September 20, 2024 • 35 mins
With over 15 years of experience, Rachad’s mission is to align organizational learning and knowledge management programs with the organizational priorities and goals, identifying and assessing learning needs, and delivering an integrated learning strategy that balances experiential, social, and formal learning. Rachad is pioneering Expertise-based knowledge management which is a key pillar to establish strategies for knowledge sharing, transferring, reusing, and applying expertise to solve technical engineering problems. Rachad considers artificial intelligence techniques as an extension to human capabilities to help in designing the enterprise knowledge architecture, retain critical knowledge, and map the expertise to support customer projects. Rachad’s purpose is to foster a culture of learning and sharing that empowers engineers to grow and innovate.
In 2024, Rachad has founded 3R knowledge Services that provides world class proven experience management services and products in the field of organizational learning. He established and pioneered expertise-based knowledge management principles in his company’s product and services offering.
Between 2013 and 2024, Rachad has lead GE Renewable Energy in the field of organizational learning and knowledge management. He was responsible for designing and implementing an integrated learning strategy, system and processes. He was accountable for defining the enterprise knowledge architecture with the right set of knowledge-sharing communities. Rachad has documented monetary savings enabled by the Knowledge sharing program.
From 2008 to 2011, Rachad acted as a knowledge management advisor for a government agency in Dubai, UAE. He directed the requirements for the EFQM Excellence Award. He implemented the enterprise knowledge architecture and the operational processes. His efforts have resulted in winning the award.
Rachad holds a doctoral degree in industrial engineering (2017) and a master’s in software engineering (2012) from the Grenoble Institute of Technology. Rachad has co-authored a recent book in the field of knowledge management and research innovation. Rachad has numerous scientific publications in prestigious conferences and journals. In his thesis, he proposed a groundbreaking framework to characterize and configure the collaboration dynamics for virtual collectives. The framework has proven its effectiveness in multiple professional contexts and organizations.
#Expertise-Based_Knowledge_Management #Learning_Organization #KM #Artificial_Intelligence #Knowledge-Sharing #Skills-Disseminators
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:11):
Pioneer Knowledge Services welcomes you to the next
because you need to know. I'm your host,
Edwin k Morris. I serve as president and
founder of this organization, and we are thrilled
to offer this educational program. These design conversations
bring you people's experiences
from all over the globe in the field
of knowledge management, nonprofit work, and innovation.
(00:40):
Assalamu alaikum.
Bonjour tout lemons. Salut.
Hello, everyone. Good morning, and welcome to this
podcast.
My name is Rashad Najjar,
and I live in Grenoble, France.
The most interesting thing near me is the
LHC,
Large Hadron Collider. It is the world's largest
(01:03):
and highest energy particle collider.
So this is somehow
scary and somehow a little bit good. So
I'm located on the French Swiss border. I
love to meet new people. That's why I'm
here on the podcast.
I'd like to connect, to share my stories,
listen,
to their stories,
and offer help if they need it. The
(01:25):
things that I have learned,
throughout all these years
is that preparation,
persistence, and patience are keys to success.
Half of the success, I believe, is preparation
and determination.
The topics that I'd like to talk for
hours and hours is knowledge management, obviously.
And I recently wrote and published a report
(01:46):
exactly
analyzing
generative AI use cases
on where and how those capabilities can augment
the learning experience,
and the knowledge management processes, of course. So
this has been my career, 10 years in
this field, organizational learning and knowledge management. The
last book that I read that I'd like
to share it with you,
is really very interesting book. It's called the
(02:09):
big on by Mariana Mazzucato.
She's a professor in
innovation economics at UCL.
Mainly, the authors,
are arguing
the myth that the consulting
industries always add value to the economy,
And they support this argument
or this counterargument
(02:29):
very brilliantly.
They said innovation should come from within, from
the inside out,
investing in the collective intelligence of the people
and empowering them to pop up ideas and
to build on the collective elaboration of those
ideas and do innovation.
Is AI gonna go away? Is artificial intelligence
(02:51):
just a fad? It's been here since
1983.
It will continue to be. But like any
other technology, there's a hype curve. It will
go and up based on the drivers, the
technology,
overall circumstances. So let's demystify what AI is.
Yes. I think there's a lot of folks
that hear it and they see it everywhere
(03:12):
now on every social media platform or every
entry point they have to
news and media.
What is it? Why should they care? Perfect.
And, I like to demystify it.
There's a misconception.
AI, artificial intelligence,
is intelligent.
This is a misconception. It's not intelligent.
(03:33):
We are really at the extreme
computing edges.
So we have very powerful computing machines that
are doing enormous computing.
Other hand,
let me ask you this question. True. Can
a machine wake up in the morning and
says, what should I do today or what
I should learn today? Well, that's self consciousness.
Right? That's a self
(03:54):
driven reality of purpose and Yes. Maybe someday
we'll get there. But right now, you're saying
that ability is not what artificial intelligence is.
Yes. And most risky thing is our expectations.
So we are expecting
from AI much more Yeah. Than AI is
capable to do. So just just so we
can add more clarity to this, so I
(04:16):
looked up Merriam Webster's definition of intelligence. Yes.
So artificial intelligence, as you say, is a
kind of a misnomer because it's not really
that yet. Yes. Intelligence in the first definition
is the ability to learn or understand
or deal with new or trying situations.
I apparently have never read that definition before
(04:37):
because that's pretty cool. So that is definitely
a human trait,
for some humans, I'll say. But that is
a humanistic type trait.
You know, let me share also with you
something. I did a PhD studies. And during
research, we are always and most of the
situations,
we are confronted in ambiguous
(04:58):
situations.
We don't know what to do.
And there's no reasoning
or there's no, let's say, evidence
if we are going to choose this correctly
or not.
The only thing that drive us toward a
direction is our gut, something inside us. Does
the machine have a gut? I'm just questioning.
(05:19):
I don't know the answer. I'm questioning. That
goes back to the 1800 with the original
concept of the thinking machine. Yes. That this
idea that we can mechanize
comprehension and understanding
has been around a long time. Definitely. And
let me give you one more thing. There's
shared GPT gains the mainstream attention.
(05:39):
But do you know that, the training because,
you know, AI is a subfield of machine
learning, and ChargeGPT
is a subfield of deep machine learning. And
deep machine learning, there's 2 phases. The first
phase is the training. This is where the
machine learn. And the second phase is the
fine tuning when we tune the machine to
(06:01):
adapt to the outcomes so we have an
accurate outcome. For example, if we are applying
this, AI
in the field of cosmetics,
So we need to teach the machine
the specific lexicon or the specific
knowledge about cosmetics. What I'd like to share,
chat GPT
and all other large language models,
(06:24):
they use something called ghost army. So the
ghost army,
those are workers
who do
very millennial
tasks like labeling an object. This is a
car. This is a fish. They say, yes.
This is a car. So the machine doesn't
know what's true, what's this is a ghost
worker, and they are paid $2
(06:45):
to $9
per hour. And they used a massive army
of ghost workers
to train or or to validate the outcome
of the machine and to say, this is
true or not or to label the objects.
So now how can a machine be intelligent
and we are still Right. You're still in
need of the human perception and the human
(07:07):
expertise. Well, you're the way you just explained
it, it can't be done. Unless,
we get to the bioengineered
level of wetware,
which we can program
hardware software
by our own synaptics
structure.
Maybe. Yes.
Then you've got embedded humanity in a machine.
Well, this is a call for all our
(07:29):
entrepreneurs
to take this idea.
The Rise of the Machines is the article
I'm looking at right now from Smithsonian
Libraries. Yes. And we talk about how this
whole idea
that spurred off the industrial revolution
became this idea of self
(07:49):
manipulating
machinery.
Yes. And it's just bizarre to think that
this was back this these concepts go back
to the 1800
that people have been trying to
make machines,
so called think, but at least make a
cognition
differential
in their mechanical approach to whatever the job
(08:09):
was. And a lot of these were very
labor intensive
work for these machines.
So if we go forward Yes. So this
is 1800s
that this whole idea
of thinking machines, and that's just the general
terminology here.
1800s,
thinking machines.
2000 twenties,
(08:31):
AI is everything, but it's not really intelligent.
What's a 100 years from now, my friend?
Well, I wish I would know. You're a
pretty smart fella, so I think you can
conjecture, please. Yeah. I think we will continue
to advance in computing. That's true. Previously, during
the 19 eighties, 19 nineties,
(08:52):
Moore's Law is, his Intel CEO. He said
the number of transistors
that we can put on the same area,
fixed area, will doubles will doubles every 2
years.
However, what we saw with the Gen AI,
since
2013,
the computing power has doubled every 3 to
(09:14):
4 months.
Yes. Definitely, when computing power increases,
it will unlock for us more a lot
of, new capabilities.
And what I can foresee is really still
within the realm of pattern recognition
for complex pattern recognition. For example, if we
are studying
(09:35):
the drug effect,
if a drug if I am on a
treatment and I am taking a a drug
and I have my personal health record and
personal health characteristics,
am I on a diet or not, and
what are other drugs I'm taking? So this
kind of interactions
between
the drugs
(09:56):
or my personal health records, those are very
complex patterns. So AI here can help in
extracting or identifying
relationships
between all those different variables
and recommend
the best scenario
and the best pattern to follow so we
can have, I have can have a treatment
without any kind of Mhmm. Side effects. Well,
(10:19):
that's a great use case, and I think
that shows the value pretty easily to the
majority of people Yes. Because it's something we
all have to deal with. So it's good
to know that that use case is in
the positive. But is there a negative side
to it? Oh. Is can it be criminal
yeah. Okay. Alright. Alright. Let's let's hear about
the negative side to all of that. Yes.
(10:41):
Well, I can think of 2, negative
consequences. The first one is about,
security,
privacy
Mhmm. And personal data. How much I am
willing to open up my personal health record,
which are highly confidential information.
So maybe some commercial companies will leverage those
(11:02):
data
and build some commercial activities or some or
make some profit. So this is very critical
topics related to ethics.
How we can build ethical AI applications
that are not harmful,
that respect my personal
privacy
without being judgmental
(11:22):
or being intrusive into my life. This is
very big and vast topic and need the
involvement of multiple stakeholders, multiple perspective from legal,
from society,
from health personnel, and every single decision maker.
So is this a call to action? Do
you see this as a call to action
right now
(11:43):
that somebody on a unified
global front
get some teeth in this? Because
right now, I would suppose most countries at
different levels are all trying to combat this
in their own way,
in their own domain.
But this is a global issue. Global issue.
Correct. And we should learn from the past
from specifically the GDPR,
(12:05):
Global Data Protection
Regulation. Mhmm. We at global level, we didn't
took it seriously until it was imposed and
until it was confirmed. And then companies rushed
to comply with GDPR
data protection.
And the the European Councils has made some
law enforcement.
So we should anticipate,
(12:27):
this trend and work together in advance to
put some common, let's say, principles and values,
to deploy and develop ethical AIs.
What I'd like also to mention
that ethical AI should not be confused with
a checklist. We don't want to create a
checklist
and say, yes. We comply with being transparent
(12:49):
and being respectful. It's not about checklist. Ethical
AI is the process. It's not the end
product of the process.
Every single application, every single time
we work together and develop AI ethical, we
should consider it a case study,
and we should reevaluate
that application
with respect to the principles and values that
(13:12):
we deployed.
For example, face recognition. I am a company,
and I am developing face recognition application.
So I should go to the checklist and
say, yes, it will respect my private
life or not. Instead, what should I say?
Should I evaluate it as a case? Who
are my target audience? Should the public
(13:33):
be able to use this,
face recognition,
or I should limit it to the celebrities?
Mhmm. Or should the government agencies should do
it? And if the government agencies Or or
who's got deep pockets? Right? I mean Deep
pockets. Yes. And even if the governmental agencies
are allowed to use it, are they allowed
to use my personal data? And what type
(13:54):
of biases I should eliminate? Because in when
doing face recognition,
there's a skin tone. The dark, the yellow,
and different skin tones.
So I should my training samples should be
representatives,
so I should remove biases.
And my applications doesn't give me or favor
(14:14):
white skins. Yeah. Yeah. Dark skins. Also should
be considered
and reevaluated
carefully
before
deciding or before opening up this application.
Let's take that a notch further. So I'm
still looking at the GDPR.
So this is a year Yes. EU, European
Union Yes. Product. And I wanna get back
(14:34):
to this, so don't let me forget. But
what you're talking about relates to me in
my history as a broadcaster.
The Federal Communications
Commission
in the United States was built
to help protect people
from media.
That's my generalization
of the FCC.
As a FM radio broadcaster,
(14:56):
we had regulations and stipulations on what could
be said on the radio to protect the
public. Right? But those regulations
were regionally
constructed. Yes. What plays well in New York
City will not play well in Idaho.
You know? So there's regionality
and sensitivities
involved. So
does that call into
(15:18):
this conversation, the idea of having a regional
gatekeeper,
a regional
meaning that there's an interpretive level of the
overarching
controls, the data policies
and enforcement,
to allow for
pockets and regions and cultures to have their
own flavor. Don't get me into politics.
(15:39):
Oh, that's not politics. Come on.
What sparked that was that, as I read
the GDPR
information on the website Yes. The history of
the GDPR
started in 1950
with the European Convention
on Human Rights. Yes. So this has a
deeper level than just data protection
(16:01):
or cyber anything. Yes. It's just human rights.
Yes. Correct. And it should be globalized.
But what is the risk? The risk is
then we customize. We say this is too
generic, and let's customize it to our own
preferences.
And there's a much more customization
for every company.
Every company will say, yeah. Those are good
(16:22):
guidelines, but I have my own values. I
have my own mission. Let me customize it.
And then slowly, what will happen,
we deviate.
And then
when, diversions will happen when we have a
little of deviations,
and then we go into all directions.
We need to take it seriously and work
it at global level. So is that a
(16:42):
UN charter? Does is the UN involved in
this? I I wouldn't recommend the UN, but,
Oh, okay. Alright. So who should own this?
It should be community owned
because
involving end users
because it's all about being transparent
and being truthful to the end users, to
the customers,
to the users.
(17:03):
So the users and the customer should be
involved. And in fact, there's something also that
I'd like to highlight.
Gen AI or machines, they don't make decisions.
They are those who the developers and the
designers and the programmers, they ingest their own,
algorithms
Mhmm. To make decisions.
And this is the phase where the customers
(17:25):
need to be engaged with the developers
and the designers
as much as possible
and explain to them how the machine is
making decisions. For example, if we are taking,
the radio imagery in the medical field. If
we are developing an application
for imagery and
MRI, we should design it in a way
(17:45):
that it will help the doctor to do
better his job, not to replace the doctor.
And the doctor should
understand
how the machine is making decisions
and making the suggestions.
And so we need to to develop it
in a white box approach rather than a
black box. So who owns it? Who who
does all this? It is community owned. We
(18:07):
need multiple perspectives,
everyone to be involved, and it is a
continuous process. It's not let's do it and
forget it.
Develop it and deploy it. It is a
continuous process. So
I'm trying to think of the software development
or web development. There was an organization out
of California.
They did the protocols for HTTPS
(18:28):
or or I can't remember what what their
construction was that aided to web interfaces.
But it was just a bunch of concerned
citizens
working together
to make it happen. You know, it was
an open source Yep. Kind of reality. Yep.
So are we looking that that's what we
need so it's not part of any government?
It's not part of any corporate agenda? It
(18:50):
is just the people with human rights that
want it. Yes. And I am in favor
of a decentralized
model
and distributed models, which is which lead to
the open source community.
And I advocate this kind this is working
mode. Okay. So when when are you gonna
start that? Let's do it tomorrow.
(19:12):
Alright. Alright. Let's go. I'm for it because
I don't see anybody owning it. And if
anybody does stand up to own it, they're
only gonna represent their little piece of the
pie. This has really long and short term
consequences.
Yep. Yep. And, also, we should not forget
the environmental
impact.
So this is more related to compliance
and ethics. But, also, there's second negativity
(19:35):
back to your initial question.
To share with you some facts, let me
bring up my cheat sheet. K. Okay. So
to give you some facts, you know, the
GPT 3, the 3rd GPT 3 Mhmm. In
terms of water consumption,
every 25
to 50 questions, we ask to GPT 3,
the machine or, let's say, the whole cluster
(19:58):
consumes,
one bottle of water
every 25 questions on average.
The water consumption is 1 bottle of water,
around 1 liter.
And the c o two emissions
emitted by the cluster to train GPT 3
is equivalent to 60 flights
between London and New York back and forth.
(20:19):
And the energy consumption,
because those are really,
hungry clusters,
they consume
the for GPT 3, which is 1,000 times
less powerful than GPT 4, it consumed
the power or the electricity
equivalent to 90 households
for 1 year.
So this is a real environmental
(20:43):
impact in terms of water,
power,
and CO 2 emission.
And those are just to for GPT 3.
And now we are talking about GPT 45.
And Microsoft,
as they just published their environmental
and sustainability
report,
their water consumption
spiked
(21:03):
around 25%
year over year from 2002
to to 21 to 22 to 23.
So every year, 25%
increase in water consumption.
So this is a really a serious issue
or consequence to consider.
Those clusters
used to train Gen AI are hungry, are
(21:25):
thirsty,
and they are they pollute.
So we're back to the
birthplace
of a lot of pollution
in the globe
to industrial
revolutions
that have taken place that had huge consequences.
So in parallel to, like,
coal and other industries that made everything
(21:47):
polluted.
So now we're at the point where people
that think, oh, it's on the Internet. Oh,
it's it's somewhere. It it just happens. It's
all magic in this little box here. Mhmm.
There's no connection to any consequence
and or output requirements
or input requirements to the process
because it's not visual. There's no I'm not
(22:08):
shoveling another shovel of coal in this machine
to make this work. Yes. It's just all
magic. It's just it just happens.
So who should be looking at that? Because
this is a real world situation
that is building up and building up as
it it becomes more user friendly and more
user interface is happening,
then that consumption keeps going up.
(22:30):
When will it stop and when will it
actually hurt to where it's news? Where it's
actually covered on the news. Oh, this this
whole
farm underground
that feeds this machine
has consumed
x amount of water. Now the state of
Nevada doesn't have any water. You know, when
is that gonna happen where it becomes a
critical juncture that things have to change?
(22:52):
Well,
I'm not trying to be a pessimist,
but already we have the global warming
we are facing.
And how much actions we are seriously taking
to to stop the global warming. Yeah. And
we are seeing all the natural disasters
from,
the record
temperature
to the flood everywhere,
(23:13):
and we are seeing the consequences, the natural
crisis.
So we don't want to add a new
layer, an extra layer on top of that.
Right. And we should try to start with
education. Universities
have a critical role
When we are teaching
Java or Python at universities,
we should add a course called programming for
(23:35):
sustainability
or developers
sustainable developers.
And we should integrate the notion
of environmental
impact
into our coding and programming applications.
So we should start doing the educations.
Companies, they have their corporate social responsibility,
affinity groups, local communities.
(23:56):
Everyone should tackle it from his own perspective.
And all of us together, we should take
it seriously
and to be energy effective
and to try also to implement to do
some kind of decarbonization.
I know from my company,
we are launching
a lot of programs into being carbon neutral.
And, also, being carbon neutral will help to
(24:18):
compensate
all the energy and the all the CO
two emissions,
emitted from those clusters. So if we put
this in the context of every other Yes.
Advance
that created pollution,
I just search artificial intelligent pollution
because really we're talking about the the environmental
impact in total. Yes. Most of what I
see is how AI is helping pollution. So
(24:41):
it's kinda funny that most of what I
see written is all about, oh, it's all
great. But there is one article. Mhmm. And
this is from The Guardian. So the source
is The Guardian. As the AI industry booms,
what toll will it take on the environment?
Mhmm. And it really looks like they're digging
into
what does this all mean? So they talk
about Amazon data center in, Manassas, Virginia.
(25:04):
I think I just wanna plant enough seeds
out there that people really need to start
Mhmm. Realizing
the world's impact
on this rapid access to information and and
data. Yeah. Just because it is silicon,
at the end, also electronic
components are silicon based. It doesn't mean they
are clean and they are they don't pollute.
(25:25):
So going back on your water, I just
see this one paragraph Yes. From the same
article from The Guardian.
Google became the first this is quote.
Google became the 1st tech giant to publicize
its water usage worldwide,
but provided average figures that concealed important details
about the local impacts of its data centers.
(25:46):
After a protracted
legal battle with the Oregon
City of Dallies,
Dalles, Dales,
d a l l e s, in Oregon,
released data showing that Google data centers use
a quarter
of the town's water supply.
Wow. Huge. This is huge. And I gotta
say, I I've been in the world of
(26:07):
tech and knowledge management as,
not as,
as kind of an advancer, I'll say.
And you're the 2nd person to be on
the show that talked about the cost of
doing all this
AI stuff or the cost of data centers.
Let's just say that AI is not the
the main culprit right here. Right? I mean
(26:27):
Right. Data centers are data centers, and they're
a big footprint. They're a big energy sector,
a big environmental
impact.
And, really, that's the bigger story here is
that AI is just another user of a
data center Correct. That is the part of
the problem. So it's not we're not calling
out AI as the main culprit here. Correct.
Yes. It is a technology that is Yeah.
(26:48):
Heavy,
power consuming. Well, you've given me a lot
to think about.
As I go away with my brain in,
fast mode,
let me ask you this. What's your definition
of knowledge management?
K. Great. Thank you. That's a great question.
I
you know, I'm buffering. I'm trying to say
some compliments.
(27:08):
Yeah. Buffering exactly. It's off you. But, seriously,
let me share with you this story.
Last week, I was contacted by a headhunter,
a Kilometers Post. And while discussing with him,
he told me that also he hired another
person, another Kilometers manager
for a client. And I asked him, okay.
(27:28):
What type of missions you are doing? And
he replied,
the
my client was a banking industry, the finance
industry,
and I needed a knowledge manager
to, unify
the customer support systems because the bank had
multiple branches and multiple functions, and they all
uses multiple and different
(27:49):
customer support systems.
And he hired a knowledge manager
to unify all those systems into 1 system.
And I told him, really? Is that how
you define knowledge management? This is called information
systems,
not knowledge management. And the way I define
and understand knowledge management from day 1 when
I started this career,
(28:09):
it's all about asset knowledge. 90% of our
knowledge is tested inside our head gained from
experience, from practice,
from solving
problems, from being practitioners.
And the job of knowledge manager
is to help, first of all, to create
a space
for, the people, the the engineers, the employees,
(28:31):
to connect together in a safe environment
that will enable them to interact
and to create dialogues and conversations.
And through dialogues, conversations, they will
externalize,
and they will extract their tacit knowledge.
And the role of knowledge manager is to
capture
those tacit knowledge that were externalized,
(28:53):
formalize them, and make them available to the
whole organization.
He's a facilitator.
He's a connector of people
trying to match people together in a safe
environment where we are not persecuted
or intimidated
because we are thinking out loud, and we
are just saying our opinions and our ideas,
how I define knowledge management in terms of,
(29:16):
facilitation
interaction,
getting the people together to exchange their task
of knowledge. So you're looking at it more
as a verb. Knowledge management is a verb
because it's around
an action orientation,
And I'll use the word, you didn't say
this, but as a broker.
You're a broker to the degree where you
see where things should go or you connect
(29:37):
dots. You extract. As you said, you try
to facilitate a better work environment by
developing knowledge for everybody.
Exactly.
And, also, I belong to, a philosophical
school
where it is specifically,
the constructivism
epistemology,
where they consider
(29:58):
knowledge management as a social activity,
and knowledge is co constructed
together when people interact together.
So, fundamentally, it's a social activity.
When people interact together, we can co construct
new knowledge. I went to Indeed
career guide.
So for those in the field of trying
(30:19):
to find a job, I'm sure you know
what Indeed is. Yes. So Indeed career guide
states,
information management
is the collection, storage, management, and maintenance
of data
and other types of information.
Mhmm. It involves the gathering, dissemination,
archiving, and destruction Yes. Of information
in all its forms.
(30:40):
Information management covers the procedures and guidelines organizations
adopt
to manage and communicate information among different individuals,
departments, and stakeholders.
So going back to your guy that was
talking to you about a job,
your viewpoint of what they described that job
was was exactly Yep. Information management, and you
(31:03):
called it on him. So what was his
response?
You know? Or her response?
Yes. Ironically,
the person
considered that I don't understand knowledge management,
and the person didn't want to, to pursue
his recruitment. I said,
that's a win win for both of us.
Yeah. Right. Thanks. Alright. Yeah. You know, it
(31:25):
kinda shows the light on the idea that
a recruiter
knows everything Yep. That they're shopping for. Right?
The second paragraph I wanna wind up on
this information management because it gets more to
your point.
Information management focuses on the level of control
an organization has over the information it produces.
It requires building dedicated information management systems
(31:49):
designed to help the company use its resources.
There are nowhere in that description was any
people involved. There's no people in this. You
know? It's just machines and software. Software. And
let me recall the famous
HP code. If only HP,
news what HP knows, we would be 3
times more productive.
(32:11):
Well, I think you could replace HP with
any other organization's name, and it's the same.
The same.
Well, thank you, sir. It has been a
blast. My pleasure. I can't wait to have
it on again to talk about what's new.
My pleasure, Edwin, and I am was so
glad to have this podcast with you. Any
other great words of wisdom that have bubbled
up that you think you need to share?
(32:32):
It's all about being generous. What I have
learned in those, 10 years,
knowledge sharing, knowledge management is about being generous.
Not only was the way I share my
knowledge or I offer help, but even in
my private life, I'm generous with my family,
with my friends, with my people.
Most probably, I'll be generous enough to share
(32:54):
my knowledge, to help people,
and to say, yeah. I'm here for you
to help. How I can do it? What
I can do to support you?
So are you saying that if anybody out
there is hiring for knowledge management skills, that
should be the only question they have on
the resume
or the interview
is give me some examples of how you've
(33:14):
shared or been generous in your life. Is
that is that really out
skills and aptitude, we can figure that out
later, but
we wanna know at the nucleus of who
you are, is this part of you? If
you invite me to Chick Fil A, you
are the great
candidate to be a knowledge manager.
(33:35):
So I hear empathy in there. There's gotta
be an empathetic
part of the human framework that is always
engaged.
It's more emotional intelligence. Mhmm. We have so
many bright and clever people,
but we need more emotional intelligence, social
relationships.
Is that what we need? Behaviors. It's all
about behaviors. So does AI have emotional intelligence?
(33:58):
Let us check. Let us ask
AI and tell them, do you have emotions?
Alright, my friend. Alright. Happy days. Thank you.
Thank you.
Thank you for joining this extraordinary journey, and
(34:20):
we hope the experiences gained add value to
you and yours.
See you next time at because you need
to know. If you'd like to contact us,
please email
byntk@pioneeredashks.org,
or find us on LinkedIn.
(34:50):
Thank you for listening to because you need
to know, the reference podcast in knowledge management.
My name is Soni Toneme. As an art
administrator,
Because You Need to Know has been my
go to podcast and has helped me hone
my management skills. Please consider sponsoring the podcast
with your business.
Pioneer Knowledge Services welcomes you to the next
because you need to know. I'm your host,
Edwin k Morris. I serve as president and
founder of this organization, and we are thrilled
to offer this educational program. These design conversations
bring you people's experiences
from all over the globe in the field
of knowledge management, nonprofit work, and innovation.
(00:40):
Assalamu alaikum.
Bonjour tout lemons. Salut.
Hello, everyone. Good morning, and welcome to this
podcast.
My name is Rashad Najjar,
and I live in Grenoble, France.
The most interesting thing near me is the
LHC,
Large Hadron Collider. It is the world's largest
(01:03):
and highest energy particle collider.
So this is somehow
scary and somehow a little bit good. So
I'm located on the French Swiss border. I
love to meet new people. That's why I'm
here on the podcast.
I'd like to connect, to share my stories,
listen,
to their stories,
and offer help if they need it. The
(01:25):
things that I have learned,
throughout all these years
is that preparation,
persistence, and patience are keys to success.
Half of the success, I believe, is preparation
and determination.
The topics that I'd like to talk for
hours and hours is knowledge management, obviously.
And I recently wrote and published a report
(01:46):
exactly
analyzing
generative AI use cases
on where and how those capabilities can augment
the learning experience,
and the knowledge management processes, of course. So
this has been my career, 10 years in
this field, organizational learning and knowledge management. The
last book that I read that I'd like
to share it with you,
is really very interesting book. It's called the
(02:09):
big on by Mariana Mazzucato.
She's a professor in
innovation economics at UCL.
Mainly, the authors,
are arguing
the myth that the consulting
industries always add value to the economy,
And they support this argument
or this counterargument
(02:29):
very brilliantly.
They said innovation should come from within, from
the inside out,
investing in the collective intelligence of the people
and empowering them to pop up ideas and
to build on the collective elaboration of those
ideas and do innovation.
Is AI gonna go away? Is artificial intelligence
(02:51):
just a fad? It's been here since
1983.
It will continue to be. But like any
other technology, there's a hype curve. It will
go and up based on the drivers, the
technology,
overall circumstances. So let's demystify what AI is.
Yes. I think there's a lot of folks
that hear it and they see it everywhere
(03:12):
now on every social media platform or every
entry point they have to
news and media.
What is it? Why should they care? Perfect.
And, I like to demystify it.
There's a misconception.
AI, artificial intelligence,
is intelligent.
This is a misconception. It's not intelligent.
(03:33):
We are really at the extreme
computing edges.
So we have very powerful computing machines that
are doing enormous computing.
Other hand,
let me ask you this question. True. Can
a machine wake up in the morning and
says, what should I do today or what
I should learn today? Well, that's self consciousness.
Right? That's a self
(03:54):
driven reality of purpose and Yes. Maybe someday
we'll get there. But right now, you're saying
that ability is not what artificial intelligence is.
Yes. And most risky thing is our expectations.
So we are expecting
from AI much more Yeah. Than AI is
capable to do. So just just so we
can add more clarity to this, so I
(04:16):
looked up Merriam Webster's definition of intelligence. Yes.
So artificial intelligence, as you say, is a
kind of a misnomer because it's not really
that yet. Yes. Intelligence in the first definition
is the ability to learn or understand
or deal with new or trying situations.
I apparently have never read that definition before
(04:37):
because that's pretty cool. So that is definitely
a human trait,
for some humans, I'll say. But that is
a humanistic type trait.
You know, let me share also with you
something. I did a PhD studies. And during
research, we are always and most of the
situations,
we are confronted in ambiguous
(04:58):
situations.
We don't know what to do.
And there's no reasoning
or there's no, let's say, evidence
if we are going to choose this correctly
or not.
The only thing that drive us toward a
direction is our gut, something inside us. Does
the machine have a gut? I'm just questioning.
(05:19):
I don't know the answer. I'm questioning. That
goes back to the 1800 with the original
concept of the thinking machine. Yes. That this
idea that we can mechanize
comprehension and understanding
has been around a long time. Definitely. And
let me give you one more thing. There's
shared GPT gains the mainstream attention.
(05:39):
But do you know that, the training because,
you know, AI is a subfield of machine
learning, and ChargeGPT
is a subfield of deep machine learning. And
deep machine learning, there's 2 phases. The first
phase is the training. This is where the
machine learn. And the second phase is the
fine tuning when we tune the machine to
(06:01):
adapt to the outcomes so we have an
accurate outcome. For example, if we are applying
this, AI
in the field of cosmetics,
So we need to teach the machine
the specific lexicon or the specific
knowledge about cosmetics. What I'd like to share,
chat GPT
and all other large language models,
(06:24):
they use something called ghost army. So the
ghost army,
those are workers
who do
very millennial
tasks like labeling an object. This is a
car. This is a fish. They say, yes.
This is a car. So the machine doesn't
know what's true, what's this is a ghost
worker, and they are paid $2
(06:45):
to $9
per hour. And they used a massive army
of ghost workers
to train or or to validate the outcome
of the machine and to say, this is
true or not or to label the objects.
So now how can a machine be intelligent
and we are still Right. You're still in
need of the human perception and the human
(07:07):
expertise. Well, you're the way you just explained
it, it can't be done. Unless,
we get to the bioengineered
level of wetware,
which we can program
hardware software
by our own synaptics
structure.
Maybe. Yes.
Then you've got embedded humanity in a machine.
Well, this is a call for all our
(07:29):
entrepreneurs
to take this idea.
The Rise of the Machines is the article
I'm looking at right now from Smithsonian
Libraries. Yes. And we talk about how this
whole idea
that spurred off the industrial revolution
became this idea of self
(07:49):
manipulating
machinery.
Yes. And it's just bizarre to think that
this was back this these concepts go back
to the 1800
that people have been trying to
make machines,
so called think, but at least make a
cognition
differential
in their mechanical approach to whatever the job
(08:09):
was. And a lot of these were very
labor intensive
work for these machines.
So if we go forward Yes. So this
is 1800s
that this whole idea
of thinking machines, and that's just the general
terminology here.
1800s,
thinking machines.
2000 twenties,
(08:31):
AI is everything, but it's not really intelligent.
What's a 100 years from now, my friend?
Well, I wish I would know. You're a
pretty smart fella, so I think you can
conjecture, please. Yeah. I think we will continue
to advance in computing. That's true. Previously, during
the 19 eighties, 19 nineties,
(08:52):
Moore's Law is, his Intel CEO. He said
the number of transistors
that we can put on the same area,
fixed area, will doubles will doubles every 2
years.
However, what we saw with the Gen AI,
since
2013,
the computing power has doubled every 3 to
(09:14):
4 months.
Yes. Definitely, when computing power increases,
it will unlock for us more a lot
of, new capabilities.
And what I can foresee is really still
within the realm of pattern recognition
for complex pattern recognition. For example, if we
are studying
(09:35):
the drug effect,
if a drug if I am on a
treatment and I am taking a a drug
and I have my personal health record and
personal health characteristics,
am I on a diet or not, and
what are other drugs I'm taking? So this
kind of interactions
between
the drugs
(09:56):
or my personal health records, those are very
complex patterns. So AI here can help in
extracting or identifying
relationships
between all those different variables
and recommend
the best scenario
and the best pattern to follow so we
can have, I have can have a treatment
without any kind of Mhmm. Side effects. Well,
(10:19):
that's a great use case, and I think
that shows the value pretty easily to the
majority of people Yes. Because it's something we
all have to deal with. So it's good
to know that that use case is in
the positive. But is there a negative side
to it? Oh. Is can it be criminal
yeah. Okay. Alright. Alright. Let's let's hear about
the negative side to all of that. Yes.
(10:41):
Well, I can think of 2, negative
consequences. The first one is about,
security,
privacy
Mhmm. And personal data. How much I am
willing to open up my personal health record,
which are highly confidential information.
So maybe some commercial companies will leverage those
(11:02):
data
and build some commercial activities or some or
make some profit. So this is very critical
topics related to ethics.
How we can build ethical AI applications
that are not harmful,
that respect my personal
privacy
without being judgmental
(11:22):
or being intrusive into my life. This is
very big and vast topic and need the
involvement of multiple stakeholders, multiple perspective from legal,
from society,
from health personnel, and every single decision maker.
So is this a call to action? Do
you see this as a call to action
right now
(11:43):
that somebody on a unified
global front
get some teeth in this? Because
right now, I would suppose most countries at
different levels are all trying to combat this
in their own way,
in their own domain.
But this is a global issue. Global issue.
Correct. And we should learn from the past
from specifically the GDPR,
(12:05):
Global Data Protection
Regulation. Mhmm. We at global level, we didn't
took it seriously until it was imposed and
until it was confirmed. And then companies rushed
to comply with GDPR
data protection.
And the the European Councils has made some
law enforcement.
So we should anticipate,
(12:27):
this trend and work together in advance to
put some common, let's say, principles and values,
to deploy and develop ethical AIs.
What I'd like also to mention
that ethical AI should not be confused with
a checklist. We don't want to create a
checklist
and say, yes. We comply with being transparent
(12:49):
and being respectful. It's not about checklist. Ethical
AI is the process. It's not the end
product of the process.
Every single application, every single time
we work together and develop AI ethical, we
should consider it a case study,
and we should reevaluate
that application
with respect to the principles and values that
(13:12):
we deployed.
For example, face recognition. I am a company,
and I am developing face recognition application.
So I should go to the checklist and
say, yes, it will respect my private
life or not. Instead, what should I say?
Should I evaluate it as a case? Who
are my target audience? Should the public
(13:33):
be able to use this,
face recognition,
or I should limit it to the celebrities?
Mhmm. Or should the government agencies should do
it? And if the government agencies Or or
who's got deep pockets? Right? I mean Deep
pockets. Yes. And even if the governmental agencies
are allowed to use it, are they allowed
to use my personal data? And what type
(13:54):
of biases I should eliminate? Because in when
doing face recognition,
there's a skin tone. The dark, the yellow,
and different skin tones.
So I should my training samples should be
representatives,
so I should remove biases.
And my applications doesn't give me or favor
(14:14):
white skins. Yeah. Yeah. Dark skins. Also should
be considered
and reevaluated
carefully
before
deciding or before opening up this application.
Let's take that a notch further. So I'm
still looking at the GDPR.
So this is a year Yes. EU, European
Union Yes. Product. And I wanna get back
(14:34):
to this, so don't let me forget. But
what you're talking about relates to me in
my history as a broadcaster.
The Federal Communications
Commission
in the United States was built
to help protect people
from media.
That's my generalization
of the FCC.
As a FM radio broadcaster,
(14:56):
we had regulations and stipulations on what could
be said on the radio to protect the
public. Right? But those regulations
were regionally
constructed. Yes. What plays well in New York
City will not play well in Idaho.
You know? So there's regionality
and sensitivities
involved. So
does that call into
(15:18):
this conversation, the idea of having a regional
gatekeeper,
a regional
meaning that there's an interpretive level of the
overarching
controls, the data policies
and enforcement,
to allow for
pockets and regions and cultures to have their
own flavor. Don't get me into politics.
(15:39):
Oh, that's not politics. Come on.
What sparked that was that, as I read
the GDPR
information on the website Yes. The history of
the GDPR
started in 1950
with the European Convention
on Human Rights. Yes. So this has a
deeper level than just data protection
(16:01):
or cyber anything. Yes. It's just human rights.
Yes. Correct. And it should be globalized.
But what is the risk? The risk is
then we customize. We say this is too
generic, and let's customize it to our own
preferences.
And there's a much more customization
for every company.
Every company will say, yeah. Those are good
(16:22):
guidelines, but I have my own values. I
have my own mission. Let me customize it.
And then slowly, what will happen,
we deviate.
And then
when, diversions will happen when we have a
little of deviations,
and then we go into all directions.
We need to take it seriously and work
it at global level. So is that a
(16:42):
UN charter? Does is the UN involved in
this? I I wouldn't recommend the UN, but,
Oh, okay. Alright. So who should own this?
It should be community owned
because
involving end users
because it's all about being transparent
and being truthful to the end users, to
the customers,
to the users.
(17:03):
So the users and the customer should be
involved. And in fact, there's something also that
I'd like to highlight.
Gen AI or machines, they don't make decisions.
They are those who the developers and the
designers and the programmers, they ingest their own,
algorithms
Mhmm. To make decisions.
And this is the phase where the customers
(17:25):
need to be engaged with the developers
and the designers
as much as possible
and explain to them how the machine is
making decisions. For example, if we are taking,
the radio imagery in the medical field. If
we are developing an application
for imagery and
MRI, we should design it in a way
(17:45):
that it will help the doctor to do
better his job, not to replace the doctor.
And the doctor should
understand
how the machine is making decisions
and making the suggestions.
And so we need to to develop it
in a white box approach rather than a
black box. So who owns it? Who who
does all this? It is community owned. We
(18:07):
need multiple perspectives,
everyone to be involved, and it is a
continuous process. It's not let's do it and
forget it.
Develop it and deploy it. It is a
continuous process. So
I'm trying to think of the software development
or web development. There was an organization out
of California.
They did the protocols for HTTPS
(18:28):
or or I can't remember what what their
construction was that aided to web interfaces.
But it was just a bunch of concerned
citizens
working together
to make it happen. You know, it was
an open source Yep. Kind of reality. Yep.
So are we looking that that's what we
need so it's not part of any government?
It's not part of any corporate agenda? It
(18:50):
is just the people with human rights that
want it. Yes. And I am in favor
of a decentralized
model
and distributed models, which is which lead to
the open source community.
And I advocate this kind this is working
mode. Okay. So when when are you gonna
start that? Let's do it tomorrow.
(19:12):
Alright. Alright. Let's go. I'm for it because
I don't see anybody owning it. And if
anybody does stand up to own it, they're
only gonna represent their little piece of the
pie. This has really long and short term
consequences.
Yep. Yep. And, also, we should not forget
the environmental
impact.
So this is more related to compliance
and ethics. But, also, there's second negativity
(19:35):
back to your initial question.
To share with you some facts, let me
bring up my cheat sheet. K. Okay. So
to give you some facts, you know, the
GPT 3, the 3rd GPT 3 Mhmm. In
terms of water consumption,
every 25
to 50 questions, we ask to GPT 3,
the machine or, let's say, the whole cluster
(19:58):
consumes,
one bottle of water
every 25 questions on average.
The water consumption is 1 bottle of water,
around 1 liter.
And the c o two emissions
emitted by the cluster to train GPT 3
is equivalent to 60 flights
between London and New York back and forth.
(20:19):
And the energy consumption,
because those are really,
hungry clusters,
they consume
the for GPT 3, which is 1,000 times
less powerful than GPT 4, it consumed
the power or the electricity
equivalent to 90 households
for 1 year.
So this is a real environmental
(20:43):
impact in terms of water,
power,
and CO 2 emission.
And those are just to for GPT 3.
And now we are talking about GPT 45.
And Microsoft,
as they just published their environmental
and sustainability
report,
their water consumption
spiked
(21:03):
around 25%
year over year from 2002
to to 21 to 22 to 23.
So every year, 25%
increase in water consumption.
So this is a really a serious issue
or consequence to consider.
Those clusters
used to train Gen AI are hungry, are
(21:25):
thirsty,
and they are they pollute.
So we're back to the
birthplace
of a lot of pollution
in the globe
to industrial
revolutions
that have taken place that had huge consequences.
So in parallel to, like,
coal and other industries that made everything
(21:47):
polluted.
So now we're at the point where people
that think, oh, it's on the Internet. Oh,
it's it's somewhere. It it just happens. It's
all magic in this little box here. Mhmm.
There's no connection to any consequence
and or output requirements
or input requirements to the process
because it's not visual. There's no I'm not
(22:08):
shoveling another shovel of coal in this machine
to make this work. Yes. It's just all
magic. It's just it just happens.
So who should be looking at that? Because
this is a real world situation
that is building up and building up as
it it becomes more user friendly and more
user interface is happening,
then that consumption keeps going up.
(22:30):
When will it stop and when will it
actually hurt to where it's news? Where it's
actually covered on the news. Oh, this this
whole
farm underground
that feeds this machine
has consumed
x amount of water. Now the state of
Nevada doesn't have any water. You know, when
is that gonna happen where it becomes a
critical juncture that things have to change?
(22:52):
Well,
I'm not trying to be a pessimist,
but already we have the global warming
we are facing.
And how much actions we are seriously taking
to to stop the global warming. Yeah. And
we are seeing all the natural disasters
from,
the record
temperature
to the flood everywhere,
(23:13):
and we are seeing the consequences, the natural
crisis.
So we don't want to add a new
layer, an extra layer on top of that.
Right. And we should try to start with
education. Universities
have a critical role
When we are teaching
Java or Python at universities,
we should add a course called programming for
(23:35):
sustainability
or developers
sustainable developers.
And we should integrate the notion
of environmental
impact
into our coding and programming applications.
So we should start doing the educations.
Companies, they have their corporate social responsibility,
affinity groups, local communities.
(23:56):
Everyone should tackle it from his own perspective.
And all of us together, we should take
it seriously
and to be energy effective
and to try also to implement to do
some kind of decarbonization.
I know from my company,
we are launching
a lot of programs into being carbon neutral.
And, also, being carbon neutral will help to
(24:18):
compensate
all the energy and the all the CO
two emissions,
emitted from those clusters. So if we put
this in the context of every other Yes.
Advance
that created pollution,
I just search artificial intelligent pollution
because really we're talking about the the environmental
impact in total. Yes. Most of what I
see is how AI is helping pollution. So
(24:41):
it's kinda funny that most of what I
see written is all about, oh, it's all
great. But there is one article. Mhmm. And
this is from The Guardian. So the source
is The Guardian. As the AI industry booms,
what toll will it take on the environment?
Mhmm. And it really looks like they're digging
into
what does this all mean? So they talk
about Amazon data center in, Manassas, Virginia.
(25:04):
I think I just wanna plant enough seeds
out there that people really need to start
Mhmm. Realizing
the world's impact
on this rapid access to information and and
data. Yeah. Just because it is silicon,
at the end, also electronic
components are silicon based. It doesn't mean they
are clean and they are they don't pollute.
(25:25):
So going back on your water, I just
see this one paragraph Yes. From the same
article from The Guardian.
Google became the first this is quote.
Google became the 1st tech giant to publicize
its water usage worldwide,
but provided average figures that concealed important details
about the local impacts of its data centers.
(25:46):
After a protracted
legal battle with the Oregon
City of Dallies,
Dalles, Dales,
d a l l e s, in Oregon,
released data showing that Google data centers use
a quarter
of the town's water supply.
Wow. Huge. This is huge. And I gotta
say, I I've been in the world of
(26:07):
tech and knowledge management as,
not as,
as kind of an advancer, I'll say.
And you're the 2nd person to be on
the show that talked about the cost of
doing all this
AI stuff or the cost of data centers.
Let's just say that AI is not the
the main culprit right here. Right? I mean
(26:27):
Right. Data centers are data centers, and they're
a big footprint. They're a big energy sector,
a big environmental
impact.
And, really, that's the bigger story here is
that AI is just another user of a
data center Correct. That is the part of
the problem. So it's not we're not calling
out AI as the main culprit here. Correct.
Yes. It is a technology that is Yeah.
(26:48):
Heavy,
power consuming. Well, you've given me a lot
to think about.
As I go away with my brain in,
fast mode,
let me ask you this. What's your definition
of knowledge management?
K. Great. Thank you. That's a great question.
I
you know, I'm buffering. I'm trying to say
some compliments.
(27:08):
Yeah. Buffering exactly. It's off you. But, seriously,
let me share with you this story.
Last week, I was contacted by a headhunter,
a Kilometers Post. And while discussing with him,
he told me that also he hired another
person, another Kilometers manager
for a client. And I asked him, okay.
(27:28):
What type of missions you are doing? And
he replied,
the
my client was a banking industry, the finance
industry,
and I needed a knowledge manager
to, unify
the customer support systems because the bank had
multiple branches and multiple functions, and they all
uses multiple and different
(27:49):
customer support systems.
And he hired a knowledge manager
to unify all those systems into 1 system.
And I told him, really? Is that how
you define knowledge management? This is called information
systems,
not knowledge management. And the way I define
and understand knowledge management from day 1 when
I started this career,
(28:09):
it's all about asset knowledge. 90% of our
knowledge is tested inside our head gained from
experience, from practice,
from solving
problems, from being practitioners.
And the job of knowledge manager
is to help, first of all, to create
a space
for, the people, the the engineers, the employees,
(28:31):
to connect together in a safe environment
that will enable them to interact
and to create dialogues and conversations.
And through dialogues, conversations, they will
externalize,
and they will extract their tacit knowledge.
And the role of knowledge manager is to
capture
those tacit knowledge that were externalized,
(28:53):
formalize them, and make them available to the
whole organization.
He's a facilitator.
He's a connector of people
trying to match people together in a safe
environment where we are not persecuted
or intimidated
because we are thinking out loud, and we
are just saying our opinions and our ideas,
how I define knowledge management in terms of,
(29:16):
facilitation
interaction,
getting the people together to exchange their task
of knowledge. So you're looking at it more
as a verb. Knowledge management is a verb
because it's around
an action orientation,
And I'll use the word, you didn't say
this, but as a broker.
You're a broker to the degree where you
see where things should go or you connect
(29:37):
dots. You extract. As you said, you try
to facilitate a better work environment by
developing knowledge for everybody.
Exactly.
And, also, I belong to, a philosophical
school
where it is specifically,
the constructivism
epistemology,
where they consider
(29:58):
knowledge management as a social activity,
and knowledge is co constructed
together when people interact together.
So, fundamentally, it's a social activity.
When people interact together, we can co construct
new knowledge. I went to Indeed
career guide.
So for those in the field of trying
(30:19):
to find a job, I'm sure you know
what Indeed is. Yes. So Indeed career guide
states,
information management
is the collection, storage, management, and maintenance
of data
and other types of information.
Mhmm. It involves the gathering, dissemination,
archiving, and destruction Yes. Of information
in all its forms.
(30:40):
Information management covers the procedures and guidelines organizations
adopt
to manage and communicate information among different individuals,
departments, and stakeholders.
So going back to your guy that was
talking to you about a job,
your viewpoint of what they described that job
was was exactly Yep. Information management, and you
(31:03):
called it on him. So what was his
response?
You know? Or her response?
Yes. Ironically,
the person
considered that I don't understand knowledge management,
and the person didn't want to, to pursue
his recruitment. I said,
that's a win win for both of us.
Yeah. Right. Thanks. Alright. Yeah. You know, it
(31:25):
kinda shows the light on the idea that
a recruiter
knows everything Yep. That they're shopping for. Right?
The second paragraph I wanna wind up on
this information management because it gets more to
your point.
Information management focuses on the level of control
an organization has over the information it produces.
It requires building dedicated information management systems
(31:49):
designed to help the company use its resources.
There are nowhere in that description was any
people involved. There's no people in this. You
know? It's just machines and software. Software. And
let me recall the famous
HP code. If only HP,
news what HP knows, we would be 3
times more productive.
(32:11):
Well, I think you could replace HP with
any other organization's name, and it's the same.
The same.
Well, thank you, sir. It has been a
blast. My pleasure. I can't wait to have
it on again to talk about what's new.
My pleasure, Edwin, and I am was so
glad to have this podcast with you. Any
other great words of wisdom that have bubbled
up that you think you need to share?
(32:32):
It's all about being generous. What I have
learned in those, 10 years,
knowledge sharing, knowledge management is about being generous.
Not only was the way I share my
knowledge or I offer help, but even in
my private life, I'm generous with my family,
with my friends, with my people.
Most probably, I'll be generous enough to share
(32:54):
my knowledge, to help people,
and to say, yeah. I'm here for you
to help. How I can do it? What
I can do to support you?
So are you saying that if anybody out
there is hiring for knowledge management skills, that
should be the only question they have on
the resume
or the interview
is give me some examples of how you've
(33:14):
shared or been generous in your life. Is
that is that really out
skills and aptitude, we can figure that out
later, but
we wanna know at the nucleus of who
you are, is this part of you? If
you invite me to Chick Fil A, you
are the great
candidate to be a knowledge manager.
(33:35):
So I hear empathy in there. There's gotta
be an empathetic
part of the human framework that is always
engaged.
It's more emotional intelligence. Mhmm. We have so
many bright and clever people,
but we need more emotional intelligence, social
relationships.
Is that what we need? Behaviors. It's all
about behaviors. So does AI have emotional intelligence?
(33:58):
Let us check. Let us ask
AI and tell them, do you have emotions?
Alright, my friend. Alright. Happy days. Thank you.
Thank you.
Thank you for joining this extraordinary journey, and
(34:20):
we hope the experiences gained add value to
you and yours.
See you next time at because you need
to know. If you'd like to contact us,
please email
byntk@pioneeredashks.org,
or find us on LinkedIn.
(34:50):
Thank you for listening to because you need
to know, the reference podcast in knowledge management.
My name is Soni Toneme. As an art
administrator,
Because You Need to Know has been my
go to podcast and has helped me hone
my management skills. Please consider sponsoring the podcast
with your business.
Popular Podcasts
24/7 News: The Latest
The latest news in 4 minutes updated every hour, every day.
Therapy Gecko
An unlicensed lizard psychologist travels the universe talking to strangers about absolutely nothing. TO CALL THE GECKO: follow me on https://www.twitch.tv/lyleforever to get a notification for when I am taking calls. I am usually live Mondays, Wednesdays, and Fridays but lately a lot of other times too. I am a gecko.
The Joe Rogan Experience
The official podcast of comedian Joe Rogan.