December 10, 2024 • 46 mins
Welcome to "AI or Not," the podcast where we explore the intersection of digital transformation and real-world wisdom, hosted by the accomplished Pamela Isom. With over 25 years of experience guiding leaders in corporate, public, and private sectors, Pamela, the CEO and Founder of IsAdvice & Consulting LLC, is a veteran in successfully navigating the complex realms of artificial intelligence, innovation, cyber issues, governance, data management, and ethical decision-making.
What if you could harness the power of AI to address global climate change? Join us as we explore this compelling question with Bill Wright, Founder and Chair, Enterprise Neurosystem AI/ML open-source community. Bill shares his inspiring journey from the world of advertising artistry to becoming a trailblazer in tech, offering insights into how humility and curiosity transformed his career during the internet boom. Now, he's channeling his passion into leveraging AI for real-time planetary analysis, envisioning a network that integrates diverse data sources for a comprehensive understanding of climate change's patterns and impacts.
Bill's narrative is a testament to the potential of AI in fostering global equity. Through the Enterprise Neurosystems program, volunteers from around the globe collaborate to innovate for the greater good, using the UN as a platform for international cooperation. Our conversation highlights AI advancements in Tanzania, where technology is empowering farmers with irrigation mapping and early warning systems, supported by UNFCCC and CTCN. These efforts not only enhance food security but also contribute to the conservation of ecosystems and wildlife, emphasizing how open-source technology can promote accessibility and drive equitable solutions.
We also explore the fascinating interplay between AI and nature, delving into projects that investigate the communication patterns of bees, potentially unlocking an "internet for nature." Our discussion underscores the importance of data protection and privacy in AI, with insights into frameworks like GDPR and initiatives aimed at minimizing risks. Throughout the episode, we celebrate the power of collaboration, curiosity, and openness to learning, reminding us all that innovation and responsibility can lead to transformative changes for our planet.
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Episode Transcript
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Pamela Isom (00:22):
This podcast is for informational purposes only.
Personal views and opinionsexpressed by our podcast guests
are their own and not legaladvice, neither health tax, nor
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Guest views may not be those ofthe host.
(00:50):
Hello and welcome to AI or Not,the podcast where business
leaders from around the globeshare wisdom and insights that
are needed now to address issuesand guide success in your
artificial intelligence anddigital transformation journey.
I'm Pamela Isom and I'm yourpodcast host.
We have a unique and specialguest with us today, and that is
(01:15):
Bill Wright.
Bill is founder and chair ofthe Enterprise Neurosystem open
source community and I certainlyappreciate teaming with Bill.
Bill, you and I have alwaysdone some things together, and I
remember the AIM for ClimateConference that I was able to
(01:36):
attend thanks to you, and thatwas really good.
I've maintained contact withfolks, by the way, and kept some
activities going on, but what awonderful program.
So, ill, welcome to AI or Not.
Bill Wright (01:51):
Oh, thank you so much for having me.
I really appreciate this, thankyou.
Pamela Isom (01:55):
So you're welcome.
As we get started here, willyou please tell me more about
yourself, your career journey,talk about what drove your
decision to establish theEnterprise Neurosystem open
source community.
Go for it.
Bill Wright (02:10):
Oh, thank you.
I have a very unusualbackground going into tech,
probably a little unconventional, but that is kind of the story
of my career arc, so I guess Ican go into that a little bit.
I started out after going tocollege as an artist.
I was working at advertisingagencies and I was drawing
storyboards and I love comicbook art.
(02:31):
That was my thing.
It was pretty classic.
For the people that know me itdidn't surprise them at all.
I was a huge comic book fan andI love to draw, I love to do
the art.
And after a few years I wasworking freelance for
advertising agencies in SanFrancisco.
But I always had a very strongsciences background.
I had an arts, I had kind ofthat arts and sciences focus, I
(02:51):
guess you could say and focusmore on astronomy and dabbling
in physics and other areas.
And one day my brother who wasworking for a computer company
called me up and he said youknow, I know you're freelancing
and doing these kind ofinteresting projects, but would
you be intrigued if you cameover and you started to work at
my at this it firm that I'mworking for and I like as a
(03:13):
contractor and you would helppeople like reach out to them
and do different kinds ofinteractions to get them to sign
contracts and renewals, and Iwas like, yeah, I'd be happy to
do that.
That sounds like a lot of fun.
And so I started out and didthat for a few years and then
that led to another job andanother job, and another job,
and then ended up having afull-blown career in IT.
(03:33):
And this was during theinternet boom.
So I'm dating myself with thatas I wave my cane in the air,
you know.
But I think what's great aboutit is it really gave me a trial
by fire.
It was like the classic firehose everybody talks about.
I mean, the internet boom wasreally spectacular and it was
both an amazing ascendance andan incredible fall for the
(03:55):
industry.
And I was just watching thewhole thing from the sidelines,
like literally with a front rowseat.
It was fascinating and itreally caught my attention and
I've always been pretty diligentabout learning and about being
curious and taking notes.
I get that probably from, I'dsay, my brothers, who are both
very much the same way, and Iconstantly would listen to
(04:17):
presentations.
I would sit down with thearchitects and the CTOs after
the meetings and I would be like, well, tell me about this, tell
me about that, how do thepieces work together, how does
this work?
And the willingness to be ofservice and the willingness to
be humble or have that humilityto ask those questions is kind
of the key to success, I think,in this industry.
And so I just kept doing thatover and over again and then
(04:40):
eventually get more into thestrategy side, the, I guess you
could say, looking for newtechnology movements that might
be emerging for companies,something I did at a couple of
different firms, and soeventually one of the movements
(05:00):
I targeted and this is gettinginto how I am doing what I'm was
in the climate world.
There were a lot of differentprojects that were either
competing in different countries, different satellite
organizations For example, youhave the European Space Agency,
you have NOAA and NASA, and youlook at all of them and they all
collaborate and everybody knowseach other and in some ways
(05:21):
they do interact and they dointerconnect.
And in some ways they dointeract and they do
interconnect.
But what was missing to me inmy role and where I thought this
was going to congeal at somepoint, was what if there was an
over-the-top network for theplanet where every external
project could plug in and thenbasically share their data in
whatever fashion they decide.
(05:41):
It can be completely open.
It can be just results of thedata that are resident in the
country, so they don't have toworry about data privacy etc.
Just like the outputs, we'retraining AI models in that
country and then sending themout.
There are all sorts of differentthings you can do, but in
essence it was.
There is no common framework foreverybody to plug in and then
apply AI on the back end to lookfor the patterns across the
(06:04):
planet in real time, and notjust from a certain subset of
satellites, but from the entirepicture.
So, atmospheric projects,oceanic projects, the one thing
that's missing really are thenature-based sensors in
different populations in nature,like beehives and mycorrhizal
networks and mussel farms, whichare usually in the mouths of
(06:25):
rivers, right by humansettlements where all the
pollution comes through.
So there are all theseinteresting species that are
really alarm bells for climatechange and they really give you
a sense of where the impacts mayarise in the future.
And if you took all that dataand you plugged it all together
with AI on the back end,grinding those patterns over a
(06:46):
24-7 period, just think aboutwhat you could discover, and I
think there would be newdiscoveries there, new
recommendations for coursecorrection.
A lot of exciting things couldtake place.
So that's my journey and that'swhere I am today.
Pamela Isom (06:59):
Is that what caused you to get interested in
artificial intelligence?
Bill Wright (07:04):
Artificial.
It's funny because AI is it's avery broad term, to be honest,
and, as you're well aware,because I know you're one of
those kind of very prominentexperts out there, I worked in
business intelligence for acompany a number of years ago
maybe 15 years ago and therewere a lot of vendors out there
very prominent vendors that hadthese linear algebra
(07:24):
applications that wouldbasically track different
business metrics and give youpredictive analytics, and it was
a very interesting area ofscience or data science, and so
I used to actually work with onecompany to get those
applications virtualized and theidea was to put them into these
virtualized environments andthen understanding their
behavior and their performance,and I learned some really
(07:46):
valuable lessons as part of thatprocess.
I remember going to SAS, thefamous company in Cary, north
Carolina, and meeting with oneof their VPs technology and it
was a very funny conversationbecause I was early to learning
about data sciences and I thinkit was like a good 15 years ago
and I sat down and we had aconversation and I was really
enthusiastic about virtualizingall their workloads and this is
(08:09):
an important point to make.
That's why I'm spending time onit.
He looked at me and he kind ofsmiled and he said Bill, you're
a great guy, but you don'tunderstand anything about what
we're doing.
And I looked at him and Ismiled and I said actually I
don't, that's why I'm here, sowhy don't you tell me?
And he laughed and he said well,when you take our applications
and you put them in a containeror virtualized environment,
(08:31):
which is really just putting aparameter around an application
so it can operate with a subsetof resources in a server there's
a long explanation behind thatand I won't go into that.
But really what it was wascreating a encapsulation of that
software application that couldreside with other operating
systems and softwareapplications in one single
(08:51):
server, which really hadn't beendone before.
And he said well, the problemwith that, bill, is that the
minute you load our softwareonto a server and this is what
he said at the time it'sprobably not the case today,
given the way the industry hasevolved, but I'd have to look
into that.
He said well, we actually turnon the server when you load our
application and it's running allthe time.
(09:12):
All the resources are activeand running and it's programmed
and designed in that way forcertain parameters.
And if you take our resourcesin that server and you subdivide
them into a little tiny box,one of many in that server,
you're going to really impactour performance and that's how
it behaves and it's reallyinteresting.
(09:33):
I didn't realize that there wasthat whole behavioral and
resource subset you really hadto pay attention to, and that
was a very valuable lesson atthat time for a younger person,
just kind of getting in thebusiness, and so it kind of all
these different experiences kindof came together to create what
really is the enterpriseneurosystem today, with the help
of all of our members.
(09:54):
I mean, I'm very blessed tohave some incredible, incredible
contributors there and you know, all the way from marketing all
the way into you know, extremedata science experts.
What's neat about that is wehave to look at the overall AI
environment in a large-scalenetwork like that, in terms of
the behavior of the applications, how they interact with one
(10:14):
another.
It's almost like creating anecology in some respects.
And I keep drawing the parallelsback to biology, because really
, what is a mobile network thana neural system?
It extends out.
You have these nodes, drawingthe parallels back to biology,
because really, what is a mobilenetwork than a neuro system?
It extends out.
You have these nodes, they'revery, you know, sensitive.
They can tell you your, yourhealth information.
You know, on a mobile phonethere's ai on most mobile phones
(10:36):
today, anyway, at least all theones that are being sold and so
, when you think about it, it'sa network of all these different
sensors, just like different,like senses in the human body,
that really collect andaggregate all that data and then
you can use it creatively andproductively in different ways.
Pamela Isom (11:00):
Right that you were interested, regardless of what
it was called.
You were interested in neuralnetwork type of capability and
virtualization and you were atthe forefront.
So I love the concept oflooking at the world from a
(11:21):
common lens, right which is whatyou described so that we can
understand global patterns.
And that is how we ended upworking together, because I
liked what Ian is all about theenterprise neural system and I
wanted.
I believe in equity, as you do,and I'm trying to figure out
what can be done and what can wedo to drive and fuel global
(11:45):
equity, and I think AI is one ofthe tools and capabilities.
That whole ecosystem, the wholeAI ecosystem it seems just
right for driving and fuelingequity from a global perspective
.
So that's how we ended upworking together and I'm so glad
that we did and I'm so gladthat you're here today.
I want to talk some more aboutyour program.
(12:09):
So I said all that to lead intoyour program, but what I really
love is what you said aboutthat whole bringing the nations
together and providing nationswith the tools that are needed
to be efficient and looking atit from that global perspective.
So can you just tell me alittle bit more about the EM
(12:32):
program, enterprise Neurosystemsprogram, where we are today and
what do you see as some nextsteps.
Bill Wright (12:40):
It's.
I'll take a second to go intothat and thank you so much.
The enterprise neurosystem nowis turning into something pretty
unique.
It's an open source group offolks from the private sector
largely, but also academia andgovernment and some different
government agencies, but it'sactually I think we've got about
(13:02):
18 or 19 different countriesnow of folks from those
countries.
We're all volunteers, sonobody's getting paid, and
that's been both.
You know, something for us tonavigate.
That's been tough, but alsoit's really been core to our
mission, because everybody has aday job, everybody does
something for a living or, ifthey have time between you know
their jobs, they can come in andwork on it, you know, more
(13:23):
diligently and then they go backout into the private sector
again.
However, that works, I thinkwhat we're answering is a need
for people to want to expresstheir creativity and to do
something good for the world,and I think yeah, like the
beehive right.
Pamela Isom (13:39):
The one that I'm involved with or was involved
with, yeah, and there's so manyfun different ways to look at it
.
Bill Wright (13:45):
So what we decided to do was, if we're going to
make a global impact, there isone table that attracts all the
countries of the world whereeverybody will gather and talk
and not everybody's always goingto agree, but everybody's going
to have an open dialogue or aactive and participatory
dialogue and that's the UnitedNations.
(14:05):
And so one day I just sat thereand I was like, okay, let me do
some research.
Okay.
And this is when we werestarting out early days and I'd
already gotten a core group ofmembers together and I just was
typing around and I was like, oh, the UNFCCC hosts the COP meet,
the big COP conferences, andthey're right at the forefront
of, you know, really bringingtogether policies that help the
(14:27):
world from an adaptation andresilience perspective for
climate change.
And I took a look at thedifferent organizations and I
reached out to a number ofdifferent people in the UNFCCC,
tec and the CTCN, some wonderfulchampions there who really,
really like the concept ofcreating this network where all
(14:49):
these different projects couldintegrate and then basically
share data and come to theselarger conclusions.
But also, in getting to yourquestion, getting to your
statement, there has to be a wayto help least developed nations
and the islands themselves thatare about to be, you know that
are having challenges withrising sea levels, et cetera.
Many of those are in the leastdeveloped category, and so the
(15:15):
idea also was to make it a veryeven playing field and to give
them the advanced technologiesthat everybody has or at least
many of the nations of the worldhave and really up-level them
quickly so they're at an equityposition in terms of technology
access.
That was the idea, and so, withthe United Nations, now we have
a couple different tracksunderway, three in particular.
(15:35):
We have the UNFCCC TEC AIInnovation Grand Challenge, and
you can go to theenterpriseneurosystemorg website
.
You can access the applicationand the contest site there, and
what that is is really just away to open up the door to all
the different innovators, allthe different academics, all the
students, all the startupsaround the world and have them
(15:58):
submit their ideas.
It's just like a paragraph,something we can scan and really
get a sense of what the valueis, and then everybody gets, you
know, everybody gets anevaluation.
Some people make it to thesemifinals, some people make it
to the finals, and then we takethe winner to COP 29, where they
get incredible access to globalleaders and different folks
that are working on theforefront of climate technology
(16:18):
and also get them engaged withventure capital and all these
other different avenues.
That's the idea is to reallyhelp get these applications off
the ground.
Pamela Isom (16:26):
Now, I was involved with that one before At Aim for
Climate.
Bill Wright (16:29):
Yeah, that's the UP .
Pamela Isom (16:30):
Yeah, yeah yeah, give me an example of one of the
winning ones.
Bill Wright (16:36):
So that was incredible.
Agrospace is a group oftechnologists out of Chile and
Spain who came up with a greatidea for satellite monitoring of
plastics on beaches, but thenalso irrigation and water
patterns as well, and so theywere the winners of the Aim for
Climate Grand Challenge, and itwas very difficult to decide.
We had some great entries.
(16:56):
We had another entry that wasbasically a giant global
database of all the differentplant species and their genetic
information to basically lookfor resilient strains of plants
in the face of, like, a higherheat environment, and also crops
that could withstand heat, butalso ways to merge the genetics
of different plants to createcrops that would really
(17:17):
withstand a heating planet.
You could say, and that wasanother great idea, and that was
, I mean, so hard to decidebecause there were so many great
ideas.
Pamela Isom (17:27):
It was hard.
Bill Wright (17:28):
I'm telling you and you remember, there was some
really creative thought there.
Pamela Isom (17:33):
Yeah, you wanted them all to be the winner.
Bill Wright (17:36):
Yeah, that was the idea, and we definitely kept in
touch with many of them, andwe've still got folks engaged to
this day.
Rocky, who was one of thefinalists as well, he actually
is working with us on the newproject and he's basically going
to assist us with the Tanzaniaproject that we will discuss in
a second here.
But we've kept them engaged, aswill AgroSpace, as a matter of
(17:56):
fact, they will be working onthat as well.
So the neat thing about this iswe're trying to take these
innovators forward into newprojects that we uncover and
that we can involve in.
We want to create a communityaround this, and so now the
second stage, beyond the UNFCCCAI Grand Challenge, is the AI
application hub, and the ideathere is to provide an area
(18:16):
where any nation but really wewant to target those developing
nations and least developednations and get them engaged to
basically go and download opensource AI applications free of
charge and then basically usethose applications to assist
them in dealing with whateverclimate crisis they're facing,
and so to make those free ofcharge and to get them into the
hands of everybody quickly isreally the idea.
(18:39):
And again, we are a nonprofit.
This is free of charge.
We're here to assist the UNwith their mission or the UNF
CCC with their mission.
The third thing I'll mention.
That's the second thing.
The third is basically throughthe UNF CCC.
Ctcn, that's a greatorganization, that's the project
arm, or rather the climateproject arm of the UNF CCC from
(19:00):
that particular perspective, andthey have a variety of
different projects that theyhave that they bring people
together to help and basicallyprovide technology, and much of
it is done.
You know they'll give a littleKickstarter or a reasonable
Kickstarter to get things moving, but then you know these
external companies come togetherand try to help and make
contributions as well to theprojects, and so it's really a
(19:22):
nice kind of synergy that theycreate with this environment,
and many of the projects arecovered by their budget, and
other ones you require morefunding.
So one of the neat things iswe've been working with the
government of Tanzania and theNDE, or the representative of
that country from a sciencesperspective, and he's a
wonderful person, and we've hada really, really good experience
(19:43):
.
We've been working with them ona irrigation I guess you could
say mapping and early morningsystem for the farmers in
Tanzania, and so what's neatabout this is we'll be using
AgriSpace for taking a look atthe irrigation levels around the
country, you know, in terms ofactually mapping the width of
rivers and different liketributaries and what's going on
there.
We'll be looking at thegroundwater as well and that'll
(20:07):
be through the GRACE satellitesthat basically look at the
groundwater, the gravimetrics ofthe groundwater underneath
Tanzania and mapping where it'smoving and where it's located,
and then also putting inriverway sensors to understand,
I guess almost from a Dopplerradar perspective, what the
speed of the river is if it'srising, what the levels are, how
quickly it's rising, et cetera.
(20:28):
And then you take these threedifferent areas and you map them
together and you get a veryaccurate sense of what's
happening.
But then there are other thingsyou can do.
You can actually plug intodifferent species around the
rivers, you can plug intospecies near the agricultural
areas, you can begin tounderstand the impacts on
multiple levels of a country andthat is the Neurosystem concept
(20:51):
is getting a sense of what thatentire picture looks like in
real time, not two months later,eight months later, et cetera,
but in as close to real time asyou can reasonably get and then
send warnings out to thepopulace, the farmers, to the
governments, letting them know.
Hey look, this is coming inthree weeks and eight weeks
tomorrow and this is what youmay want to take a look at and
(21:15):
do, and that can be an AIrecommendation system as well.
So there are different waysthat we're looking at assisting.
But that project in Tanzania,we've got the proposal finished,
we're working with thegovernment right now, we're
getting the adaptation fundengaged and we'll see where that
goes, and it's been veryexciting so far.
Pamela Isom (21:30):
So, yeah, it's been a lot of fun that fish in the
water streams were suffering andthe farmers were trying to
(21:51):
understand what was it that iscausing the issues with the
fisheries and with the fishthemselves, and so I remember
that that one was kind of nearand dear to my heart because of
food and safety.
You know, food, food securityand food safety is important and
and fish is is healthy for us,and so as long as the fish is
(22:12):
healthy, right.
And so I remember payingattention to that, because we've
not only got the issue with therising sea levels and then in
other places around the world isdry as a bone, right.
But then we also have this.
We have this situation, and Idistinctly remember some of the
officials pointing out that thisis a concern.
(22:36):
So I'm hoping that thisdiscovery from the Tanzania
efforts will start, start toidentify how best to preserve
wildlife and some of our marinelife, right, ocean life so, and
river life and whatever you callit right.
So I hope that this will helpwith that, because we definitely
(22:57):
need that.
That's part of our honestlypart of our food supply chain.
But even if it wasn't, say,you're a person that is not
necessarily a fish eater, it'salso part of just nature and
letting nature exist and thrivein a healthy way.
So it's a wonderful project andprogram, and so I'm I.
(23:20):
As I said, I'll be back, I'llbe back.
Bill Wright (23:24):
Excellent, we'd be really excited to have you too.
That would be a lot of fun,seriously.
Pamela Isom (23:29):
Yeah, and you mentioned open source and I'm
thinking that part of therationale behind open source is
for the accessibility.
And, going back to the equitydiscussion, this is a part of
that equity playbook as well,which is why we're pushing the
open source, is that?
Bill Wright (23:47):
correct?
Oh, that's totally correct.
Yes, if you think about it,open source is usually a
community of developers who cometogether, build applications
that are needed in either theenterprise or for public good or
different parts of, I guess,any walk of society, when you
think about it wherever it'sneeded and provide those
software applications free ofcharge and support them as a
community, and then somecompanies will take them on and
(24:09):
support them in their own Iguess, flavor, you could say,
and make a very good living atthat, and then others will
basically take whatever the freeversion is and use that as they
need to.
So there are differentofferings in the open source
domain in terms of what issupported and how that support
is delivered.
The thing that's interesting toremember, though, is a lot of
the open source developers whoare out there also work in the
(24:31):
private sector and are verycompetent.
They're writing really strongapplications in many ways, and
so it isn't as fraught withperil, I think, as some people
make it out to be.
In some ways, and in other ways,you have to be careful with
open source.
You have to be diligent aboutthe code that is used and the
support and maintenance of thatcode.
(24:51):
That's really critical.
The neatest way to get it outthere is kind of a IP, I guess a
looser IP perspective to get itout.
You don't want to have to worryabout those constraints.
You want to be able to developthings and customize them if you
need to, if you're a user.
But also you can engagesomebody and contract them to
customize them as well andenable that support.
(25:14):
It's a very dynamic environmentand some of the greatest
creativity taking place in AI isin the open source domain
Because, if you think about it,a private company has its own
roadmap and has its owndirection and resource
allocation.
Hey, we'll work on this featurefor this amount of time.
We'll get to that feature muchlater and that's all good.
That's part of a veryprogrammatic way of doing
(25:35):
business and it's very logical.
And then in open source it'svery dynamic.
It's okay, so-and-so needs thisparticular feature.
How long can we get that done?
Oh, it might take 30 days.
And then somebody goes out andcodes it.
They do the requisite testingand then they just push it out
and they get it out there, andso people are doing it in their
spare time.
It can be academics.
There are all these differentpeople that work on these
(25:57):
projects, and so it's a muchmore kind of I guess you could
say fast-moving environment fromthat perspective.
So open source, the fast-movingnature of it and the advances
that take place so quickly,probably cause some people a
little concern, but honestly, itis where the most interesting
work is being done.
The greatest innovations,largely, are coming out of open
(26:18):
source.
Many private companies come upwith great ideas too, don't get
me wrong, but I think it's agreat petri dish for a lot of
creativity right now intechnology, and so some of the
greatest ideas in AI are poppingliterally out of open source.
And you've seen all the storiesin the news about open source
and some of the constraints andthe regulations that are coming
in.
And the regulations, you know,in many ways are a very good
(26:40):
idea and you don't want toconstrain the creativity, but
you also don't want to putthings out that could, you know
like, cause some imbalance, andso you have to look what that
looks like and be rational aboutit.
And in the enterprise neuralsystem I'll wrap our data is
climate data and our sensors arelistening to beehives, you know
(27:01):
like, and watching the planetfrom above, like riverways and
things like that.
Our data is relatively benign.
We're not looking for personalinformation.
We're not.
We're listening to bees.
We're not listening to people.
That's the whole idea.
Pamela Isom (27:15):
Why the bees, since you've mentioned it a couple of
times and I know I was involvedwith that, but you can tell the
story better than me.
Bill Wright (27:24):
So you can tell it too, but I'll give it a try,
yeah.
Pamela Isom (27:27):
I'll chime in, but talk about the bees.
Bill Wright (27:30):
Well, you were in the middle of that, so I and
you're funny, I you could tellit better than I could.
But I think what's fascinatingis there are a lot of species in
nature.
Right now they're applying AIto sperm whales is one, and
there was a big news articleabout that and how they've
finally begun to decipher thelanguage between whales by using
ai and listening to you knowhow they communicate and there
(27:52):
are many, many species in naturethat do communicate, like how
do bees communicate and get tothe different locations where
all the pollen is located andthen bring it back again and if
there's a new source, how dothey communicate that?
And they have different waysand modalities of doing that.
But one of the suspected waysof doing it is through acoustic
information.
Acoustic patterns in a beehiveare very interesting and
(28:15):
beekeepers on occasion have usedstethoscopes to listen to their
beehives if they're in distressor if they're getting ready to
swarm or do something else likethat.
And again, I'm not a bee expert, but this is how I've learned
about it is from people that dothis for a living, and I think
what's really interesting aboutit is on my side.
It was like a natural thing toto talk to my friends that are
(28:36):
in the community and we all cametogether and this was the idea
of somebody named DennisO'Connell, who worked at a at
time at Yahoo, was the head ofthe performance engineering lab
there, and he was like, oh, weshould use beehives as
environmental sensors, and itwas a brilliant idea.
And then Ryan Coffey, who worksat Stanford Slack, was like, oh
my gosh, in addition to workingin the physics domain and in AI,
(28:58):
I'm also a beekeeper.
And we had this dynamicconversation that led to this
stream of research, and I givethem full credit for it.
And dynamic conversation thatled to this stream of research,
and I give them full credit forit.
And what was neat about it waswe began to realize you could
probably take ai and apply it toa species, and ai runs around
the clock and it's listeningaround the clock.
This is what's great about itand, and taking in all the
different patterns ofcommunications of that one,
(29:19):
particular clustering and thenexactly, and then over time,
it's oh, we're seeing patterns.
And then you could take an AIpowered camera and you could
identify the species and theircommunications and what their
behaviors are in relation tothose communications.
And you cross, correlate it,you begin to look across those
data patterns.
It's like, oh, when they issuedthis communication, this is the
(29:41):
next action.
And then you start to get asense of what that dictionary is
.
And then you're creating aneffect, an internet for nature.
But you're also potentially andI don't want to get ahead of my
skis here, but you could createa rosetta stone for nature and
begin to understand how naturecommunicates.
And so, yeah, and that's theoutcome of all the different
(30:02):
people in this community.
I mean, I, I just got the ballrolling at the top of the hill,
but everybody has beenincredible, like you know, and
the ideas come from all thesedifferent corners, like from you
and from Ryan and from Dennisand everybody.
And so what's neat about it isthat's the power of an open
source community.
You get this brilliantpreponderance of ideas and we
(30:23):
just throw them against the wallfearlessly, because we're just
not afraid of looking bad.
We're all friends and we don'tcare if it works or if it
doesn't.
Let's just try it and see whereit goes, and if it works, then
we've got another step further,you know.
Pamela Isom (30:37):
Yeah, and we're using the acoustics from the
bees to help with farming, togive some clues to farmers, also
to help to influence how weaddress weather patterns,
Because I think that theacoustics help us understand
what is coming.
It's kind of like when you workwith, when you listen.
I don't know if you like dogs,but I like dogs.
(30:58):
Right, I love dogs.
And dogs hear everything Likethey don't miss, they don't miss
, and long before you hear it,they hear it, and so they give
you a warning.
And so what we're doing is andwhat I enjoyed about the work
with the acoustics from the beesis it's giving us a different
(31:19):
type of warning, but it's awarning Right.
But it's a warning right, andwe're studying the data to
understand what the warningswould be, and we're wanting to
use it to guide farming andthings around the weather, and I
don't think they want me to saytoo much more than that.
But it is an open sourceinitiative and it's fun, right
and so, but it's a great program.
(31:42):
The EN is a great program initself, so, if I build on top of
that.
So now we're talking about aglobal program framework that
helps us understand what'shappening and what are some
commonalities and patternsaround the globe.
Okay, so then let's talk aboutregulatory requirements.
(32:05):
So we're dealing with AI.
There's emerging regulations,there's regulations that already
exist, like GDPR, which reallyisn't about AI, but it's about
data, which makes it about AI,because all this is about data.
But are there anything inspecific, like for the Tanzania
initiative?
(32:25):
I know we were talking aboutdata protection Are there legal
frameworks around the dataprotection space specifically
for Africa that we should beconsidering or that you're
looking into.
Bill Wright (32:38):
Yes, africa has a data framework that they've
enacted that basically acts asthe foundation of a lot of what
they're looking at from an AIperspective, as is the EU, as is
North America, the White House,the White House.
There are a lot of differentinitiatives that are dealing
with data protection, privacyand also how it's utilized, how
LLMs, large language models, aretaking data, you know, scraping
(33:00):
it from all over, if it'scopyrighted or not.
How do we deal with that?
All these are very kind oflarge, open questions, but the
initial data protectionframeworks are I don't want to
say largely in place, but manyare already in place and they're
putting new ones in every day.
So there's a lot of I guess I'dsay prudent caution around that.
I think it's actually a verygood thing in many respects.
(33:22):
You don't want to inhibit thecreativity, but you also don't
want to create a wild westscenario.
You want to be able to applysome good, orderly guidance to
the whole process, and so Ithink that's what's happening at
the governmental level, andI've been seeing some really
good things coming out of that.
And I'm not saying that to be,you know, to just say the right
thing.
I mean literally.
I think it's a great idea.
(33:44):
It's a balance you have tostrike between the creativity
and between the protection ofindividual rights and individual
creativity and copyright andfreedoms.
I think there's a very carefulway to do that and some major
companies are being very brightabout that, very smart.
I know that IBM has basicallygone ahead and they've created
these large language models onlytrained on data that doesn't
(34:09):
have any copyright issuewhatsoever, and they'll actually
indemnify the use of thosemodels from that perspective,
and so you see a lot of theprivate sector companies taking
those kinds of approaches to getahead of that, to basically not
take the companies they workwith down that path, and so
there's been some really goodwork from that perspective.
I've seen out there and I yeah,I think it's good to really
(34:33):
think through the whole arc ofthe use of AI, from the data to
the systems, to the populationsthat will be affected.
All those things need to bepart of that broad view and I've
seen some great thinking aroundthat and some great policy
enacted.
Pamela Isom (34:48):
And the use cases, because the reason why I said
that is because earlier youmentioned that, hey, we're just
looking at sensor data from bees, right, and so one would think,
well, okay, so maybe that's nota reason to look at the various
use cases, because it's allgoing to depend on how we use
the data, how we use the sensorsand how we apply the AI.
(35:10):
What kind of decisions is goingto be making?
So I just kind of wanted to saythat in order to bring that
(35:31):
point home.
There's HIPAA, there's the GDPR, right, so the data protection
most of it is about dataprotection and then there's the
AI in Africa.
I think we said it's called thehas something to do with Malabo
, which I don't know, so I won'tsay any more than that.
Bill Wright (35:49):
That is the framework, I believe.
Pamela Isom (35:50):
Yes.
Bill Wright (35:51):
The privacy and protection framework that
they've enacted, and that'sacross the continent from what I
understand.
But I think, yeah, it's very,very nice to see all the
countries of the world taking itseriously but also embracing it
at the same time.
Okay.
And.
I think that's what's been neatto see, because the benefits far
(36:12):
outweigh the liabilities.
With every new technology, younormally get both, and so in
this case, this is one way forjust understanding what's
happening in nature around uswith climate change in a very
rapid fashion and getting tosolutions that much faster using
data that's largelynon-controversial.
So this is an easier one totackle, I'd say, than many other
(36:36):
, I guess, industries orverticals where you'd have to
apply AI.
In this regard, I think it'seminently doable.
Pamela Isom (36:44):
I guess you could say I think for the Tanzania
initiative and the wholeirrigation system and the
analysis, and then the, the beeexample, but there's others,
right, the, the award-winninginnovation, uh, what's it called
?
agro tech or yeah yeah, yeah,yeah you still have to be
(37:08):
careful with, like thesurveillance, but so I still
think that they're good usecases and they're things that we
want to be mindful of.
So my message to myself wouldbe don't underestimate the
(37:29):
implications, the use cases.
Make sure you fully understandthem and look into them so that
we are mitigating any potentialrisks.
So, no matter how simple theymay seem or are non-invasive,
let's make sure we double check.
But I agree with everythingthat you've been saying.
(37:50):
I want to know.
There's a couple of things thatI was looking at, and one of
them has to do with using AI todetect disease, which we didn't
mention that in the Tanzaniaeffort, but it could be right
Using it to detect disease inthe water or in the water supply
(38:14):
, and I was talking to someoneand they were discussing using
AI to detect disease in poultry.
So I was just looking into thatfurther, and so that may be
(38:37):
something that we dig into alittle bit more to support the
effort that we have going on.
So I'm going to throw that outthere as food for thought.
Is disease detection in thefarmland, in support of some of
those initiatives that we have.
I know we're doing it to someextent, but I'd like to bring
that to the forefront.
I usually ask my guests to sharewords of wisdom or experiences
(39:01):
for the listeners, but usuallybefore I want to know, before
you do that, is there anythingelse that we wanted to talk
about?
Is there anything else that youwanted to share or anything
else that you wanted me todiscuss during this talk with
you, before you share your wordsof wisdom?
Bill Wright (39:20):
Oh, certainly, and all I would say is I really
appreciate the opportunity toshare in this kind of a forum.
I mean, this is really exciting.
But also, if any of you have anidea that you'd like to get
into the world from an AIclimate perspective, please go
to the Enterprise Neurosystemwebsite.
It's EnterpriseNeurosystemorg.
There's a kind of a banner atthe top of the website.
(39:42):
Click on that banner.
That'll take you to the pagethat basically acts as the
contest page.
You can submit your ideas there.
We really encourage everybodyto submit their 250-word
proposals from that perspective.
So I would say that andotherwise glad to take it from
here.
Pamela Isom (40:05):
Okay.
Well then, I would like to knowabout your words of wisdom or
experiences that you want toshare with the listeners, so
that they can take it away withthem and kind of ponder over it
and apply to their daily livesand it's for me as well.
Bill Wright (40:17):
So what you got, I think you know the power of the
individual is overlooked a lotin the technology business, so
it was the hot startup or thebig companies that get all the
attention.
But a very small community orgroup of individuals can make a
real big difference.
If you get the right peopletogether with the right, with
(40:38):
the right heart, you know theright kind of feeling around how
things should go and unfold.
And I was very fortunate to besurrounded early on in the
creation of this community bypeople just like that.
And I started the community andI'll just try to wrap it up
because one day my son and I Iwas already doing work on this
area at the company that I was,that I'm at, and working on AI,
(41:01):
for I guess you could say mobilenetworks and networking
technologies and I rememberlooking outside the window right
out here in the next room withmy son at that time, who was
about four years younger, and itwas the day that those
wildfires created those reallydeep red skies.
I mean literally horizon tohorizon in the Bay Area.
It was like a Martian landscape.
(41:24):
You just looked out the windowand the sky was actually like
fire engine red Pictures you sawI have to add this the pictures
you saw that came out later,like on Facebook.
Those were like the iPhonefilters that kicked in and made
them orange.
It was actually red, it wasreally and looking out the
window at that, my son justlooked at that and he just had
(41:45):
this kind of stunned look on hisface.
And then he looked up at me andhe goes dad, what are we going
to do about this?
And you know from you know it.
Just, he's a lot smarter than Iam, cause I I mean, that was
the first thing he said and I'dlooked at him and I was like
that's a really good questionand it was like the heck with it
.
Let me call up my friends,let's try to do something.
(42:07):
You know, and I can give my, myson, credit for that, you know
honestly, because if he hadn'tasked that question, it would
have taken me a much longer timeto get to that.
But so what's neat about this isyou just don't know where this
inspiration is going to comefrom.
You have to always give peoplethe credit they're due and just
be humble and open to learning,because there's so much to learn
about nature that I just I'mlearning every day.
(42:29):
It's fascinating.
You know all the different waysspecies communicate and how you
can listen to them and how youcan track them.
It's like and understand.
You know what we can do to helpthem.
And, yeah, it's, if you justget the right people together,
there's a lot of neat stuff thatcan happen.
And so have faith in I'd say tothe listening community, have
faith in your own abilities.
It doesn't matter if you'recoming out of whatever, whatever
(42:52):
area of you can contribute andyou can get things moving.
You know, that's what I'velearned.
Pamela Isom (42:57):
I think that's really good feedback and really
good insights.
I'll just say I remember beingat Energy, so you know we've
been involved.
You and I will stay connected.
We'll keep doing things.
I remember being at theDepartment of Energy and I
remember getting involved withmany things pertaining to equity
(43:18):
and equitable outcomes throughthe use of AI, from the water
supply to energy.
But I distinctly remember thewildfire situation, and you just
made me think about that.
So in the wildfire situation,there were helicopters.
The wildfire situation, therewere helicopters and via the
helicopters, they wanted to beable to zoom in on what's
(43:45):
happening on the ground from thebird I'm going to call it the
bird.
So from the bird, zoom in onwhat's happening on the ground
and be able to detect thetrajectory of the wildfire as
well as where are the triggersor the fuel loads.
They called it.
And I was so honored to beinvolved with that effort.
I was involved with theDepartment of Defense, the
(44:08):
Department of Energy, and I wasthe leader of the effort from an
AI perspective.
So we had to look at how wecould use AI and the cool thing
was it's like what your son sayslike what are we going to do
about this?
Because it's irritating to methat we deal with this wildfire
situation every year and we knowthat it's going to happen every
(44:30):
year, and we know that it'sgoing to get worse every year.
Every year it gets worse andworse and worse.
So we started looking into whatcould we do to address this
problem.
And so kudos to your son,because he is right, it's a
problem, and I can rememberliving in Colorado and the ashes
from the wildfire was so baduntil it was resting on the
(44:55):
furniture inside the homes rightwhen the wildfires would break
out for whatever reasonsometimes it was man caused and
sometimes not, but I canremember that.
And so it is good to look atengineers, look at what to do to
solve problems.
I don't know an engineer thatdoesn't see a problem and
(45:16):
doesn't want to do something tofix it.
So kudos to your son and kudosto you, and tell him that there
was work going on at Energy tohelp solve these types of
problems, working with the USForest Service, and we ended up
disallowing controlled burnsbecause that was not helping,
(45:36):
because you got to get some goodpractices in place when it
comes to controlled burns andall that and examine the fuel
loads and what's the trigger.
And so he's absolutely rightand I hope you will encourage
him to continue that mindsetbecause you must recognize and I
know you do, just like you haveit that's that engineering in
(45:56):
him.
So I'm happy and there is workgoing on and I know there was
probably when he mentioned it toyou and it has to continue and
we're using AI and data scienceto help address some of these
challenges.
Bill Wright (46:10):
Couldn't agree more .
No, and I'll pass that on tohim.
Actually, I'll be seeing himright after this call.
Pamela Isom (46:14):
Couldn't agree more .
No, and I'll pass that on tohim.
Actually.
I'll be seeing him right afterthis call and thank you for the
words of wisdom because it is upto them.
We should not put limits onourselves, never, and we should
never think that a problem istoo small or de minimis.
So a very good point.
So thank you.
This podcast is for informational purposes only.
Personal views and opinionsexpressed by our podcast guests
are their own and not legaladvice, neither health tax, nor
professional nor officialstatements by their
organizations.
Guest views may not be those ofthe host.
(00:50):
Hello and welcome to AI or Not,the podcast where business
leaders from around the globeshare wisdom and insights that
are needed now to address issuesand guide success in your
artificial intelligence anddigital transformation journey.
I'm Pamela Isom and I'm yourpodcast host.
We have a unique and specialguest with us today, and that is
(01:15):
Bill Wright.
Bill is founder and chair ofthe Enterprise Neurosystem open
source community and I certainlyappreciate teaming with Bill.
Bill, you and I have alwaysdone some things together, and I
remember the AIM for ClimateConference that I was able to
(01:36):
attend thanks to you, and thatwas really good.
I've maintained contact withfolks, by the way, and kept some
activities going on, but what awonderful program.
So, ill, welcome to AI or Not.
Bill Wright (01:51):
Oh, thank you so much for having me.
I really appreciate this, thankyou.
Pamela Isom (01:55):
So you're welcome.
As we get started here, willyou please tell me more about
yourself, your career journey,talk about what drove your
decision to establish theEnterprise Neurosystem open
source community.
Go for it.
Bill Wright (02:10):
Oh, thank you.
I have a very unusualbackground going into tech,
probably a little unconventional, but that is kind of the story
of my career arc, so I guess Ican go into that a little bit.
I started out after going tocollege as an artist.
I was working at advertisingagencies and I was drawing
storyboards and I love comicbook art.
(02:31):
That was my thing.
It was pretty classic.
For the people that know me itdidn't surprise them at all.
I was a huge comic book fan andI love to draw, I love to do
the art.
And after a few years I wasworking freelance for
advertising agencies in SanFrancisco.
But I always had a very strongsciences background.
I had an arts, I had kind ofthat arts and sciences focus, I
(02:51):
guess you could say and focusmore on astronomy and dabbling
in physics and other areas.
And one day my brother who wasworking for a computer company
called me up and he said youknow, I know you're freelancing
and doing these kind ofinteresting projects, but would
you be intrigued if you cameover and you started to work at
my at this it firm that I'mworking for and I like as a
(03:13):
contractor and you would helppeople like reach out to them
and do different kinds ofinteractions to get them to sign
contracts and renewals, and Iwas like, yeah, I'd be happy to
do that.
That sounds like a lot of fun.
And so I started out and didthat for a few years and then
that led to another job andanother job, and another job,
and then ended up having afull-blown career in IT.
(03:33):
And this was during theinternet boom.
So I'm dating myself with thatas I wave my cane in the air,
you know.
But I think what's great aboutit is it really gave me a trial
by fire.
It was like the classic firehose everybody talks about.
I mean, the internet boom wasreally spectacular and it was
both an amazing ascendance andan incredible fall for the
(03:55):
industry.
And I was just watching thewhole thing from the sidelines,
like literally with a front rowseat.
It was fascinating and itreally caught my attention and
I've always been pretty diligentabout learning and about being
curious and taking notes.
I get that probably from, I'dsay, my brothers, who are both
very much the same way, and Iconstantly would listen to
(04:17):
presentations.
I would sit down with thearchitects and the CTOs after
the meetings and I would be like, well, tell me about this, tell
me about that, how do thepieces work together, how does
this work?
And the willingness to be ofservice and the willingness to
be humble or have that humilityto ask those questions is kind
of the key to success, I think,in this industry.
And so I just kept doing thatover and over again and then
(04:40):
eventually get more into thestrategy side, the, I guess you
could say, looking for newtechnology movements that might
be emerging for companies,something I did at a couple of
different firms, and soeventually one of the movements
(05:00):
I targeted and this is gettinginto how I am doing what I'm was
in the climate world.
There were a lot of differentprojects that were either
competing in different countries, different satellite
organizations For example, youhave the European Space Agency,
you have NOAA and NASA, and youlook at all of them and they all
collaborate and everybody knowseach other and in some ways
(05:21):
they do interact and they dointerconnect.
And in some ways they dointeract and they do
interconnect.
But what was missing to me inmy role and where I thought this
was going to congeal at somepoint, was what if there was an
over-the-top network for theplanet where every external
project could plug in and thenbasically share their data in
whatever fashion they decide.
(05:41):
It can be completely open.
It can be just results of thedata that are resident in the
country, so they don't have toworry about data privacy etc.
Just like the outputs, we'retraining AI models in that
country and then sending themout.
There are all sorts of differentthings you can do, but in
essence it was.
There is no common framework foreverybody to plug in and then
apply AI on the back end to lookfor the patterns across the
(06:04):
planet in real time, and notjust from a certain subset of
satellites, but from the entirepicture.
So, atmospheric projects,oceanic projects, the one thing
that's missing really are thenature-based sensors in
different populations in nature,like beehives and mycorrhizal
networks and mussel farms, whichare usually in the mouths of
(06:25):
rivers, right by humansettlements where all the
pollution comes through.
So there are all theseinteresting species that are
really alarm bells for climatechange and they really give you
a sense of where the impacts mayarise in the future.
And if you took all that dataand you plugged it all together
with AI on the back end,grinding those patterns over a
(06:46):
24-7 period, just think aboutwhat you could discover, and I
think there would be newdiscoveries there, new
recommendations for coursecorrection.
A lot of exciting things couldtake place.
So that's my journey and that'swhere I am today.
Pamela Isom (06:59):
Is that what caused you to get interested in
artificial intelligence?
Bill Wright (07:04):
Artificial.
It's funny because AI is it's avery broad term, to be honest,
and, as you're well aware,because I know you're one of
those kind of very prominentexperts out there, I worked in
business intelligence for acompany a number of years ago
maybe 15 years ago and therewere a lot of vendors out there
very prominent vendors that hadthese linear algebra
(07:24):
applications that wouldbasically track different
business metrics and give youpredictive analytics, and it was
a very interesting area ofscience or data science, and so
I used to actually work with onecompany to get those
applications virtualized and theidea was to put them into these
virtualized environments andthen understanding their
behavior and their performance,and I learned some really
(07:46):
valuable lessons as part of thatprocess.
I remember going to SAS, thefamous company in Cary, north
Carolina, and meeting with oneof their VPs technology and it
was a very funny conversationbecause I was early to learning
about data sciences and I thinkit was like a good 15 years ago
and I sat down and we had aconversation and I was really
enthusiastic about virtualizingall their workloads and this is
(08:09):
an important point to make.
That's why I'm spending time onit.
He looked at me and he kind ofsmiled and he said Bill, you're
a great guy, but you don'tunderstand anything about what
we're doing.
And I looked at him and Ismiled and I said actually I
don't, that's why I'm here, sowhy don't you tell me?
And he laughed and he said well,when you take our applications
and you put them in a containeror virtualized environment,
(08:31):
which is really just putting aparameter around an application
so it can operate with a subsetof resources in a server there's
a long explanation behind thatand I won't go into that.
But really what it was wascreating a encapsulation of that
software application that couldreside with other operating
systems and softwareapplications in one single
(08:51):
server, which really hadn't beendone before.
And he said well, the problemwith that, bill, is that the
minute you load our softwareonto a server and this is what
he said at the time it'sprobably not the case today,
given the way the industry hasevolved, but I'd have to look
into that.
He said well, we actually turnon the server when you load our
application and it's running allthe time.
(09:12):
All the resources are activeand running and it's programmed
and designed in that way forcertain parameters.
And if you take our resourcesin that server and you subdivide
them into a little tiny box,one of many in that server,
you're going to really impactour performance and that's how
it behaves and it's reallyinteresting.
(09:33):
I didn't realize that there wasthat whole behavioral and
resource subset you really hadto pay attention to, and that
was a very valuable lesson atthat time for a younger person,
just kind of getting in thebusiness, and so it kind of all
these different experiences kindof came together to create what
really is the enterpriseneurosystem today, with the help
of all of our members.
(09:54):
I mean, I'm very blessed tohave some incredible, incredible
contributors there and you know, all the way from marketing all
the way into you know, extremedata science experts.
What's neat about that is wehave to look at the overall AI
environment in a large-scalenetwork like that, in terms of
the behavior of the applications, how they interact with one
(10:14):
another.
It's almost like creating anecology in some respects.
And I keep drawing the parallelsback to biology, because really
, what is a mobile network thana neural system?
It extends out.
You have these nodes, drawingthe parallels back to biology,
because really, what is a mobilenetwork than a neuro system?
It extends out.
You have these nodes, they'revery, you know, sensitive.
They can tell you your, yourhealth information.
You know, on a mobile phonethere's ai on most mobile phones
(10:36):
today, anyway, at least all theones that are being sold and so
, when you think about it, it'sa network of all these different
sensors, just like different,like senses in the human body,
that really collect andaggregate all that data and then
you can use it creatively andproductively in different ways.
Pamela Isom (11:00):
Right that you were interested, regardless of what
it was called.
You were interested in neuralnetwork type of capability and
virtualization and you were atthe forefront.
So I love the concept oflooking at the world from a
(11:21):
common lens, right which is whatyou described so that we can
understand global patterns.
And that is how we ended upworking together, because I
liked what Ian is all about theenterprise neural system and I
wanted.
I believe in equity, as you do,and I'm trying to figure out
what can be done and what can wedo to drive and fuel global
(11:45):
equity, and I think AI is one ofthe tools and capabilities.
That whole ecosystem, the wholeAI ecosystem it seems just
right for driving and fuelingequity from a global perspective
.
So that's how we ended upworking together and I'm so glad
that we did and I'm so gladthat you're here today.
I want to talk some more aboutyour program.
(12:09):
So I said all that to lead intoyour program, but what I really
love is what you said aboutthat whole bringing the nations
together and providing nationswith the tools that are needed
to be efficient and looking atit from that global perspective.
So can you just tell me alittle bit more about the EM
(12:32):
program, enterprise Neurosystemsprogram, where we are today and
what do you see as some nextsteps.
Bill Wright (12:40):
It's.
I'll take a second to go intothat and thank you so much.
The enterprise neurosystem nowis turning into something pretty
unique.
It's an open source group offolks from the private sector
largely, but also academia andgovernment and some different
government agencies, but it'sactually I think we've got about
(13:02):
18 or 19 different countriesnow of folks from those
countries.
We're all volunteers, sonobody's getting paid, and
that's been both.
You know, something for us tonavigate.
That's been tough, but alsoit's really been core to our
mission, because everybody has aday job, everybody does
something for a living or, ifthey have time between you know
their jobs, they can come in andwork on it, you know, more
(13:23):
diligently and then they go backout into the private sector
again.
However, that works, I thinkwhat we're answering is a need
for people to want to expresstheir creativity and to do
something good for the world,and I think yeah, like the
beehive right.
Pamela Isom (13:39):
The one that I'm involved with or was involved
with, yeah, and there's so manyfun different ways to look at it
.
Bill Wright (13:45):
So what we decided to do was, if we're going to
make a global impact, there isone table that attracts all the
countries of the world whereeverybody will gather and talk
and not everybody's always goingto agree, but everybody's going
to have an open dialogue or aactive and participatory
dialogue and that's the UnitedNations.
(14:05):
And so one day I just sat thereand I was like, okay, let me do
some research.
Okay.
And this is when we werestarting out early days and I'd
already gotten a core group ofmembers together and I just was
typing around and I was like, oh, the UNFCCC hosts the COP meet,
the big COP conferences, andthey're right at the forefront
of, you know, really bringingtogether policies that help the
(14:27):
world from an adaptation andresilience perspective for
climate change.
And I took a look at thedifferent organizations and I
reached out to a number ofdifferent people in the UNFCCC,
tec and the CTCN, some wonderfulchampions there who really,
really like the concept ofcreating this network where all
(14:49):
these different projects couldintegrate and then basically
share data and come to theselarger conclusions.
But also, in getting to yourquestion, getting to your
statement, there has to be a wayto help least developed nations
and the islands themselves thatare about to be, you know that
are having challenges withrising sea levels, et cetera.
Many of those are in the leastdeveloped category, and so the
(15:15):
idea also was to make it a veryeven playing field and to give
them the advanced technologiesthat everybody has or at least
many of the nations of the worldhave and really up-level them
quickly so they're at an equityposition in terms of technology
access.
That was the idea, and so, withthe United Nations, now we have
a couple different tracksunderway, three in particular.
(15:35):
We have the UNFCCC TEC AIInnovation Grand Challenge, and
you can go to theenterpriseneurosystemorg website
.
You can access the applicationand the contest site there, and
what that is is really just away to open up the door to all
the different innovators, allthe different academics, all the
students, all the startupsaround the world and have them
(15:58):
submit their ideas.
It's just like a paragraph,something we can scan and really
get a sense of what the valueis, and then everybody gets, you
know, everybody gets anevaluation.
Some people make it to thesemifinals, some people make it
to the finals, and then we takethe winner to COP 29, where they
get incredible access to globalleaders and different folks
that are working on theforefront of climate technology
(16:18):
and also get them engaged withventure capital and all these
other different avenues.
That's the idea is to reallyhelp get these applications off
the ground.
Pamela Isom (16:26):
Now, I was involved with that one before At Aim for
Climate.
Bill Wright (16:29):
Yeah, that's the UP .
Pamela Isom (16:30):
Yeah, yeah yeah, give me an example of one of the
winning ones.
Bill Wright (16:36):
So that was incredible.
Agrospace is a group oftechnologists out of Chile and
Spain who came up with a greatidea for satellite monitoring of
plastics on beaches, but thenalso irrigation and water
patterns as well, and so theywere the winners of the Aim for
Climate Grand Challenge, and itwas very difficult to decide.
We had some great entries.
(16:56):
We had another entry that wasbasically a giant global
database of all the differentplant species and their genetic
information to basically lookfor resilient strains of plants
in the face of, like, a higherheat environment, and also crops
that could withstand heat, butalso ways to merge the genetics
of different plants to createcrops that would really
(17:17):
withstand a heating planet.
You could say, and that wasanother great idea, and that was
, I mean, so hard to decidebecause there were so many great
ideas.
Pamela Isom (17:27):
It was hard.
Bill Wright (17:28):
I'm telling you and you remember, there was some
really creative thought there.
Pamela Isom (17:33):
Yeah, you wanted them all to be the winner.
Bill Wright (17:36):
Yeah, that was the idea, and we definitely kept in
touch with many of them, andwe've still got folks engaged to
this day.
Rocky, who was one of thefinalists as well, he actually
is working with us on the newproject and he's basically going
to assist us with the Tanzaniaproject that we will discuss in
a second here.
But we've kept them engaged, aswill AgroSpace, as a matter of
(17:56):
fact, they will be working onthat as well.
So the neat thing about this iswe're trying to take these
innovators forward into newprojects that we uncover and
that we can involve in.
We want to create a communityaround this, and so now the
second stage, beyond the UNFCCCAI Grand Challenge, is the AI
application hub, and the ideathere is to provide an area
(18:16):
where any nation but really wewant to target those developing
nations and least developednations and get them engaged to
basically go and download opensource AI applications free of
charge and then basically usethose applications to assist
them in dealing with whateverclimate crisis they're facing,
and so to make those free ofcharge and to get them into the
hands of everybody quickly isreally the idea.
(18:39):
And again, we are a nonprofit.
This is free of charge.
We're here to assist the UNwith their mission or the UNF
CCC with their mission.
The third thing I'll mention.
That's the second thing.
The third is basically throughthe UNF CCC.
Ctcn, that's a greatorganization, that's the project
arm, or rather the climateproject arm of the UNF CCC from
(19:00):
that particular perspective, andthey have a variety of
different projects that theyhave that they bring people
together to help and basicallyprovide technology, and much of
it is done.
You know they'll give a littleKickstarter or a reasonable
Kickstarter to get things moving, but then you know these
external companies come togetherand try to help and make
contributions as well to theprojects, and so it's really a
(19:22):
nice kind of synergy that theycreate with this environment,
and many of the projects arecovered by their budget, and
other ones you require morefunding.
So one of the neat things iswe've been working with the
government of Tanzania and theNDE, or the representative of
that country from a sciencesperspective, and he's a
wonderful person, and we've hada really, really good experience
(19:43):
.
We've been working with them ona irrigation I guess you could
say mapping and early morningsystem for the farmers in
Tanzania, and so what's neatabout this is we'll be using
AgriSpace for taking a look atthe irrigation levels around the
country, you know, in terms ofactually mapping the width of
rivers and different liketributaries and what's going on
there.
We'll be looking at thegroundwater as well and that'll
(20:07):
be through the GRACE satellitesthat basically look at the
groundwater, the gravimetrics ofthe groundwater underneath
Tanzania and mapping where it'smoving and where it's located,
and then also putting inriverway sensors to understand,
I guess almost from a Dopplerradar perspective, what the
speed of the river is if it'srising, what the levels are, how
quickly it's rising, et cetera.
(20:28):
And then you take these threedifferent areas and you map them
together and you get a veryaccurate sense of what's
happening.
But then there are other thingsyou can do.
You can actually plug intodifferent species around the
rivers, you can plug intospecies near the agricultural
areas, you can begin tounderstand the impacts on
multiple levels of a country andthat is the Neurosystem concept
(20:51):
is getting a sense of what thatentire picture looks like in
real time, not two months later,eight months later, et cetera,
but in as close to real time asyou can reasonably get and then
send warnings out to thepopulace, the farmers, to the
governments, letting them know.
Hey look, this is coming inthree weeks and eight weeks
tomorrow and this is what youmay want to take a look at and
(21:15):
do, and that can be an AIrecommendation system as well.
So there are different waysthat we're looking at assisting.
But that project in Tanzania,we've got the proposal finished,
we're working with thegovernment right now, we're
getting the adaptation fundengaged and we'll see where that
goes, and it's been veryexciting so far.
Pamela Isom (21:30):
So, yeah, it's been a lot of fun that fish in the
water streams were suffering andthe farmers were trying to
(21:51):
understand what was it that iscausing the issues with the
fisheries and with the fishthemselves, and so I remember
that that one was kind of nearand dear to my heart because of
food and safety.
You know, food, food securityand food safety is important and
and fish is is healthy for us,and so as long as the fish is
(22:12):
healthy, right.
And so I remember payingattention to that, because we've
not only got the issue with therising sea levels and then in
other places around the world isdry as a bone, right.
But then we also have this.
We have this situation, and Idistinctly remember some of the
officials pointing out that thisis a concern.
(22:36):
So I'm hoping that thisdiscovery from the Tanzania
efforts will start, start toidentify how best to preserve
wildlife and some of our marinelife, right, ocean life so, and
river life and whatever you callit right.
So I hope that this will helpwith that, because we definitely
(22:57):
need that.
That's part of our honestlypart of our food supply chain.
But even if it wasn't, say,you're a person that is not
necessarily a fish eater, it'salso part of just nature and
letting nature exist and thrivein a healthy way.
So it's a wonderful project andprogram, and so I'm I.
(23:20):
As I said, I'll be back, I'llbe back.
Bill Wright (23:24):
Excellent, we'd be really excited to have you too.
That would be a lot of fun,seriously.
Pamela Isom (23:29):
Yeah, and you mentioned open source and I'm
thinking that part of therationale behind open source is
for the accessibility.
And, going back to the equitydiscussion, this is a part of
that equity playbook as well,which is why we're pushing the
open source, is that?
Bill Wright (23:47):
correct?
Oh, that's totally correct.
Yes, if you think about it,open source is usually a
community of developers who cometogether, build applications
that are needed in either theenterprise or for public good or
different parts of, I guess,any walk of society, when you
think about it wherever it'sneeded and provide those
software applications free ofcharge and support them as a
community, and then somecompanies will take them on and
(24:09):
support them in their own Iguess, flavor, you could say,
and make a very good living atthat, and then others will
basically take whatever the freeversion is and use that as they
need to.
So there are differentofferings in the open source
domain in terms of what issupported and how that support
is delivered.
The thing that's interesting toremember, though, is a lot of
the open source developers whoare out there also work in the
(24:31):
private sector and are verycompetent.
They're writing really strongapplications in many ways, and
so it isn't as fraught withperil, I think, as some people
make it out to be.
In some ways, and in other ways,you have to be careful with
open source.
You have to be diligent aboutthe code that is used and the
support and maintenance of thatcode.
(24:51):
That's really critical.
The neatest way to get it outthere is kind of a IP, I guess a
looser IP perspective to get itout.
You don't want to have to worryabout those constraints.
You want to be able to developthings and customize them if you
need to, if you're a user.
But also you can engagesomebody and contract them to
customize them as well andenable that support.
(25:14):
It's a very dynamic environmentand some of the greatest
creativity taking place in AI isin the open source domain
Because, if you think about it,a private company has its own
roadmap and has its owndirection and resource
allocation.
Hey, we'll work on this featurefor this amount of time.
We'll get to that feature muchlater and that's all good.
That's part of a veryprogrammatic way of doing
(25:35):
business and it's very logical.
And then in open source it'svery dynamic.
It's okay, so-and-so needs thisparticular feature.
How long can we get that done?
Oh, it might take 30 days.
And then somebody goes out andcodes it.
They do the requisite testingand then they just push it out
and they get it out there, andso people are doing it in their
spare time.
It can be academics.
There are all these differentpeople that work on these
(25:57):
projects, and so it's a muchmore kind of I guess you could
say fast-moving environment fromthat perspective.
So open source, the fast-movingnature of it and the advances
that take place so quickly,probably cause some people a
little concern, but honestly, itis where the most interesting
work is being done.
The greatest innovations,largely, are coming out of open
(26:18):
source.
Many private companies come upwith great ideas too, don't get
me wrong, but I think it's agreat petri dish for a lot of
creativity right now intechnology, and so some of the
greatest ideas in AI are poppingliterally out of open source.
And you've seen all the storiesin the news about open source
and some of the constraints andthe regulations that are coming
in.
And the regulations, you know,in many ways are a very good
(26:40):
idea and you don't want toconstrain the creativity, but
you also don't want to putthings out that could, you know
like, cause some imbalance, andso you have to look what that
looks like and be rational aboutit.
And in the enterprise neuralsystem I'll wrap our data is
climate data and our sensors arelistening to beehives, you know
(27:01):
like, and watching the planetfrom above, like riverways and
things like that.
Our data is relatively benign.
We're not looking for personalinformation.
We're not.
We're listening to bees.
We're not listening to people.
That's the whole idea.
Pamela Isom (27:15):
Why the bees, since you've mentioned it a couple of
times and I know I was involvedwith that, but you can tell the
story better than me.
Bill Wright (27:24):
So you can tell it too, but I'll give it a try,
yeah.
Pamela Isom (27:27):
I'll chime in, but talk about the bees.
Bill Wright (27:30):
Well, you were in the middle of that, so I and
you're funny, I you could tellit better than I could.
But I think what's fascinatingis there are a lot of species in
nature.
Right now they're applying AIto sperm whales is one, and
there was a big news articleabout that and how they've
finally begun to decipher thelanguage between whales by using
ai and listening to you knowhow they communicate and there
(27:52):
are many, many species in naturethat do communicate, like how
do bees communicate and get tothe different locations where
all the pollen is located andthen bring it back again and if
there's a new source, how dothey communicate that?
And they have different waysand modalities of doing that.
But one of the suspected waysof doing it is through acoustic
information.
Acoustic patterns in a beehiveare very interesting and
(28:15):
beekeepers on occasion have usedstethoscopes to listen to their
beehives if they're in distressor if they're getting ready to
swarm or do something else likethat.
And again, I'm not a bee expert, but this is how I've learned
about it is from people that dothis for a living, and I think
what's really interesting aboutit is on my side.
It was like a natural thing toto talk to my friends that are
(28:36):
in the community and we all cametogether and this was the idea
of somebody named DennisO'Connell, who worked at a at
time at Yahoo, was the head ofthe performance engineering lab
there, and he was like, oh, weshould use beehives as
environmental sensors, and itwas a brilliant idea.
And then Ryan Coffey, who worksat Stanford Slack, was like, oh
my gosh, in addition to workingin the physics domain and in AI,
(28:58):
I'm also a beekeeper.
And we had this dynamicconversation that led to this
stream of research, and I givethem full credit for it.
And dynamic conversation thatled to this stream of research,
and I give them full credit forit.
And what was neat about it waswe began to realize you could
probably take ai and apply it toa species, and ai runs around
the clock and it's listeningaround the clock.
This is what's great about itand, and taking in all the
different patterns ofcommunications of that one,
(29:19):
particular clustering and thenexactly, and then over time,
it's oh, we're seeing patterns.
And then you could take an AIpowered camera and you could
identify the species and theircommunications and what their
behaviors are in relation tothose communications.
And you cross, correlate it,you begin to look across those
data patterns.
It's like, oh, when they issuedthis communication, this is the
(29:41):
next action.
And then you start to get asense of what that dictionary is
.
And then you're creating aneffect, an internet for nature.
But you're also potentially andI don't want to get ahead of my
skis here, but you could createa rosetta stone for nature and
begin to understand how naturecommunicates.
And so, yeah, and that's theoutcome of all the different
(30:02):
people in this community.
I mean, I, I just got the ballrolling at the top of the hill,
but everybody has beenincredible, like you know, and
the ideas come from all thesedifferent corners, like from you
and from Ryan and from Dennisand everybody.
And so what's neat about it isthat's the power of an open
source community.
You get this brilliantpreponderance of ideas and we
(30:23):
just throw them against the wallfearlessly, because we're just
not afraid of looking bad.
We're all friends and we don'tcare if it works or if it
doesn't.
Let's just try it and see whereit goes, and if it works, then
we've got another step further,you know.
Pamela Isom (30:37):
Yeah, and we're using the acoustics from the
bees to help with farming, togive some clues to farmers, also
to help to influence how weaddress weather patterns,
Because I think that theacoustics help us understand
what is coming.
It's kind of like when you workwith, when you listen.
I don't know if you like dogs,but I like dogs.
(30:58):
Right, I love dogs.
And dogs hear everything Likethey don't miss, they don't miss
, and long before you hear it,they hear it, and so they give
you a warning.
And so what we're doing is andwhat I enjoyed about the work
with the acoustics from the beesis it's giving us a different
(31:19):
type of warning, but it's awarning Right.
But it's a warning right, andwe're studying the data to
understand what the warningswould be, and we're wanting to
use it to guide farming andthings around the weather, and I
don't think they want me to saytoo much more than that.
But it is an open sourceinitiative and it's fun, right
and so, but it's a great program.
(31:42):
The EN is a great program initself, so, if I build on top of
that.
So now we're talking about aglobal program framework that
helps us understand what'shappening and what are some
commonalities and patternsaround the globe.
Okay, so then let's talk aboutregulatory requirements.
(32:05):
So we're dealing with AI.
There's emerging regulations,there's regulations that already
exist, like GDPR, which reallyisn't about AI, but it's about
data, which makes it about AI,because all this is about data.
But are there anything inspecific, like for the Tanzania
initiative?
(32:25):
I know we were talking aboutdata protection Are there legal
frameworks around the dataprotection space specifically
for Africa that we should beconsidering or that you're
looking into.
Bill Wright (32:38):
Yes, africa has a data framework that they've
enacted that basically acts asthe foundation of a lot of what
they're looking at from an AIperspective, as is the EU, as is
North America, the White House,the White House.
There are a lot of differentinitiatives that are dealing
with data protection, privacyand also how it's utilized, how
LLMs, large language models, aretaking data, you know, scraping
(33:00):
it from all over, if it'scopyrighted or not.
How do we deal with that?
All these are very kind oflarge, open questions, but the
initial data protectionframeworks are I don't want to
say largely in place, but manyare already in place and they're
putting new ones in every day.
So there's a lot of I guess I'dsay prudent caution around that.
I think it's actually a verygood thing in many respects.
(33:22):
You don't want to inhibit thecreativity, but you also don't
want to create a wild westscenario.
You want to be able to applysome good, orderly guidance to
the whole process, and so Ithink that's what's happening at
the governmental level, andI've been seeing some really
good things coming out of that.
And I'm not saying that to be,you know, to just say the right
thing.
I mean literally.
I think it's a great idea.
(33:44):
It's a balance you have tostrike between the creativity
and between the protection ofindividual rights and individual
creativity and copyright andfreedoms.
I think there's a very carefulway to do that and some major
companies are being very brightabout that, very smart.
I know that IBM has basicallygone ahead and they've created
these large language models onlytrained on data that doesn't
(34:09):
have any copyright issuewhatsoever, and they'll actually
indemnify the use of thosemodels from that perspective,
and so you see a lot of theprivate sector companies taking
those kinds of approaches to getahead of that, to basically not
take the companies they workwith down that path, and so
there's been some really goodwork from that perspective.
I've seen out there and I yeah,I think it's good to really
(34:33):
think through the whole arc ofthe use of AI, from the data to
the systems, to the populationsthat will be affected.
All those things need to bepart of that broad view and I've
seen some great thinking aroundthat and some great policy
enacted.
Pamela Isom (34:48):
And the use cases, because the reason why I said
that is because earlier youmentioned that, hey, we're just
looking at sensor data from bees, right, and so one would think,
well, okay, so maybe that's nota reason to look at the various
use cases, because it's allgoing to depend on how we use
the data, how we use the sensorsand how we apply the AI.
(35:10):
What kind of decisions is goingto be making?
So I just kind of wanted to saythat in order to bring that
(35:31):
point home.
There's HIPAA, there's the GDPR, right, so the data protection
most of it is about dataprotection and then there's the
AI in Africa.
I think we said it's called thehas something to do with Malabo
, which I don't know, so I won'tsay any more than that.
Bill Wright (35:49):
That is the framework, I believe.
Pamela Isom (35:50):
Yes.
Bill Wright (35:51):
The privacy and protection framework that
they've enacted, and that'sacross the continent from what I
understand.
But I think, yeah, it's very,very nice to see all the
countries of the world taking itseriously but also embracing it
at the same time.
Okay.
And.
I think that's what's been neatto see, because the benefits far
(36:12):
outweigh the liabilities.
With every new technology, younormally get both, and so in
this case, this is one way forjust understanding what's
happening in nature around uswith climate change in a very
rapid fashion and getting tosolutions that much faster using
data that's largelynon-controversial.
So this is an easier one totackle, I'd say, than many other
(36:36):
, I guess, industries orverticals where you'd have to
apply AI.
In this regard, I think it'seminently doable.
Pamela Isom (36:44):
I guess you could say I think for the Tanzania
initiative and the wholeirrigation system and the
analysis, and then the, the beeexample, but there's others,
right, the, the award-winninginnovation, uh, what's it called
?
agro tech or yeah yeah, yeah,yeah you still have to be
(37:08):
careful with, like thesurveillance, but so I still
think that they're good usecases and they're things that we
want to be mindful of.
So my message to myself wouldbe don't underestimate the
(37:29):
implications, the use cases.
Make sure you fully understandthem and look into them so that
we are mitigating any potentialrisks.
So, no matter how simple theymay seem or are non-invasive,
let's make sure we double check.
But I agree with everythingthat you've been saying.
(37:50):
I want to know.
There's a couple of things thatI was looking at, and one of
them has to do with using AI todetect disease, which we didn't
mention that in the Tanzaniaeffort, but it could be right
Using it to detect disease inthe water or in the water supply
(38:14):
, and I was talking to someoneand they were discussing using
AI to detect disease in poultry.
So I was just looking into thatfurther, and so that may be
(38:37):
something that we dig into alittle bit more to support the
effort that we have going on.
So I'm going to throw that outthere as food for thought.
Is disease detection in thefarmland, in support of some of
those initiatives that we have.
I know we're doing it to someextent, but I'd like to bring
that to the forefront.
I usually ask my guests to sharewords of wisdom or experiences
(39:01):
for the listeners, but usuallybefore I want to know, before
you do that, is there anythingelse that we wanted to talk
about?
Is there anything else that youwanted to share or anything
else that you wanted me todiscuss during this talk with
you, before you share your wordsof wisdom?
Bill Wright (39:20):
Oh, certainly, and all I would say is I really
appreciate the opportunity toshare in this kind of a forum.
I mean, this is really exciting.
But also, if any of you have anidea that you'd like to get
into the world from an AIclimate perspective, please go
to the Enterprise Neurosystemwebsite.
It's EnterpriseNeurosystemorg.
There's a kind of a banner atthe top of the website.
(39:42):
Click on that banner.
That'll take you to the pagethat basically acts as the
contest page.
You can submit your ideas there.
We really encourage everybodyto submit their 250-word
proposals from that perspective.
So I would say that andotherwise glad to take it from
here.
Pamela Isom (40:05):
Okay.
Well then, I would like to knowabout your words of wisdom or
experiences that you want toshare with the listeners, so
that they can take it away withthem and kind of ponder over it
and apply to their daily livesand it's for me as well.
Bill Wright (40:17):
So what you got, I think you know the power of the
individual is overlooked a lotin the technology business, so
it was the hot startup or thebig companies that get all the
attention.
But a very small community orgroup of individuals can make a
real big difference.
If you get the right peopletogether with the right, with
(40:38):
the right heart, you know theright kind of feeling around how
things should go and unfold.
And I was very fortunate to besurrounded early on in the
creation of this community bypeople just like that.
And I started the community andI'll just try to wrap it up
because one day my son and I Iwas already doing work on this
area at the company that I was,that I'm at, and working on AI,
(41:01):
for I guess you could say mobilenetworks and networking
technologies and I rememberlooking outside the window right
out here in the next room withmy son at that time, who was
about four years younger, and itwas the day that those
wildfires created those reallydeep red skies.
I mean literally horizon tohorizon in the Bay Area.
It was like a Martian landscape.
(41:24):
You just looked out the windowand the sky was actually like
fire engine red Pictures you sawI have to add this the pictures
you saw that came out later,like on Facebook.
Those were like the iPhonefilters that kicked in and made
them orange.
It was actually red, it wasreally and looking out the
window at that, my son justlooked at that and he just had
(41:45):
this kind of stunned look on hisface.
And then he looked up at me andhe goes dad, what are we going
to do about this?
And you know from you know it.
Just, he's a lot smarter than Iam, cause I I mean, that was
the first thing he said and I'dlooked at him and I was like
that's a really good questionand it was like the heck with it
.
Let me call up my friends,let's try to do something.
(42:07):
You know, and I can give my, myson, credit for that, you know
honestly, because if he hadn'tasked that question, it would
have taken me a much longer timeto get to that.
But so what's neat about this isyou just don't know where this
inspiration is going to comefrom.
You have to always give peoplethe credit they're due and just
be humble and open to learning,because there's so much to learn
about nature that I just I'mlearning every day.
(42:29):
It's fascinating.
You know all the different waysspecies communicate and how you
can listen to them and how youcan track them.
It's like and understand.
You know what we can do to helpthem.
And, yeah, it's, if you justget the right people together,
there's a lot of neat stuff thatcan happen.
And so have faith in I'd say tothe listening community, have
faith in your own abilities.
It doesn't matter if you'recoming out of whatever, whatever
(42:52):
area of you can contribute andyou can get things moving.
You know, that's what I'velearned.
Pamela Isom (42:57):
I think that's really good feedback and really
good insights.
I'll just say I remember beingat Energy, so you know we've
been involved.
You and I will stay connected.
We'll keep doing things.
I remember being at theDepartment of Energy and I
remember getting involved withmany things pertaining to equity
(43:18):
and equitable outcomes throughthe use of AI, from the water
supply to energy.
But I distinctly remember thewildfire situation, and you just
made me think about that.
So in the wildfire situation,there were helicopters.
The wildfire situation, therewere helicopters and via the
helicopters, they wanted to beable to zoom in on what's
(43:45):
happening on the ground from thebird I'm going to call it the
bird.
So from the bird, zoom in onwhat's happening on the ground
and be able to detect thetrajectory of the wildfire as
well as where are the triggersor the fuel loads.
They called it.
And I was so honored to beinvolved with that effort.
I was involved with theDepartment of Defense, the
(44:08):
Department of Energy, and I wasthe leader of the effort from an
AI perspective.
So we had to look at how wecould use AI and the cool thing
was it's like what your son sayslike what are we going to do
about this?
Because it's irritating to methat we deal with this wildfire
situation every year and we knowthat it's going to happen every
(44:30):
year, and we know that it'sgoing to get worse every year.
Every year it gets worse andworse and worse.
So we started looking into whatcould we do to address this
problem.
And so kudos to your son,because he is right, it's a
problem, and I can rememberliving in Colorado and the ashes
from the wildfire was so baduntil it was resting on the
(44:55):
furniture inside the homes rightwhen the wildfires would break
out for whatever reasonsometimes it was man caused and
sometimes not, but I canremember that.
And so it is good to look atengineers, look at what to do to
solve problems.
I don't know an engineer thatdoesn't see a problem and
(45:16):
doesn't want to do something tofix it.
So kudos to your son and kudosto you, and tell him that there
was work going on at Energy tohelp solve these types of
problems, working with the USForest Service, and we ended up
disallowing controlled burnsbecause that was not helping,
(45:36):
because you got to get some goodpractices in place when it
comes to controlled burns andall that and examine the fuel
loads and what's the trigger.
And so he's absolutely rightand I hope you will encourage
him to continue that mindsetbecause you must recognize and I
know you do, just like you haveit that's that engineering in
(45:56):
him.
So I'm happy and there is workgoing on and I know there was
probably when he mentioned it toyou and it has to continue and
we're using AI and data scienceto help address some of these
challenges.
Bill Wright (46:10):
Couldn't agree more .
No, and I'll pass that on tohim.
Actually, I'll be seeing himright after this call.
Pamela Isom (46:14):
Couldn't agree more .
No, and I'll pass that on tohim.
Actually.
I'll be seeing him right afterthis call and thank you for the
words of wisdom because it is upto them.
We should not put limits onourselves, never, and we should
never think that a problem istoo small or de minimis.
So a very good point.
So thank you.
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