We are fully engulfed in the era of massive data collection. 
All those data represent the most critical and valuable strategic assets of modern organizations that are undergoing digital disruption and digital transformation. Advanced analytics tools and techniques drive insights discovery, innovation, new market opportunities, and value creation from data. However, our enthusiasm for “big data” is tempered by the fact that this data flood also drives us to sensory input shock and awe.
Among the countless amazing foresights that appeared in Alvin Toffler’s Future Shock was the concept of information overload. His discussion of the topic came long before the creation and proliferation of social networks, the World Wide Web, the internet, enterprise databases, ubiquitous sensors, and digital data collection by all organizations—big and small, public and private, near and far. The clear and present human consequences and dangers of infoglut were succinctly called out as section headings in Chapter 16, “The Psychological Dimension,” of Toffler’s book, including these: “the overstimulated individual,” “bombardment of the senses,” and “decision stress.”
While these ominous forecasts have now become a reality for our digitally drenched society, especially for the digital natives who have known no other experience, there is hope for a lifeline that we can grasp while swimming (or drowning) in that sea of data. And that hope emanates from the same foundation that is the basis of the information overload shock itself. That foundation is data, and the hope is AI—artificial intelligence. The promise of AI is entirely dependent on the flood of sensory input data that fuels the advanced algorithms that activate and stimulate the actionable insights (representing another definition of A.I.), which is what AI is really aimed at achieving.
AI is a great producer of dichotomous reactions in society: hype versus hope, concern versus commercialization, fantasy versus forward-thinking, overstimulation versus overabundance of opportunity, bombardment of the senses versus bountiful insights into solving hard problems, and decision stress versus automated decision support. Could we imagine any technology that has more mutually contradictory psychological dimensions than AI? I cannot.
AI takes its cue from data. It needs data—and not just small quantities, but large quantities of data, containing training examples of all sorts of behaviors, events, processes, things, and outcomes. Just as a human (or any cognitive creature) receives sensory inputs from its world through multiple channels (e.g., our five senses) and then produces an output—a decision, an action—in response to those inputs, similarly that is exactly what an AI does. The AI relies on mathematical algorithms that sort through streams of data to find the most relevant, informative, actionable, and pertinent patterns in the data. From those presently perceived patterns, AI then produces an output (decision and/or action). When a human detects and then recognizes a previously seen pattern, the person knows how to respond—what to decide and how to act. This is what an AI is being trained to do, automatically and with minimal human intervention through the data inputs that cue the algorithms.
AI helps with infoglut and thereby addresses the themes of Toffler’s writings (information overload, overstimulation and bombardment of the senses, and decision stress) by the pattern-learned process of automating the essential triage of input data streams that cognitive beings do autonomically (i.e., through autonomic reflex). This is what an “intelligence” does.
Ever since the beginning of our existence, we humans go through our lives constantly bombarded by sensory inputs. Imagine walking out the front door of your house and paying detailed attention to every single bit of information that both natural and human-constructed objects present to us—every leaf on every tree, every cloud in the sky, every twist and turn of the roads and pathways, every scent or odor in the air, every pleasant or cacophonous sound in the environment. Our brains (which are natural neural networks) know how to categorize and sort those billions of sensory inputs (information glut) into three categories of information, without even deliberately thinking about them (i.e., without suffering from information overload). As noted above, those three categories—the triage—of information are: (1) those that are common and unimportant in that moment (thus, ignored); (2) those that are critically important (thus, used promptly for the next decision and action); and (3) those that might be important later (thus, stored in our minds for later recall). This information categorization triage happens without any conscious action on our part.
AI (which includes artificial neural networks) can and will provide a similar triage on the infoglut and big data inputs that would otherwise have the potential to produce the deleterious psychological outcomes that Toffler writes about: confusion, disorientation, anxiety, analysis paralysis, and eventually apathy and withdrawal. AI will do what it is built for doing and tuned to do well (through large amounts of input training data), which is to accelerate, amplify, assist, and augment human intelligence. Those more meaningful and representative forms of AI (Accelerated, Amplified, Assisted, and Augmented Intelligence) also include Actionable, Automated, and Adaptable Intelligence. These represent the real power of AI—there is nothing artificial at all in those applications of AI.
Therefore, AI ultimately enables us to adapt to the information overload, sensory bombardment, and decision stress in the modern data-intense world. We are in a transition period, between our ability to generate massive quantities of data and our ability to assimilate all of that information through AI. Data are then simply the input, albeit a significantly large amount of input, to our business logic processes that have always been the fundamental means of decision-making and taking actions. This applies not only to organizations, but also to individuals. AI can manage the information flood that we encounter daily through news sources, social networks, entertainment devices, and more.
Adaptability to shock (rapid change, too much novelty, and overstimulation) is hard, and Toffler provided many examples where humans have been unable to adapt in such situations (including on the battlefield, during natural disasters, and when thrust into a new cultural environment). But Adaptable Intelligence (A.I.) is within reach through modern digital tools, technologies, algorithms, and devices that are embedded with AI algorithms acting on streams of sensory inputs.
Consider the example of a universal translator, which is no longer in the realm of science fiction but is part of scientific reality today. These in-ear devices can perform real-time bilingual translation that enables conversation between two speakers who are each using their own native language. If I used this AI-enabled device on my next international journey to a country that I have never visited, I would hear the other speaker’s words in my native language, even though the words were spoken in their language. Part of my culture shock will essentially disappear. Furthermore, since language has the power to build bridges and bonds between peoples, then maybe most of the remaining culture shock will dissipate also.
When we think of AI as assisted, augmented, and amplified intelligence, we should see this as a two-way interaction: humans assisting machines, and machines assisting humans. The flood of data that comes at us continuously from ubiquitous sensors, social networks, and the digitization of society has the potential to cause us psychological shock, distress, disorientation, and anxiety. But an informed AI will assist us in adapting to this data shock and in achieving its greater potential for human-centered discovery, innovation, and value creation in a shockingly large number of situations that can deliver positive societal impact for all. That will be an aftershock that we can all live and thrive with.
Dr. Kirk Borne is the Principal Data Scientist, an executive advisor, and the first Data Science Fellow at the global technology and consulting firm Booz Allen Hamilton based in McLean, Virginia. Before 2015, he was professor in the data science program at George Mason University for 12 years. Prior to that, Dr. Borne spent nearly 20 years supporting data systems activities for NASA space science missions. Dr. Borne has a BS degree in physics from LSU, and a PhD in astronomy from Caltech. He is an elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. Since 2013 he has been listed each year as one of the top worldwide influencers in big data and data science on social media. https://www.linkedin.com/in/kirkdborne/