Can You Afford To Ignore The Artificial Intelligence Wave?

Recent news of a a computer program that passed the Turing Test is a great achievement for artificial intelligence (AI).  Pulling down the barrier between human and machine has been a decades long holy grail pursuit.  Right now, it is a novelty.  In the near future, the implications are immense.

Which brings us to why should you care.

Earlier this week the House majority leader, Eric Cantor, suffered an enormous defeat in Virginia's Republican primary by Tea Party candidate David Brat.  No one predicted this - the polls were wrong, by a long shot.  Frank Luntz, a Republican pollster and communication advisor, offered up his opinion on what was missing in a New York Times Op-Ed piece - lack of face-to-face discussions and interviews with voters.  He asserts that while data collection was limited to discrete survey questions, what it lacked was context.  Information such as voter mood, perceptions, motives, and overall mind set were missing. Even if you collected quantitative data across a variety of sources, you don't get to these prescient indicators.  

The new wave of AI (the next 2 - 5 years) makes capturing this insight possible and at scale.  Marketing organizations are already using such capabilities to test advertising messages and positioning in focus group settings.  But, if you took this a step further and allowed pollsters to ingest full discussions in person or through transcripts in research interviews, street polls, social media, news discussions and interviews, and other sources where citizen points of view manifest directly and indirectly to voting, that rich content translates into more accurate and insightful information.

Intelligence becomes a cognitive engagement, elevating AI from strictly an analytic capability. Rather than forcing a method, engagement model, or instrument upon the environment, ingestion is adaptive and natural to the environment.  Information is unfiltered and uncurated, rich in context in a way that traditional data and content management erase.

This example demonstrates the blind spots of data analysis, even analysis that is done on data collected and stored in big data environments.  Curate too early and you create ambiguity.  Curate at all can strip away much needed insight.  Artificial intelligence is filling this gap by infusing engagement with analysis in a way that reflects natural communication and listening to ideas, questions, and facts.

Early capabilities for AI are available

  • IBM Watson has a chat module that companies like Genesys and Fluid are using to improve customer support and eCommerce experiences).  
  • Narrative Science and Yseop translate volumes of content and data into financial overviews for investors.
  • Microsoft and Apple provide virtual assistance in their mobile devices and software platforms.

But, these are only scratching the surface of the potential.  At every point conversation occurs between company and customer, employee to employee, organization to organization, and government to citizen, AI provides a window on engagement and our experience that allows us to filter through the noise and get to the point of enriching experience, relationships, and achieving goals.

Early sophisticated commercial pilots of IBM Watson in healthcare for cancer diagnosis with Sloan Kittering is such an example, and certainly where the majority of penetration has occurred in enterprise settings.  It remains to be seen if the complexity of establishing the corpus of knowledge and training the systems is a repeatable and scalable opportunity that still allows differentiation and competitive advantage between organizations and other industries.  Also, will cognitive engagement take hold in consumer scenarios first where access to large content, data sources, and foundational analytics is more advantageous, such as Google's Internet of Things path to connect search algorithms, consumer devices, and shopping patterns that can translate into another wave of consumer empowerment like social media?  The answer has largely to do with the ability of organizations to look broadly at their business processes, engagement models, communication channels, and collection of market content.  Pilot small to 'learn' the model, but the goal is not strictly efficiency or better answers, competitive advantage from cognitive engagement will come by aligning these to a moment in need.  

The recent past of technology adoption by organizations has shown a lag between consumer adoption (first) and organization adoption (second).  The internet created the environment that changed the information paradigm.  Mobile changed the engagement paradigm.  Social changes the influence paradigm.  Companies are still playing catch up.  Cognitive engagement will change all these simultaneously and in convergence, and sooner rather than later.  If artificial intelligence capabilities aren't on your technology adoption watch list, they should be.


Don't overlook expert systems

Michele -- I agree that achievements like IBM's Watson herald a shift in how we deal with complex scenarios.

One factor that is also affecting this issue is the rise of expert systems. As the author of a computer language expert system, I don't define such systems as AI "out of the box", because they typically do not come equipped with built-in learning capability. However, it is not only possible but eminently practical to create rules for them that do learn, which edges them into the AI arena.

The objectives of an expert system are twofold: a) to capture human expertise, in the form of rules, and deploy that expertise in the absence of the expert; and b) to scale that deployment far beyond what an individual can handle.

For instance, in the case of our expert system, its rules language can automate software engineering tasks, including the assessment / analysis, transformation / re-engineering, translation, and generation of code, in a broad range of computer languages. This makes possible an approach to software development that is unattainable in the absence of such capability. We routinely create rules for our system that learn from their analysis of a code body and apply what they have learned to its transformation.

In my view, the implications of such capability are profound. For example, as automation sinks its teeth into software development, the number of developers will dramatically decrease, and the average skill and talent level of the survivors will just as dramatically increase. They will be either creating & maintaining systems using the automation tools, or creating & maintaining those tools. Because of their advanced level of skill and experience, they will be in short supply, so their geographic location will be irrelevant, and they will earn globally-based salaries. This will eliminate labor cost arbitrage as a business driver, which will wipe out the whole concept of "off-shoring". All of this will not happen tomorrow, but sooner than people think. At that point, the concepts of migration and modernization (and even software maintenance) will be obsolete as we know them now.

I believe that the implications of successful deployment of expert systems in other fields are similarly profound.

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