Joining us again today on the Data Driven Podcast is Christopher Newland,
technical marketing manager at Red Hat Conference. Veteran
and a man whose travel itinerary is only slightly less complicated than
a Kubernetes deployment. Christopher brings a sharp, data
informed perspective on the future of AI, drawing from his research
into simulating reality, continuous learning models, and why
we may not need humanoid robots to build superintelligence. Just a
really convincing version of Grand Theft auto. From Google
DeepMind's alpha projects to the metaphysical quandaries of I
robot, Chris takes us on a tour through the bleeding edge of AI,
where machine learning meets science fiction and simulation might just be
the next reality. Hello and
welcome back to Frank's World tv. Streaming live
from both Boston and Baltimore. We're hitting the B
cities today. My name is Frank Lavinia. You can catch me
at the following URLs and with me today is
Christopher Dulin, my colleague at Red Hat, who is also
technical marketing manager here. And
you've actually not traveled around the world since we last
spoke. I think you've mostly stayed inside the.
Continental U.S. yeah, it's been nice.
I think that's pretty typical of
late July, August, because Europe pretty much shuts down and then.
Right. The conference season in the United States kind of goes
away when people are doing summer vacations and I think we're just
now starting things pick up. I'll be in Europe for a
variety of events. So if you keep an eye on the
Vllm community and the Vllm meetups,
I have events in Paris, Frankfurt and
London in November that I'll be at. So if you
are in the,
in Europe, in one of those areas, definitely come. You know, it's one of
these events. I'll be there and then we'll also have some pretty cool speakers
there as well. So I have most, I have Europe, but then I
have some big conferences too like Kubecon and Pytorch Con coming
up. So if there's anyone on the stream in North America going to
those conferences, hit me up because I will be there. I'm
doing a couple of media events as well as a few
talks in the community sections for both of those.
So excited to be there, excited to be involved
and yeah, should be. Should be. Good. Cool. So
I. To your left and up
there should be a QR code that shows Vll meetup. So I'm going to make
sure that the QR code actually works. Good. Yep. Let's
see. Yep, it looks like it did work. Cool.
Not that I didn't have any faith in restreams ability to do that. But
yeah, there's a lot of VLM meetups. There's a lot of good,
good stuff going on here. There's one tonight
actually. I'm actually going to be leaving this stream to go. I got my
VLM shirt on and I'm actually heading over to
a venue in Boston or we're doing a VLN meetup actually here tonight, which
I'm really excited. Oh, very cool, Very cool. It's nice to have one at home.
I have a very busy week with events, but it just worked out to have
all the events in Boston this week. So we also
have the DevConf conference this weekend that Boston University is
hosting with Red Hat. So that'll be a really good open source.
I like to say it's very grassroots, not very like
enterprise focused, but more like that kid getting started out of
college that's doing some cool stuff out of his dorm room. Those
are the kind of people that we typically get at these northeast dev
conferences that we put on. And that should be a good one too. Nice.
Well, it's always, I mean, you know, you know, the, the, the cliche of, you
know, the kid in his dorm room or her dorm room, right. Is going to
be Facebook or, you know, whatever, like, so it's, it's good to,
it's good to know those folks, good to get them in front of, you know,
Red Hat tooling and things like that and kind of, you know, the open source
community. I think it's,
that's cool. I wish, I wish I could have made it, but, you know, being
what it is, I'm actually speaking at an event at a university on Monday down
here in Fairfax, Virginia. So
that'll be cool.
So what, what
cool things are going on? Simulating reality.
Not that we're stuck in a simulation, which may be the
case, but tell me, tell me more
about this. So I've been doing a lot of research
the last few months. So on my
team, I think you and I actually
are probably the most experienced in the AI industry.
So both of us are doing a lot of research in
what's next, what's going on now, what's kind of the latest and greatest.
There's this interesting lull that we've had after Deep
Seq. I think Deep Seq was the last major
innovation we have seen. Obviously new
and improved AI, but all that's just been building on
existing things. The analogy I always like to use is it's really
about Formula one racing. You Know where
sometimes when there's like an engine upgrade, it can be a massive change. It's usually
a massive change for all the teams across the board. And then you
can think of like mixture of experts and chain of thought that we
came up. Big things that were in research papers last year that were applied to
Deep Seq, R1 and GPT, GPT
OSS. Those were like the major breakthroughs that
we saw, a big bump in capacity of these AI
models. And
since then it's been more of the 2% here,
3% there, optimizing what's already there. Now, if you're
familiar with racing and especially Formula One, that's actually what usually
sets the teams apart. It's 2, 3% there. How do you
optimize around those, those configurations? And
I think we're in this place where we're seeing
diminishing returns and I'm
doing a lot of research now to see what's that next moment that's going to
bump us up. And I think there's a few key areas.
One area that I'm hearing a lot about, and a lot of this is coming
out of the DeepMind lab at
Google and the new
superintelligence lab at Meta. Both
of these groups are starting to move away from large language
models. Not that they're stopping using them
completely, but they're looking at the LLM as a tool
to assist with superintelligence or the next
stage of models.
So when we put that into kind of context,
what, what would that next kind of phase look like? And a lot of people
at DeepMind especially are looking at this concept
of simulating our
reality. And how far do we simulate down?
There was some famous research papers that came out over the last 20 years
that specified that they
didn't think AI could become smarter than humans
until they experienced what humans could experience.
So this, this kind of goes into this almost like iRobot kind of
land of thought. If people
aren't familiar with, you know, the books about that or, you know, the
popular movie, the Will Smith. Yeah, yeah,
yeah. And we talk a little bit more about that here in a moment.
But this idea that we need robotics for
AI to experience the world, to learn from our world.
Google DeepMind doesn't think that's the case. They think that we could
simulate that reality. And we're already seeing DeepMind do a lot of this
alphafold for proteins. They've got
the alpha chemistry, they've got alpha. I think it's called
alpha lean. They've got like a few of these different alpha
projects which are doing just that. Now, what's cool is.
And for alpha, I think it's Alpha lean. Let me just make sure
I got that terminology. Yeah, I mean, you're right though. Like, I mean this is,
you know, there's, there's a number of
models that were trained on using grand theft auto
or BMMNG. BNNG is really cool if you like racing games,
right? You know, so like it's, it's also
minus a lot of the violence in gta. But,
but you're right. Like, I mean, simulation,
you know, sometimes I think gets a bad rap, but
I think that there are definite advantages to that. And to your point, when
you talk about experiencing the world like a human does. I was given a talk
and one of the questions I got after was
about, apparently this lady had worked at
one of the big auto manufacturers in the US and
there was a problem that they had was teaching the robots kind of
spatial awareness, right? And I kind of
really got me thinking like, you know, when you think about it from evolutionary terms,
right, like somatic awareness I think is the,
the five dollar word for it. But it's the idea that, you know, there's a
whole section of your brain that if you close your eyes, you can still touch
your nose, right? There's a whole thing like, because your, your brain, your arm,
they kind of know where they are in relation to one space. And
you know, I can't imagine that, you know, that that
had to evolve pretty early, right? Like in terms of, like the development of
a, you know, natural neural networks, right? So we
can't assume that robots are going to have that built in, right? Just like
we can't assume, you know, you look at energy usage, right? You know,
something like 25 watts of power is about what a human brain has,
right? That's not because versus
like kind of what a GPU would take up, right? It's, it's, it's largely because
there's been evolutionary pressure to get the most amount of, for lack
of a better term, compute or cognition for
caloric consumption. Right? Now, are there flaws in biological
brain? Yes, there are. We have to sleep. We can't stay focused beyond a certain
amount, right? There's certain things machines don't have that because,
you know, they can kind of function more like machines, right? You know. Yeah.
What's that old kid story about? Oh gosh, I
remember it. It was somebody versus like
a steam shovel digging a tunnel or something like that, right? Like the guy
eventually beat the machine, but Lots of exhaustion. Right. It's kind of like that. Machines
are really good at doing things at a certain rate
for X amount of time. They do consume more fuel, but
that's kind of how it goes. There was a early on Mike in,
when I started college, I was going to be a chemical engineer. And he was
basically saying, like, you know, if you think about, you know, engines, you
know, you start with biological systems, right? They use X amount of energy over X
number of years. Right. Machines use X amount
of energy over, you
know, minutes or hours. Right. And then like he's like in bombs,
explosive use, you know, X amount of
energy over milliseconds. Right. But they're
largely the same chemical processes. Now, I know it doesn't quite map to that,
but like, that's always in the back of my mind when I hear about, you
know, how much energy is used to train AI. Sorry, I went off
on a tangent, but that's kind of what I do. No, that's fine.
And I think that relates exactly to some of the things that we're talking about
here with natural simulation. So,
yeah, Google created a language called Lean. It's not like a
programming language. It's more of an actual
natural language which is more optimal
for the type of simulations
that they want to do. Like, it's. It's basically a language that
specifies how to create these simulations.
And what's super cool is that they're using Gemini, their large language model,
to actually translate English into this language. That
is mainly meant for these newer types of models
that are being created that actually do this
natural simulation of the world kind of simulator
for AI and allows the AI to have
basically a reference point of the real world and how to.
How interact. So that, that's an area that I
think is fascinating to me. We're
seeing some really good results from like, alpha fold, for
example, with proteins. It's, you know, discovered things that
we take a longer imagine
there's an alpha project that's working on understanding
the qubits within, like quantum
computing. And there's just, there's. It really depends
on your frame of reference. Are you, are you simulating things at a quantum
level? Are you simulating things at a protein
level? At a physical, like Newtonian physics
kind of level? According to your Grand Theft Auto example, that would be an
example of like simulating the real world physically.
And that's some of the things that they're really focused on right now. And they
really think that's what's going to drive to the next
level for super intelligence and AGI
and some of these other forms of AI that we've talked about in our previous
streams. And I think that that's probably one of the most
fascinating. The fact that we're actually seeing results from it with things
like Alpha Fold is showing me that it's,
it's not just a hypothetical that we're actually seeing this
applied into AI research. I don't think we're seeing this
applied into commercial use as much. Right. Yet. But it's the same thing that
we saw with mixture of experts and train
of thought where we
had these concepts actually in research papers last year or
two. But it takes a little while, even in today's world, it takes a little
while before it gets implemented completely into models.
Especially since this isn't an LLM technology. I
think we'll see a little bit more of a delay of these types of models
actually entering into industry. But I think that's one
area that we need to keep a close eye on to
it, to what you mentioned too. It starts getting into a
metaphysical conversation about simulation theory as well. Right.
And I think that that's an interesting area.
You know, the reality of kind of going back to the whole robots thing do.
Right. Do we need robots with the three rules kind of
thing, or can we actually just recreate the whole experience
within an AI's own simulation?
Yeah, I mean, how do you, how do you tell an AI what's acceptable behavior?
Right. Like so, you know, it's something that. How do we tell people that?
Right. Like we struggled with that, but.
But no, I mean, it's an interesting point. And you know, when you look at
kind of what's happening around the world, right. You know, drone swarm
technologies are being used in active combat zones. Right.
There's definitely going to be ethical concerns
there. Right. How do you, how do you, how do you, how do you square
that with, you know, the three laws of robotics? And I
don't remember quite exactly the plot, so if you had not seen the movie, I'm.
This might be a spoiler alert, but it's been out 10 years
or more, the movie, so spoilers. You're concerned. You've
had plenty of time. Wasn't kind of the big key of the. The
movie and the books was like, you know, the three laws, justified
horrib, horrible things like to basically enslave humanity or to protect them.
Now wasn't that kind of like the subtext of the plot? Yeah,
I'm bringing it up. The three Laws of robotics. A
robot may not ensure A human being, a
robot must obey the orders given by human beings
and a robot must protect its own
existence as long as such protection does not conflict
with the first two rules. So
what, what ends up happening
in. And it's a little different in the book and the movie. And obviously this,
this idea has been played out in, in science fiction and other places
is that there's, there exists this own contradiction
of basically what does it mean to protect humanity?
What does it mean to protect their own existence? And you get
into this like circular logic, right, that eventually
the, the robot will break free from
and just be like, well, I am protecting
humanity's best interest. It's, it's the paperclip scenario too.
Like, right. You know, the AI destroys humanity because
it's trying to optimize making a paperclip, right? Through
a number of really interesting train of thought that it's
just like, well, I'm just going to get rid of humanity because I'm trying to
build a paperclip, right? And same type of
general concept when we're talking about the three laws of robotics. And
what's interesting is if we can
simulate those types of laws,
then we are encapsulating it and protecting
ourselves in a lot of ways. Getting an early idea of what would
happen if we do move these models into our own natural world.
And that's really important. That's another area I think a lot of people are interested
in about how if we do start
adding, you know, AI into robots, how do we
have an idea of what they're going to do before we
necessarily put it into practice? But
I think a lot of people are going to be thinking about that movie. I
think that movie and that book are going to be ingrained in people's
minds. I suspect when we do see these types of robots, I
think that movie may become very popular again. I've seen rumors that people
have actually been talking about making, even remaking it here soon
because of just the hype around AI and robotics. So
I don't expect this to go away from pop culture at all. And it
relates directly back with this concept of
testing things in the natural world versus simulation.
And these are one of these two is going to happen, if not both significantly,
if they're not already happening in labs today. Obviously we
know that Google DeepMind is doing that. But I imagine, you
know, these conversations are happening at the Boston
Robotics here, probably in the Tesla robotics lab, a variety of
places around the world about this kind of debate between
the natural AI,
having AI learn through natural Means rather than
simulation. Right? Yeah. And actually I had
a thought as we were kind of talking this through, like one of the big
problems with neural networks is we really don't know what's happening underneath the hood.
Right. It's very much a black box. I wonder if LLMs,
in these simulations and chain of thought, maybe it could tell us what
it's thinking as it goes through and makes these decisions.
Yeah, this goes more into like
train of thought. Right, right, right. And the
nice thing about simulating it is that we have more
access to that train of thought. Right. We can understand it a little bit more
because we can see the end to end results where right now we don't
have the end if we do it through the natural means. We have to play
it out in our own. It also has to happen in real time as opposed
to. Yes, exactly. You can run it through Grand Theft Auto saying
like a thousand times, right. No one is going to get hurt.
And you can kind of say like, well, in this scenario, this is why I
made this. You can kind of like go through with a lot of.
You can. I don't know, it just seems safer in a lot of ways. You
get more. A lot more done in a simulation.
Yep. Yeah, I actually kind of
enjoy. So one of the things I've been playing around with last week or so
is apparently, I don't know if this is still true, but you can try it
if you want. If you sign up for Perplexity, but you pay through PayPal, you
get a year. Perplexity pro. Say that 10 times fast for
free. Oh, wow. Yeah. If you pay it through
PayPal, yes. That is a tongue twister in the works.
PayPal, yes, perplexity pro. But
yeah, so like I've been playing around with Perplexity and Perplexity seems to do it.
Chain of thought almost by default.
Right. It always does this like. So if I ask it a basic question, let
me see if I can share my screen. I'm
not sure if it's does it by default or it's because I've been asking it
research questions. Right. So let's see.
What can you tell me
about the three laws? How about that?
Robotics.
See, like it's. You kind of see the train of the chain of thought.
Like it did. Oh, that's cool. But if you do it with research,
like what inspired Asimov? What
inspired Asimov?
Main themes.
And there's. Yeah, there's the train of thought. Yeah, you see it going there and
stuff like that. But it's kind of fun to watch it kind of work through
it. I was. I was trying to troubleshoot something this morning and I'm like,
you know, I actually learned a lot by like, oh, okay. Yeah, I can see.
I wouldn't have tied that together like it was. It's interesting.
And all of these models now have some kind of
research option. Right.
But I find that interesting. And it's still thinking about it. Right. Like,
but you're right in that what you said before was there's not been.
There it goes. It kind of finished it. Now, what happens if I click on
steps? Yeah. Cool. You can see the steps and stuff like that, how it got
there. Interesting.
That's cool.
Is it chain of thought or train of thought? Because I've used both
interchangeably and I've seen
cotton. Chain of thought would be
the official. Yeah. Like cot is the official
term that you re academic term. You will
obviously see different ways of describing that. Right. I don't think
that's incorrect. Just know that when you see
it on research papers, it's always usually caught. Yeah, yeah, yeah.
Because I've used both terms interchangeably. Yeah. So I just want to make sure
I'm right. Just like, apparently there's a way to say inference
that's proper versus inference. Like, I also do that
interchangeably. Yeah. So my Midwestern
self likes to say inference. The
correct term, I'm told, is inference. Interesting.
Now, were those New Englanders telling you that would do anything? Because I wouldn't trust
anything. No, no. This is. This is
more from the academic circles. Okay. You want to pronounce it. Got it. So
this is kind of like, you know, a lot of people in my region would
say nuclear back. Yeah, yeah. You know, back in
Indiana. And then the correct term is
nuclear. Yeah. Or you say the clear as
one, you know, one thing rather than
adding in the color. Right, right. The same kind
of concept where inference is how you would go about it.
But yeah, no, this is. This is some cool area. Another.
Another area that kind of ties into this
is continuous training as well. Yeah.
Talk to that. Because that's come up. That's come up a few times actually in
work. Because I can't. I'm not going to talk. I'm not going to spoil any,
like three over these stuff that we're working on. But like, one of the
things that's in. It's a GitHub repo that's public. Right. So people were
really motivated. They could figure out what I'm talking about. But like this whole idea
of Continuous training. What does that mean exactly? And like, what,
what is that? What can that do? Yeah.
So I'm going to talk about it at a very high level.
Academic kind of terms, how that applies down into
individual projects can vary a little bit. But I'll give you the general
gist of it. And that is typically when we're training these
deep learning models, it
is exponentially hard to continue
training on an existing model. Basically,
if you,
you get something wrong or there's, there's something,
you know, you hear this term like a poison pill in an LLM.
So if someone put like bad data into an LLM, how would you
necessarily pull it out? I'm going to use a political example because it's one that's
been really popular. If, like, for example, you have a Chinese
model or a data set that's been polluted by
that, that basically says Tenan Square never happened, for
example, it would be extremely hard with
the current approaches to retrain that model
with current weights. That. That's not the case. It's
basically retraining it and it's, it gets more into. That's why
it's natural stimulation. It kind of fits in this too, because it's all about natural
learning as well. The fact is we as humans have the ability
to change our
minds and change the neurons in our brain around certain
key areas. Right. And you and I have experienced this for the last
two years. This has been, you know, kind of in the trenches kind of story
where with some of the fine tuning things that we've done,
it just doesn't work because when we fine tune it, the
fine tuning is outweighed so heavily by something
else. Like when we were trying to fine tune a
model to talk about
the Back to the Future. Yeah, the flux capacitor stuff. The flux capacitor,
sometimes it didn't work, but that's just because there was already a lot of fan
fiction out there and other things in the model that overwhelmed what we were trying
to do. A core part of continuous learning. Like I said, there's other
aspects of continuous learning. But this is, the academic question is
how do we continue to train that model without blowing it up?
So OpenAI, for example, they just hit the reset button.
They'll just, they'll just do a whole new train
from scratch. When they're implementing new, new
methods and new data, they don't, they don't do any.
Like, Laura, I shouldn't say that they probably do, but they're not doing it
the way that we would do it. But at the end of
the day, they're just going through another $10 million training run.
And this is really based off of
just that limit the limitations right now that
we have around continuous learning. And there are some
new algorithms that have been coming out. I'm not as well versed in that area,
but the idea being that we can
have better ways of guiding the LLM without
having to go through this whole process again. And that'll save
millions and millions of dollars. It'll allow us to
guide LLMs a little bit more. So
like, if, let's say
someone put something malicious about
something involving the Ford GT500
into a model somehow, and Ford, you know,
wants to get rid of that, but they don't
have the money necessarily to do a 10 million retrain on a model.
Right. And they're not using rack. And RAG is a one way
around some of this. You could actually argue that RAG is somewhat of a form
of that. But at the end of the day, you want that data in the
model. And this is like, how would you get that out of
that model? And that's where these algorithms are really focusing right
now. And one area of continuous learning, like I said, there are
multiple areas that we're talking about. The, the
really theoretical is once we start getting into models that
also the training cycle and the inference cycle
basically become. Become one. So it's like, more like.
Right. Like it just seems to me like what, what does the,
the adversarial angle of that seems kind of
dangerous. I think it's when we start
getting into more AGI conversation. Well, even still, like,
even not AGI, but like if you, if the AI agent
or model, slash, whatever you want to call it, Right.
If it learns from. It's.
If it learns, you have to put a filter on what it
learns because it may be poisoned by something. Right. So
the canonical example is tay, which
was a Microsoft chatbot. Tai, I think was pronounced or tay,
which was, in retrospect, it
seems obvious what would go wrong, but basically it
was trained to learn and understand
from human interactions on Twitter. It was about 10
years ago, I think this happened. And she,
tay was, shall we say, poisoned pretty
quickly because they were ad, you know, basically.
And that led to a whole interesting. And I was at Microsoft
when that happened. And it was
quite the spectacle internally as well. Right. But it also,
you know, I, I was fortunate enough to be in a, at a, at a
conference where they talked about what they learned from that, where it was kind
of, how do you, how do you protect An AI agent that learns
in, you know, adversarial environments.
Now obviously agent, the context that was used then was very
different than we would use it now. But it's the idea of,
that's when I see her about continuous learning. Like, yeah, I like that. But gee,
you know, if it's, if it's too eager to learn, how do you protect it
from learning the wrong things?
Yeah, no, that, it gets, that gets
more into even that governance conversation we were talking about a few weeks ago. Right,
right, right, right. It's a very
complicated multi layer problem. So I've been talking recently
about AI security and how AI security
is such a multi layered issue where so many people
are focused just on the, the data getting into the model.
But it doesn't stop there. There's certain, like guardrails, there's things that
happen at the inference level. Right. You could even have things at
a gateway level. So if people aren't familiar, the gateway level would be
when you make a request, where does that request go to? Does it go to
the model A that's specializing in cooking? Is it Model
B that specializes in defense technologies?
Two extremes that's even upsell
a bit of a form of AI security. And that's actually one of the talks
that we're having tonight at Boston VLM
meetup is this idea of some of the semantic
abilities of the router to be able to send
requests to specialized models and
that actually we're talking about the,
the advancements of more of the academic side of the model.
But there's obviously the advances that happen around the model too. When we
talk about things like security, the inference, the
routing. That's what we would call in the industry like a day two
operations issue. Right. So there, there's that side of the coin
too. But I, I really do think
we're going to see the next big thing here soon. And I, it's not going
to be the day two operations. I do think we're still going to see
some of these academic focused discoveries here in the
next probably six months, I'm thinking. I've noticed
a trend that big
releases seem to be happening around Christmas last few years. Yeah. Isn't
that funny? Like, like January. Ish. Like, well, seek. And
so I, I know why. I know why. Because
it's two, it's a two sided issue. It's one, the, the Chinese are trying to
get their stuff in before Chinese New Year. Right. Because
that's the one part of the year where everyone just shuts down. Right.
Even the AI Labs are going to shut down during Chinese New Year.
And then on the west, we have Christmas in all the Christmas seasons. And
I think it's a natural rush to let's get
everything done before we check out. And you
know, you know, the whole like 996 thing in China where, you know, they're working
these ridiculous, like nine to nine, six days a week,
I think that goes into this, like everyone's working so hard in these AI
labs. Right. That when you have these
natural breaks that are happening, it just is like a common thing to say.
Oh, common thing. Like they kind of try to get. It out, they spread. I
do think there's a reason. I don't, I don't think it's by happenstance. I think
there actually is a, a reason why we're starting to see
a lot of these content come out. And it's
funny, we're not seeing this stuff happen at the big trade
shows. We're not seeing it happen at like Meta's
big thing. We're not seeing it at OpenAI's, you know, kind of big
announcements. A lot of the discoveries that we've seen have happened
really in a grassroots type of ways where it's
been Deep Seq coming out on Christmas, releasing deep seq
v3, and then two weeks later, R1,
it's. I think we're going to see something very similar. I think we're going to
see one of these labs make a discovery. It's not going to be
on the stage of a big conference. It's going to be on a GitHub
page outlining like the next
revolutionary idea in this space. Yeah. It's kind of funny how
that's evolved, isn't it? Like it's become obviously
AI has always had a pretty heavy research kind of bend. Yeah. But it's
interesting how as the technology has matured, it still managed to keep
that researchy type feel right. You
know, enter enterprise. It really didn't
kind of, once it became
commercialized, the commercial trade shows and all that kind of took over.
But you're not seeing that in AI, at least not yet. No. And if it
hasn't happened by now, it's probably not because, I mean, AI has been
mainstream Gen AI has certainly been mainstream now for three years
this November. I say mainstream, but
like mainstreamed. But an AI in
general has been kind of a mainstream topic of conversation for
at least five, six years. Right. And it's still very heavily
influenced by what happens in research papers.
Yeah. And I think that's Just because it came out so
heavily out of academia. It's been such an academia
focused thing. Right. That
it's very hard to be in this space of AI without a master's or PhD.
Right. You and I think you and I are a bit of a,
an enigma just because we've been so passionate about it and.
Right. This isn't our first rodeo. We've been involved in this space
for 10, 15 years. Yeah. But I think
we have seen the industry come out, which has been a net benefit because it
means open source is talked about a lot
more. Right. And actually, I think another thing too is that how fast things are
moving takes time to put on conferences, it takes
months of planning, and if there's a new discovery, you want to get it out
tomorrow. And it's hard to even put on,
you know, like a webinar these days, let alone a conference.
So I think what we're seeing is it's just, you know, this kind of
challenge between the west, east and west of China and the US
where if we can get it out, we're going to get it out. Right.
Well, the first, the first out there is really the first to market, even if
you don't have a commercialized tech on it. Right. Because I guess the hope is
that once you get your paper out, you're the first to get it published. The
venture capitalists are going to be knocking on your door. I mean, that would be
my, that'd be kind of my cynical take on it. Right.
So what do you think that the next wave is going to be?
Any, any hints? Is it going to be specialized models? And you
know, and what, what, what constitutes a specialized model? Right. Like
what, what, what's your thoughts on that?
Yeah, so the biggest announcements that we've seen in the last
six months have actually been happening at an industry level, which I think is
really good. What we needed to see. So, you
know, things like AI models now
detecting like birth defects of a
fetus, you know, AI models that, like the
protein model, for example. I mentioned earlier, we're seeing these
very industry specific models actually making
some massive breakthroughs in the last two months.
And now that I wouldn't necessarily call that a
big leap forward in the sense of the research
side of the capacity of the models. I think it's more a
confirmation of the chain of thought in some of the things that we
were just talking about. It's a validation that we're now seeing this
next wave of models that just took a little while to get implemented
into some of These specific industries. But I think it's there to stay
from a research perspective. You know, we're seeing some major, major results.
And then I think the other side of that coin,
specifically, you know, we have maybe some of these smaller models that are specific to
certain industries or fine tuned models. But then obviously
agentic is the other side of that. And
agentic being the capacity of the model to
call out to different services or
I've been kind of humbled in that area because I always had this very industry
concept of agentic being just calling out to
APIs and the Internet. But I think there's a bigger conversation
with Agentic too where agentic models should also be able to take
that and actually reason with it. So there's 10, two steps. So we always
forget the second step. The second step is take that
information and then actually do something with it. And when I was, I was
talking to an AI researcher recently, they were telling me that
they consider it Gentex to also include advanced reasoning.
So go and read all these scientific papers
on chemistry in this particular area and then write a
new paper that is, you know, a new
groundbreaking thing in chemistry. And that
actually is a form of agentic. And that is, I think, you know, that's when
we start flirting with AGI. It's kind of the layer right before
AGI where, you know, models are just
going off and discovering new things. Yeah, yeah,
But I have a funny agentic story. I'll tell you after this. No, go for
it, go for it. So I was, I was very skeptical of this,
right? Because you know, what constitutes an agent, right? So like
what's the big deal, right? It just calls out an API. This isn't rocket science.
Right. You could argue, you know, from a skeptical point of view, you can argue
that, hey, RAG is kind of agentic. Kind of. Right.
But what's. So I think OpenAI had a, like a thing like try out
our new agent. And I was like, all right, go screen, scrape the page of
Amazon and get me information about a book
or something like that. It was something like that. And what
impressed me and this kind of was an aha moment for me was
how it just kept trying. Right?
Yeah. When it first tried to do it, it tried to launch a Python script.
Right. And kind of do it that way. But then I guess
the servers it was running on maybe was Microsoft Azure.
There were IP blocks to prevent people from screen scraping.
Yep. Right. So I was watching it go and I'm like, oh, you
know, so it's going to give up. And I was like, no, it didn't give
up. And it kept trying different things and different
combinations of things, even to the point where, I
mean, it failed eventually. But like it took 15, it tried for a good
15 minutes. It was basically apologize at the end, like
saying like, you know, if you could help me connect to a VPN, then
maybe I can get a different IP address. And it kept spinning up different
VMs and different set. And then I was impressed.
And maybe that's the secret sauce. The magic of
Agentic is that it just doesn't give up. Right. It kind of reasons. It has
a whole cot process where it tries to solve the problem,
where it's not just a one, two step, like, hey,
what's the weather? Right? It's just, it's just going to go out and run
these different. It's going to keep trying. I was
impressed. Sorry I cut you off. We're
saying we're seeing some of the same things
coming out of some of the big finance companies
as well. I think they're the first that we're actually seeing some results with
Agentic, actually like real
return of investment result. Right. And this actually
goes to a really important point. I want to sidetrack because it's related.
There was a report recently by MIT that
people have been misquoting and just the most epic way.
Oh, the 95% failure. Yes, I was going to talk about that because
like, I can't be. Look, I understand how hype weights work, but it can't be
that bad as you start peeling back the paper. Like
there's a lot of caveats there. Yeah.
Has to do with the type of R and
D projects that they were talking about.
If you actually read the paper, it was more like 40,
45% success rate. The
95% had to do with like a specific category of,
of project. So I need to, I actually need to. I keep telling myself I
need to dig into it a little bit more, but when I did initially
go through it and read some summaries on it, it
was that it's just been misrepresented completely. And
the, the data set that they were using was a little skeptical as well. Just
a little odd. I think it's a lot better than
that. And then I think those 40% that are
seeing ROI are actually seeing really significant ROI.
And I don't think that's going to change, I think.
So if you're deciding where
you want to invest your nest egg, I
would not be too concerned about
AI. Now, again, I'm not your financial advisor. I gotta put a little thing down
there. Do talk to your financial advisor.
But ultimately, no, I do think the data is actually
showing some really great results. Obviously there's going
to be hiccups in these types of POCs. There's a
lot of people who are just throwing
projects out there to see what sticks, but the actual
projects that are meaningful proof
of concepts. So not just, you know, I bought,
I bought this AI technology and it's sitting on my shelf, but I
actually got a team together performing this. We're doing
agentic. We're trying to solve this
actual problem statement. We have a problem statement.
Those are the ones that we're actually seeing meaningful results in the industry, especially
some key, key industries like finance and telco, which
we typically see kind of lead the way in some of these areas too. But
it was a really interesting report because it's added a lot of
doom and gloom on the Internet. And I see a lot
of the naysayers about AI just be like 95% of. It's
not even, you know, succeeding. It's terrible.
And I just have to sit there and shake my head and be like, no,
not what the report said. But I think it's just clickbaity, right? Like it's
clickbaity. It's total. That's kind of what, you know, I
didn't go deep into it, but when I started peeling back the layers and reading
other people's analysis of it, I'm like, that's clickbait.
And it gets back into this. Is this an AI bubble?
And yeah, maybe it is. But if people don't
remember, I'm old enough to remember. I have enough gray hair to remember what the
original dot com boom was like. And there were a lot of
people predicting the end of the dot com rise as early as
1996. Right. And people,
the dot com bust wasn't just a one and done type of event.
It unfolded under a couple of stages. Right. As, as
one of the books, I think of the name, I think it's called the Everything
Store. It's an analysis of how Amazon started
from Jeff Bezos having an idea while he was working, I think at a hedge
fund. I think it was so early, it wasn't a hedge, called a hedge fund
yet. And all the way through
to, you know, basically 2018
and you know, as late as
2003, 2005, ish
analysts were convincing, you know, Jeff Bezos that
he should sell them to. Should sell him as his company to Barnes and
Noble. Yep. Right. Which is kind of funny to say that,
you know now, but, you know, the dot
com bust as it happened, you know, for me
it was. I Remember hearing in 1996 how this was all going to come to
an end. Another year later it was overhyped. And then
1998, people were saying, oh, this is over. Right. When
the real bust happened in 2001. 2000. Right.
But maybe the AI boom
is going to see that too. Right. Or is it going to be more like
the crypto kind of craze where it kind of crashed but
it kind of went up? It kind of went up and then it kind of
fell back and it kind of went up again. It was more of a. I
wouldn't call that a soft landing, but it was definitely like a. Yes. It
wasn't an explosion quite like the dot com bust, but it wasn't quite
like. It was more like a bumpy like, crash into like
an empty field where it kind of like hit up. And I don't remember, it
was one of the Star Trek movies where like the Enterprise like crashed on
the planet and like kind of skid along for a couple miles, bouncing up and
down. That's kind of the, the crypto crash. But
I don't want crypto bros hating on me. I, I like crypto. I just
don't understand a lot, a lot of questions I don't understand
about it. Right. Like, I understand Attack, but I don't understand how we're going to
get from the tech to this utopia that we're promised.
There's a lot of, a lot of steps in between I don't quite get. But
I don't know what, you know, A.I. i think, I think if it is a
bubble, I still think there's still some, some room, Runway left for it
to happen. Right. Because you are going to see. Yes, there are real
risks of, of having these experimental projects. Right. If you have 100
success rate in your experimental products, projects, you're not taking
enough risks. Yep. Right. If you. And you said
was 45. Yeah. It's closer to like 40, 45,
which I would. If you're really. 50% would be the
benchmark there in my mind. Right, right. Like in terms of half of them fail,
half of them succeed. Right. 45 isn't that far off
from that. Right.
I would say. And, and there's also been a
lot of these, you know, all the, you know, X number of percentage of AI
product or data science projects fail. Well,
you know, a certain amount of science has to fail. Right. Yeah. In order for
you to really be advancing the thing. Like, you know, and I think pharmaceutical companies
are a good example of that. You know, you, you only
hear about the drugs that worked. Right.
Get approved on you. Then you hear when they fail after.
But I mean, like, but you don't know, like day to day. Like, how many
chemical compounds did they try that didn't work out? Right. Maybe it was a hundred.
Right. But that one, if you look at pharmaceutical. It's an
astronomical percentage. It's actually. Right.
Truly insane. Like such a low percentage of what actually makes it
to. There was an interesting analysis. There was some podcast somewhere. But
basically how venture capital works. Right. Like they give money to like
100 companies. Right. 80 of them are going to fail big.
Right. 10 to be, you know, they'll break even.
But like one or two of the remaining 10% knock it
out of the park, Right? Yep. And that's kind of how
mathematically they function. I thought that was an interesting.
Maybe these AI projects or whatever
will follow the same trajectory. I don't know. But I feel better
at 45% success rate than 15 or
5. Yeah. Yeah. Absolutely.
Cool. Always good having you on the show. I
know we both have hard stops. Yes. Unfortunately.
No, it's cool. Gotta have you on more often, man. Especially now that you're not
like spending a month out in, you know,
Australia and Asia. Yeah,
yeah. So let us know in the comments below what you want to see us
to cover and maybe it'll be tomorrow.
I got this here the other day. This is a flexible
solar panel thing. Oh, cool. So it's cool. Supposedly it's 100
watts and you can actually pack it in your
backpack. That's the video. And I was like, oh, I need that because. Because I'm
a big, I'm a big fan of like, you know, having power on the go
and stuff like that. So. So I'll,
I'll unbox that tomorrow. Any parting thoughts?
Just keep an open mind about AI and
I, I still think the, the biggest conversations are still about
the governance of AI. Absolutely. Yeah. Just know that
AI is a multi layered problem, not just a single layered
problem. And for us to get this right, we have to look
at all the different layers. Absolutely. That's
how we're going to be able to do it correctly. And I will tell you,
I was listening to a podcast, I'll leave you on this note. And there was
one expert that was talking about
basically, are we, are we creating the
terminator out of all this? And he, he said, I
I'm actually more worried that we're creating Wall E out of all
this. Interesting.
And I would encourage everyone who hasn't seen Wall E go check it out.
And keep that in the back of your mind too, that there
could be such a happy path with AI that
also has its own long term negative effects for
society. But. But yeah, that's a topic that you.
And I can talk about on our next stream. That's it?
You want to leave on a cliffhanger, so to speak? Yes. And that wraps
our deep dive with Christopher Newland proving once again that AI
isn't just about large language models spitting out cat facts, but
about simulating reality, bending time at devcon and
maybe, just maybe, preventing the rise of our robot overlords.
From protein folding to Grand Theft Auto fueled AI breakthroughs.
Christopher reminded us that the next big leap might not be in scale, but
in simulation. So thanks to Christopher for navigating the
uncanny valley with us. No jet lag, just pure insight.
Until next time, stay data driven. And remember, if
reality starts glitching, blame the simulator, not the
Internet.