40 ISE Magazine | www.iise.org/ISEmagazine

Engineering

the intelligent

enterprise

Augmented intelligence can’t match human

thinking but can optimize business processes

By Joseph Byrum

January 2019 | ISE Magazine 41

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 un-

til 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 pos-

sible with man and machine? The starting point must be to

distinguish the strengths and weaknesses of each.

The limits of human understanding

Based on the number of neurons and connections in the hu-

man brain, Northwestern University psychology professor

Paul Reber, Ph.D., estimates we have a memory capacity

roughly equivalent to 2.5 petabytes of storage, though di-

rect 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 impres-

sive mental feats. Two millennia ago, bards would travel

from village to village to recite works like Homer’s Iliad

from memory. It might take a few evenings to get through

the entire 15,693-line epic poem that was passed around for

generations by word-of-mouth long before books and wide-

spread literacy were common.

Multinight campﬁre readings have gone out of style, so

a more modern bard might resemble Rajveer Meena, who

in 10 hours can recite pi to 70,000 digits. Other exemplars

of human processing ability include Scott Flansburg, who

took just 15 seconds to add a randomly selected two-digit

number to itself over and over 36 times, 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, are trivial compared to machines

able to calculate pi to 22 trillion digits.

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 simplest questions asked of a child can trip up a computer.

This is evident to anyone who has used the amazing tools of

text recognition, live translation and voice recognition. 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.

Similarly, most children can walk before they reach the

age of 2, but only the most highly engineered robots are able

to walk on complex terrain without falling over. YouTube is

ﬁlled with videos documenting these less successful efforts.

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; even random number generation

must be faked.

Sure, AI has made art, movies, poetry and music. Those

are works of art in the same sense as the canvas painted by an

elephant trained to wield a brush as seen at www.elephantart.

com, or the “selﬁe” taken by a monkey that presses the remote

trigger for a tripod-mounted camera. 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 mem-

ory – with the most powerful aspect of humans – judgment

and creativity.

Digging deeper into how it works

Judgment and creativity aren’t the province of machines

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. Causal infer-

ence is the essential ingredient for achieving human-level

intelligence. Otherwise you just have systems that mimic

human speech patterns like a digital parrot unable to un-

derstand the words being said.

Pearl has done a masterful job outlining the mathemati-

42 ISE Magazine | www.iise.org/ISEmagazine

Engineering the intelligent enterprise

cal models of causation necessary to assist machines in help-

ing us 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 is the cause. This form of reasoning

provides an insight not available from simple statistics that

can only point us toward associations.

A visit to Spurious Correlations at www.tylervigen.com

provides plenty of examples where associative reasoning

leads to hilariously misleading results, such as the surprising

link between per capita consumption of mozzarella cheese

and the number of civil engineering doctorates awarded

each year as depicted in Figure 1.

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 on its own. There is only so much

that can be accomplished by processing data, evaluating

the most relevant factors and simulating potential actions.

When an experienced human user takes that output and

spots data that conventional analytical methods would have

overlooked, the value of augmented intelligence becomes

clear. Such systems allow humans to act on better intel-

ligence, 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.

Evariant 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

FIGURE 1

A cheesy view of cause and effect

This humorous graph from Spurious Correlations at www.tylervigen.com shows that cause and effect aren’t always linked, even if the data

indicate otherwise.

Credit: Spurious Correlations

January 2019 | ISE Magazine 43

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. 

Joseph Byrum is the chief data scientist at Principal Financial

Group. He was previously senior R&D and strategic marketing ex-

ecutive in life sciences-global product development, innovation and

delivery at Syngenta. In that role, he was chief architect of initia-

tives that won Syngenta the 2016 ANA Genius Award in Mar-

keting Analytics and 2015 Franz Edelman prize for contributions

in operations research and the management sciences. He holds an

MBA from the University of Michigan’s Stephen M. Ross School

of Business and a doctorate in genetics from Iowa State University.

Smarter AI: Machine learning

without negative data

A research team from the RIKEN Center for Advanced Intelligence

Project has developed a method for machine learning that

allows artiﬁcial intelligence to make classiﬁcations without what

is known as “negative data,” a ﬁnding that could lead to wider

application to a variety of classiﬁcation tasks.

Classifying things is critical for humans seeking to detect

spam mail, fake political news or read objects or faces. When

using AI, such tasks are based on “classiﬁcation technology” in

machine learning – having the computer learn via the boundary

separating positive and negative data. “Positive” data would be

photos featuring a happy face; “negative,” photos with a sad

face. Once a classiﬁcation boundary is learned, the computer

can categorize the data.

Yet such technology requires both positive and negative

data for the learning process, and negative data are not always

available. In a real-life application, a retailer trying to predict

customer habits can ﬁnd data on those who purchased from

them (positive) but not those who did not (negative).

Researchers succeeded in developing a method to let

computers learn a classiﬁcation boundary only from positive

data and information on its conﬁdence (positive reliability)

against classiﬁcation problems of machine learning that divide

data positively and negatively.

“This discovery could expand the range of applications where

classiﬁcation technology can be used,” lead author Takashi

Ishida said.