Welcome back everyone to the deep dive.
Ready for another exciting deep dive today?
Absolutely, always ready to explore the latest in AI.
Awesome.
Well, today we're diving into something pretty remarkable.
Amazon's brand new family of AI models.
They call it Amazon Nova.
These are foundation models, right?
Exactly.
Think of them like a Swiss army knife of AI.
Oh, I like that analogy.
Yeah, they're built to be super versatile,
ready to tackle all sorts of different tasks.
And get this, it's not just one model, it's a whole family.
Nova Pro, Light, Micro, Canvas, and Real.
Wow, a whole family.
So each one has its own unique strengths.
You got it.
And not only are they designed to be incredibly intelligent,
but they're also fast and cost effective.
Speed and efficiency, those are big wins in the AI world,
especially as these models get more and more complex.
Absolutely.
But let's break it down a bit.
We've got Pro, Light, and Micro.
What's the deal with those three?
So those three are all built on what are called the transformer
architecture.
You can think of it like a powerful engine that
allows them to really understand and work
with all different types of information.
Like what?
Give us some examples.
Well, we're talking text, images, videos, even documents.
They can take all of that as input and then generate text,
output, provide insights, answer questions, that kind of thing.
So it's not just about understanding words on a page
anymore.
These models are processing the world around us
in all its forms.
That's pretty mind blowing.
It is.
And they do really well on industry standard tests.
For example, there's MMLU.
It measures language understanding
across all kinds of subjects.
OK, like a comprehensive language exam for AI.
Exactly.
And then there's Flores, which evaluates translation skills
across a bunch of different languages.
So these benchmarks are like a way
to see how well rounded these AI models are,
how well they can grasp information
and apply their knowledge.
Right.
And it's interesting to see how each of those models
Pro, Light, and Micro perform on those tests.
They each have their own strengths.
But one that's really been making waves is Nova Micro.
You mentioned that before.
What makes it so special?
Well, for its size, it's punching way above its weight.
It's actually outperforming some of the bigger, more complex
models in certain areas.
Like what kinds of areas?
Especially when it comes to things like logic, reasoning,
following complex instructions, really impressive stuff
for a model of its size.
So does that mean we might start seeing more powerful AI
capabilities becoming available to, say,
smaller companies or individual developers who might not
have the resources of a big tech company?
It's definitely a possibility.
This kind of efficiency could be a game changer
for making advanced AI more accessible.
That's super exciting.
Now, you mentioned that these models can be customized.
What does that look like in practice?
Well, developers can actually fine tune them
for specific tasks.
It's really cool.
They can adjust things like accuracy, speed, and even
cost to fit their needs.
So it's not a one-size-fits-all situation.
You can actually mold these models
to create the perfect AI assistant for a specific job.
Exactly.
It's all about flexibility and making these models work
for you, not the other way around.
And they're using some pretty sophisticated techniques
to do it.
Like what?
Things like supervised fine tuning,
where they train the models further on specific data sets.
And then there's reinforcement learning
from human feedback, which is super interesting.
Oh, yeah.
I've heard about that.
It's like the AI is learning from our feedback
and getting better over time.
Precisely.
It's constantly learning and evolving.
That's wild.
But we've only talked about one part of the family so far.
What about those creative models, Nova Canvas and Real?
Yes, those are the artists of the family.
Nova Canvas focuses on generating images
from text descriptions, while Nova Real takes it a step
further and creates videos.
Wait, AI that can make art?
This is where things start to feel like science fiction.
It does, doesn't it?
Imagine typing in a cat wearing a tiny hat,
riding a unicorn through a rainbow field,
and having Canvas generate a stunning high resolution
image of exactly that.
That's amazing.
But do you have any control over the image,
or does it just kind of do its own thing?
Oh, you have control.
With Canvas, you can specify the resolution, the aspect ratio
you're not stuck with, just a standard square image.
And get this, you can actually edit the image
using text commands.
Hold on.
So I can talk to the AI and tell it to make changes
to the image.
Like, what if I wanted the cat's hat to be blue instead of red?
Exactly.
You could say, change the cat's hat to blue,
and it would actually make the change.
No way.
That's incredible.
And what about real?
How does that work?
It's similar, but focuses on videos.
You could give it a prompt, like a robot learning
to dance in a bustling city square,
and it would generate a short video
bringing that scene to life.
Wow.
So I could basically write a mini movie script,
and this AI would direct it for me.
In a way, yes.
You could even control the camera movement
using natural language.
Imagine saying, zoom in on the robot's face,
or pan across the cityscape, and having the AI
seamlessly incorporate those camera moves into the video.
That's insane.
It sounds like these models could revolutionize filmmaking,
animation, even advertising.
The possibilities are endless.
The potential applications are vast.
And what's even more impressive is how they achieve
this level of creative output.
They use something called latent diffusion models.
OK, latent diffusion models.
Now you're just using big words to make me feel dumb.
Break that down for us.
How do they actually work?
All right, imagine you have a perfectly clear picture,
and then you start slowly adding noise to it,
making it blurrier and blurrier until it's just
a mess of random pixels.
OK, following so far.
Kind of like those scrambled pictures
you used to see in puzzle books.
Yeah, kind of like that.
So latent diffusion models learn to reverse that process.
They start with a jumbled mess, and then, based on your text
prompts, they gradually remove the noise
to create a coherent image or video.
So it's like taking that blurry pixelated image
and having the AI de-blur it based on your description,
except instead of an image, it's a whole video.
That's wild.
How do they train these models to do this?
Well, the training process is extensive.
There are two main phases.
First, they pre-train the models on a massive data
set of images and videos.
OK, hold on.
Where do they get all this data from?
Are they just grabbing everything off the internet?
They don't go into specific details,
but they mention using a mix of sources.
Like what?
They use licensed data, their own data, open source data sets,
and publicly available data, where it's appropriate to use it.
So it's a blend of different sources.
And I'm assuming they're being super careful about things
like data privacy and responsible use from them.
Absolutely.
In fact, they have a whole section of their report
dedicated to responsible AI.
It's all about ensuring these powerful tools are used ethically.
Maybe we can delve into that a little bit more after a quick.
Oh, we don't really have breaks here, do we?
Nope.
This is the deep dive.
We just keep on diving.
So let's jump right into responsible AI
and see what Amazon is doing to make sure these incredible models
are used for good.
So responsible AI, it's a big topic these days
and for good reason.
It is.
It's one thing to create powerful AI,
but it's another to make sure it's used ethically.
Responsibly, it's a must-have.
Right, with great power comes great responsibility
or something like that.
So how is Amazon approaching this with the Nova models?
Well, they've got this framework.
They're calling it the eight core dimensions
of responsible AI.
Eight dimensions, OK.
Lay it on us.
What are they?
Fairness, explainability, privacy and security,
safety, controllability, and veracity and robustness, governance,
and transparency.
OK, that's quite the list.
So those are the dimensions.
But what do they actually mean when it comes to these AI models?
How does it all play out in the real world?
Good question.
Let's start with fairness.
What we're talking about here is making sure these models don't
have any bias baked in.
Baked in, OK.
So no unfair treatment of certain groups of people.
Exactly.
Like let's say you're using Nova to analyze resumes.
You want to make sure it's a value in candidates fairly,
regardless of their background, their gender, their ethnicity,
all of that.
That's crucial.
We've seen examples in the past where AI systems have actually
perpetuated existing biases.
So it's good to know they're addressing this head on.
What about explainability?
What does that mean in this context?
Explainability is all about understanding
how the model makes its decisions.
Like why did it recommend this candidate?
Or why did it generate this particular image?
So it's like a peek behind the curtain.
We get to see the reasoning behind the AI's actions.
Exactly.
That transparency is so important for building trust,
for holding these AI systems accountable.
I can see that.
And privacy and security, I imagine,
those are huge priorities as well,
especially with these models handling so much data.
Absolutely.
I mean, you're talking about people's personal information,
sensitive data.
Amazon has implemented measures to protect all of that
and to prevent these models from being misused.
Misused?
How so?
Give us an example.
Well, you don't want someone using these powerful AI models
to say generate harmful content or spread misinformation.
It's a real concern.
Definitely.
It's like we're giving these AI's incredibly powerful tools,
and we need to make sure they use them for good, not for harm.
Exactly.
That's where the safety dimension comes in.
It's about ensuring these models are being used in a way that
doesn't put people at risk.
Makes sense.
It's like putting safeguards around these tools,
like safety rails.
But how do they actually control these models?
How do you make sure they stay within the bounds
of responsible use?
It seems like a tough challenge.
It is, but that's what controllability is all about.
They've built in mechanisms to monitor the AI's behavior,
to steer it in the right direction, to keep it on track.
So it's not just about setting rules.
It's about having the power to actually enforce those rules,
to make sure the AI doesn't go off the rails, so to speak.
Right.
You need both.
Clear guidelines and the ability to make sure
those guidelines are followed.
Now, you mentioned veracity, robustness, governance,
transparency, want to dig into those a bit more.
Let's do it.
Break those down for us.
All right.
So veracity and robustness, that's
about making sure the models are giving us
accurate information, reliable results,
even when they're faced with something unexpected.
Like a curveball.
Yes, like a curveball.
Yeah.
A challenging input.
You need these models to be tough, resilient,
to stand up to the test and still give you
trustworthy outputs.
So it's about building them to be reliable,
no matter what we throw at them.
What about governance?
Where does that fit in?
Governance is all about setting up
clear guidelines and procedures for developing and deploying
these AI systems responsibly.
It's about having a solid framework in place
to bake those ethical considerations
into every step of the process.
Got it.
And lastly, transparency.
Transparency is all about being open and honest,
making sure all the information about the model's
capabilities, its limitations, all of that
is easily accessible to users and stakeholders.
No secrets.
Right.
No secrets.
It's about building trust by being upfront about how
these models work, what they're good at,
and where they might fall short.
So it's not just about building responsible AI.
It's about being open and communicating clearly
about the whole process.
I like that.
It sounds like they're really taking
a holistic approach to all of this.
They are.
And they've taken some concrete steps.
They've been really careful about using
a wide range of data for training,
making sure it's diverse, unbiased.
Plus they've done a ton of these red teaming exercises.
Red teaming exercises.
What are those?
Imagine this.
They bring in these internal and external experts
and basically ask them to try to break the models.
Break them?
How?
Well, by trying to find ways to trick them
into generating harmful content, inappropriate outputs,
that kind of thing.
So like a stress test.
Yeah.
Finding the weak points and fixing them
before these models get released into the wild.
Exactly.
It's a proactive way to identify and address
potential problems before they become real world issues.
And they don't stop there.
Once these models are out in the world,
they've got more safeguards in place.
Like what?
Things like runtime input and output moderation.
Basically, they have systems that filter out
harmful or inappropriate prompts and responses.
So even if someone tries to use these models for bad stuff,
these moderation systems are there to stop them,
to prevent misuse.
Exactly.
They're like the gatekeepers, making sure everything
stays within the bounds of responsible use.
And get this, they even came up with this really
innovative watermarking technique for images and videos
generated by Canvas and Reel.
A watermark?
What does that do?
It's like a digital fingerprint.
It helps you tell the difference between AI-generated
content and real content, which is super important when
you think about the rise of deep fakes and all the concerns
about misinformation.
So you can always tell something was created by an AI.
It's like a built-in truth detector.
In a way, yes.
And again, that transparency is key to building trust
and encouraging responsible use.
OK, so we've talked about the what of responsible AI,
the principles, the different dimensions.
But I'm also curious about the how.
What kind of infrastructure and technology
did it take to actually build and train
these massive AI models?
Well, it's safe to say they went all out.
They used custom design chips, the most powerful GPUs out
there, huge, massive computing systems.
They really pushed the limits of what's possible with the AI
infrastructure.
It sounds like we're about to get really technical.
All right, so let's get into the nuts and bolts of it all.
What kind of tech firepower did they
need to create these Amazon Nova models?
Well, they definitely didn't hold back.
For starters, they used their own Trainium One chips.
Trainium One chips.
Yeah, these chips are custom designed
for machine learning, high performance stuff.
And they didn't stop there.
They also used those NVIDIA A100 and H100 GPUs.
GPUs.
Those are the graphics cards, right?
It's like the gamers use.
You got it, but these are like the supercharged versions.
Some of them was powerful out there,
perfect for handling those massive AI workloads.
So they've got the hardware, but these AI models are huge.
How do they even manage the training process
with all that data?
That's where AWS comes in.
They used Amazon SageMaker.
SageMaker, that's their cloud-based machine
learning platform, right?
Exactly.
They used it to orchestrate the whole training process.
And to make things even smoother,
they used Amazon's Elastic Kubernetes Service, or EKS
for short.
Kubernetes, no, that's a word I've
heard thrown around a lot, but I'm not
sure I fully understand it.
Think of it like this.
You've got this massive training workload, right?
Kubernetes helps distribute that workload
across a whole bunch of different servers.
It's like having an army of computers all working together
in Perfect Sync.
So it's all about teamwork.
Makes sense.
But we're still talking about mountains of data, text, images,
video.
Where do they even store all of that?
They used a smart combination of storage solutions.
For frequently used data, they went with Amazon FSx.
It's a high-performance file system, so super fast access.
And then for storing all those massive data sets
and model checkpoints, they used Amazon S3.
Amazon S3, that's their cloud storage service.
Pretty much everyone uses it these days.
Yeah, it's known for being super reliable and scalable.
You can store pretty much anything on there,
perfect for those giant AI models.
So they built this whole ecosystem
for training these models, optimizing every step of the way.
It's pretty impressive.
But beyond the hardware and the cloud stuff,
did they do anything to make the training process itself
more efficient?
They did.
They actually came up with this new activation checkpointing
scheme.
Activation checkpointing scheme?
Try saying that five times fast.
I know, right?
It's a mouthful.
But basically, it's a way to reduce the amount of memory
needed during training.
So they found a way to make it less memory intensive.
How does that work?
Well, I won't bore you with the technical details,
but it's pretty clever.
They call it super selective activation checkpointing.
Super selective, I like it.
It sounds very precise.
It is.
And this allows them to either train even larger models
or use even more data during training.
It opens up a lot of possibilities.
That's amazing.
Any other optimizations they made?
Oh, yeah.
They also made some tweaks to their communication protocols.
Communication protocols.
Yeah.
Basically, it's how different parts of this whole distributed
system talk to each other.
And by improving that communication,
they're able to minimize the time it takes for everything
to sync up.
So it's not just about individual components being fast.
It's about making sure everything works together
smoothly, efficiently.
Kind of like a well-oiled machine.
Exactly.
A well-oiled machine.
That's a great way to put it.
Well, I think we've covered a lot of ground today.
We've explored these fascinating new AI models
from Amazon, the Nova family.
We've dived into the complex world of responsible AI
and how they're working to make sure these powerful tools are
used for good.
And we even got a little technical,
exploring the infrastructure and the clever optimizations
they made to make all of this possible.
It's been a really exciting deep dive.
The Amazon Nova family represents a real step forward
in the world of AI.
They're pushing the limits of what we thought was possible.
And the most exciting part is that we're just
at the beginning of this journey.
As developers get their hands on these models,
who knows what incredible applications they'll come up
with.
The possibilities are truly endless.
So to our listeners, we'd love to hear your thoughts.
What applications of the Amazon Nova family
are you most excited about?
Head over to our website and let us know.
And be sure to check out the show notes for links
to all the reports and resources we mentioned today.
Thanks for joining us on this deep dive.
We'll see you next time.