson, perhaps because we can all imagine playing Jeopardy, and the question and answer approach is so “human”.

Which brings us to current events. Google’s DeepMind research team has developed AlphaGo which beat Fan Hui 5-0 last October. Hui is the current European Go champion and 2 dan master. This was impressive enough, but today saw AlphaGo win 4-1 playing Lee Sodol, the current World Champion, a South Korean 9 dan Grandmaster.   Send Lee Sodol a message of support somehow, because being on the coalface of human defeat by computers must be tough.

What is happening here?

Closed system games like Chess and Go are complicated, but have simple rules and a known, although massive, number of variables. There are more possible Go board move sequences than the estimated 1080 atoms in the visible universe. This is a formidable problem, but a different sort to the open ended question and answer format of a game like Jeopardy

AlphaGo’s algorithms use a combination of value-weighted, Monte-Carlo tree search techniques and a neural network implementation. The DeepMind team’s approach to machine learning involved extensive training from both human and computer play. AlphaGo played itself to rapidly learn the outcomes of numerous different options.

Watson used Hadoop to store masses of unstructured information, including the entire text of Wikipedia, that it could search with analytical techniques in real time. Equally significant in Watson’s case is that it was responding to natural language questions that it had first to understand using similar search techniques.

A third powerhouse for change, Facebook is also experimenting with AI systems and has their own Go-playing system Darkforest, also based on combining machine learning and tree search techniques.

Between them and the numerous other AI projects underway in different domains, we have the building blocks for HAL’s arrival.

So what?

I hear some saying “So what David?”. “This is interesting to learn about, and with the election I had missed it in the news, but of what importance is it to me?”

DeepMind is targeting smartphone assistants, healthcare, and robotics as the practical outcome for their experimental work with AlphaGo. From their website:

“The algorithms we build are capable of learning for themselves directly from raw experience or data, and are general in that they can perform well across a wide variety of tasks straight out of the box.”

IBM has already applied versions of Watson to practical problems, offers it as a service for anyone to buy and a developer community to encourage experimentation. An example of a practical application is the partnership with Sloan Kettering to fine tune cancer treatment. Similarly DeepMind is partnering with the UK’s National Health Service to improve its services.

Although for specific solutions much secret sauce is often preserved, the framework of these systems is usually Open-Source software. An important component of Watson is the Apache Unstructured Information Management Architecture (UIMA) software. These same tools and techniques will be what disrupt your business soon and you will want to be an early adopter.

Fed with the right data, a Watson-type system could answer new questions that nobody yet knows the answers too. Or applied to real-world problems an AlphaGo-type system could decided on the best course of action given many variables and alternatives. Leading the field in practical solutions IBM calls this ‘cognitive business’ and it is definitely a part of our future.

You Control your Future

In the panorama of the Digital Transformation, AI is out there as a wildcard with seeming limitless possibilities. We are both familiar with, and scared of, these futures because of numerous science fiction dramas. HAL is not here yet, but its coming. For you it’s really a case of whether your company or the competition deploy machine learning systems first. You don’t need an AI system to answer that question.


Image credit: NYTimes