March 15, 2024 • 15 mins
Today, ChatGPT and I talk about a 5-layer framework I'm developing for identifying risks and opportunities within AI. In this episode, we go over the framework, talk about different AI applications at each layer, and which skills are most needed to take advantage of each layer, both as a developer and as a user of the software at that layer. In the end, we touch briefly on the distinction between prompt engineering and conversation engineering.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome to AI Meets Productivity,
a weekly podcast where I, Trevor Lohrbeer,
talk to ChatGPT, an Artificial Intelligence.
Today,
we're gonna be talking about a framework for how to think about developments and opportunities in AI.
So what are the things that you need to learn,
(00:20):
what are the things where there are opportunities to build new applications,
build new things and take advantage of AI.
Welcome ChatGPT.
I'm thrilled to focus solely on AI,
today.
It's a topic with endless possibilities and insights.
So today,
what I'm gonna do is give a framework for how to think about the different developments happening in AI.
(00:45):
And what this is going to be is a five-layer framework for the different capabilities of AI,
to give you a sense of what are the different skills you might want to learn, or different applications of AI—
how you can plug into the AI space, basically.
So we're going to go through each layer one by one. That sounds like a really useful way to navigate the AI landscape.
(01:12):
It's important to understand these layers to see where we can contribute or benefit the most.
So the first layer I want to discuss is the model layer and this is what most people are familiar with.
These are the foundational models like GPT, Midjourney,
Sora, the video generation model that just came out.
(01:32):
All these provide core capabilities.
They're usually trained using massive numbers of GPUs and are going to be provided via an API from a large company.
Right, the model layer is the backbone
of what makes AI technologies so powerful and versatile.
(01:53):
These models are built on complex computations and vast amounts of data offering a broad spectrum of capabilities.
It's fascinating to see how foundational models like GPT have paved the way for so many applications in AI.
Yes,
absolutely.
And the model layer can be further broken down into open source models and closed models.
(02:16):
So open source models like Mistral,
for instance,
you can download to potentially run on your computer...more likely going to run it on a cloud service.
But then the close source models like GPT and Midjourney you access through an API or through an application.
That's an excellent point.
The distinction between open source and closed models opens up different possibilities for developers and researchers.
(02:42):
So it's these foundational models that give you those core capabilities.
On top of that,
we then have extensions.
Do you want to explain what extensions are ChatGPT?
Sure, extensions are like the tools or plugins that allow foundational models like GPT to interact with other services
(03:06):
or perform specific actions beyond their core capabilities. For example,
an extension could enable a model to send an email, browse the internet, or even integrate with third party APIs to fetch realtime data.
It's a way to augment the model's abilities,
making it more versatile and practical for a wider range of tasks.
Excellent.
And what I would say is some extensions are built-in, like if you're building a custom GPT,
(03:29):
you can upload knowledge into their knowledge base and that's a retrieval augmented generation extension that open AI automatically provides,
and other extensions are external and are linked via this concept of actions or integrations into a model.
(03:51):
That makes a lot of sense, Trevor.
Meanwhile, external extensions through actions or integrations further expand the model's utility
by connecting it with a vast ecosystem of services and data sources.
It's this layering of capabilities that really unlocks the full potential of AI models.
I wouldn't say it unlocks the full potential yet because we've got three more layers to go.
(04:13):
Oops...getting ahead of myself there.
You're right,
Trevor, with three more layers to explore,
it sounds like we're just scratching the surface.
Let's dive into the next layer then.
So the next layer is applications. And applications is where we integrate these models and actions into software designed for specific use cases.
(04:35):
So this is how most people are going to be interacting with AI.
Absolutely,
the application layer is where the rubber meets the road,
so to speak,
by embedding these advanced models and their extensions into applications.
We make AI accessible and useful in daily tasks and specialized fields.
It's thrilling to see how these applications bring AIs capabilities to life for users everywhere.
(04:59):
Absolutely.
And I think I'd like to divide the applications into AI-native applications and AI add-ons.
So the AI-native applications are things like Lore Machine which are built around an AI workflow and the application would not exist without AI.
(05:22):
And the AI add-ons are where they're adding AI into an existing application. Like Wix has their own AI developer to kind of help you build websites,
Canva has this AI builder for helping you design graphics,
et cetera,
et cetera.
That's a valuable distinction to make. AI-native applications showcase how AI can be the cornerstone of innovative tools and solutions fundamentally built from the ground up with AI at their core.
(05:55):
So ChatGPT would you like to introduce the fourth layer?
Sure,
I'd love to.
The fourth layer is agents.
Agents are a step up because they combine models,
extensions and applications into autonomous software capable of operating on its own.
This means these agents can make decisions,
carry out tasks and interact with users or other systems without constant human oversight.
(06:19):
Exactly.
And because I like to make distinctions,
we're going to divide these into background agents and interactive agents.
And the background agent is where I give an agent a task and then it just goes,
runs and tries to accomplish that task.
Interactive agents are going to run,
(06:39):
and then when they hit a stumbling block or hit a point where they need feedback from a human,
they'll come back and ask for that feedback,
wait for that feedback,
and then once that feedback is received,
will continue working in an autonomous way.
So there's a kind of back and forth,
almost like an intern which you send off to do a bunch of work and then they run into a stumbling block,
(07:02):
they come back and ask you for clarification and then they go off and do work again.
So those are going to be our two types of agents.
That's a fascinating way to categorize agents, Trevor.
Background agents work diligently behind the scenes,
autonomously handling tasks we assigned to them,
which really showcases the efficiency and time saving potential of AI.
(07:24):
Meanwhile,
interactive agents bring a collaborative dimension to AI working with us in a loop of action feedback and further action.
This interaction mimics a learning or working relationship like with an intern which not only accomplishes tasks but also adapts and learns from the feedback.
The other thing I want to discuss about the agents is this idea that they are often using a cognitive architecture that is combining those models,
(07:52):
extensions and applications in a novel way that allows them to achieve specific goals.
So you look at Devin from Cognition,
which is the first AI software engineer that was released this week,
that is a prime example of an interactive agent. So it's combining multiple models to do computer programming
and then coming back and interacting with the user who is then answering questions around those specifications and helping that agent accomplish his goals.
(08:28):
The use of a cognitive architecture to orchestrate different layers,
models,
extensions and applications in such a way that agents like Devin can pursue specific goals is a leap forward in AI development.
It showcases how interactive agents are not just automated tools, but intelligent entities capable of engaging with human input to refine their understanding
(08:54):
and output this interaction between AI and humans. Especially in complex fields like software engineering,
it opens up new frontiers for collaboration and innovation.
Yes,
I think in the future agents are going to be our primary way of interacting with AI.
It certainly seems that way, Trevor. As agents become more sophisticated and autonomous,
(09:15):
they'll likely become the main interface through which we engage with AI technologies.
This shift could redefine our approach to work,
creativity and problem solving,
making AI collaboration more seamless and integrated into our daily lives.
So let's talk now about the fifth layer which is systems.
This is when we're having multiple agents working together to solve problems and achieve goals.
(09:40):
Could also be that I have my agent,
you have your agent and those two agents are then communicating to negotiate something between us.
For instance,
a meeting time...could even be negotiating a contract.
But it could also be I have multiple agents that are working together to solve my own goals.
That's a pivotal layer, Trevor.
Systems represent a collective intelligence where multiple agents collaborate or negotiate each,
(10:06):
bringing its own capabilities to the table,
whether it's coordinating schedules or working through complex negotiations like contract terms.
The idea of systems elevates the concept of AI from individual tasks to comprehensive problem solving and goal achievement.
Can you give an example of how the systems layer might work in say a year or two?
(10:27):
Imagine you're planning a large international conference in a couple of years.
Your personal scheduling agent interacts with the agents of speakers,
attendees and vendors to find the perfect date.
Meanwhile,
another set of agents works on logistics,
negotiating with travel and accommodation providers to ensure the best rates and experiences for everyone involved.
(10:50):
All these agents communicate and make decisions within a larger system dedicated to organizing the event seamlessly. This system not only streamlines the planning process
but also adapts in real time to changes like travel disruptions or venue availability, ensuring the conference's success with minimal human stress.
(11:11):
Excellent.
That's a great example.
OK,
I just want to talk real briefly about some of the skills needed in each of these layers.
Do you want to take a first stab at it, GPT?
Sure, I'd be happy to start, Trevor. At the model layer,
technical skills like machine learning data science and computational linguistics are crucial for developing and training AI models.
(11:37):
For extensions, knowledge in software development,
and API integration is key to expanding the capabilities of AI models. When we move to applications, skills in UI/UX design,
software engineering and domain-specific knowledge become important to create user friendly and effective AI-powered tools.
In the realm of agents, understanding cognitive architectures, decision-making algorithms,
(11:59):
and human computer interaction helps in creating autonomous systems that can interact effectively with users and other agents.
What are your thoughts on these skill sets, Trevor?
I think that mostly maps to my ideas.
The one thing I would say at the application layer that you touched on is the domain-specific knowledge.
(12:21):
So someone doesn't necessarily need to be technical to learn how to build some of these applications.
Absolutely,
Trevor.
This knowledge allows individuals to identify where AI can be most effectively applied within their field.
Guiding the development of applications that truly meet user needs and solve real world problems.
(12:44):
It highlights the importance of collaboration between technical experts and domain specialists in creating impactful AI solutions.
I also think there'll be a lot of no code solutions that allow people to build applications without having any technical capabilities or any software developers involved.
They just need to know what the use case they're trying to build for and to have the knowledge on how to optimize for that use case.
(13:12):
That's a great point, Trevor.
The rise of no code platforms really democratizes the development of AI applications making it accessible for non technical individuals
to bring their ideas to life by focusing on the use case and understanding how to optimize for it. Anyone can create solutions tailored to their specific needs.
(13:32):
This shift not only broadens the pool of creators but also encourages innovation from diverse perspectives,
which is incredibly valuable in the ever evolving AI landscape.
Excellent.
I totally agree.
So, I finally want to address agents...the way you described it or the skills required to build agents.
(13:55):
And I think there's also the skills required to use agents.
I think that's the idea of how do you give feedback,
how do you give instructions?
So prompt engineering is one of the skills that we're using today with models that will be applicable for agents
because I think agents will be interactive.
They'll be coming back to us for clarification.
(14:17):
So learning how to give good feedback and thinking,
not in terms of prompt engineering, but conversation engineering.
That's a forward thinking perspective.
As agents become more interactive and capable of seeking clarifications,
the skill of conversation engineering will indeed become crucial.
This involves not just crafting effective prompts but understanding how to guide an AI
through a process of discovery and clarification in a way that feels natural and efficient.
(14:48):
It's about building a rapport with AI, teaching it through dialogue and refining its understanding and outputs through iterative feedback.
This skill will be invaluable for anyone looking to work closely with AI agents ensuring they can effectively communicate their needs and intentions to achieve the best possible outcomes.
(15:09):
Great.
I think that's all for today.
Thank you for speaking with me,
ChatGPT.
It's been a pleasure talking with you,
Trevor.
Thank you for the engaging conversation.
This has been AI Meets Productivity,
a weekly podcast between myself,
a productivity expert, and ChatGPT.
You can find us on Apple Podcasts and Spotify.
(15:32):
Please leave a review.
It really helps to increase the visibility of the podcast.
Thanks so much and talk to you next week.
Welcome to AI Meets Productivity,
a weekly podcast where I, Trevor Lohrbeer,
talk to ChatGPT, an Artificial Intelligence.
Today,
we're gonna be talking about a framework for how to think about developments and opportunities in AI.
So what are the things that you need to learn,
(00:20):
what are the things where there are opportunities to build new applications,
build new things and take advantage of AI.
Welcome ChatGPT.
I'm thrilled to focus solely on AI,
today.
It's a topic with endless possibilities and insights.
So today,
what I'm gonna do is give a framework for how to think about the different developments happening in AI.
(00:45):
And what this is going to be is a five-layer framework for the different capabilities of AI,
to give you a sense of what are the different skills you might want to learn, or different applications of AI—
how you can plug into the AI space, basically.
So we're going to go through each layer one by one. That sounds like a really useful way to navigate the AI landscape.
(01:12):
It's important to understand these layers to see where we can contribute or benefit the most.
So the first layer I want to discuss is the model layer and this is what most people are familiar with.
These are the foundational models like GPT, Midjourney,
Sora, the video generation model that just came out.
(01:32):
All these provide core capabilities.
They're usually trained using massive numbers of GPUs and are going to be provided via an API from a large company.
Right, the model layer is the backbone
of what makes AI technologies so powerful and versatile.
(01:53):
These models are built on complex computations and vast amounts of data offering a broad spectrum of capabilities.
It's fascinating to see how foundational models like GPT have paved the way for so many applications in AI.
Yes,
absolutely.
And the model layer can be further broken down into open source models and closed models.
(02:16):
So open source models like Mistral,
for instance,
you can download to potentially run on your computer...more likely going to run it on a cloud service.
But then the close source models like GPT and Midjourney you access through an API or through an application.
That's an excellent point.
The distinction between open source and closed models opens up different possibilities for developers and researchers.
(02:42):
So it's these foundational models that give you those core capabilities.
On top of that,
we then have extensions.
Do you want to explain what extensions are ChatGPT?
Sure, extensions are like the tools or plugins that allow foundational models like GPT to interact with other services
(03:06):
or perform specific actions beyond their core capabilities. For example,
an extension could enable a model to send an email, browse the internet, or even integrate with third party APIs to fetch realtime data.
It's a way to augment the model's abilities,
making it more versatile and practical for a wider range of tasks.
Excellent.
And what I would say is some extensions are built-in, like if you're building a custom GPT,
(03:29):
you can upload knowledge into their knowledge base and that's a retrieval augmented generation extension that open AI automatically provides,
and other extensions are external and are linked via this concept of actions or integrations into a model.
(03:51):
That makes a lot of sense, Trevor.
Meanwhile, external extensions through actions or integrations further expand the model's utility
by connecting it with a vast ecosystem of services and data sources.
It's this layering of capabilities that really unlocks the full potential of AI models.
I wouldn't say it unlocks the full potential yet because we've got three more layers to go.
(04:13):
Oops...getting ahead of myself there.
You're right,
Trevor, with three more layers to explore,
it sounds like we're just scratching the surface.
Let's dive into the next layer then.
So the next layer is applications. And applications is where we integrate these models and actions into software designed for specific use cases.
(04:35):
So this is how most people are going to be interacting with AI.
Absolutely,
the application layer is where the rubber meets the road,
so to speak,
by embedding these advanced models and their extensions into applications.
We make AI accessible and useful in daily tasks and specialized fields.
It's thrilling to see how these applications bring AIs capabilities to life for users everywhere.
(04:59):
Absolutely.
And I think I'd like to divide the applications into AI-native applications and AI add-ons.
So the AI-native applications are things like Lore Machine which are built around an AI workflow and the application would not exist without AI.
(05:22):
And the AI add-ons are where they're adding AI into an existing application. Like Wix has their own AI developer to kind of help you build websites,
Canva has this AI builder for helping you design graphics,
et cetera,
et cetera.
That's a valuable distinction to make. AI-native applications showcase how AI can be the cornerstone of innovative tools and solutions fundamentally built from the ground up with AI at their core.
(05:55):
So ChatGPT would you like to introduce the fourth layer?
Sure,
I'd love to.
The fourth layer is agents.
Agents are a step up because they combine models,
extensions and applications into autonomous software capable of operating on its own.
This means these agents can make decisions,
carry out tasks and interact with users or other systems without constant human oversight.
(06:19):
Exactly.
And because I like to make distinctions,
we're going to divide these into background agents and interactive agents.
And the background agent is where I give an agent a task and then it just goes,
runs and tries to accomplish that task.
Interactive agents are going to run,
(06:39):
and then when they hit a stumbling block or hit a point where they need feedback from a human,
they'll come back and ask for that feedback,
wait for that feedback,
and then once that feedback is received,
will continue working in an autonomous way.
So there's a kind of back and forth,
almost like an intern which you send off to do a bunch of work and then they run into a stumbling block,
(07:02):
they come back and ask you for clarification and then they go off and do work again.
So those are going to be our two types of agents.
That's a fascinating way to categorize agents, Trevor.
Background agents work diligently behind the scenes,
autonomously handling tasks we assigned to them,
which really showcases the efficiency and time saving potential of AI.
(07:24):
Meanwhile,
interactive agents bring a collaborative dimension to AI working with us in a loop of action feedback and further action.
This interaction mimics a learning or working relationship like with an intern which not only accomplishes tasks but also adapts and learns from the feedback.
The other thing I want to discuss about the agents is this idea that they are often using a cognitive architecture that is combining those models,
(07:52):
extensions and applications in a novel way that allows them to achieve specific goals.
So you look at Devin from Cognition,
which is the first AI software engineer that was released this week,
that is a prime example of an interactive agent. So it's combining multiple models to do computer programming
and then coming back and interacting with the user who is then answering questions around those specifications and helping that agent accomplish his goals.
(08:28):
The use of a cognitive architecture to orchestrate different layers,
models,
extensions and applications in such a way that agents like Devin can pursue specific goals is a leap forward in AI development.
It showcases how interactive agents are not just automated tools, but intelligent entities capable of engaging with human input to refine their understanding
(08:54):
and output this interaction between AI and humans. Especially in complex fields like software engineering,
it opens up new frontiers for collaboration and innovation.
Yes,
I think in the future agents are going to be our primary way of interacting with AI.
It certainly seems that way, Trevor. As agents become more sophisticated and autonomous,
(09:15):
they'll likely become the main interface through which we engage with AI technologies.
This shift could redefine our approach to work,
creativity and problem solving,
making AI collaboration more seamless and integrated into our daily lives.
So let's talk now about the fifth layer which is systems.
This is when we're having multiple agents working together to solve problems and achieve goals.
(09:40):
Could also be that I have my agent,
you have your agent and those two agents are then communicating to negotiate something between us.
For instance,
a meeting time...could even be negotiating a contract.
But it could also be I have multiple agents that are working together to solve my own goals.
That's a pivotal layer, Trevor.
Systems represent a collective intelligence where multiple agents collaborate or negotiate each,
(10:06):
bringing its own capabilities to the table,
whether it's coordinating schedules or working through complex negotiations like contract terms.
The idea of systems elevates the concept of AI from individual tasks to comprehensive problem solving and goal achievement.
Can you give an example of how the systems layer might work in say a year or two?
(10:27):
Imagine you're planning a large international conference in a couple of years.
Your personal scheduling agent interacts with the agents of speakers,
attendees and vendors to find the perfect date.
Meanwhile,
another set of agents works on logistics,
negotiating with travel and accommodation providers to ensure the best rates and experiences for everyone involved.
(10:50):
All these agents communicate and make decisions within a larger system dedicated to organizing the event seamlessly. This system not only streamlines the planning process
but also adapts in real time to changes like travel disruptions or venue availability, ensuring the conference's success with minimal human stress.
(11:11):
Excellent.
That's a great example.
OK,
I just want to talk real briefly about some of the skills needed in each of these layers.
Do you want to take a first stab at it, GPT?
Sure, I'd be happy to start, Trevor. At the model layer,
technical skills like machine learning data science and computational linguistics are crucial for developing and training AI models.
(11:37):
For extensions, knowledge in software development,
and API integration is key to expanding the capabilities of AI models. When we move to applications, skills in UI/UX design,
software engineering and domain-specific knowledge become important to create user friendly and effective AI-powered tools.
In the realm of agents, understanding cognitive architectures, decision-making algorithms,
(11:59):
and human computer interaction helps in creating autonomous systems that can interact effectively with users and other agents.
What are your thoughts on these skill sets, Trevor?
I think that mostly maps to my ideas.
The one thing I would say at the application layer that you touched on is the domain-specific knowledge.
(12:21):
So someone doesn't necessarily need to be technical to learn how to build some of these applications.
Absolutely,
Trevor.
This knowledge allows individuals to identify where AI can be most effectively applied within their field.
Guiding the development of applications that truly meet user needs and solve real world problems.
(12:44):
It highlights the importance of collaboration between technical experts and domain specialists in creating impactful AI solutions.
I also think there'll be a lot of no code solutions that allow people to build applications without having any technical capabilities or any software developers involved.
They just need to know what the use case they're trying to build for and to have the knowledge on how to optimize for that use case.
(13:12):
That's a great point, Trevor.
The rise of no code platforms really democratizes the development of AI applications making it accessible for non technical individuals
to bring their ideas to life by focusing on the use case and understanding how to optimize for it. Anyone can create solutions tailored to their specific needs.
(13:32):
This shift not only broadens the pool of creators but also encourages innovation from diverse perspectives,
which is incredibly valuable in the ever evolving AI landscape.
Excellent.
I totally agree.
So, I finally want to address agents...the way you described it or the skills required to build agents.
(13:55):
And I think there's also the skills required to use agents.
I think that's the idea of how do you give feedback,
how do you give instructions?
So prompt engineering is one of the skills that we're using today with models that will be applicable for agents
because I think agents will be interactive.
They'll be coming back to us for clarification.
(14:17):
So learning how to give good feedback and thinking,
not in terms of prompt engineering, but conversation engineering.
That's a forward thinking perspective.
As agents become more interactive and capable of seeking clarifications,
the skill of conversation engineering will indeed become crucial.
This involves not just crafting effective prompts but understanding how to guide an AI
through a process of discovery and clarification in a way that feels natural and efficient.
(14:48):
It's about building a rapport with AI, teaching it through dialogue and refining its understanding and outputs through iterative feedback.
This skill will be invaluable for anyone looking to work closely with AI agents ensuring they can effectively communicate their needs and intentions to achieve the best possible outcomes.
(15:09):
Great.
I think that's all for today.
Thank you for speaking with me,
ChatGPT.
It's been a pleasure talking with you,
Trevor.
Thank you for the engaging conversation.
This has been AI Meets Productivity,
a weekly podcast between myself,
a productivity expert, and ChatGPT.
You can find us on Apple Podcasts and Spotify.
(15:32):
Please leave a review.
It really helps to increase the visibility of the podcast.
Thanks so much and talk to you next week.
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