To AI or not to AI

Welcome to another special edition of „Mediocrity and Madness“!
Usually this Podcast is dedicated to the ever-widening gap
between talk and reality in our big organizations, most notably
in our global corporates. Well, I might have to admit that in
some cases the undertone is a tiny bit angry and another bit
tongue-in-cheek. The title might indicate that.



Today’s episode is not like this. Well, it is but in a different
way. Upon reflection, it still addresses a mighty chasm between
talk and reality but the reason for this chasm appears more
forgivable to me than those many dysfunctions we appear to have
accepted against better judgement. Today’s podcast is about
artificial intelligence and our struggles to put it to use in
businesses.


This podcast is to some measure inspired by what I learned in and
around two programs of Allianz, “IT Literacy for top executives”
and “AI for the business”, which I had the privilege and the
pleasure to help developing and facilitating.


I am tempted to begin this episode with the same claim I used in
the last (German) one: With artificial intelligence it is like
with teenage sex. Everybody talks about it, but nobody really
knows how it works. Everybody thinks that everyone else does it.
Thus, everybody claims he does it.


And again, Dan Ariely gets all the credits for coining that
phrase with “Big Data” instead of “artificial intelligence” which
is actually a bit related anyway. Or not. As we will see later.


To begin with, the big question is:
What is “artificial intelligence” after all?

The straightforward way to answering that question is to first
define what intelligence is in general and then apply the notion
that “artificial” is just when the same is done by machines. Yet
here begins the problem. There simply is no proper definition of
intelligence. Some might say, intelligence is what discerns man
from animal but that’s not very helpful, too. Where’s the
boarder.


When I was a boy, I read that a commonplace definition was that
humans use tools while animals don’t. Besides the question
whether that little detail would be one that made us truly proud
of our human intelligence, multiple examples of animals using
tools have been found since.


To make a long story short, there is no proper and general
definition of intelligence. Thus, we end up with some
self-referentiality: “It’s intelligent if it behaves like a
human”. In a way, that’s quite a dissatisfying definition, most
of all because it leaves no room for types of intelligences that
behave – or “are” – significantly non-human. “Black swan” is
greeting. But we’re detouring into philosophy. Back to our
problem at hand: What is artificial intelligence after all?


Well, if it’s intelligent, if it behaves like a human, then the
logical answer to this question is: “artificial intelligence is
when a computer/machine behaves like a human”. For practical
purposes this is something we can work with. Yet even then
another question looms: How do we evaluate whether it behaves
like a human?


Being used to some self-referentiality already, the answer is
quite straight forward: “It behaves like a human if other humans
can’t tell the difference from human behavior.” This is actually
the essence of what is called the “Turing test”, devised by the
famous British mathematician Alan Turing who next to basically
inventing what we today call computer sciences helped solving the
Enigma encryption during World War II.


Turing’s biography is as inspiring as it is tragic and I wouldn’t
mind if you stopped listening to this humble podcast and explored
Turing in a bit more depth, for example by watching “The
imitation game” starring Benedict Cumberbatch. If you decide to
stay with me instead of Cumberbatch, that’s where we finally are:


“Artificial intelligence is when a machine/robot behaves in a way
that humans can’t discern that behavior from human behavior.”


As you might imagine, the respective tests have to be designed
properly so that biases are avoided. And, of course, also the
questions or problems designed to ascertain human or less human
behavior have to be designed carefully. These are subjects of
more advanced versions of the Turing test but in the end, the
ultimate condition remains the same: A machine is regarded
intelligent if it behaves like a human.
(Deliberately) stupid?

It has taken us some time to establish this somewhat flawed,
extremely human-centric but workable definition of machine
intelligence. It poses some questions and it helps answering some
others.


One question that is discussed around the Turing test is indeed
whether would-be artificial intelligences should deliberately put
a few mistakes into their behavior even despite better knowledge,
just in order to appear more human. I think that question comes
more from would-be philosophers than it is a serious one to
consider. Yet, you could argue that if taking the Turing test
seriously, in order to convince a human of being a fellow human
the occasional mistake is appropriate. After all, “to err is
human”. Again, the question appears a bit stupid to me. Would you
really argue that it is intelligent only if it occasionally errs?


The other side of that coin though is quite relevant. In many
discussions about machine intelligence, the implicit or explicit
requirement appears to be: If it’s done by a machine, it needs to
be 100%.


I reason that’s because when dealing with computer algorithms,
like calculating for example the trajectory of a moon rocket,
we’re used to zero errors; given that the programming is right,
that there are no strange glitches in the hardware and that the
input data isn’t faulty as such. Writing that, a puzzling thought
enters my mind: We trustin machine perfection and expect human
imperfection. Not a good outlook in regard to human supremacy.


Sorry, I’m on another detour. Time to get back to the question of
intelligence. If we define intelligence as behavior being
indiscernible from human one, why then do we wonder if machine
intelligence doesn’t yield 100% perfect results. Well, for the
really complex problems it would actually be impossible to define
what “100% perfect” even is, neither ex ante nor ex post but
let’s stick to the simpler problems for now: pattern recognition,
predictive analysis, autonomous driving … . Intelligent beings
make mistakes. Even those whose intelligence is focused onto a
specific task. Human radiologists identify some spots on their
pictures falsely as positive signs of cancer whilst they overlook
others that actually would be malicious. So do machines trained
to the same purpose.
Competition

I am rather sure that the kind listener’s intuitive reaction at
this point is: “Who cares? – If the machine makes less errors
than her human counterpart, let her take the lead!” And of
course, this is the only logical conclusion. Yet quite often,
here’s one major barrier to embracing artificial intelligence.
Our reaction to machines threatening to become better than us but
not totally perfect is poking for the outliers and inflating them
until the use of machine intelligence feels somewhat
disconcerting.


Well, they are competitors after all, aren’t they?


The radiologist case is especially illuminating. In fact, the
problem is that amongst human radiologists there is a huge, huge
spread in competency. Whilst a few radiologists are just
brilliant in analyzing their pictures, others are comparatively
poor. The gap not only results from experience or attitude, there
are also significant differences from county to country for
example. Thus, even if the machine would not beat the very best
of radiologists, it would be a huge step ahead and saving many,
many lives if one could just provide a better average across the
board;  – which is what commonly available machines geared
to the task do. Guess what your average radiologist thinks about
that. – Ah, and don’t mind, if the machine would not yet be
better than her best human colleagues, it is but a matter of
weeks or months or maybe a year or two until she is as we will
see in a minute.


You still don’t believe that this impedes the adaption of
artificial intelligence? – Look this example that made it into
the feuilletons not long ago. Autonomous driving. Suppose you’re
sitting in a car that is driven autonomously by some kind of
artificial intelligence. All of a sudden, another car – probably
driven by a human intelligence – comes towards you on the rather
narrow street you’re driven through. Within microseconds, your
car recognizes its choices: divert to the right and kill a group
of kids playing there, divert to the left and kill some adults in
their sixties one of which it recognizes as an important advisor
to an even more important politician or keep the track and kill
both, the occupants of the oncoming car … and unfortunately you
yourself.


The dilemma has been stylized to a kind of fundamental question
by some would-be philosophers with the underlying notion of “if
we can’t solve that dilemma rationally, we might better give up
the whole idea of autonomous driving for good.” Well, I am
exaggerating again but there is some truth in that. Now, as the
dilemma is inextricable as such: bye, bye autonomous driving!


Of course, the real answer is all but philosophical. Actually, it
doesn’t matter what choice the intelligence driving our car
makes. It might actually just throw a dice in its random access
memory. We have thousands of traffic victims every year anyway.
Humankind has decided to live with that sad fact as the
advantages of mobility outweigh these bereavements. We have
invented motor liability insurance exactly for that reason. Thus,
the only and very pragmatic question has to be: Do the advantages
of autonomous driving outweigh some sad accidents? – And
fortunately, probability is that autonomous driving will
massively reduce the number of traffic accidents so the question
is actually a very simple one to deal with. Except probably for
motor insurance companies … and some would-be philosophers.
Irreversible

Here’s another intriguing thing with artificial intelligence:
irreversibility. As soon as machine intelligence has become
better than man in a specific area, the competition is won
forever by the machines. Or lost for humankind. Simple: as soon
as your artificial radiologist beats her human colleague, the
latter one will never catch up again. On the contrary. The
machine will improve further, in some cases very fast. Man might
improve a little, over time but by far not at the same speed as
his silicon colleague … or competitor … or potential replacement.


In some cases, the world splits into two parallel ones: the
machine world and the human world. This is what happened in 1997
with the game of Chess when Deep Blue beat the then world
champion Gary Kasparow. Deep Blue wasn’t even an intelligence. It
was just a brute force with input from some chess savvy
programmers but then humans have lost the game to the machines,
forever. In today’s chess tournaments not the best players on
earth compete but the best human players. They might use
computers to improve their game but none of them would stand the
slightest chance against a halfway decent artificial chess
intelligence … or even a brute force algorithm.


The loss of chess for humankind is a rather ancient story
compared to the game of Go. Go being multitudes more complex than
chess resisted the machines about twenty years more. Brute force
doesn’t work for Go and thus it took until 2016 until AlphaGo, an
artificial intelligence designed to play Go by Google’s DeepMind
finally conquered that stronghold of humanity. That year, AlphaGo
defeated Lee Sedol, one of the best players in the world. A few
months later, the program also defeated Ke Jie, the then
top-ranking player in the world.


Most impressive though it is that again only a few months later
DeepMind published another version of its Go-genius: AlphaGo
Zero. Whilst AlphaGo had been trained with huge numbers of Go
matches played by human players, AlphaGo Zero had to be taught
only the rules of the game and developed its skills purely by
playing against versions of itself. After three days, this
version beat her predecessor that had won against Lee Sedol
100:0. And again only three months later, another version was
deployed. AlphaZero learnt the games of Chess and Go and Shogi,
another highly complex strategy game, in only a few hours and
defeated all previous versions in a sweep. By then, man was out
of the picture for what can be considered an eternity by measures
of AI development cycles.


AlphaZero not only plays a better Go – or Chess – than any human
does, it develops totally new strategies and tactics to play the
game, it plays moves never considered reasonable before by its
carbon-based predecessors. It has transcended its creators in the
game and never again will humanity regain that domain.


This, you see, is the nature of artificial intelligence: as soon
as it has gained superiority in a certain domain, this domain is
forever lost for humankind. If anything, another technology will
surpass its predecessor. We and our human brains won’t. We might
comfort ourselves that it’s only rather mundane tasks that we
cede to machines of specialized intelligence, that it’s a long
way still towards a more universal artificial intelligence and
that after all, we’re the creators of these intelligences … . But
the games of Chess and Go are actually not quite so mundane and
the development is somewhat exponential. Finally, a look into
ancient mythology is all but comforting. Take Greece as an
example: the progenitor of gods, Uranos, was emasculated by his
offspring, the Titans and these again were defeated and punished
by their offspring, the Olympians, who then ruled the world, most
notably Zeus, Uranos’ grandson.


Well, Greek mythology is probably not what the kind listener
expects from a podcast about artificial intelligence. Hence, back
to business.
AI is not necessarily BIG Data

Here’s a not so uncommon misconception: AI or advanced analytics
is always Big Data or – more exactly: Big Data is a necessary
prerequisite for advanced analytics.


We could make use of the AlphaZero example again. There could
hardly be less data necessary. Just a few rules of the game and
off we go! “Wait”, some will argue, “our business problems aren’t
like this. What we want is predictive analysis and that’s Big
Data for sure!”. I personally and vehemently believe this is a
misconception. I actually assume, it is a misconception with a
purpose but before sinking deeper into speculation, let’s look at
an example, a real business problem.


I have spent quite some years in the insurance business. Hence
please apologize for me using an insurance example. It is very
simple. The idea is using artificial intelligence for calculating
insurance premiums, specifically motor insurance third party
liability (TPL). Usually, this is a mandatory insurance. The risk
it covers is that you in your capacity of driving a car – or
parking it – damage an object that belongs to someone else or
that you injure someone else. Usually, your insurance premium
should reflect the risk you want to cover. Thus, in the case of
TPL the essential question from an actuary’s point of view is the
following one: Is the person under inspection a good driver or a
not so good one? “Good” in the insurer’s sense: less prone to
cause an accident and if so, one that usually doesn’t come with a
big damage.


There are zillions of ways to approach that problem. The best
would probably be to get an individual psychological profile of
the respective person, add a decently detailed analysis of her
driving patterns (where, when, …) and calculate the premium based
on that analysis, maybe using some sort of artificial
intelligence in order to cope with the complex set of data.


The traditional way is comparatively simplistic and indirect. We
use a mere handful of data, some of them related to the car like
type and registration code, some personal data like age or
homeownership and some about driving patterns, mostly yearly
mileage and calculate a premium out of these few by some rather
simple statistical analysis.


If we were looking for more Big Data-ish solutions we could
consider basing our calculation on social media timelines. Young
males posting photos that show them Friday and Saturday nights in
distant clubs with fancy drinks in their hands should emerge with
way higher premiums than their geeky contemporaries who spend
their weekends in front of some computers using their cars only
to drive to the next fast food restaurant or once a week to the
comic book shop. The shades in between might be subtle and an
artificial intelligence might come up with some rather delicate
distinctions.


And you might not even need a whole timeline. Just one picture
might suffice. The forms of our faces, our haircut, the glasses
we fancy, the jewelry we wear, the way we twinkle our noses …
might well be very good indicators of our driving behavior.
Definitely a job for an artificial intelligence.


I’m sure, you can imagine other avenues. Some are truly Big Data,
others are rather small in terms of data … and fancy learning
machines. The point is, these very different approaches may well
yield very similar results ie, a few data related to your car
might reveal quite as much about the question at hand as an
analysis of your Instagram story.


The fundamental reason is that data as such are worthless.
Valuable is only what we extract from that data. This is the
so-called DIKW hierarchy. Data, Information, Knowledge, Wisdom.
The true challenge is extracting wisdom from data. And the rule
is not: more data – more wisdom. On the contrary. Too much data
might in fact clutter the way to wisdom. And in any case, very
different data might represent the same information, knowledge or
wisdom.


As what concerns our example, I have first of all to admit that I
have nor analytical proof – or wisdom – about specifics I am
going to discuss but I feel confident that the examples
illustrate the point. Here we go.


The type of car – put into in the right correlation with a few
other data -- might already contain most of the knowledge you
could gain from a full-blown psychological analysis or a
comprehensive inspection of a person’s social media profile. Data
representing a 19 year old male, living in a certain area of
town, owning a used but rather high powered car, driving a
certain mileage per year might very well contain the same
information with respect to our question about “good” driving as
all the pictures we find in his Facebook timeline. And the other
way around. The same holds true for the information we might get
out of a single static photo.


Yet the Facebook timeline or the photo are welling over with
information that is irrelevant for our specific problem. Or
irrelevant at all. And it is utterly difficult to get a) the
necessary data in a proper breadth and quality at all and b) to
distill relevant information, knowledge and wisdom from this
cornucopia of data. 


Again: more data does not necessarily mean more wisdom! It might.
But one kind of data might – no: will – contain the same
information as other kinds. Even the absence of data might
contain information or knowledge. Assume for instance, you have
someone explicitly denying her consent to using her data for
marketing purposes. That might mean she is anxious about her data
privacy which in turn might indicate that she is also concerned
about other burning social and environmental issues which then
might indicate she doesn’t use her car a lot and if so in a
rather responsible way … .


You get the point. Most probably that whole chain of reasoning
won’t work having that single piece of data in isolation but put
into the context of other data there might actually be wisdom.
Actually, looking at the whole picture, this might not even be a
chain of reasoning but more a description of the certain state of
things that denies decomposition into human logic. Which leads us
to another issue with artificial intelligence.
The unboxing problem

Artificial intelligences, very much like their human
contemporaries, can’t always be understood easily. That is, the
logic, the chain of reasoning, the parameters that causally
determine certain outcomes, decisions or predictions are in many
cases less than transparent. At the same time, we humans demand
from artificial intelligence what we can’t deliver for our own
reasoning: this very transparency. Quite like us demanding 100%
machine perfection, some control-instinct of ours claims: If it’s
not transparent to us (humans), it isn’t worth much.


Hence, a line of research in the field of artificial intelligence
has developed: “Unboxing the AI”.


Except for some specific cases yet, the outlook for this
discipline isn’t too bright. The reason is the very way
artificial intelligence works. Made in the image of the human
brain, artificial intelligences consist of so-called “neural
networks”. A neural network is more or less a – layered – mesh of
nodes. The strength of the connections between these nodes
determines how the input to the network determines the output.
Training the AI means varying the strengths of these connections
in a way that the network finally translates the input into a
desired output in a decent manner. There are different topologies
for these networks, tailored to certain classes of problems but
the thing as such is rather universal.


Hence AI projects can be rather simple by IT standards: define
the right target function, collect proper training data, plug
that data to your neural network, train it … . It takes but a
couple of weeks and voila, you have an artificial intelligence
thatyou can throw on new data for solving your problem.


In short, what we can call “intelligence” is the state of
strengths of all the connections in your network. The number of
these connections can be huge and the nature of the neural
network is actually agnostic to the problem you want it to solve.
“Unboxing” would thus mean to backwardly extract specific
criteria from such a huge and agnostic network. In our
radiologist case for example, we would have to find something
like “serrated fringes” or “solid core” in nothing but this set
of connection strengths in our network. Have fun!


Well, you might approach the problem differently by simply
probing your AI in order to learn that and how it actually reacts
to serrated fringes. But that approach has its limits, too. If
you don’t know what to look for or if the results are determined
not by a single criterion but by the entirety of some data,
looking for specifics becomes utterly difficult. Think of
AlphaZero again. It develops strategies and moves that have been
unknown to man before. Can we really claim we must understand the
logic behind, neglecting the fact that Go as such has been quite
resistant to straightforward tactics and logic patterns for the
centuries humans have played it.


The question is: why “unboxing” after all? – Have you ever asked
for unboxing a fellow human’s brain? OK, being able to do that
for your adolescent kids’ brains would be a real blessing! But
normally we don’t unbox brains. Why are we attracted by one
person and not by another? Is it the colour of her eyes, her
laughter lines, her voice, her choice of words …? Why do we find
one person trustworthy and another one not? Is it the way she
stands, her dress, her sincerity, her sense of humour? How do we
solve a mathematical problem? Or a business one? When and how do
the pieces fall into place? Where does the crucial idea emerge
from?


Even when we strive to rationalize our decision making, there
always remain components we cannot properly “unbox”. If the
problem at hand is complex – and thus relevant – enough. We
“factor in” strategic considerations, assumptions about the
future, others’ expectations … . Parts of our reasoning are
shaped by our personal experiences, our individual preferences,
like our risk-appetite, values, aspirations, … . Unbox this!


Humankind has learnt to cope with the impossibility of “unboxing”
brains or lives. We probe others and if we’re happy with the
results, we start trusting. We cede responsibilities and continue
probing. We cede more responsibilities … and sometimes we are
surpassed by the very persons we promoted. Ah, I am entering
philosophical grounds again. Apologies!


To make it short. I admit, there are some cases in which you
might need full transparency, complete “unboxing”. And in case
you don’t get it, abolish the idea of using AI for the problem
you had in mind.


But there are more cases in which the desire for unboxing is just
another pretense for not chartering new territory. If it’s
intelligent if it behaves like a human why do we ask for so much
more from the machines than we would ask from man?


Again, I am drifting off into questions of dangerously
fundamental nature. Let’s assume for once that we have overcome
all our concerns, prejudices and excuses, that despite all of
them, we have a business problem we full-heartedly want to throw
artificial intelligence at. Then comes the biggest challenge of
all.
The biggest challenge of all: how to operationalize it

Pretty much like in our discussion at the beginning of this post,
on the face of it, it looks simple: unplug the human intelligence
occupied with the work at hand and plug in the artificial one. If
it is significant – quite some AI projects are still more in the
toy category – this comes along with all the challenges we are
used to in what we call change management. Automating tasks comes
with adapting to new processes, jobs becoming redundant, layoffs,
re-training and rallying the remaining workforce behind the new
ways of working.


Yet changes related to artificial intelligence might have a very
different quality. They are about “intelligence” after all,
aren’t they? They are not about replacing repetitive, sometimes
strenuous or boring work like welding metal or consolidating
accounting records, they dig to the heart of our pride. Plus, the
results are by default neither perfect nor “unboxable”. That
makes it very hard to actually operationalize artificial
intelligence. Here’s an example.


It is more than fifteen years old, taking place at a time when a
terabyte was an still an incredible amount of storage, when data
was still desired to be stored in warehouses and not floating
around in lakes or oceans and when true machine learning was
still a purely academic discipline. In short: the good old times.
This gives us the privilege to strip the example bare of
complexity and buzz.


At that time, I was together with a few others responsible for
developing Business Intelligence solutions in the area of
insurance sales. We had our dispositive data stored in the
proverbial warehouse, some smart actuaries had applied
multivariate statistics to that data and hurrah, we got
propensities to buy and rescind for our customers.


Even with the simple means we had by then, these propensities
were quite accurate. As an ex-post analysis showed, they hit the
mark at 80% applying the relevant metrics. Cutting the ranking at
rather ambitious levels, we pushed the information to our agents:
customers who with a likelihood of more than 80% were to close a
new contract or to cancel one … or both. The latter one sounds a
bit odd, but a deeper look showed that these were indeed
customers who were intensely looking for a new insurance without
a strong loyalty. – If we won them, they would stay with us and
loyalty would improve, if a competitor won them, they would
gradually transfer their portfolio to him.


You would think that would be a treasure trove for any salesforce
in the world, wouldn’t you? Far from it! Most agents either
ignored the information or – worse – they discredited it. To the
latter purpose, they used anecdotal evidence: “My mother in law
was on the list”, they broadcast, “she would never cancel her
contract”. Well, some analysis showed that she was on the list
for a reason but how would you fight a good story with the
intricacies of multivariate statistics? Actually, the
mother-in-law issue was more of a proxy for a deeper concern.
Client relationship is supposed to be the core competency of any
salesforce. And now, there comes some algorithm or artificial
intelligence that claims to understand at least a (major) part of
that core competency as good as that very salesforce … .
Definitely a reason to fight back, isn’t it?


Besides this, agents did not use the information because they
regarded it not too helpful. Many of the customers on the
high-propensity-to-buy-list were their “good” customers anyway,
those with who they were in regular contact already. They were
likely indeed to make another buy but agents reasoned they would
have contacted them anyway. So, don’t bother with that list.


Regarding the list of customers on the verge of rescinding, the
problem was a different one. Agents had only very little
(monetary) incentive to prevent these from doing so. There was a
recurring commission but asked whether to invest valuable time
into just keeping a customer or going for new business, most were
inclined to choose the latter option.


I could continue on end with stories around that work, but I’d
like to share only one more tidbit here before entering a brief
review of what went wrong: What was the reaction of management
higher up the food-chain when all these facts trickled in? Well,
they questioned the quality of the analysis and demanded to
include more – today we would say “bigger” – data in order to
improve that quality, like buying sociodemographic data which was
the fad at that time. Well, that might have increased the quality
from 80% to 80+% but remember the discussion we had around
redundancy of data. The type of car you drive or the sum covered
by your home insurance might say much more than sociodemographic
data based on the area you live in. … Not to speak of that
eternal management talk that 80% would be good enough.


What went wrong?


First, the purpose of the action wasn’t thought through well
enough from the start. We more or less just choose the easiest
way. Certainly, the purpose couldn’t have been to provide agents
with a list of leads they already knew were their best customers.
From a business perspective the group of “second best customers”
might have been much more attractive. Approaching that group and
closing new contracts there would have not only created new
business but also broadened the base of loyal customers and thus
paved the way for longer term success. The price would of course
have been that these customers would have been more difficult to
win over than the “already good” ones so that agents would have
needed an incentive to invest effort into this group. Admittedly
going for the second-best group would have come with more
difficulties. We might have faced for example many more
mother-in-law anecdotes.


Second, there was no mechanism in place to foster the use of the
information. Whether the agents worked on the leads or not didn’t
matter so why should they? Worse even with the churn-list. From a
long-term business perspective, it makes all the sense in the
world to prevent customer churn as winning new customers is way
more expensive. It also makes perfect sense to try making your
second-best customers more loyal but from a short-term salesman’s
or -woman’s perspective boiling the soup of already good
customers makes more short-term sense. Thus, in order to
operationalize AI target systems might need a thorough overhaul.
If you are serious, that is. The same holds true if you would for
example want to establish machine assisted sentiment analysis in
your customer care center.


Third, there was no good understanding of data and data analytics
neither on the supposed-to-be users’ side nor on the management
side. This led to the “usual” reflexes on both sides: resistance
on the one side and an overly simplified call for “better” on the
other one. Whatever “better” was supposed to mean.


Of course, neither the example nor the conclusions are
exhaustive, but I hope they help illustrate the point: more often
than not it is not the analytics part of artificial intelligence
that is the tricky one. It is tricky indeed but there are smart
and experienced people around to deal with that type of tricky
business.


More often than not, the truly tricky part is to put AI into
operations,


to ask the right questions in the first place,

to integrate the amazing opportunities in a consistent way
into your organization, processes and systems,

to manage a change that is more fundamental than simple
automation and

to resist the reflex that bigger is always better!



 


So much for today from “Mediocrity and Madness”, the podcast that
usually deals with the ever-growing gap between corporate
rhetoric and action. I dearly thank all the people who provided
inspiration and input to these musings especially in and around
the programs I mentioned in the intro, most notably Gemma
Garriga, Marcela Schrank Fialova, Christiane Konzelmann,
Stephanie Schneider, Arnaud Michelet and the revered Prof. Jürgen
Schmidhuber!


Thank You for listening … and I hope to have you back soon!