NBC News Radio. I'm Chris Garagio. The Jacksonville County sheriff says the gunman
who killed three people at a store in a predominantly black community yesterday had no
criminal record and apparently purchased both of the weapons legally. The gunman took his
own life minutes after carrying out the attack. President Biden has condemned it as
a potential hate crime. A second victim has died following the mass shooting at
a bar in Louisville early this morning. Four other people were shot and remain
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accomplishments, but added that corporate greed is the reason sixty percent of Americans are
living paycheck to paycheck, and Democrats need to have the guts to take on
the large corporations. And Governor de Santis is urging Florida West Coast residence to
get ready for tropical Stormy Dalia. He's placed thirty three counties under a state
of emergency. Landfall is expected Tuesday. I'm Chris Garraggio, NBC News Radio.
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This is the man from yesterday and back in time, this time to nineteen
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from Yesterday dot Com, Miss Something Today, Yesterday last week. Check out
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listener behind. It require or to kind of work together, but the beautiful
thing. And I think open source has a lot to do with this,
quite frankly, because the spirit of open source said, look, let's all
collaborate on the foundations and then each of us can build out our unique characteristics
and function out if it makes our company special. Right, yeah, I
think you know. I would say that going back to the Gibson quote you
started with, Right, the future is here, but it's not evenly distributed.
I think the potential is here, but it's not evenly distributed. There's
a there's a tendency where I find engineers are optimizers by nature, and so
a lot of the times, even when they have the opportunity to say,
forget about all that complexity underneath, just build what you want to build,
they still like to tink current way with. You know, let's I think
we can we can make that wheel spin a little bit faster. Let's reinvent
that wheel one more time. So that's just to say that I think that
one of the things that's really important in the in the world of cloud is
constraints, and constraints come in different forms. So open sources is an interesting
example there because open sources at the code level, it's very wide open in
terms of what you can do. I find that can lead to a little
of that tinkering I was describing. That may be superfluous, but APIs play
a huge role in the cloud native space, and I think if you look
at Kuberneties, if you look at Kuberneties, if you look at AWS,
if you look at all these different cloud based services, APIs are usually the
demarcation point between the consumer and the provider. And the place where you can
hide all that you can abstract away all that complexity that's unnecessary for the consumer
side. And I've been, you know, focusing on API design best do
this for a long time. Again, there's ways to design good APIs that
are durable and abstract things. S three and AWS is probably a great example
of that, where it's they launch this API in two thousand and six and
it's hardly changed in seventeen years. And then there's other examples where maybe the
complexity bleeds through. But definitely for organizations I've seen really taking advantage of the
cloud native opportunity. That's what they're doing. They're really using the demarcation points
and the constraints and the abstractions to help people move at the fastest speed they
can to get their job done. Well. Yeah, and you know,
when the target stays the same, that allows you to practice and practice and
get closer to perfection. But when the target keeps moving, you know,
when the goal posts move, that really affects every last bit of the process.
The planning, the roadmap, the execution, the maintenance. Everything becomes
Lucy goosey if the goalposts keep moving. But if at least the target stays
the same, then we can marshal resources it's necessary, and at least we
can track our progress getting to where we want to go right exactly. And
I think complexity is an interesting word here where I think a lot of times
and this is you'll see in a lot of marketing materials in the industry treating
complexity like it's the enemy. Right, complexity is bad. We're going to
simplify everything. I think, like Fred Brookstead his paper in nineteen eighty five
or something talking about different types of complexity, I think the reality is like,
for the most part, look at what we can do with software,
the complex problems that we can solve, and we're going to get into talking
about ahi where it just takes it to all other levels. Right. It's
really the organizations I've seen the work most effectively are the ones that understand that
they have to make complexity of their friend and really learn how to tame complexity,
right, take the wild beast and have it working for you as opposed
to working against you. And a big part of that is delineating, like
what's the complexity that we're solving for versus what's the complexity that we're introducing in
the solution. And so I think with Cloud, that's why it's so powerful
is we could never invent a single appliance that was going to hide all the
complexity inside a box, right, But with with cloud and all the levels
of abstraction that we have and the way that we can interconnect, you know,
the things that we can do in an interconnected way allows us to dance
with the right complexity and kind of and and not lead over too much of
the complexity that we don't need. But to your point, like it's it's
a potential, it's always a potential house of cards because if and that's why
I think embracing complexity is an important thing for organizations to do, because if
you pretend that it's gone right, this is like the fallacies of the network,
right you like they will it will come back and bite you. That's
right. I love what you said, dancing with the right level of complexity.
That's pretty cool stuff. And you gave me a great segue to bring
in Mike Bachman out of architecture and AI for booming Mike, So I'll bring
you in. You know, the beautiful power of AI is again, if
the target stays the same, then we can build our intelligent automation, our
intelligent analysis around all that. Let's go to Mike Bachmann. Actually William mc
knight is on the screen here. Just i'll get some quick thoughts from you.
Go ahead me, well, yeah, yeah, go ahead, okay,
okay, Well, I like what we'll said about cloud native. I
do want to stress that it's more than just cloud computing. So it's more
than just utilizing the cloud for all of the benefits that you get from the
cloud where you don't have to have your own data center and the staff that
goes along with that and all that sort of thing, but you get access
to a wide variety of resources, which is great. But to be cloud
data, you go beyond that, go beyond your lift and shift from your
on prem to the cloud and you get into some of the things that we're
talking about. Specifically, the broad term is micro services, and so to
get there, you have to take your code, your historical code that is
probably not in my pro services, but take that and shrink it down into
manageable, bite sized pieces where each piece does a very finite function, orchestrate
them together with orchestration tools like Kubernetti's was mentioned make sure that everything is containerized,
and this gives the code ability to be portable and manageable across all environments,
as you have been stressing, Eric, but also I think take full
advantage of the cloud and be cloud native. You have to be agile,
and you have to code with CICD as we say, or continuous integration continuous
delivery in mind, and that will help allow you to take full advantage of
the cloud capabilities for your applications, and your DevOps should be more streamlined as
well around this the ICD concept. So putting that all together, it's not
for everything in most enterprises, but a lot of development is going that way,
or it might be going to a place where you still got your on
prem and you're adding on to it in the cloud. Is sort of a
hybrid cloud fashion. So while I wouldn't call cloud native a current standard,
I would say it's a great best practice and definite trend. M Yeah,
and we'll bring Mike Bachman again into the next segment. I suppose I'll go
back to you, William and kind of comments on what you just mentioned there,
which is hybrid cloud. Right. So we have this very hybrid world
right now because I you know, I think the rumors of on prem's demise
have been greatly exaggerated. I think on prem data centers are going to stick
around for a long time. They're gonna be a long tail at least for
seven years, right. Isn't that the amorization schedule for those kinds of investments.
So it's like the accountants and the senior executives are like, no,
no, no, I'm amortizing that for another three or four years. You
can't make it go away just yet. But I think you're going to start
seeing it. We're already seeing this. In fact, before the PANDEM I
went to a territated conference there was a company I can't remember what their name
was, but what they do is they take your data center and they make
it look like S three. So I think you're going to see some interesting
innovations going on on the data center side to be able to leverage these things.
And then things like personally Identifiable Information PII or you're the data you want
to train your outgrams on, you probably want that data on prem. So
they're going to be use cases to leverage on prem. It's not going to
go away soon. So we really have to be careful about where we're going
and how we're getting there, right William one minute, Yeah, that's right.
As I mentioned, some newer parts of applications are going to the cloud,
whereas it still has an on prem foundation to that application, but furthermore
nonsensitive or less critical or you might say voluminous data that is data that's going
into the cloud. And a lot of companies are still are still concerned about
the cloud and exposing their data on the cloud. Some of them, though,
are replicating data in both places, so they get it ask a recovery
type of architecture going as well. So there's a lot of options in here
with hybrid cloud. And you know, you might use the public cloud for
your birsty type of workloads where in the month you've got some excess capacity that
you're going to need, just use the cloud for that and avoid over provisioning.
Yeah, I know, these are all factors that come into play.
And I'm glad you brought that up because you do buy cloud in credits and
if you have extra capacity and there's a wind time window on using it,
and this is the job of a CIO these days, and a cdo and
a CTO for these folks to talk to each other and understand where they're going.
Plan these things out. Be as agile as possible, but don't lose
your moorings. And then, of course we're talking about AI and training AI.
Well, you're gonna want your own data to train that AI. Folks,
you don't want to train your data on someone else's train your on someone
else's data. But don't touch dot I'll be right back. You are listening
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inzialysis first team. All right, folks, take us to the future.
Indeed, but he's bit to have Matt mcclardy, CTEO of Boomy, and
Mike Bachman, he's the head of architecture and AI, and our good buddy
William mcknights, who scored a one hundred and one of these Cloud Analyst Influencer
scores to track. I'm like, dude, how'd you get a one hundred?
The second place was like fifty eight? Mike, all right, my
boy here is he's figured it out. But we were talking about AI and
your data and on prem data centers not going away and I'll throw this one
over to Mike Bachman because you know, think or two about all this I
mentioned you don't want your data training someone else's AI. Of course that's what
chat GPT did. There are now some lawsuits out there and it's going to
be interesting to see how that pans out. But you've got a lot of
experience in the space at these are amazing times. What are your thoughts about
this nexus of AI and cloud native. Yeah, so, first of all,
Eric, thanks for having having me on today. This is a lot
of fun. The conversation has been great where AI is going. You know,
I loved where you left that off because I often say, do you
want your IP training somebody else's IP? And interfacing with cloud based you know,
machine and tell eligence services such as something like chet GPT, Barred Claude.
There are different ways that each of those different sas services uses their foundation
models under the covers, but some you don't know exactly how your data is
being used in spite of the fact that there are different governance tools that each
one of these types of services offers. Still it's something that we're all impressed
with. Chet GPT really changed a lot. It brought into the zeitgeist.
You know, this concept of something that feels very anthropomorphic, something that feels
like we can talk with a system of computation and get back something of value,
and so it becomes seductive. I guess to want to interface with it.
But I think if we use it as a system of production, or
to put something like a generative, pre trained transform large language model in a
typical data pipeline, we might want to think about how that use case really
plays out, what type of governance needs to be thought through, and then
what ultimately our desired outcome is from that particular use of this technology. Because
it's really really neat stuff. However, it's just scratching the service. Yeah,
you bring up an excellent point here, which is to remember what are
we trying to accomplish? Right And I think one of the hurdles that organizations
will have to get over here and it's happening and fits and starts right now,
is how do we open our minds to the total recall open your mind,
remember Arnold Shorten, But how do we open our minds to this new
set of possibilities? Because there is a tendency to do things the old way
and to get accustomed to our constraints that we have. Well, those constraints
are now largely out the window. So you always want any focus you have
about leveraging AI to be bordered and driven by some business need. What are
we trying to accomplish? Do we want lower costs? Do we want a
better conversion rate? Do we want to better understand the customer? And then
let your team run wild in terms of coming up with how to leverage it.
Give them a sandbox to play with to do something, but be careful
have the guardrails. Do this in your on prime environment, for example,
don't just throw it into one of these large language models. But focusing on
the business outcome and then using these technologies to get there, that's the key
to success. What do you think? Yeah, I agree with with all
of that more or less. And I think you know the idea of being
you know, at the nexus of where the art of the possible and the
reality of the feasible meat is sort of where you know, both Matt and
I have spent much of our career and with that comes you know, new
and emergent technologies such as these really suffici sisticated sets of computation artificial intelligence,
or I like to refer to it as machine intelligence, because I don't know
what intelligence is, whether something's real or artificial. That's a philosophical conversation.
But that said, fathom, you know, this is really powerful, and
so before we use a powerful technology, we probably want to test it out
in the sandbox environment. And one of the things that I love about how
you're bringing together the conversation between cloud native and artificial intelligence is because they see
them go hand at hand, you know, in terms of agile process,
micro services, all of these things I think AI is today is really a
lot of the and computation at scale. The ability to have memory systems and
networks at scale as we do today happened iteratively for you know, really seven
decades, and this was sort of the Turing's vision was what we're now realizing,
where asian is actually capable of feeling and so how you know, data
evolved with you know, with these different systems that we've built over the course
of the last seven or so decades. We're at a point now where we
get to see just the incipient phases of where AI or machine intelligences could take
us, and so to use machine intelligence inside of our processes can be really,
really seductive, and so it's important that we really think through all of
the realm of possibilities that artificial intelligence can give an organization, look at our
use cases and really become ready to receive AI in ways that we haven't.
In fact, right now we're developing whole whole programs around AI readiness because this
seems to be where folks want to know. Where can I use chat GPT?
Is chat GPT the only thing I can use? Short answer, no,
it's not. What are these models that I can take advantage of?
Do I have to go through assas service to use them? Yes? Or
no? How do I make sure that I can trust whatever this AI engine
is giving me? Because I hear it it hallucinates? What does that mean?
How is that relevant to my business? Right? These are just some
of many concepts that we have to think through to become AI ready. And
you know, I'm so glad you brought that up. That is a huge
issue to tech. Maybe that's structured, it's probably more like ten, which
means you've got ninety percent unstructured data. It's in documents, pdf, spreadsheets,
emails, all sorts, of unstructured data. Well, guess what that's
the context for your structured data now when the executives reports down and know the
general context. But machines don't crawling around in your data, so you need
to be ready to use AI. And you made another really interesting point here
I'd like you to expand upon, which is we're not really gonna know until
we play around how these things operate in what they do. And if folks
WO haven't done it yet, you should definitely start playing around with these large
language models. Just the different prompts you can get. We did all show
on that last week about sequential prompts and strategically aligned prompts basically, and you
can give lots of details. And then of course there's d MS into a
vector dease of truth as Brian Raymond referred to it. A guy we had
on the show last week. I love that concept anchors of truth. But
you need a skunk works team to play around with this stuff, just to
hash out the range of possibilities of what's going to be plausible to spend your
time and effort on. And if you do that carefully, you know what
was Eisenhower's line about planning I've always found that plans are useless, but planning.
Planning is indispensable. You're going to learn a lot by playing around with
this stuff, right, absolutely. And I do want to talk about something
you just said, and that is I'm glad you had a conversation about embeddings
and vector databases last week because really, you know, people off asked me,
what changed? How did this thing come to be? Was it?
Remember when chat GPT was introduced, it seemed to come out of nowhere.
Well, if you've been in the space, theres that have happened the decades
leading up to twenty seventeen. In December of twenty seventeen, there was a
paper that came out that was sort of seminal for what where we're at today
with something called Transformers. And since you had the conversation last week, we'll
have your listeners go back to that. But really easily to distill what changed
is humans no longer have to think like computers in order to retrieve information.
Now the computer we can offload that function to the computer to do this,
and so these anchors of truth that your previous guest had talked about is exactly
that sort of thing wherein you know, instead of us having to think in
terms of sequel statements or compute logic and in orgates, we can now ask
in our own native languages, whether that's English, German, Russian, whatever,
you can ask in your own native language a query question and you can
get a response that really downs relevant to whatever the answer is going to be.
And there's a you know, another thing to think about here too.
The more that you provide context based data into that particular question, or you
I view the machine intelligence with relevant data, the better your answers are going
to be. And you brought something else up that I want to point out
too, which is talking about different types of prompts. I like lazy prompting
because what that means is that I can have conversations with the machine intelligent system
that I'm talking to, whether it's Claude barred or chat GPT or something else,
and be able to ask a very simple, root ammentary question about a
particular topic and then riff with the computer until I come up with the answer
that is sufficient for me, and so I don't need necessarily and certainly over
time, I do not anticipate that these systems are going to require a well
prompt engineered sort of input in order to achieve achieve a desired outcome. Instead,
what it's going to require is domain efficiency with the kinds of questions that
you're asking, and the ability to have two interlocutors, one human one machine,
being able to rip off of each other until a defined answer is achieved.
Yeah, lazy prompting, that's brilliant. It's like the open ended question.
Right, maybe I'll bring let's see, I'll bring Matt mcclardy back into
the conversation. The point is you have to play around with these things to
understand how they work. It's like I got back into playing ping pong and
reminding myself about spin, top spin, back spin. You have to do
it a few times before you're oh, that's right. If he does backspen,
I got to do backs. But if he does forcement, I got
to do forcemen that kind of thing. That's what you're going to learn when
you play around with these things. But there is so much to learn.
We've only just begun, right. Yeah, you know, innovation in the
technology space is a really it's a tricky. It's it's more art than science.
I've been you know, I think it's The point was made earlier about
really zoning in on the business need. But until you know what the range
of possibilities are, it's hard to even figure out what business possibilities are going
after And we've seen this. This isn't just a new thing for the AI
space, but in any sort of technology domain, like how do you when
a new technology comes along, where do you apply it? There can be
a huge temptation to just you know, take the new hammer and everything is
a nail. And at the other end of the spectrum you may be myoptically
focused on some particular business needs. So I think the point about just having
people get into that world and play around with it and get an understanding is
important. But again I was talking earlier about the practical application and where there.
So I think that AI has for the most part the world of out
of the world of analytics right in data and analytics. It's it's essentially this
is a massive oversimplification, but it's like code written by data, right.
This is this is kind of if we if we were to classify all the
all the AI stuff that's out there, and so garbage in, garbage out.
It's putting the focus on the data is extremely important, but it's easy
because of all the complexity in that world to get lost and forget about if
you're a business evaluating the usage of this technology, how is it actually moving
the needle from my business? Where am I going to realize value? And
so, you know, I think for companies that are pure play building new
models, you know, doing all the innovation on the AI side, fundamentally
important where we sit and boomy is we we have a ten to span those
worlds just by virtue where a platform resides, right, we're going from the
data bringing that to the surface where it's used in by customers, for companies,
by other user interactions, by automated process that drive the business. So
I think this is where we've been able to observe, even pre AI other
technologies, like where the value gets realized in all of this isn't so much
in the latest and greatest model that gets invented, But how does the insights
coming out of the models, how do they actually get leveraged in the flow
of business to drive the business model for companies. So and I've seen a
lot of companies sort of approach this from the data side, from the supply
side, almost like hey, get our data or in order to get our
AI strategy in order we need to look at the data, think about all
the types of data that we have. I think it's actually quite useful to
invert that and look at things from the consumer side and think of Okay,
if if we know the place that the value is going to be realized is
going to be in the execution of our business model, whether that's how seamlessly
we integrate or how seamlessly we interact with our customers, how we support our
customer channels through you know, assisted channels right, like you can see it
becomes make it more bite size and you can, you know, taking the
principles of cloud native and micro services, you can you can right anyway,
Yeah, that's an excellent point. Well, folks, don't touch it.
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Welcome back to Inside Analysis. Here's your host, Eric Kavanaugh. All
right, ladies and gentlemen, back here on Inside Analysis, your host Eric
Kavanaugh with an all star cast. Here we've got Matt McCarty CTO, and
Mike Bachmann, head of AI and architecture over at Boomy, and William McKnight
number one cloud analysts in the world according to some sources, and I'm fine
with that. And William, you've made a couple of really good points about
cloud native as a concept and about principles of engineering and software development and design
and program management. I mean, that's really what a CIO needs to be
doing, is program management, understanding where to allocate resources, what to put
in the cloud, what to do on prem's it's a fun time, but
it's a bit of a dizzying time. What's your advice for how to navigate
through these waters. Well, you're going to get a typical enterprise. It's
not going to get there all at once. So I take an inventory of
your on prem assets that could move to the cloud. The better architecture they
are today, the easier they will be to migrate to the cloud. And
there's there's a host of things that need to be done. Obviously, you
need to select a cloud provider, adapt a micro services architecture. That's a
big one there. If you really want a cloud native. Now some things
you're just going to move, lock and load cloud computing. Okay, we
get some of those benefits like I mentioned before, but to get the cloud
native, which is that next level of value, next level of value that
the application portfolio is going to be delivering back to the business, you want
to adopt the micro services architecture and understand that when you do this and move
to the cloud, you're now in for the most parked serverless computing. So
you don't have to worry about things like provisioning and managing servers, trying to
guestimate how scalable this needs to be where it's going to go. That's all
taken care of, as well as backup recovery, all your kind of operational
things as well. So there's a lot of good things in moving to the
cloud and getting to cloud native. You've got to containerize and package your applications,
you got to choose an orchestration platform and implements the ICD. But I
will also add that what the cloud allows is much more visibility in two operations,
and so you can use tools like Prometheus or Grafana to monitor your applications
at a much lower level than you ever did before. You can also selective
move to manage services in the cloud in order to minimize your overall operational overhead.
Beyond that, you're just going to have to iterate and improve until your
environment visaly the cloud is where you want it to be, and you're never
going to get there because there's mings coming in the door all the time.
Maybe you push those to the cloud and by the time you think you might
get there or something else will come in play, and you'll just keep moving.
But keep moving, keep going forward. Remember holp to make how to
do priversation, to want to do things that are easy to do, that
you can do low risk. We've been talking about kind of reading the same
thing for cloud. You want to set yourself up for the future by doing
things now or immediately that help you do bigger and better things in the future.
And so some ways that you divide up your applications inside of your enterprise
can help you do that. Finally, and most importantly, is you want
to add value back to the enterprise. We've been talking a lot about ROI,
and I'm all about ROI when it comes to AI, though, yes,
we do need to explore how can we ever project and we really don't
know what we're doing. We don't know what possibilities are, we don't know
how long it's going to take or what resources. Organizations really need to be
learning a lot now and where to apply it. Let's see, I'll throw
it over to I guess Mike Bachmann. I see you nodding your head.
A lot of great stuff from William there. It's a lot to digest and
understand. Again. Prioritization, I mean to me, this is one of
the most important things in any organization these days. How do you prioritize what
you're working on now? How investment ling future innovation versus keeping the train running
on time and all that stuff. You got to make sure that stuff works.
But it's about intelligent automation and integration. And you begin to learn from
these models how to optimize. Me possibly more than the generative AI stuff is
learning to optimize these incredibly complex architectures to save time, improve performance, and
by the way, if you do it right, you're also improving security.
What do you think, Michael, Yeah, I think that the three often
stated rules of value are day out of jail, you know there's risk,
and so as long as you can tie technical value to one of those three
areas, I think you have a good case to make about how you can
go about prioritizing. In fact, what William just said sounds an awful lot
like how you determine whether your principles is a little bit of a heavy word
for me, but we could say a list of six and growing things to
think about when being AI ready, having clear goals, Having great documentation one
of the biggest inhibitors to whether you're talking about breaking up an application into smaller
pieces, or whether you talk intelligence or machine intelligence. A dearth of a
documentation creates a lengthy road for you to get to your destination. So documentation
is key account This isn't artificial intelligence isn't just about stuff that makes your bow
tie spin for you know, for tech geeks like myself, but it's it's
about bringing the line of business together with the technology in order to have a
way to make money, save money or reduce risk as important and we go
through in our workshop, needs to be had by all of the stakeholders involved
in these coprocesses, which should be documented. And then one of the biggest
things in the area is automating manual processes. AI works at SPET. We
be able to, you know, sort of get our heads wrapped around a
lot of manual prospated processes are dependent upon and so I implore our enterprise organizations
with which we work to automate those manual processes. And the last thing is
really getting good understanding our data, data quality, data governance, data lineage,
line of data that you know, we call the context is the stuff
the AI for data that's going to be sore to your orgation in some particular
way. And so the idea is if you can create a good context pipeline
to ground the teenager to which I read before, this hallucinogenic precosition as an
advisor to your business, well, that's one pipeline that we can serve today.
Okay, now fonds that you're getting from this new advisor to your business,
what do you do with that okay's great, we should have we should
act in certain way. But if you don't have an automated way to do
that sort of thing, well, that makes sure keep you in a place
of being moored on the side of the ocean, so the on the shore,
off the shore. And so the idea is you want to have a
context pipeline as input feeding the AI action pipeline as a response to what the
AI is suggesting from a predictive state statement and whether that's predicting you know,
how how to restock your inventory payments on time to your veil under a different
So there's a whole panel plate of different options that we have of use cases
where that action pipeline needs to be automated as well. So, yeah,
those are all excellent points. And you use that term suggest and I'll throw
this over guests to mep here before the live section in our podcast put a
segment comes up. But I believe that by and large the power of AI
will manifesttions, and so the key is to be open to those suggestions and
then track when the suggestion helped and when the suggested didn't help, And over
time you're going to get better and better suggestions. From your AI real quick,
what do you think completely? I think this is all about it.
As we've heard, it's all about learning. We don't intelligence. It's all
about learning. So those suggestions, so data creates data. When we put
those insights into action, we get more insights and we can put those into
action and you create these feedback loops here and everything that Williams is describing around
the principles of native and the practices that come along with that. Breaking things
down, frequent change, smaller, more inventable bits is fundamental to even more
so for AI, and this is something as a company actually with Boomy we
we actually have a feature called Boomy Suggestment around for a while our own AI
journey. All those learnings that we've gone through you integrate automated, we've put
them. So it's definitely probably that when it comes to AI. That's that
is such an excellent point. You know what, I don't think I've heard
someone say that per se, which is interesting because you're right, even though
we're talking about this artificial intelligence, I have folks who talk about authentic intelligence,
which I like even more. But really it's all about learning and learning
from this reflection that we see from AI because it was built upon or as
upon US. Lifestyle expert Megan Murphy has more try Airborne NBC News Radio.
I'm Chris Garagio. The Jacksonville County sheriff says the gunman who killed three people
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added that corporate greed is the reason sixty percent of Americans are living paycheck to
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The first thing you're going to have to learn is that until you stop expecting
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