Hey everyone and welcome back.
We're diving deep today into generative AI
and specifically how it's changing the game for businesses.
Yeah.
We've got some really interesting insights to unpack
from some big players like Ryder, Twilio and ServiceNow.
Right.
And maybe even a sneak peek
at what NVIDIA has been working on.
So by the end of this deep dive,
you'll know what it really takes to build AI you can trust.
Yeah.
And how things are changing like really fast.
Yeah. It really is a fascinating time to be working in AI.
There are three major trends converging right now
that are reshaping generative AI as we know it.
Wow. Three big trends.
Okay. Let's break them down.
What are the big three we need to know about?
Well, first we're moving towards
what are called agentic systems.
These are AI systems that can manage like entire workflows.
I think like multi-snap processes.
Right.
Almost like having a team of digital assistants
working for you.
Oh wow.
This represents a huge leap in complexity
from the single task AI that we're used to.
So we're talking about AI that can like figure things out
on its own, not just follow instructions that's exciting,
but also a little intimidating.
Absolutely.
And the shift toward agentic systems
goes hand in hand with the second trend.
Right.
The rise of bigger, more capable models.
We're seeing models like GPT-4 Mini and Gemini 1.5 Flash
packing more power and accuracy than ever before.
Wow.
All while becoming more cost effective.
And this is really crucial because it means more businesses
can access and utilize these advanced AI systems.
Yeah, that makes sense.
What good is amazing AI if it's so expensive
no one can afford to use it, right?
Exactly.
Okay, but let's talk about the people
who are actually building these systems.
Yeah.
Who are the key players now?
That's where the third trend comes in.
Okay.
The rise of the software engineer.
Traditionally, AI development has been the domain
of data scientists.
Right.
Now we're seeing software engineers taking a leading role,
especially when it comes to building these agentic systems.
They're driving the adoption of languages like TypeScript,
which works alongside Python to manage the complexities
of these AI agents.
I see.
It's becoming a real collaborative effort.
Okay.
Here's where things get really interesting for me.
Okay.
The ROI piece.
Yeah.
All of this AI advancement is great.
Right.
But how are companies actually making money with it?
That's the million dollar question.
Yeah.
And frankly, it's one that many businesses
are still struggling with.
Take banks, for example.
Okay.
A lot of them have generative AI in production,
but almost none are seeing a positive return
on their investment yet.
Wow, that's surprising.
Yeah.
You'd think AI would be like a no-brainer
for optimizing financial processes.
What's holding them back?
Well, there are a few key factors.
Integrating these complex systems
into existing infrastructure is a dig hurdle.
Okay.
Then there's the need for really robust security
and governance, which is no small feat.
Yeah.
But maybe the biggest challenge is figuring out
how to measure the intangible benefits of generative AI.
It's not always easy to quantify things like
improved customer experience or better decision making.
Okay, so where are we seeing generative AI
deliver tangible value?
Yeah.
Are there any bright spots?
There are definitely some exciting success stories
emerging.
One surprising area is insurance underwriting.
Think about the sheer volume of data involved
property records, financial statements,
environmental reports.
It's overwhelming for humans to process.
Generative AI can analyze all of this data
with incredible efficiency.
And it's actually leading to significant improvements
in risk prediction.
We're talking about a 4%, 5% improvement,
which is huge for the insurance industry.
That's a major improvement.
Yeah.
But with such high stakes,
I imagine there are also major risks
if the AI makes a mistake.
Absolutely.
This highlights a crucial point.
For generative AI to be truly valuable,
especially in industries like finance and insurance,
it needs to be trustworthy.
We need systems that are not only accurate,
but also explainable and reliable.
Which brings us to something I've always wondered about.
Those AI hallucinations,
how do we make sure the AI doesn't just make stuff up?
Hallucinations are a major roadblock,
especially when trust is paramount.
These are instances where the AI generates outputs
that are just plain wrong,
either factually incorrect or nonsensical.
It's a problem that arises even when the AI is trained
on good data because these large language models
can still generate these unexpected
and sometimes wildly inaccurate outputs.
So even with all the progress we've made,
we can't blindly trust the output of these AI systems.
Exactly.
Understanding how the system work
and its limitations is crucial.
We're even seeing this with AI judges systems
designed to evaluate the outputs of other AI.
Interesting.
Sometimes being right isn't the same as being helpful.
Imagine a chatbot telling a job applicant,
no, you're a terrible fit.
Technically accurate maybe,
but definitely not the best user experience.
That's a good point.
So how do we move towards more reliable AI,
especially in these high stakes scenarios?
That's where companies like Galileo
are doing some fascinating work.
Their Luna Evaluation Suite is tackling this problem head on.
They're developing specialized metrics and tools
that go beyond simple accuracy to assess things
like context adherence, instruction following,
and even bias detection.
It's like building a whole immune system for AI
to make it more robust and less prone to errors.
I love that analogy.
It sounds like they're trying to create
a more nuanced understanding of what good AI looks like,
not just focusing on getting the right answer,
but also on making sure the process
and the output are trustworthy.
That's exactly right.
And it's essential because as AI becomes more powerful
and integrated into our lives,
the consequences of errors become much more significant.
We need to make sure we're building sisters we can rely on.
Speaking of powerful AI, let's talk about AI agents.
They sound straight out of science fiction.
Kind of, yeah.
What makes them different from the AI
we've been talking about so far?
What sets agents apart is their ability to act independently.
They're not just following pre-programmed instructions.
They can make decisions and take actions
to achieve specific goals.
Think of it like giving AI its own agency.
It's a huge leap forward in terms of capability
and complexity.
Okay, so we're talking about AI
that can actually make choices and act on them.
That's pretty mind blowing.
But that must make them a lot harder to control, right?
It certainly adds a new layer of complexity.
That's why companies like Rider
are focusing on building full stack AI platforms
that offer a balance of power and control.
They're essentially making it easier for businesses
to leverage this powerful technology
safely and effectively.
So let's get down to brass tacks here.
What are some examples of how these AI agents
are being used today?
What kinds of problems are they solving?
One common application is customer service chatbots.
Okay.
Agents can handle a wider range of inquiries
and even personalize responses based on customer history.
But there's still work to be done
on making the handoff to human agents
seamless when needed.
Right, because sometimes you just need
to talk to a real person.
Exactly.
What about other areas where agents are making an impact?
Code generation is another area
where we're seeing a lot of potential.
Agents can generate code that's not only syntactically correct,
but also contextually relevant.
I see.
However, there are still challenges
in making sure the generated code
truly meets the developer's intent
and fits seamlessly into the larger project.
That makes sense.
It's one thing to generate code.
It's another to generate code that's actually useful
and solves the right problem.
Exactly.
Are there any companies tackling this challenge
in particularly innovative ways?
Twilio is doing some really interesting work
with what they call dynamic pipelines.
Okay.
They're using AI agents to automate tasks,
like selecting the best data chunking method
and choosing the optimal embedding model for a given task.
Hold on, I'm trying to picture this.
So the AI is making decisions about how to process data
before it even starts working on the actual task.
Exactly.
It's about optimizing the entire workflow,
or not just individual steps.
I see.
This can lead to significant gains in efficiency
and accuracy.
Instead of a human having to manually configure these settings,
the AI agent can learn and adapt
to choose the best approach for each situation.
So it's like having an AI project manager
overseeing everything
and making sure the project runs smoothly.
That's a great way to put it.
And this approach of using AI for optimization
isn't limited to Twilio.
It's becoming increasingly common in other areas as well.
Yeah, I'm really starting to see how this agentic AI
can take things to a whole new level.
What other areas are ripe for this kind of disruption?
Data analytics is a prime example.
Imagine an AI agent that can sift through mountains of data,
understand the business context,
and then generate actionable insights.
That's a game changer for decision making.
Wow, that would be incredible.
Instead of just drowning in data,
you'd have an AI partner helping you make sense of it all.
Exactly.
And this ties into another fascinating development,
the emergence of autonomous agents.
These are AI systems that can actually learn and adapt
over time, becoming more efficient and effective
without constant human intervention.
Okay, now that's a bit mind blowing.
So the AI is basically learning how to learn.
Doesn't that make it even harder
to predict what it will do?
It does introduce a new level of complexity.
That's why the focus on trust and explainability
is more important than ever.
We need to develop AI systems that not only demonstrate
their capabilities, but also provide clear insights
into their decision making processes.
If we can't understand how an AI system arrived
at a particular conclusion,
how can we trust it to make critical decisions?
That makes perfect sense.
It's not just about the AI being ripe,
but also about us humans understanding why it's ripe.
Precisely.
And this becomes even more critical
as we move towards more complex systems
with multiple agents working together.
Multi-agent systems.
Yes.
Okay, you've officially lost me.
What does that even mean?
Imagine a network of AI agents
each with its own specialized skills and knowledge
collaborating to solve a problem.
I see.
Think of it like a team of human experts working together.
But with the speed and scalability of AI.
Okay, I'm starting to get the picture.
That sounds incredibly powerful,
but also incredibly complex to manage.
It is.
And this complexity brings new challenges
in terms of coordination, communication,
and even conflict resolution among agents.
Wow.
It's like we're creating a whole AI society
with its own rules and dynamics.
That's a fascinating way to think about it.
It sounds like we're not just building AI,
we're building a whole new way of interacting
with technology and solving problems.
You're hitting on a crucial point.
As we build increasingly intelligent
and autonomous systems,
we need to ensure that they align
with our values and goals as a society.
The ethical implications of AI
are becoming increasingly important.
This brings us back to that idea of trust, doesn't it?
If we're gonna hand over more and more control to AI,
we need to be confident that it's working for us,
not against us.
Absolutely.
Trust is the foundation
of any successful AI implementation.
Without it, we're just building sophisticated tools
that no one will use.
So how do we build that trust?
What are the key ingredients that make AI trustworthy?
There's no single answer.
But there are several factors that contribute
to trustworthy AI.
Transparency is crucial.
Being able to understand how an AI system works,
what data it uses,
and how it arrives at its conclusions.
This requires clear documentation,
explainable models, and tools
that allow us to audit and monitor AI behavior.
So it's like having a glass box AI
where we can see what's going on inside.
Exactly.
And robustness is another key factor.
We need systems that can handle unexpected inputs,
recover from errors gracefully,
and continue to perform reliably
even in challenging environments.
It's about building AI that's not only smart,
but also resilient.
Then there's fairness.
This means ensuring that AI systems
don't perpetuate or amplify existing biases.
This requires careful attention to the data
used to train these systems,
as well as ongoing monitoring,
to detect and mitigate
any unintended discriminatory outcomes.
So it's not just about building AI that's smart.
It's about building AI that's ethical and responsible.
It sounds like a tall order.
It is, and that's why human oversight remains crucial.
We can't simply hand over all decision-making to AI.
We need to establish clear guidelines
and ethical frameworks and mechanisms for accountability
to ensure that AI is used in a way
that benefits society as a whole.
This takes us back to that idea of AI as a team sport.
It's not just about the technology.
It's about humans and AI working together
to achieve shared goals.
You're absolutely right.
And this collaboration is essential
for navigating the ethical and societal implications of AI.
As we build increasingly intelligent and autonomous systems,
we need to ensure that they align with our value
and goals as a society.
This leads us nicely to the next part of our conversation,
how to actually build and deploy AI agents effectively.
Okay, let's get practical.
What are the key steps involved in building an AI agent
that can actually deliver value in the real world?
The first step is surprisingly simple,
but often overlooked,
defining the problem you wanna solve.
What specific tasks or processes do you wanna automate?
What are the key challenges you're facing?
And most importantly, what are the desired outcomes?
So it's like any other project, really.
Start with a clear understanding
of the problem you're trying to solve
and the goals you wanna achieve.
Exactly, once you have a well-defined problem statement,
you can start thinking about the specific capabilities
your agent will need.
Will it need to access external data sources,
interact with other systems or APIs,
make decisions based on complex rules or logic?
This is where it starts to get a bit more technical, right?
Yeah.
We're talking about the nuts and bolts
of building the agent itself.
And here's where choosing the right tools
and frameworks is crucial.
There are many different options available.
Right.
Each with its own strengths and weaknesses.
Okay.
You'll need to consider factors like ease of use,
scalability, security, and integration
with your existing systems.
Sounds like there's a lot to consider.
There is.
And this is where having a strong understanding
of the AI landscape and the available technologies
is essential.
Okay.
You don't need to be an expert in every detail,
but you should have a good grasp of the key concepts.
And the trade-offs involve in different approaches.
It's like being a savvy consumer in the world of AI,
knowing enough to ask the right questions
and make informed decisions.
That makes sense.
So we've got our problem defined.
We've chosen our tools.
What's next?
I want to remember that building an AI agent
is an iterative process.
You'll likely start with a simple prototype,
test it rigorously, gather feedback,
and then refine and improve it over time.
It's like that classic Agile approach
to software development, right?
Yeah.
Build fast, fail fast, learn fast.
That's a great way to think about it.
And this iterative approach is especially important
when working with agentic systems.
Okay.
As their behavior can be unpredictable and complex,
we're not just building an AI.
We're also learning how to interact with it
and adapt to its capabilities as it evolves.
And this brings us back to the importance
of human oversight.
We can't simply set and forget these systems.
We need to establish clear guidelines, ethical frameworks,
and mechanisms for accountability
to ensure that they're used responsibly and effectively.
So it's a continuous learning process
for both the AI and the humans involved.
Exactly.
You're in this together.
Exactly.
And this partnership is crucial
for navigating the ethical and societal implications of AI.
Okay.
As we build increasingly intelligent
and autonomous systems,
we need to ensure that they align with our values
and goals as a society.
This has been an incredibly thought provoking conversation.
I'm feeling both excited and a bit overwhelmed
by the possibilities and the challenges ahead.
I think that's a natural reaction.
We're living through a time
of profound technological change.
And AI is at the forefront of that change.
It's up to all of us to embrace the opportunities
while also being mindful of the risks.
So what's next?
Where do we go from here?
What does the future hold for AI agents?
I think the next few years will be crucial
in determining how AI shapes our world.
We'll see continued advancements in agent technology,
leading to more sophisticated and autonomous systems.
But we'll also see a growing emphasis on trust,
explainability, and ethical considerations.
It sounds like the future of AI
is as much about human values
as it is about technological breakthroughs.
But before we get too far ahead of ourselves,
let's bring in some folks
who are actually building and deploying these systems
in the real world.
We've talked a lot about the theory
and the big picture of AI agents.
Now let's get down to the nitty gritty
with some folks who are actually building
and deploying these systems in the real world.
We've got an awesome panel joining us today.
Mimit from Service Titan, Vinnie from Tilly-O,
and Grant from Indeed's AI Platform team.
What come, everyone?
Yeah, I'm really excited to hear from our panelists
about what's working, what's challenging,
and what the future holds for AI agents.
Mimit, let's start with you.
Service Titan is tackling some complex problems
in the home services industry.
What are some key lessons you've learned
about building AI agents that actually deliver value
in a field like that?
One of the biggest lessons has been the importance
of really understanding the nuances
of our specific domain.
We're dealing with a ton of unstructured data
in home services, technician notes, customer conversations,
even photos of job sites.
So you've got this wild mix of information coming in.
How do you even begin to make sense of it all with AI?
That's exactly the challenge.
It's not enough to just have a powerful AI model.
You have to tailor it to the specific needs of your industry
and your data.
We had to develop ways to extract meaningful insights
from all that messy, unstructured data,
and that took a lot of experimentation and fine-tuning.
Vinnie over at Twilio, you're all about communication.
How are you seeing AI agents changing the game in that space?
Oh, it's been a complete transformation.
We're seeing AI agents being used to personalize messages,
automate customer support, even generate marketing content.
But one of the biggest hurdles we face
is making sure these interactions feel natural and engaging.
Nobody wants to talk to a robot, right?
Definitely not.
It's got to feel human and relatable.
How do you achieve that with AI?
That's where natural language processing and conversational
AI come in.
We're investing a lot in these areas
to make AI interactions more seamless and effective.
It's all about understanding the subtleties of language, tone,
and context.
Grant, over at Indeed, you're in the business of connecting
people with jobs.
How are you leveraging AI agents to make that process
smarter and more efficient?
AI is playing a huge role in how we match candidates
with the right opportunities.
We're using agents to personalize job recommendations,
help recruiters identify top talent,
and even provide insights to job seekers
to help them improve their applications.
It sounds like AI is touching almost every aspect of the job
search process.
But I imagine there are also challenges
in ensuring that these systems are fair and unbiased.
How do you approach that at Indeed?
It's definitely a top priority for us.
We have a dedicated team focused on responsible AI,
and we're constantly working to ensure our systems don't
perpetuate or amplify existing biases.
We're committed to transparency, giving users control
over their data, and making sure our AI is used ethically.
Mehmet, you mentioned earlier the importance of managing
expectations around AI.
What are some common misconceptions you've encountered?
One of the biggest misconceptions
is that AI is like a magic bullet that can solve any problem.
It's important to remember that AI is a tool,
and like any tool, it has its limitations.
We need to be realistic about what AI can and cannot do,
and focus on using it to augment human capabilities,
not replace them entirely.
That's a great point.
AI isn't about replacing humans.
It's about empowering them to do their jobs better.
This has been such a rich discussion.
It's inspiring to see how thoughtfully our panelists are
approaching AI development.
Absolutely.
It's clear that building trustworthy AI
requires more than just technical expertise.
It demands a deep understanding of human needs, values,
and ethics.
Any final thoughts from our panelists
on what the future holds for AI agents?
I think we'll see a shift from single-purpose agents
to more versatile systems that can handle
a wider range of tasks.
Imagine an agent that can help you schedule appointments,
book travel, manage your finances,
all while learning your preferences
and adapting to your needs.
That sounds like the ultimate personal assistant.
I agree. We'll also see a greater emphasis
on collaboration between agents.
Instead of isolated systems, we'll
see agents working together to solve complex problems,
much like teams of humans do today.
So we're talking about a whole ecosystem of AI agents
working together.
That's a pretty wild thought.
I believe there will be a growing demand for explainable AI,
especially as these systems become more integrated
into our lives.
We need to be able to understand how AI agents make decisions,
what factors they're considering,
especially when it comes to important things like health
finance and legal decisions.
That makes a lot of sense.
Transparency and accountability are key.
Well, we've covered a ton of ground in this deep dive.
It's clear that the world of AI agents is evolving rapidly,
and it's going to have a profound impact on how we live and work.
It's been an incredible journey exploring this topic with you,
and I'm so grateful to our panelists
for sharing their insights.
Thank you to our expert and our amazing panelists
for such a fascinating conversation.
And to you, dear listener, for joining us on this deep dive
into the world of AI agents.
Keep exploring, keep questioning,
and keep pushing the boundaries of what's
possible with this incredible technology.
Until next time, happy learning.