Travis Yeik joins us for part two of our deep dive into the interaction of artificial intelligence and humans in agriculture, exploring current applications and future possibilities of this transformative technology in farming.
We chat about...
• Weather forecasting is already utilizing AI to provide crucial planning information to farmers
• Precision agriculture applications include weed identification, disease monitoring, and targeted spraying
• Livestock operations benefit from AI monitoring of animal health, feed consumption, and production metrics
• Irrigation management systems use AI to optimize water usage and placement
• Biogenetics represents a revolutionary frontier where AI is accelerating crop development
• Protein structure mapping has advanced from mapping dozens to millions of proteins in just years
• Future AI applications could include profit maximization planning and automated stand count analysis
• Adoption challenges mirror previous farm technology introductions like auto-steer systems
• Data collection remains a critical challenge for AI implementation in agriculture
• Human oversight will remain essential even as AI capabilities continue to advance
Join us as we continue exploring how technology is transforming agriculture and what it means for the future of farming.
https://gktechinc.com/
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Transcript
Episode Transcript
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Sarah (00:00):
And now it's time for Ag Geek Speak with GK Technology's,
Sarah and Jodi, friends and Ican't wait to get in the fields
again.
No, I can't wait to get in thefields again.
Jodi (00:31):
Welcome back to another episode of Ag Geek Speak, where
I am joined with my co-host,Sarah Lovas, and GK Technology's
own Travis Yeik, and in thispart two of our episode about
artificial intelligence, we aregoing to continue our excellent
conversation that we startedlast week, where Travis helped
us define what artificialintelligence is, what its
(00:53):
history has been and, kind oflike, what its limitations are,
and we thought for this episodewe're going to go into more
depth about you know what AItools are here now and how could
they help us be more efficientas agronomists and farmers,
Because there's a lot ofcapacity for AI to do that.
Sarah (01:12):
My question is what is AI doing in agriculture right now?
How is AI helping us right now?
Travis, can you help usunderstand that, maybe a little
bit?
Travis Yeik (01:26):
Yeah.
So there's several applications, I think, where we can
definitely say AI is already inthe workforce and some of those
are, as we discussed here lastweek is weather right.
Being able to have farmers know, you know ahead of time what is
going to happen, whether thatis you know two hours ahead of
(01:48):
time or even two days ahead oftime, is super important for
making those decisions.
Yeah, so weather is one we goon and there's a lot of
precision agriculture for roomfor AI to grow and where AI
already is, and some of thoseinclude weed control and disease
(02:09):
monitoring.
I think there's John Deere hasa scene spray thing where they
use AI in it and they use imagesto come out and say, hey, this
is this type of weed and sprayspecifically just that weed.
Yeah, we don't get into it alot.
But livestock health monitoring,I think is a big one.
You'll see it in a bunch ofdairies, whether it monitors
(02:32):
milk outputs or the type of feed, uh, the animal when it comes
into the, to the bunk, orgetting water.
That is as a big one for fordoing animal production that way
, and we talked other ways Iguess would be irrigation, I
think is another one.
There's several companies doingirrigation, on determining when
(02:55):
and how much and where to water, so some of that precision ag
stuff there, that way again.
Uh, then we, we can move outand go broader into such as, uh,
being able to determine wherewe need products or or just, you
know, shipping and distributionin that way with agriculture,
(03:15):
that's, that's a, that's a hugepart of agriculture.
Um, and doing predictability ofwhat, what crops are being
grown and what needs to be grown, and doing commodities and
stuff.
Those are some of the majorones just off the top of my head
.
I think that is big inagriculture here already.
Jodi (03:37):
So, now that I think about this and where AI is and where
it isn't in agriculture rightnow, I feel like we're seeing AI
in places where there's alreadydata in ag right.
So like yield data, satelliteimagery these are pieces of
agriculture where we alreadyhave data.
Is that why we've got AI therealready, because we've got data,
(04:01):
or are we seeing a push for AI?
And you know, maybe some of themore challenging pieces of what
I'm trying to say here is likeis this really, is this being
driven?
And like where AI is and whereits potential.
Is it driven from low-hangingfruits or is it really trying to
address, like, problems thatexist in the market?
(04:22):
Now, maybe that's too too like,uh, theoretical for this
conversation, but like I mean,there has to be data to do ai
right, but you also have tostart with the theoretical idea.
Sarah (04:39):
And if in a, in a in a concept of where you want to go,
you got to dream a little bit,right?
You know, I have this problem.
Travis Yeik (04:45):
And it also calls money, of course.
Jodi (04:47):
Yeah, people have to pay for it, right yeah?
Travis Yeik (04:51):
Yeah, I think there's some you know statistics
that you know.
Like right now I can't rememberwhat it was, maybe I had it in
my presentation.
Let me see if I had that.
It was interesting, Like, letme see if I had that it was
interesting Like where the moneyin AI development is.
Yeah.
Sarah (05:06):
So Travis actually attended GK Technology's first
precision agriculture symposiumthat we ever put on and he was a
speaker for us.
He spoke about AI and precisionagriculture.
It was a great presentation.
It was fantastic.
So if anybody's interested inthat sort of stuff, come to the
(05:27):
GK Symposium into the future,because there's going to be
another one, not that I wouldever, Travis, do you want to
give your same presentationagain?
Jodi (05:34):
Just kidding Not that I would ever throw out a shameless
plug for our programming.
Sarah (05:38):
But there it is.
Travis Yeik (05:41):
There's your commercial so, yeah, we are
spending like billions ofdollars already and it's just
crazy to think of how muchenergy is going to be put into
ai in agriculture how do we keepthat money focused on products
that actually benefit decisionmaking in the field from a
(06:03):
practical standpoint?
Sarah (06:04):
and I and grant that.
You know there has to be acertain amount of dreaming that
goes into you know, what if Icould do this, what if I could
do that?
You kind of have to dream aboutthings that you want to make
better, but what are some thingsthat could go in our industry
to help make sure that thatmoney is going into places where
(06:25):
it's actually going to affectthe outcome for agriculture in a
very positive way?
Travis Yeik (06:30):
I think there's like four or five like major
agriculture sectors that we canthink of on where where AI is
going to be focused, and I thinkso for us, obviously it's
precision ag, right.
That's that's a big deal.
So for us, obviously it'sprecision ag right that's a big
deal and that's being able touse satellite images and be able
(07:03):
to to use some of thatprecision ag stuff to make those
decisions and enhance likeefficiencies of how we're
managing our crops and then Ithink another part is is
robotics, right?
uh, some of these, these bigcompanies, will get into John
Deere and Case and whatnot andthey'll get into the robotics
(07:24):
and being able to use the AI toautomate stuff or to automate
machinery.
And then I think another onewill be supplies and change and
distribution.
It's another major sector there.
That's another major sectorthere.
I guess we could includelivestock, even though we are in
(07:56):
the part of the different partof agriculture is what we're
dealing with, and then I thinkthe very last one would be some
of these other companies such asMonsanto Say Bayer, Bayer,
Bayer.
Yeah, Monsanto doesn't existanymore.
Yep.
Sarah (08:04):
They were purchased by Bayer.
Say Bayer yeah, sorry, Monsantodoesn't exist anymore.
Yep.
They were purchased by Bayer ohokay, Thanks.
Travis Yeik (08:09):
And then the other big one then would be companies
such as Bayer and doingbiogenetics right To breed and
to genetically alter some plantsand make them crop resistant,
disease resistant, droughtresistant, and that'll be a huge
, huge money making adventurethere for AI coming into
(08:32):
agriculture.
Jodi (08:33):
To speed up the process of choosing which genes, which
genetics, make up the bestcombination to maximize yield
and survivability in a givenarea to maximize yield and
survivability in a given area.
Travis Yeik (08:49):
Yeah, yeah, yeah.
And really it's being able tohave AI do that and be able to
look at whether the genetics orthe proteins of the plant.
I was listening to a podcasthere the other day of they were
doing that and whether you callit GMO or genetic altering right
, but it's just taking a plantand carrying it down about 10
(09:10):
evolutions, uh, or combining andsaying, hey, we can see this
crop, that that grows in thispart of the country, and take
out that specific piece of dnasequence and put it into a
different plant that that needs,that needs that trout
resistance or whatever it is.
Sarah (09:28):
So AI is going to be able to help identify those genes.
Travis Yeik (09:32):
Yeah, help identify them or help figure out.
Hey, how can it be implementedinto this different cell
structure?
Sarah (09:43):
Wow, Wow.
To actually help with theactual implementation of where
it needs to go, I suppose in theDNA sequence itself when you
splice it out and put it back in.
Jodi (09:55):
Can you send that link to that podcast?
Travis Yeik (10:00):
Yeah, it was a good one.
It was this girl in Europe.
Another really good one, though, is he's a really famous
YouTuber guy and he's calledVeritesium, and he does.
He had one that come out on.
What are the future benefits ofAI and one that we're currently
using right now?
Is it's actually the samealgorithm that I was talking
(10:21):
about here last week, about theAlphaGo and the Moo0ero and the
EfficientZero, where they'reusing this one that was able to
play games now and they changed.
Instead of playing a game, itis able to change protein
structures.
And this came out here.
I remember reading about it.
It was about eight years agoand we didn't have any protein
(10:41):
structures, hardly mapped out.
And so this uh or thisprofessor, he came in and he
made a program and he gave it togamers and these gamers from
all over the world were able tofigure out this protein
structure in like three days,something that we hadn't been
able to do in like 50 years,right holy buckets and now they
(11:03):
changed it so that not only cangamers do it, well, now this ai
can do it, and within, like thelast, I want to say two or three
years.
Right, we had maybe 20 proteinsmapped out.
Now we have like a million ofthem mapped out.
That's amazing, so so we'reable to, and not only can it map
(11:24):
out the proteins, but it cancreate new proteins.
Sarah (11:29):
Could it change the protein that's there?
Travis Yeik (11:31):
So like let's say that you're lactose intolerant,
although that's a sugar, butlike but it's an enzyme, right,
which is a protein then thatcontrols and how we can process
that sugar is a protein than thecontrols and how we can, we can
process that sugar.
Jodi (11:49):
So I don't know a whole lot about most things, but I
know some things about weedscience.
Um, and that was a lot of what Iworked on was like herbicide
resistance and like the coolthing that I'm thinking about
right now is like a lot of theselike we talk about target
sequence mutations that causeherbicide resistance and you
know, for like ALS inhibitorresistance, most of those are
target resistance.
(12:10):
But the point is is like whathappens there is you have just
maybe a single amino acid orlike a single nucleotide change
and that causes an amino acidinside of a protein to change
shape and so maybe that changesthe protein to change shape, so
where an ALS inhibitor can nolonger fit in that binding site
and can't kill that plant, andwhat we always wanted like when
(12:32):
I was in grad school just acouple of years ago, like the
thought was like how can wepredict where new mutations
might occur that might causeherbicide resistance?
And if you could model orpredict, you know what changes
on the gene could change theshape of it and make it so the
herbicide couldn't fit thereanymore, you'd know like what,
what could be likely to happenfor herbicide resistance?
(12:54):
But on the other hand too, likeif you can predict how these
proteins take shape, then youcan hypothetically design better
herbicides that fit thesemolecules to kill plants right.
I mean, most of our herbicidesthat we work with are all about
inhibiting enzymes in plants.
So if we can model theseproteins in weeds and figure out
(13:17):
how to back engineer a moleculeof herbicide to fit in that
protein and make it stop, thatcould help us develop new
herbicides too.
Sarah (13:27):
Or we actually have a weed geneticist at North Dakota
State University who works onthe genetics of of weed
resistance, like all the time.
Wouldn't it be cool if we couldchange the weed back so it was
not resistant anymore and makeit susceptible to the herbicides
(13:49):
that are already in themarketplace?
Yeah, right, likehypothetically, could you just
change it back and it's ai thatcould find that spot and do that
.
That's, that's fun.
Travis Yeik (14:01):
I think about the scary side of it now like we're
changing proteins right, whichis a prion, and so we can change
and create our own prions, andyou know, so you got so mad cow
disease right.
Jodi (14:15):
Yeah, yeah.
So prions is like, if we canmake a prion like mad cow
disease, right, yeah, yeah.
So prions is like, if we canmake a prion like mad cow
disease, like our ability tobioengineer, like weapons, would
be drastically better so so youmentioned the other episode.
If ai can learn how to killhumans, is this the approach.
Travis Yeik (14:33):
We should probably not transcribe this, so this
doesn't end up in a largelanguage model for ai to
discover and get back at us cutthis part out no but I was
listening, you know, so thatpodcast I mentioned earlier
about this uh girl, she wasusing some of that biogenetic
(14:53):
engineering and so one of herthings that she mentioned in the
podcast was being able to takea plant right.
It takes, you know, 15, 16weeks or whatever for the plant
to grow.
Well, she was just cutting outthe plant tissues and, uh, so
you can get it within a day ortwo and change it and see how
how these genetic markers reactwithin the plant tissues, rather
(15:14):
than yeah and I I think just toprovide some context.
Jodi (15:19):
So like I read a novel about like barbara mcclintock's
life, so barbara mcclintock, shewas a geneticist that
discovered jumping genes, um,and basically was helped helped
to map out the corn chromosomesback in like the 1930s.
But at that that point in time,right, that was before Watson
and Crick.
So like we didn't even knowwhat DNA looked like.
(15:41):
And so in the 1930s, the 1940s,we were still trying to figure
out like it was during that timeperiod that we even found out
that one piece of DNA codes fora protein.
Like that's not even a hundredyears ago, that's almost a
hundred years ago we didn't evenknow that DNA is coded for
proteins.
And now we're talking aboutusing an AI technology to
(16:04):
predict, like if we made achange in this tissue, you know
how could it.
It's crazy to think how fastthis technology is progressing,
based on how recently we'velearned about a lot of these
things.
Just nuts.
Sarah (16:20):
Okay.
So, jodi, I got an idea.
And this might be scary, buthere we go.
I have this idea.
What if we play a little gameright now called if AI could do
anything for me in the field?
It would be.
And then fill in the blank, andthen Travis can tell us whether
we're pie in the sky,ridiculous with the idea, or
(16:41):
whether you know there's not a.
You know there's no way itwould ever work.
So, okay, jodi, you go first.
If.
Think about farming, thinkabout agronomy, think about
whatever you want, if there wasone thing that AI could do for
you in the field, what would itbe?
Jodi (16:58):
It would be profit maximization decisions at the
beginning of the year.
Help me decide what herbicides,what mix of crops, given like
market conditions, what sets ofcrops should I grow, given my
equipment makeup, given theprices of what I can have and
then what I can hire, and thengiven the possible range of like
(17:20):
prices I could get for the, thecommodities at like.
There's a lot of variabilitiesof it but we'll say I'll give it
a data set of like what I thinkI can market it for.
But if they could help meprofit maximize and help me with
that decision making, that'swhat I'd want it for.
Travis Yeik (17:35):
Okay, I think that's definitely coming'd want
it for.
Okay, that's definitely comingright.
One of those algorithms Italked about was a mu zero and
efficient zero, and what they dospecifically is they look not
only at the reward now with thisobservation, but it plans it
out.
So, 30 steps ahead.
What will my reward be?
Jodi (17:55):
And so is, and if it can.
Travis Yeik (17:57):
And then also the other part of the model is
saying hey, if we have thisobservation now and I take this
action, what is my nextobservation going to be in my
next one and my next one.
And so it gets really good atplanning the future and it makes
those those future rewardsbased on what is happening and
where you're, where your currentsituation is at now, and that's
(18:19):
why it does really well and itdoes a lot of times better than
humans at understanding thesevery complex models, because it
has ran through thesesimulations millions of times
and it knows, hey, based on this, this is where the most likely
action will be, this way or,probability wise, this will be
(18:39):
the best outcome based on mydecision now.
Sarah (18:42):
That's fun.
That's really fun Becausethat's a lot of different
factors that go in and I feellike you know, in the spring of
the year, you know we're reallywell.
It actually starts like thefall before, like after harvest,
when you're trying to figureout what am I going to plant
next year and and what.
Then you're going through thewhole year of fertilizer, of
(19:03):
everything else.
So that's that's prettyexciting.
Travis Yeik (19:06):
And then obviously the tough part is, as we talked
about, is this combining allthese different decisions into
having that data for it to makethose decisions.
So we, uh, we are.
The algorithms are beingdeveloped, but having that data
put together and processed, um,it could be ways in the future,
but, um, I, I could foresee ithappening.
Jodi (19:29):
And I have a question for later.
I'm I'm going to put this on mylater notes.
Okay, sarah, okay, samequestion for you Okay.
Sarah (19:38):
So this time of the year as an agronomist, you know we're
recording this over like rightafter Memorial day weekend and
pretty much if you are anagronomist in North Dakota,
Manitoba, Saskatchewan,Minnesota, South Dakota, if you
were an agronomist and you'vegot a four-wheeler, you are
putting on probably 100 miles aday right now, at least on your
(20:01):
four-wheeler, if not more.
It's go time and one of the theparts of scouting crops at this
time of the year that I findtime consuming and annoying is
stand counts.
Now I am aware that we can goout and we can log stand counts
with a drone.
You know you can take a picture.
It's high, detailed.
You can get those pictures to acertain extent.
(20:22):
Like sugar beets can be reallyhard if they're in the cotyledon
stage because they're so smallthat even sometimes the
resolution isn't good enough ondrones UAS.
But what I think would bereally great is if we could get
accurate stand counts before andafter an event and then have
(20:46):
some sort of a decision model inthere that can take into effect
the previous weather, thefuture predicted weather and the
market conditions to determinewhether we should replant or not
.
Travis Yeik (21:01):
So that is a lot right, that's.
That's like several differentsystems coming into one.
I think about what was.
We were in grade school and youhad the man versus the machine.
Right it was.
It was this guy digging atunnel or a for the, for the
railroad, and then he had amachine and he was trying to
beat the machine.
You guys remember what that wascalled.
Jodi (21:20):
No.
Travis Yeik (21:21):
Oh, okay, maybe this was a Wyoming thing.
No.
Sarah (21:27):
You're younger than me too.
I mean, when I was inelementary school we had Apple
II computers with basic language.
Travis Yeik (21:35):
I'm going to look at that real quick, just because
I'm curious.
Sarah (21:37):
I know you guys have heard this the chalk all looked
pink when I got back to myclassroom after computer class
because of the green screen onthe computers.
Oh yeah, and, by the way, weactually had chalkboards with
chalk.
Jodi (21:56):
Oh.
I went to an elementary schoolthat had about 50, 60 kids total
.
So yeah, all we could affordwere Apple computers and
chalkboards too.
Sarah (22:04):
Then I moved and look at how he turned out.
Jody.
The legend of John.
Travis Yeik (22:10):
Henry.
Sarah (22:11):
What was it called Travis ?
Travis Yeik (22:13):
So it's the legend of John Henry, and it was this
drill that could drill fasterthan any man, and he was
drilling through a tunnel.
And so he went and said youknow what?
We don't need these machines.
These machines are going toreplace us.
Right, I can beat this machine.
And so he went up against thismachine and I believe I'm trying
(22:36):
to remember the outcome, but Ithink he lost.
But it's the same thing, right?
I'm thinking about automationand how we have assembly lines.
Right, when they came and Ithink a lot of people were
scared at that time too thatassembly lines are going to
replace jobs, and in a lot ofcases, they did and I think AI
(23:01):
with robots, I think they couldbe there someday where they take
over these processes.
It's just an assembly machinethat can move around, and so,
whether you're pitching hay ordoing manual labor tasks, fixing
fence and stuff like that, Ithink you could have an ai
someday that has the ability toto move um and make these very
(23:23):
simple um task orienteddecisions on putting a post in
the ground or doing this ordoing that, and I think think in
this case, it could be in yourexample, driving a food out to
this place, or a drone, flying adrone out to this location and
being able to make these tasksand bring back the data for some
(23:46):
of these tasks.
And I think once that happens,then AI will be able to be
carried along quite a ways,because now we have that data
that is being automaticallygenerated by robots, by AI
itself, and it just compoundsright even more and more.
And already, who knows whenthat's going to happen?
That could be 50 years down theroad, I don't know.
(24:08):
But you know, robots and ai aregetting a lot more intelligent
even now, and so, yeah, if wehave a moving assembly line that
can do manual tasks like thatand then being able to come in
and process that data, whichwhich we are already doing in
some sense, um, you know, withsatellite imagery or aerial
(24:31):
imagery, and processing thatdata to make decisions, I feel
like it could be.
That is a lot farther away,though.
It may not be in our lifetime,but yeah, someday I think it
could be.
Jodi (24:44):
That's you know you brought up.
You know robots would collectthe data instead of having to
rely on humans to collect thedata.
I never thought about that.
Right, how fast or how muchbetter will AI models get when
they don't have to rely onimperfect humans to collect data
, but they can just rely onrobots to collect data?
That's interesting becausethat's what I keep thinking back
(25:07):
to Someone that did a researchproject in grad school.
Like it's it's not easy tocollect good data.
Like it's a hard process.
Travis Yeik (25:15):
It's a lot of yeah, it's a lot of resources, it's a
lot of time and, yeah, a lot,yeah, and, as you said, it has
to be accurate, right?
So you have one person thatdoes it here versus another
person that does it over there.
Jodi (25:28):
They're going to do things entirely different ways and
maybe that I think that leadsinto my next question too is
like, okay, so in order for allof us, because at the end of
this, like I'll just cut to thechase and say I don't think that
AI will replace agronomists orfarmers anytime soon, but I do
(25:49):
think, you know, with the legendof John Henry and thinking
about assembly lines replacinghumans and like the Luddites
deciding that they didn't wantto embrace technology, but it
seems to be the best.
At least, looking at history, itseems to be that those that can
(26:16):
utilize it, learn about it andgrow with it are the ones that
quote unquote win in society.
So, thinking about that, youknow, like us, as farmers and
agronomists, what can we do nowto start growing with AI?
And I God, that sounds likesuch a weird question, but like,
(26:37):
what can we do now to make allof us better right now, with our
current you know what we do indecision-making and farming and
agriculture.
What can we do now so that wecan take advantage of this as
technology progresses, collectbetter data?
I don't know?
Travis Yeik (26:57):
well, I mean, that may be part of it.
I, I think for me, um, it boilsdown to one of the very basic
things is accepting it right,like anything new that comes in
it?
It's just like this is nevergoing to be right, or I'm
against this, like I don't know.
It took me it was probably 10years after your smartphone came
(27:21):
out that I had a smartphone.
It's been within maybe the lasteight years.
I had that.
I switched from a flip phone.
Even though I'm a developer,I'm kind of anti-technology.
Sarah (27:32):
Even though I'm a developer, I'm kind of
anti-technology.
Do you think that's justbecause you're skeptical of new
technology that's coming out?
Do you think it's because youquestion the validity of what
the claims are?
And the reason I ask that isbecause you know we find this in
(27:54):
farming all the time.
Where so, for example, we hadauto steer.
Well, it wasn't that long agoand I remember you know my dad's
generation saying who is solazy that they can't drive their
own tractor?
And why would you ever not wantto drive your tractor?
(28:15):
And now, like, honestly, mostfarms right around here in North
Dakota, they usually have.
God bless them but a retired guythat wants to come out and do
tillage.
Well, part of that tillageapplication is going to involve
auto steer, and if the autosteer quits, that guy is calling
and he's going to be like, hey,I don't know what to do, the
(28:36):
auto steer isn't working.
It's like that's all right,grab the wheel and drive what?
No, yes, you know, and so Ithink that's that's yeah.
How do we?
How do we handle thatskepticism?
Travis Yeik (28:54):
well, I think what you bring in is great point is
the cost of what it is right,not just the financial cost of
it.
I mean that is a huge deal likedoes using this technology
offset what I, what I would bedoing otherwise?
does getting that uh you knowthat rtk gps and using it to it
for spraying or for auto steer.
(29:15):
Does that offset my cost ofwhat this technology is and
using it or the support if itbreaks down?
Right, you have to havesomebody there that can give you
the support and know how to usethat technology, and that's
another turnaround as well.
I think you know to use thattechnology, and that's another
(29:35):
turnaround as well, I think.
And with AI, for now or even inthe near future, we're going to
need that human oversight.
So even with a robot, I wouldthink if you had one, you're not
just going to let that thing goand, you know, terrorize the
farm and all your animals.
You're going to have to havesomeone watching it and making
(29:56):
sure that what what it is doingis right, cause if it breaks
something that could be way morecostly than what it's actually
worth.
Sarah (30:02):
That's a really good point.
And you know, going back to theauto steer example, yeah, we're
getting to have some autonomoustractors in the field, but you
know we don't have the operatorthat far away and I realize
we're getting further away fromit to the point where we can
trust it.
But think about how long autosteer has been in a tractor to
(30:23):
get to this point and it's notlike it's driving down the
highway.
You know, going between fields,you know going between fields,
uh, and in some instances youknow with the with the grain
cart, that that tractor with thegrain cart behind it is not
loading the truck, it's justgoing over to the docking area
and then the truck pulls in andthen the truck driver gets out
(30:46):
and jumps in the grain cart andloads that.
So it's it's.
It has taken us so long to getto this point in in the whole
concept of autonomous tractors.
So the idea that we're notgoing to have oversight in AI,
at least for a while, is blowsmy mind.
(31:07):
I mean, we're going to have tohave that.
Yeah, that's such a great point,sarah.
Well, and Travis kind ofbrought that point up.
But you know, another questionthat kind of goes with this is
it.
It feels like Travis, when,when we visit about what you're
doing with AI.
You are actively training orteaching that AI model how to do
(31:29):
something, so it has to learn.
Teaching that AI model how todo something, so it has to learn
.
So in a way, it kind of almostfeels like it's a child that
you're actually like traininghow to do something.
Travis Yeik (31:41):
Yeah, would you trust your four-year-old to go
out in the tractor by himself?
I don't know.
Sarah (31:47):
Yeah, is that way off, though, on the way that I'm
thinking about that, that youare actually like teaching that
AI model how to do something.
Travis Yeik (31:58):
Yeah no I think that's one of like the one of
the cruxes, I guess, in teachingAI is that we are teaching
every AI from infant stage,right, and there's not an AI
that's already like 10 years oldthat we can just, hey, okay,
now teach you to do this, andteach you to do that, and make
these connections, and I thinkthat's part of the point, or,
(32:21):
you know, one of the struggleswith ai, and I don't see this
perhaps happening, and then Idon't know, let's say, the next
20, 50 years, even of being ableto make these important
decisions that humans do, thatrequire this oversight.
Sarah (32:38):
Imagine what this is going to be like when AI reaches
its teenage years and young 20s, when it's going to make a few
bad decisions along the way.
Uff da maida, huh prions, justkidding pre-ons oh, that's.
(32:58):
this is a really interestingconversation.
It's.
There's so much here to thinkabout.
Hopefully, ai is going to be agreat thing for agriculture into
the future, but it really doessound like it is up to us as
humans to decide what that'sgoing to look like, how we train
(33:20):
it and how we choose toincorporate it into our daily
lives.
Travis, I want to thank you somuch for this great conversation
.
The last two episodes thatwe've done here this one and the
one before just it was veryinsightful and there was a lot
of thoughts, so thank you forthat.
Jodi, as always, it's super funto co-host with you and I guess
(33:44):
with that at GK Technology.
We have a map and an app Forthat.
I can't wait to get in thefields again.
Jodi (33:54):
No, I can't wait to get in the fields again and an app for
that.
And now it's time for Ag Geek Speak with GK Technology's,
Sarah and Jodi, friends and Ican't wait to get in the fields
again.
No, I can't wait to get in thefields again.
Jodi (00:31):
Welcome back to another episode of Ag Geek Speak, where
I am joined with my co-host,Sarah Lovas, and GK Technology's
own Travis Yeik, and in thispart two of our episode about
artificial intelligence, we aregoing to continue our excellent
conversation that we startedlast week, where Travis helped
us define what artificialintelligence is, what its
(00:53):
history has been and, kind oflike, what its limitations are,
and we thought for this episodewe're going to go into more
depth about you know what AItools are here now and how could
they help us be more efficientas agronomists and farmers,
Because there's a lot ofcapacity for AI to do that.
Sarah (01:12):
My question is what is AI doing in agriculture right now?
How is AI helping us right now?
Travis, can you help usunderstand that, maybe a little
bit?
Travis Yeik (01:26):
Yeah.
So there's several applications, I think, where we can
definitely say AI is already inthe workforce and some of those
are, as we discussed here lastweek is weather right.
Being able to have farmers know, you know ahead of time what is
going to happen, whether thatis you know two hours ahead of
(01:48):
time or even two days ahead oftime, is super important for
making those decisions.
Yeah, so weather is one we goon and there's a lot of
precision agriculture for roomfor AI to grow and where AI
already is, and some of thoseinclude weed control and disease
(02:09):
monitoring.
I think there's John Deere hasa scene spray thing where they
use AI in it and they use imagesto come out and say, hey, this
is this type of weed and sprayspecifically just that weed.
Yeah, we don't get into it alot.
But livestock health monitoring,I think is a big one.
You'll see it in a bunch ofdairies, whether it monitors
(02:32):
milk outputs or the type of feed, uh, the animal when it comes
into the, to the bunk, orgetting water.
That is as a big one for fordoing animal production that way
, and we talked other ways Iguess would be irrigation, I
think is another one.
There's several companies doingirrigation, on determining when
(02:55):
and how much and where to water, so some of that precision ag
stuff there, that way again.
Uh, then we, we can move outand go broader into such as, uh,
being able to determine wherewe need products or or just, you
know, shipping and distributionin that way with agriculture,
(03:15):
that's, that's a, that's a hugepart of agriculture.
Um, and doing predictability ofwhat, what crops are being
grown and what needs to be grown, and doing commodities and
stuff.
Those are some of the majorones just off the top of my head
.
I think that is big inagriculture here already.
Jodi (03:37):
So, now that I think about this and where AI is and where
it isn't in agriculture rightnow, I feel like we're seeing AI
in places where there's alreadydata in ag right.
So like yield data, satelliteimagery these are pieces of
agriculture where we alreadyhave data.
Is that why we've got AI therealready, because we've got data,
(04:01):
or are we seeing a push for AI?
And you know, maybe some of themore challenging pieces of what
I'm trying to say here is likeis this really, is this being
driven?
And like where AI is and whereits potential.
Is it driven from low-hangingfruits or is it really trying to
address, like, problems thatexist in the market?
(04:22):
Now, maybe that's too too like,uh, theoretical for this
conversation, but like I mean,there has to be data to do ai
right, but you also have tostart with the theoretical idea.
Sarah (04:39):
And if in a, in a in a concept of where you want to go,
you got to dream a little bit,right?
You know, I have this problem.
Travis Yeik (04:45):
And it also calls money, of course.
Jodi (04:47):
Yeah, people have to pay for it, right yeah?
Travis Yeik (04:51):
Yeah, I think there's some you know statistics
that you know.
Like right now I can't rememberwhat it was, maybe I had it in
my presentation.
Let me see if I had that.
It was interesting, Like, letme see if I had that it was
interesting Like where the moneyin AI development is.
Yeah.
Sarah (05:06):
So Travis actually attended GK Technology's first
precision agriculture symposiumthat we ever put on and he was a
speaker for us.
He spoke about AI and precisionagriculture.
It was a great presentation.
It was fantastic.
So if anybody's interested inthat sort of stuff, come to the
(05:27):
GK Symposium into the future,because there's going to be
another one, not that I wouldever, Travis, do you want to
give your same presentationagain?
Jodi (05:34):
Just kidding Not that I would ever throw out a shameless
plug for our programming.
Sarah (05:38):
But there it is.
Travis Yeik (05:41):
There's your commercial so, yeah, we are
spending like billions ofdollars already and it's just
crazy to think of how muchenergy is going to be put into
ai in agriculture how do we keepthat money focused on products
that actually benefit decisionmaking in the field from a
(06:03):
practical standpoint?
Sarah (06:04):
and I and grant that.
You know there has to be acertain amount of dreaming that
goes into you know, what if Icould do this, what if I could
do that?
You kind of have to dream aboutthings that you want to make
better, but what are some thingsthat could go in our industry
to help make sure that thatmoney is going into places where
(06:25):
it's actually going to affectthe outcome for agriculture in a
very positive way?
Travis Yeik (06:30):
I think there's like four or five like major
agriculture sectors that we canthink of on where where AI is
going to be focused, and I thinkso for us, obviously it's
precision ag, right.
That's that's a big deal.
So for us, obviously it'sprecision ag right that's a big
deal and that's being able touse satellite images and be able
(07:03):
to to use some of thatprecision ag stuff to make those
decisions and enhance likeefficiencies of how we're
managing our crops and then Ithink another part is is
robotics, right?
uh, some of these, these bigcompanies, will get into John
Deere and Case and whatnot andthey'll get into the robotics
(07:24):
and being able to use the AI toautomate stuff or to automate
machinery.
And then I think another onewill be supplies and change and
distribution.
It's another major sector there.
That's another major sectorthere.
I guess we could includelivestock, even though we are in
(07:56):
the part of the different partof agriculture is what we're
dealing with, and then I thinkthe very last one would be some
of these other companies such asMonsanto Say Bayer, Bayer,
Bayer.
Yeah, Monsanto doesn't existanymore.
Yep.
Sarah (08:04):
They were purchased by Bayer.
Say Bayer yeah, sorry, Monsantodoesn't exist anymore.
Yep.
They were purchased by Bayer ohokay, Thanks.
Travis Yeik (08:09):
And then the other big one then would be companies
such as Bayer and doingbiogenetics right To breed and
to genetically alter some plantsand make them crop resistant,
disease resistant, droughtresistant, and that'll be a huge
, huge money making adventurethere for AI coming into
(08:32):
agriculture.
Jodi (08:33):
To speed up the process of choosing which genes, which
genetics, make up the bestcombination to maximize yield
and survivability in a givenarea to maximize yield and
survivability in a given area.
Travis Yeik (08:49):
Yeah, yeah, yeah.
And really it's being able tohave AI do that and be able to
look at whether the genetics orthe proteins of the plant.
I was listening to a podcasthere the other day of they were
doing that and whether you callit GMO or genetic altering right
, but it's just taking a plantand carrying it down about 10
(09:10):
evolutions, uh, or combining andsaying, hey, we can see this
crop, that that grows in thispart of the country, and take
out that specific piece of dnasequence and put it into a
different plant that that needs,that needs that trout
resistance or whatever it is.
Sarah (09:28):
So AI is going to be able to help identify those genes.
Travis Yeik (09:32):
Yeah, help identify them or help figure out.
Hey, how can it be implementedinto this different cell
structure?
Sarah (09:43):
Wow, Wow.
To actually help with theactual implementation of where
it needs to go, I suppose in theDNA sequence itself when you
splice it out and put it back in.
Jodi (09:55):
Can you send that link to that podcast?
Travis Yeik (10:00):
Yeah, it was a good one.
It was this girl in Europe.
Another really good one, though, is he's a really famous
YouTuber guy and he's calledVeritesium, and he does.
He had one that come out on.
What are the future benefits ofAI and one that we're currently
using right now?
Is it's actually the samealgorithm that I was talking
(10:21):
about here last week, about theAlphaGo and the Moo0ero and the
EfficientZero, where they'reusing this one that was able to
play games now and they changed.
Instead of playing a game, itis able to change protein
structures.
And this came out here.
I remember reading about it.
It was about eight years agoand we didn't have any protein
(10:41):
structures, hardly mapped out.
And so this uh or thisprofessor, he came in and he
made a program and he gave it togamers and these gamers from
all over the world were able tofigure out this protein
structure in like three days,something that we hadn't been
able to do in like 50 years,right holy buckets and now they
(11:03):
changed it so that not only cangamers do it, well, now this ai
can do it, and within, like thelast, I want to say two or three
years.
Right, we had maybe 20 proteinsmapped out.
Now we have like a million ofthem mapped out.
That's amazing, so so we'reable to, and not only can it map
(11:24):
out the proteins, but it cancreate new proteins.
Sarah (11:29):
Could it change the protein that's there?
Travis Yeik (11:31):
So like let's say that you're lactose intolerant,
although that's a sugar, butlike but it's an enzyme, right,
which is a protein then thatcontrols and how we can process
that sugar is a protein than thecontrols and how we can, we can
process that sugar.
Jodi (11:49):
So I don't know a whole lot about most things, but I
know some things about weedscience.
Um, and that was a lot of what Iworked on was like herbicide
resistance and like the coolthing that I'm thinking about
right now is like a lot of theselike we talk about target
sequence mutations that causeherbicide resistance and you
know, for like ALS inhibitorresistance, most of those are
target resistance.
(12:10):
But the point is is like whathappens there is you have just
maybe a single amino acid orlike a single nucleotide change
and that causes an amino acidinside of a protein to change
shape and so maybe that changesthe protein to change shape, so
where an ALS inhibitor can nolonger fit in that binding site
and can't kill that plant, andwhat we always wanted like when
(12:32):
I was in grad school just acouple of years ago, like the
thought was like how can wepredict where new mutations
might occur that might causeherbicide resistance?
And if you could model orpredict, you know what changes
on the gene could change theshape of it and make it so the
herbicide couldn't fit thereanymore, you'd know like what,
what could be likely to happenfor herbicide resistance?
(12:54):
But on the other hand too, likeif you can predict how these
proteins take shape, then youcan hypothetically design better
herbicides that fit thesemolecules to kill plants right.
I mean, most of our herbicidesthat we work with are all about
inhibiting enzymes in plants.
So if we can model theseproteins in weeds and figure out
(13:17):
how to back engineer a moleculeof herbicide to fit in that
protein and make it stop, thatcould help us develop new
herbicides too.
Sarah (13:27):
Or we actually have a weed geneticist at North Dakota
State University who works onthe genetics of of weed
resistance, like all the time.
Wouldn't it be cool if we couldchange the weed back so it was
not resistant anymore and makeit susceptible to the herbicides
(13:49):
that are already in themarketplace?
Yeah, right, likehypothetically, could you just
change it back and it's ai thatcould find that spot and do that
.
That's, that's fun.
Travis Yeik (14:01):
I think about the scary side of it now like we're
changing proteins right, whichis a prion, and so we can change
and create our own prions, andyou know, so you got so mad cow
disease right.
Jodi (14:15):
Yeah, yeah.
So prions is like, if we canmake a prion like mad cow
disease, right, yeah, yeah.
So prions is like, if we canmake a prion like mad cow
disease, like our ability tobioengineer, like weapons, would
be drastically better so so youmentioned the other episode.
If ai can learn how to killhumans, is this the approach.
Travis Yeik (14:33):
We should probably not transcribe this, so this
doesn't end up in a largelanguage model for ai to
discover and get back at us cutthis part out no but I was
listening, you know, so thatpodcast I mentioned earlier
about this uh girl, she wasusing some of that biogenetic
(14:53):
engineering and so one of herthings that she mentioned in the
podcast was being able to takea plant right.
It takes, you know, 15, 16weeks or whatever for the plant
to grow.
Well, she was just cutting outthe plant tissues and, uh, so
you can get it within a day ortwo and change it and see how
how these genetic markers reactwithin the plant tissues, rather
(15:14):
than yeah and I I think just toprovide some context.
Jodi (15:19):
So like I read a novel about like barbara mcclintock's
life, so barbara mcclintock, shewas a geneticist that
discovered jumping genes, um,and basically was helped helped
to map out the corn chromosomesback in like the 1930s.
But at that that point in time,right, that was before Watson
and Crick.
So like we didn't even knowwhat DNA looked like.
(15:41):
And so in the 1930s, the 1940s,we were still trying to figure
out like it was during that timeperiod that we even found out
that one piece of DNA codes fora protein.
Like that's not even a hundredyears ago, that's almost a
hundred years ago we didn't evenknow that DNA is coded for
proteins.
And now we're talking aboutusing an AI technology to
(16:04):
predict, like if we made achange in this tissue, you know
how could it.
It's crazy to think how fastthis technology is progressing,
based on how recently we'velearned about a lot of these
things.
Just nuts.
Sarah (16:20):
Okay.
So, jodi, I got an idea.
And this might be scary, buthere we go.
I have this idea.
What if we play a little gameright now called if AI could do
anything for me in the field?
It would be.
And then fill in the blank, andthen Travis can tell us whether
we're pie in the sky,ridiculous with the idea, or
(16:41):
whether you know there's not a.
You know there's no way itwould ever work.
So, okay, jodi, you go first.
If.
Think about farming, thinkabout agronomy, think about
whatever you want, if there wasone thing that AI could do for
you in the field, what would itbe?
Jodi (16:58):
It would be profit maximization decisions at the
beginning of the year.
Help me decide what herbicides,what mix of crops, given like
market conditions, what sets ofcrops should I grow, given my
equipment makeup, given theprices of what I can have and
then what I can hire, and thengiven the possible range of like
(17:20):
prices I could get for the, thecommodities at like.
There's a lot of variabilitiesof it but we'll say I'll give it
a data set of like what I thinkI can market it for.
But if they could help meprofit maximize and help me with
that decision making, that'swhat I'd want it for.
Travis Yeik (17:35):
Okay, I think that's definitely coming'd want
it for.
Okay, that's definitely comingright.
One of those algorithms Italked about was a mu zero and
efficient zero, and what they dospecifically is they look not
only at the reward now with thisobservation, but it plans it
out.
So, 30 steps ahead.
What will my reward be?
Jodi (17:55):
And so is, and if it can.
Travis Yeik (17:57):
And then also the other part of the model is
saying hey, if we have thisobservation now and I take this
action, what is my nextobservation going to be in my
next one and my next one.
And so it gets really good atplanning the future and it makes
those those future rewardsbased on what is happening and
where you're, where your currentsituation is at now, and that's
(18:19):
why it does really well and itdoes a lot of times better than
humans at understanding thesevery complex models, because it
has ran through thesesimulations millions of times
and it knows, hey, based on this, this is where the most likely
action will be, this way or,probability wise, this will be
(18:39):
the best outcome based on mydecision now.
Sarah (18:42):
That's fun.
That's really fun Becausethat's a lot of different
factors that go in and I feellike you know, in the spring of
the year, you know we're reallywell.
It actually starts like thefall before, like after harvest,
when you're trying to figureout what am I going to plant
next year and and what.
Then you're going through thewhole year of fertilizer, of
(19:03):
everything else.
So that's that's prettyexciting.
Travis Yeik (19:06):
And then obviously the tough part is, as we talked
about, is this combining allthese different decisions into
having that data for it to makethose decisions.
So we, uh, we are.
The algorithms are beingdeveloped, but having that data
put together and processed, um,it could be ways in the future,
but, um, I, I could foresee ithappening.
Jodi (19:29):
And I have a question for later.
I'm I'm going to put this on mylater notes.
Okay, sarah, okay, samequestion for you Okay.
Sarah (19:38):
So this time of the year as an agronomist, you know we're
recording this over like rightafter Memorial day weekend and
pretty much if you are anagronomist in North Dakota,
Manitoba, Saskatchewan,Minnesota, South Dakota, if you
were an agronomist and you'vegot a four-wheeler, you are
putting on probably 100 miles aday right now, at least on your
(20:01):
four-wheeler, if not more.
It's go time and one of the theparts of scouting crops at this
time of the year that I findtime consuming and annoying is
stand counts.
Now I am aware that we can goout and we can log stand counts
with a drone.
You know you can take a picture.
It's high, detailed.
You can get those pictures to acertain extent.
(20:22):
Like sugar beets can be reallyhard if they're in the cotyledon
stage because they're so smallthat even sometimes the
resolution isn't good enough ondrones UAS.
But what I think would bereally great is if we could get
accurate stand counts before andafter an event and then have
(20:46):
some sort of a decision model inthere that can take into effect
the previous weather, thefuture predicted weather and the
market conditions to determinewhether we should replant or not
.
Travis Yeik (21:01):
So that is a lot right, that's.
That's like several differentsystems coming into one.
I think about what was.
We were in grade school and youhad the man versus the machine.
Right it was.
It was this guy digging atunnel or a for the, for the
railroad, and then he had amachine and he was trying to
beat the machine.
You guys remember what that wascalled.
Jodi (21:20):
No.
Travis Yeik (21:21):
Oh, okay, maybe this was a Wyoming thing.
No.
Sarah (21:27):
You're younger than me too.
I mean, when I was inelementary school we had Apple
II computers with basic language.
Travis Yeik (21:35):
I'm going to look at that real quick, just because
I'm curious.
Sarah (21:37):
I know you guys have heard this the chalk all looked
pink when I got back to myclassroom after computer class
because of the green screen onthe computers.
Oh yeah, and, by the way, weactually had chalkboards with
chalk.
Jodi (21:56):
Oh.
I went to an elementary schoolthat had about 50, 60 kids total
.
So yeah, all we could affordwere Apple computers and
chalkboards too.
Sarah (22:04):
Then I moved and look at how he turned out.
Jody.
The legend of John.
Travis Yeik (22:10):
Henry.
Sarah (22:11):
What was it called Travis ?
Travis Yeik (22:13):
So it's the legend of John Henry, and it was this
drill that could drill fasterthan any man, and he was
drilling through a tunnel.
And so he went and said youknow what?
We don't need these machines.
These machines are going toreplace us.
Right, I can beat this machine.
And so he went up against thismachine and I believe I'm trying
(22:36):
to remember the outcome, but Ithink he lost.
But it's the same thing, right?
I'm thinking about automationand how we have assembly lines.
Right, when they came and Ithink a lot of people were
scared at that time too thatassembly lines are going to
replace jobs, and in a lot ofcases, they did and I think AI
(23:01):
with robots, I think they couldbe there someday where they take
over these processes.
It's just an assembly machinethat can move around, and so,
whether you're pitching hay ordoing manual labor tasks, fixing
fence and stuff like that, Ithink you could have an ai
someday that has the ability toto move um and make these very
(23:23):
simple um task orienteddecisions on putting a post in
the ground or doing this ordoing that, and I think think in
this case, it could be in yourexample, driving a food out to
this place, or a drone, flying adrone out to this location and
being able to make these tasksand bring back the data for some
(23:46):
of these tasks.
And I think once that happens,then AI will be able to be
carried along quite a ways,because now we have that data
that is being automaticallygenerated by robots, by AI
itself, and it just compoundsright even more and more.
And already, who knows whenthat's going to happen?
That could be 50 years down theroad, I don't know.
(24:08):
But you know, robots and ai aregetting a lot more intelligent
even now, and so, yeah, if wehave a moving assembly line that
can do manual tasks like thatand then being able to come in
and process that data, whichwhich we are already doing in
some sense, um, you know, withsatellite imagery or aerial
(24:31):
imagery, and processing thatdata to make decisions, I feel
like it could be.
That is a lot farther away,though.
It may not be in our lifetime,but yeah, someday I think it
could be.
Jodi (24:44):
That's you know you brought up.
You know robots would collectthe data instead of having to
rely on humans to collect thedata.
I never thought about that.
Right, how fast or how muchbetter will AI models get when
they don't have to rely onimperfect humans to collect data
, but they can just rely onrobots to collect data?
That's interesting becausethat's what I keep thinking back
(25:07):
to Someone that did a researchproject in grad school.
Like it's it's not easy tocollect good data.
Like it's a hard process.
Travis Yeik (25:15):
It's a lot of yeah, it's a lot of resources, it's a
lot of time and, yeah, a lot,yeah, and, as you said, it has
to be accurate, right?
So you have one person thatdoes it here versus another
person that does it over there.
Jodi (25:28):
They're going to do things entirely different ways and
maybe that I think that leadsinto my next question too is
like, okay, so in order for allof us, because at the end of
this, like I'll just cut to thechase and say I don't think that
AI will replace agronomists orfarmers anytime soon, but I do
(25:49):
think, you know, with the legendof John Henry and thinking
about assembly lines replacinghumans and like the Luddites
deciding that they didn't wantto embrace technology, but it
seems to be the best.
At least, looking at history, itseems to be that those that can
(26:16):
utilize it, learn about it andgrow with it are the ones that
quote unquote win in society.
So, thinking about that, youknow, like us, as farmers and
agronomists, what can we do nowto start growing with AI?
And I God, that sounds likesuch a weird question, but like,
(26:37):
what can we do now to make allof us better right now, with our
current you know what we do indecision-making and farming and
agriculture.
What can we do now so that wecan take advantage of this as
technology progresses, collectbetter data?
I don't know?
Travis Yeik (26:57):
well, I mean, that may be part of it.
I, I think for me, um, it boilsdown to one of the very basic
things is accepting it right,like anything new that comes in
it?
It's just like this is nevergoing to be right, or I'm
against this, like I don't know.
It took me it was probably 10years after your smartphone came
(27:21):
out that I had a smartphone.
It's been within maybe the lasteight years.
I had that.
I switched from a flip phone.
Even though I'm a developer,I'm kind of anti-technology.
Sarah (27:32):
Even though I'm a developer, I'm kind of
anti-technology.
Do you think that's justbecause you're skeptical of new
technology that's coming out?
Do you think it's because youquestion the validity of what
the claims are?
And the reason I ask that isbecause you know we find this in
(27:54):
farming all the time.
Where so, for example, we hadauto steer.
Well, it wasn't that long agoand I remember you know my dad's
generation saying who is solazy that they can't drive their
own tractor?
And why would you ever not wantto drive your tractor?
(28:15):
And now, like, honestly, mostfarms right around here in North
Dakota, they usually have.
God bless them but a retired guythat wants to come out and do
tillage.
Well, part of that tillageapplication is going to involve
auto steer, and if the autosteer quits, that guy is calling
and he's going to be like, hey,I don't know what to do, the
(28:36):
auto steer isn't working.
It's like that's all right,grab the wheel and drive what?
No, yes, you know, and so Ithink that's that's yeah.
How do we?
How do we handle thatskepticism?
Travis Yeik (28:54):
well, I think what you bring in is great point is
the cost of what it is right,not just the financial cost of
it.
I mean that is a huge deal likedoes using this technology
offset what I, what I would bedoing otherwise?
does getting that uh you knowthat rtk gps and using it to it
for spraying or for auto steer.
(29:15):
Does that offset my cost ofwhat this technology is and
using it or the support if itbreaks down?
Right, you have to havesomebody there that can give you
the support and know how to usethat technology, and that's
another turnaround as well.
I think you know to use thattechnology, and that's another
(29:35):
turnaround as well, I think.
And with AI, for now or even inthe near future, we're going to
need that human oversight.
So even with a robot, I wouldthink if you had one, you're not
just going to let that thing goand, you know, terrorize the
farm and all your animals.
You're going to have to havesomeone watching it and making
(29:56):
sure that what what it is doingis right, cause if it breaks
something that could be way morecostly than what it's actually
worth.
Sarah (30:02):
That's a really good point.
And you know, going back to theauto steer example, yeah, we're
getting to have some autonomoustractors in the field, but you
know we don't have the operatorthat far away and I realize
we're getting further away fromit to the point where we can
trust it.
But think about how long autosteer has been in a tractor to
(30:23):
get to this point and it's notlike it's driving down the
highway.
You know, going between fields,you know going between fields,
uh, and in some instances youknow with the with the grain
cart, that that tractor with thegrain cart behind it is not
loading the truck, it's justgoing over to the docking area
and then the truck pulls in andthen the truck driver gets out
(30:46):
and jumps in the grain cart andloads that.
So it's it's.
It has taken us so long to getto this point in in the whole
concept of autonomous tractors.
So the idea that we're notgoing to have oversight in AI,
at least for a while, is blowsmy mind.
(31:07):
I mean, we're going to have tohave that.
Yeah, that's such a great point,sarah.
Well, and Travis kind ofbrought that point up.
But you know, another questionthat kind of goes with this is
it.
It feels like Travis, when,when we visit about what you're
doing with AI.
You are actively training orteaching that AI model how to do
(31:29):
something, so it has to learn.
Teaching that AI model how todo something, so it has to learn
.
So in a way, it kind of almostfeels like it's a child that
you're actually like traininghow to do something.
Travis Yeik (31:41):
Yeah, would you trust your four-year-old to go
out in the tractor by himself?
I don't know.
Sarah (31:47):
Yeah, is that way off, though, on the way that I'm
thinking about that, that youare actually like teaching that
AI model how to do something.
Travis Yeik (31:58):
Yeah no I think that's one of like the one of
the cruxes, I guess, in teachingAI is that we are teaching
every AI from infant stage,right, and there's not an AI
that's already like 10 years oldthat we can just, hey, okay,
now teach you to do this, andteach you to do that, and make
these connections, and I thinkthat's part of the point, or,
(32:21):
you know, one of the struggleswith ai, and I don't see this
perhaps happening, and then Idon't know, let's say, the next
20, 50 years, even of being ableto make these important
decisions that humans do, thatrequire this oversight.
Sarah (32:38):
Imagine what this is going to be like when AI reaches
its teenage years and young 20s, when it's going to make a few
bad decisions along the way.
Uff da maida, huh prions, justkidding pre-ons oh, that's.
(32:58):
this is a really interestingconversation.
It's.
There's so much here to thinkabout.
Hopefully, ai is going to be agreat thing for agriculture into
the future, but it really doessound like it is up to us as
humans to decide what that'sgoing to look like, how we train
(33:20):
it and how we choose toincorporate it into our daily
lives.
Travis, I want to thank you somuch for this great conversation
.
The last two episodes thatwe've done here this one and the
one before just it was veryinsightful and there was a lot
of thoughts, so thank you forthat.
Jodi, as always, it's super funto co-host with you and I guess
(33:44):
with that at GK Technology.
We have a map and an app Forthat.
I can't wait to get in thefields again.
Jodi (33:54):
No, I can't wait to get in the fields again and an app for
that.
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