Engineering The Intelligent Enterprise
ISE Magazine January 2019 Volume: 51 Number: 1
By Joseph Byrum
Science ﬁction writers say the businesses of the future will be run by self-aware robots. After all, these devices can make intelligent decisions unclouded by fear or other emotions. They will work 24/7 without the need for pay, break time, unionizing or vacation. Such an enterprise would outperform the fallible human competition at least until the last act of the story, when the robots inevitably go rogue.
While last minute twists are critical to an entertaining plot, more sober researchers in the artiﬁcial intelligence ﬁeld explain that we are far from achieving the general intelligence needed for a robot-run enterprise to become reality.
So the question then becomes: How do we extract the greatest performance out of what we know today is possible with man and machine? The starting point must be to distinguish the strengths and weaknesses of each.
The limits of human understanding
Northwestern University psychology professor Paul Reber, Ph.D., estimates human have a memory capacity roughly equivalent to 2.5 petabytes of storage, though direct comparisons are obviously difﬁcult. Whatever the exact ﬁgure, human memory is ﬁnite, though one can always add more storage arrays to ensure a machine will never “forget.”.
Mankind, on the other hand, can perform some impressive mental feats. Rajveer Meena, who can recite pi to 70,000 digits in 10 hours, Scott Flansburg, who add a randomly selected two-digit number to itself over and over 36 times with only 15 seconds, and Vikas Sharma, who calculated 15 large number roots in one minute. These demonstrations, recognized by Guinness World Records as the height of human ability.
While the most talented human can’t come close to compet-ing against machines in arithmetic and memory, don’t take the logical leap to conclude that machines are better at reasoning. The increasingly ubiquitous digital assistants are as deeply frustrat-ing as they are impressive, with quite a way to go before they can be considered replacements for humans.
The reason for this is straightforward. The assistants rely upon preprogrammed responses and Wikipedia entries to generate answers to expected questions. This makes them more like digital parrots with a large vocabulary than intel-ligent AI systems with a grasp of language and nuance. To say they lack critical thinking abilities is not to deny their useful-ness. Rather, it is a recognition of the inherent limits of the underlying technology used in these machines.
Humans excel at judgment and creativity. We can take ideas, mix them together and think outside the box to create works that are truly new. Constrained by logic, machines cannot come up with responses that aren’t preprogrammed. Everything they do is, by deﬁnition, programmed. They can only simulate spontaneity.
Sure, AI has made art, movies, poetry and music. The AI creates what passes for art by sampling a range of different examples of paintings, movies, poems and songs. It uses learning algo-rithms to extract the various elements common to each, then generates and recombines those elements in a “new” way us-ing pseudo-random number generation.
This output has the appearance of creativity without the inspiration. There is no emotional connection to the subject matter any more than there is an understanding of the mean-ing of the brush strokes or musical notes. Where the machines fall short, humans excel. Likewise, the qualities humans most lack can be supplemented by ma-chines. The intelligent enterprise recognizes this and pairs the most powerful aspects of machines – analysis and memory – with the most powerful aspect of humans – judgment and creativity.
Digging deeper into how it works
Machines are not excel in judgment and creatvity because causal reasoning is not easily reduced to mathemat-ical calculation. As the work of UCLA computer scientist Judea Pearl has shown, mining statistics and then applying a few calculations to a data set cannot come close to creating an AI capable of matching wits with a human. Pearl has done outlining the mathematical models of causation necessary to assist machines to answer the question “Why?” He describes what he means using a classic example of causal inference in The Book of Why that he co-authored with Dana Mackenzie: “A ﬁre broke out after someone struck a match, and the question is ‘What caused the ﬁre, striking the match or the presence of oxygen in the room?’ Note that both factors are equally necessary, since the ﬁre would not have occurred absent one of them. So, from a purely logical point of view, the two factors are equally responsible for the ﬁre. Why, then, do we consider lighting the match a more reasonable explanation of the ﬁre than the presence of oxygen?”.
This problem can be reduced to the form of a counterfac-tual expression, revealing that the probability that lighting the match caused the ﬁre is greater than the probability the presence of oxygen. This form of reasoning provides an insight not available from simple statistics that can only point us toward associations.
The ability to distinguish correlations that matter from those that don’t can unlock crucial analytical capabilities. A cognitive engine that can perform some level of causal evaluation can excel at sorting data in terms of its impor-tance, separating the noise, likely a coincidence, from the signal – information that reveals signiﬁcant trends. Know-ing “why” something is the case extends the power of AI far beyond mere imitation.Technology approaching the qualities of human intelli-gence still is not capable of replacing the human mind. To-day’s cognitive engines have nowhere near the level of self-awareness created by sci-ﬁ writers for generations. They are merely tools; what we can do with them is make the most of business processes that can beneﬁt from causal analysis.
Putting it all together: The Iron Man ‘suite’
Even armed with causal reasoning, a cognitive AI system is not particularly effective by itself. There is only so much that can be accomplished by processing data, evaluating the most relevant factors and simulating potential actions. When experienced human users took output and spots data that conventional methods would have overlooked, the value of augmented intelligence became clear. Such systems allow humans to act on better intelligence, making choices informed by a solid understanding of probable outcomes.
AI in the form of augmented intelligence assists human experts in completing tasks with greater efﬁciency. With causal inference, an AI system can, for instance, better tar-get marketing efforts by understanding which groups are truly interested in a product and avoid spurious correla-tions. Civil engineering schools won’t waste efforts mar-keting their graduate programs to mozzarella cheese lovers, but more important uses are taking shape in the healthcare sector.
variant is a company that helps large hospitals optimize marketing efforts by predicting patients’ upcoming needs by analyzing medical data. At ﬁrst, the company hand-cod-ed each algorithm it used, devising a new solution for each customer. Then it realized there was a better way. It turned to DataRobot, a ﬁrm that offered a system to sort through hundreds of algorithms and ﬁnd the one for each particu-lar application that would be statistically reliable and valid. This was not a replacement for Evariant’s data scientists; rather, the system took over mundane and routine coding tasks, freeing experts to perform more effectively.
The beneﬁts of augmented intelligence extend beyond marketing departments and data science teams. Even enter-prise lawyers can now take advantage of systems like Klar-ity, which reads through standard contracts to decide if it’s worth the time for an attorney to review the terms or if it’s just the usual boilerplate with no real risks. The system draws out all the important terms of the agreement so they can be reviewed at a glance and checked in detail when necessary. The tool helps bypass the legal bottleneck and speed up the approval of important deals.
Combining the analytical power of machines with the judgment and creativity of a human is an arrangement I compare to an “Iron Man” suit. In the movies and the comics, Tony Stark is just an ordinary man when it comes to physical ability. Once he dons his AI-powered suit, his overall effectiveness grows as the suit makes suggestions and manages the small details. The ﬁctional example shows us the value of pairing the human’s best abilities with the best abilities of the machine.
For the role of AI in business, it wouldn’t be a physical suit but a software suite that endows a ﬁnancial analyst, factory manager or CEO with powers that exceed those of ordinary humans. The enhanced judgment would deliver better optimized performance, and a business built around such technology would rightly be called an intelligent enterprise. Though augmented intelligence does not make for as entertaining a story, it does make for a proﬁtable company. Considering the competitive edge that augmented intelligence can pro-vide, most future businesses will likely become intelligent enterprises. All the rest won’t be works of ﬁction; they’ll be works of history.