All right, so today we're diving into AI MV2,
this new AI model from Apple.
Yeah.
That can handle both images and text.
Pretty cool stuff.
Pretty big deal in the AI world, right?
Oh, absolutely.
It is, like, you know,
teaching a computer to see and read at the same time.
Exactly, and traditionally,
AI models have been kind of like specialists.
You know, they're either really good at images
or really good at text.
Right.
But not both.
Uh-huh.
AI MV2 is really trying to change that.
Yeah, and the way it learns is super interesting.
Okay.
Instead of just, like, matching images and text,
like, saying, okay, this picture of a cat
goes with the word cat.
Right.
It actually learns to predict what comes next,
whether it's the next part of an image
or the next word in a sentence.
So it's just memorizing pairs?
Yeah.
It's actually, like, figuring out the patterns
and the relationships.
It's more like how we learn.
Yeah.
You know, like, anticipating what's coming next.
That's really cool.
Yeah.
And the researchers found that this way of learning,
which is similar to how large language models are trained,
it actually works really well for images, too.
Like, they've unlocked a new way to teach AI
about the visual world.
So that's how it learns.
But how does it actually stack up
against other big AI models out there?
So they put it through a whole bunch of tests.
Yep.
And it seems to be outperforming
some of the, like, the heavy hitters out there.
Oh, wow.
Like, for example, it's better than DinoV2
at identifying objects and images.
And understanding descriptions like,
the cat sitting on the blue rug.
So it's not just recognizing the objects,
but also understanding how they relate to each other
in the scene.
Exactly.
It's getting the context.
Yeah.
And it's also scoring higher than CLIP and SIGLIP
on some of those classic image recognition tests,
like ImageNet.
Wow, OK.
Now, the paper mentions something called
Native Resolution Fine Tuning.
Right.
Which sounds pretty technical.
Yeah, it sounds fancy.
But basically, it means that AI and V2
can handle images of any size or shape.
It doesn't need them to be perfectly formatted or resized.
Got it.
Which is a big advantage, because in the real world,
images come in all sorts of sizes and resolutions.
Right.
Like, my phone takes a different picture
than a big camera.
Exactly.
So it's more flexible and adaptable to, like, real world
situations.
Yeah, it can work with images in the wild, so to speak.
I like that.
Which is a big step towards making AI more practical
for everyday use.
That's really interesting.
So we've talked about how it learns,
how it compares to other models, but what can it actually do?
Right.
What kinds of tasks can it handle that
involve both images and text?
Well, one of the things it excels at
is answering questions about images.
OK.
So you can show it a picture and ask, like,
what color is the car?
Or how many people are in this photo?
And it can answer pretty accurately.
So it's not just recognizing what's in the image,
but also understanding the context and the relationships
between the elements.
Exactly.
It's going beyond just basic recognition.
That's amazing.
It's starting to understand the meaning.
And it's also pretty good at something
called few-shot learning, which means
it can learn new tasks with very little training data.
So it's a fast learner.
Yeah.
It catches on quickly.
That's impressive.
And, you know, this is all coming out of Apple.
Right.
A company known more for its hardware than its AI research.
Yeah.
You think iPhones?
Exactly.
So it's interesting to see them making such strides
in this field.
Yeah.
It's like Apple is saying, hey, we're not just
about iPhones and Macs anymore.
We're serious about AI, too.
Exactly.
And they're backing it up with some really impressive research.
Yeah.
You know what's really fascinating to me
is that AI and V2's success hinges
on this simple, yet powerful idea, training AI,
to predict what comes next, whether it's
a pixel in an image or a word in a sentence.
So it's not just about the complexity of the model,
but also about the cleverness of the training approach.
Exactly. It's about finding those elegant solutions.
And this approach could have far-reaching implications
for the future of multimodal AI.
It could lead to even more powerful and versatile AI
models that can truly understand and interact
with the world around us.
OK.
So we've talked about the what and the why.
Now, let's get into the how.
Can you walk us through how AI and V2 is actually built
and trained?
What's going on under the hood, so to speak?
So at its core, AI and V2 uses a popular architecture
called the Vision Transformer, or VIT for short.
OK.
So what's a Vision Transformer?
Think of it like this.
A Vision Transformer takes an image
and breaks it down into smaller chunks,
kind of like a jigsaw puzzle.
And it analyzes the relationships between those chunks
to understand the bigger picture.
So it's not just looking at the whole image at once,
but rather dissecting it and analyzing the pieces.
Exactly.
It's breaking it down to understand the whole.
And this approach has proven to be very effective
for image recognition tasks.
Oh, yeah.
It's become very popular in recent years.
So VIT is the foundation.
But what makes AI and V2 special?
What did the researchers add to this basic architecture
to make it so good at handling both images and text?
Well, they incorporated some clever techniques
that have been successful in language modeling.
OK.
One of them is called prefix attention.
Prefix attention?
Yeah.
That sounds intriguing.
What does that do?
Think of it like a spotlight.
Prefix attention helps the model focus
on the most important parts of the input,
whether it's an image or a sentence.
OK.
It helps filter out the noise and highlight
the key information.
So it's like having a built-in attention mechanism.
Exactly.
That helps AI and V2 prioritize what's important.
It's learning what to pay attention to.
And that's crucial when you're dealing with complex data,
like images and texts.
You don't want the model to get bogged down
in irrelevant details.
You want to focus on the stuff that really matters.
Right.
It's about efficiency and accuracy.
Makes sense.
What else did they add to the mix?
They also use techniques called SUGLU and RMSNorm.
Ooh.
Those sound complicated.
They sound fancy, but they basically
help the model learn more complex relationships in the data
and prevent it from jumping to conclusions too quickly.
So it's like adding some safeguards and fine tuning
to the learning process.
Exactly.
To make sure AI and V2 is learning effectively.
It's all about optimizing the training.
And all of this is powered by a massive amount of data, right?
Oh, yeah.
What kind of data are we talking about?
A mix of public data sets and a private data set from Apple.
OK.
They used millions of images and text captions
to train AI and V2, giving it a huge knowledge base to draw from.
So it's not just random images and text
scraped from the internet.
No.
They've carefully curated the data.
They've put a lot of effort into making sure
it's high quality and relevant.
To make sure it's high quality and relevant to the task at hand.
Exactly.
And they even used a technique called synthetic captioning
to create additional captions for the images giving AI and V2
even more examples to learn from.
It's like giving it extra study material.
So they're basically creating artificial captions
to supplement the real ones.
Exactly.
That's pretty clever.
It's a smart way to boost the training data.
And it seems to have paid off AI and V2's performance
is truly impressive.
It really is.
And it points to a bright future for multimodal AI.
Yeah.
This is just the beginning.
This is all very interesting.
But I have a question.
We've talked a lot about how AI and V2
learns from paired image text data,
like images with captions.
But what about situations where you don't have those captions?
Can AI and V2 still understand images on its own?
That's a great question.
And it's something the researchers are exploring.
While AI and V2 relies heavily on paired data
for its initial training.
OK.
They've also shown that it can be adapted to handle
images without captions.
So it's not entirely dependent on having text
to go with the images.
Exactly.
It can still extract meaningful information from images
even without explicit textual descriptions.
So even though it's trained on these image text pairs,
it's not limited to those scenarios.
It can still learn and understand in a more visual way.
Right.
It's building a deeper understanding of the visual world.
That's really interesting.
It opens up a lot of possibilities
for using AI and V2 in situations where paired data is
scarce or difficult to obtain.
That makes sense.
So it's a very promising area of research.
That's really cool.
It's one of the things that makes AI and V2 so exciting.
It's pushing the boundaries of what's
possible with AI showing us that we can teach machines
to understand the world in more nuanced and sophisticated
ways.
And that's what makes this field so fascinating.
It's constantly evolving.
And we're just scratching the surface of what AI can do.
Yeah, we're just getting started.
Exactly.
And it's not just about recognizing what's in a picture.
It's about understanding the relationships between things.
Like it's not just seeing a cat and a dog,
but understanding that the cat is chasing the dog
or that they're playing together.
So it can kind of understand the story.
Exactly.
It's like AI and V2 can grasp the narrative
within the image, which is pretty amazing.
Yeah, that is amazing.
And to test this, the researchers
gave AI and V2 some tricky tasks,
like predicting what would happen next
in a series of pictures.
Oh, that's interesting.
So if you showed it pictures of someone burking a cake,
could it predict that the next step would be
to put the cake in the oven?
Exactly.
And what's remarkable is that AI and V2
showed real promise in these tasks,
even though it wasn't specifically trained for them.
So it's kind of thinking ahead.
Yeah, it's like it's learning to think about images
in a more human-like way.
That's really cool.
It seems like it's not just a one-trick pony.
It's actually learning to think about images more like we do.
Exactly.
And that kind of visual reasoning ability
just opens up so many possibilities
for how AI and V2 could be used.
Well, let's talk about some of those possibilities.
What are some real-world applications
where AI and V2 could really shine?
Well, one area that immediately comes to mind is image search.
Imagine being able to search for images,
not by typing in keywords, but by describing what you see,
like a photo of a sunset over a mountain range
with a lake in the foreground.
Wow, that was so much easier.
Right, so much more intuitive.
Than trying to come up with the perfect keywords.
Exactly.
AI and V2 could totally revolutionize the way we
search for and discover visual content.
That would be huge.
And another area is in content creation.
Like automatically generating captions for images and videos.
Yes, exactly.
That could make content more accessible to people
with visual impairments.
Oh, that's a good point.
And also help with things like organizing and managing
huge libraries of images and videos.
Yeah, that makes sense.
It's a huge time saver.
What about other creative applications?
Could AI and V2 be used to generate art or write
stories based on images?
It's definitely possible.
We're already seeing AI being used to create art and music.
And AI and V2's ability to understand the relationship
between images and text could take things to a whole new level.
That's pretty exciting.
It is.
It's like the fusion of creativity and technology.
What about areas like robotics or self-driving cars?
Oh, absolutely.
In robotics, AI and V2 could help robots better
understand their surroundings, allowing them
to navigate more complex environments
and interact with objects in a more sophisticated way.
So instead of just seeing a chair as an obstacle,
a robot equipped with AI and V2 could understand that it's
a chair and could potentially even use it to sit down
or to reach something high up.
Exactly.
It's like giving robots a deeper understanding of the world.
And in self-driving cars?
Yeah.
Could it help there?
Absolutely.
It could be used to enhance the perception systems,
helping the cars to understand complex traffic situations
and make safer driving decisions.
So it could really have a big impact on safety?
It could.
It has the potential to make our roads much safer.
That's amazing.
Yeah.
But with any powerful technology,
it's important to consider the potential downsides.
Right.
Are there any concerns about how AI and V2
might be used or misused?
One thing that's really important to remember
is that AI and V2, like any AI model,
is only as good as the data it's trained on.
So if the data used to train AI and V2 contains biases,
those biases could be reflected in the model's output.
That's right.
And that could have real world consequences,
especially if AI and V2 is used in applications that
make decisions affecting people's lives.
So it's really crucial to ensure that the data used
to train these models is as unbiased and represented
as a possible.
Absolutely.
We need to be very careful about the data we
feed these models.
And we also need to be mindful of how AI and V2 is used
and who has access to it.
Exactly.
Like any powerful tool, it could potentially be misused.
So it's important to have ethical guidelines and regulations
in place.
So it's not just about the technology itself,
but also about how we use it.
Exactly.
And what safeguards we put in place.
It's about responsible AI development and deployment.
That makes a lot of sense.
It's not just about what AI can do,
but also about what it should do.
We need to make sure it's used for good.
Exactly.
And speaking of advancements, one aspect of AI and V2
that we haven't discussed yet is its potential for scalability.
Right.
What do you mean by scalability?
Well, the researchers found that AI and V2
gets even better as it's trained on more data.
And as the size of the model increases.
So the more data you feed it, the smarter it gets.
Exactly.
And that scalability is what makes AI and V2 so exciting.
It means that it has the potential
to keep learning and improving as we gather more data
and develop more powerful computing resources.
So we're just seeing the tip of the iceberg with AI and V2
as it continues to learn and grow.
Who knows what amazing things it'll be capable of.
It's really mind blowing to think about.
It is.
But as we delve into those possibilities,
it's important to remember that AI and V2 is still a tool.
Its impact on the world will ultimately
depend on how we choose to use it.
That's a good point.
Technology is neutral.
It's up to us to use it wisely and ethically.
And that brings us to another interesting point.
The paper on AI and V2 focuses mainly
on its technical capabilities.
But it doesn't delve deeply into the broader societal
implications.
So it tells us what AI and V2 can do.
But not necessarily what it should do.
Exactly.
And that's a question that goes beyond the scope
of any single research paper.
It's something that we as society
need to grapple with as AI technology becomes
more and more integrated into our lives.
So it's a bigger conversation.
It is.
That needs to involve not just AI experts,
but also ethicists, policymakers,
and the general public.
Absolutely.
We need to have a broad societal discussion
about the future of AI.
That's a great point.
We can't just leave it to the tech companies
to figure this out on their own.
Right.
It needs to be a collaborative effort.
Absolutely.
Yeah.
Yeah, it really highlights the need
for a more holistic approach to AI development, one that
considers not just the technical aspects,
but also the societal and ethical implications.
Absolutely.
And it's not just about putting safeguards in place.
It's about actively shaping the development of AI
in a way that aligns with our values and goals as a society.
So it's not just about what AI can do,
but what AI should do.
Exactly.
We need to be asking ourselves, what kind of future
do we want to create with AI, and then guide its development
accordingly?
That's a really great point.
It's a reminder that we're not just passive observers
in this technological revolution.
We have a role to play in shaping its trajectory.
And one way to do that is by staying informed and engaged.
The more people understand about AI, its potential,
and its limitations, the better equipped
will be to make those decisions.
That's why I think this research on AI and V2 is so important.
It's pushing the boundaries of what's possible with AI
and forcing us to think about the implications.
And it's sparking some really interesting discussions
in the AI community.
Oh, yeah.
People are excited about the potential
of multimodal models like AI and V2,
but also cautious about the potential risks.
Yeah, it's good to have that balance of excitement and caution.
Right.
We need both to navigate this new landscape responsibly.
Absolutely.
And as we continue to explore this landscape,
I think it's important to remember
that AI is still in its early stages of development.
Right.
We're just getting started.
We're just starting to scratch the surface of what's possible.
So what we're seeing with AI and V2
is just a glimpse of what the future of AI might hold.
Exactly.
Imagine a world where AI can not only understand images and text,
but also generate new content, combining those modalities
in creative and innovative ways.
So like an AI that could write a song based on a painting
or create a 3D model from a sketch.
Precisely.
The possibilities are pretty mind-boggling.
Yeah, it is kind of mind-boggling.
It's an exciting time to be following the world of AI.
We're witnessing the births of a new era of intelligent machines
capable of things we never thought possible.
And as we move forward, it's up to all of us
to ensure that these advancements are used for good
and that they benefit all of humanity.
Well said.
It's been a fascinating deep dive into AI and V2,
a model that's truly pushing the boundaries of what's
possible with AI.
And what's remarkable is that this groundbreaking research
is coming out of Apple, a company known more
for its consumer products than for its contributions
to fundamental AI research.
Right.
You think iPhones?
Exactly.
It's a reminder that innovation can come from unexpected places
and that the field of AI is constantly evolving
with new players emerging and pushing the boundaries of what
we thought was possible.
And speaking of pushing boundaries,
one aspect of AI and V2 that we haven't touched on yet
is its potential for multimodal generation.
Multimodal generation?
Yeah.
That sounds intriguing.
What exactly does that mean?
Well, so far we've mostly talked about AI and V2's ability
to understand images and text.
Right.
But the researchers also suggest
that it could be used to generate new content,
combining both modalities in novel ways.
So it's not just about analyzing existing content,
but also about creating something entirely new.
Exactly.
Blending images and text in ways we haven't even imagined yet.
Taking things to a whole other level.
So imagine being able to give AI and V2 a text prompt,
like create a painting of a futuristic city.
And it could actually generate a unique and visually stunning
image based on that description.
That's the idea.
Or conversely, you could show it an image
and ask it to write a poem or a story inspired by what it sees.
Wow, that's amazing.
It sounds like something straight out of science fiction.
But it's happening right now in research labs around the world.
And as AI and V2 and other similar models continue to evolve,
we can expect to see even more mind-blowing applications
emerge, transforming industries, and challenging
our understanding of what's possible with AI.
It's an exciting time to be alive.
It really is.
We're witnessing the birth of a new era of intelligent machines
capable of understanding and interacting with the world
in ways we never thought possible.
And as with any powerful technology,
it's crucial that we approach its development and deployment
with both enthusiasm and a healthy dose of caution.
We need that balance, right?
We do.
Absolutely.
We need to ensure that these technologies are used
ethically and responsibly, promoting fairness, transparency,
and accountability.
And we need to have those open and honest conversations
about the potential societal impact of these advancements,
involving not just AI experts, but also
policymakers, ethicists, and the general public.
Yeah, those conversations are so important.
It's clear that AI is not just a technological revolution,
but also a societal one.
And it's up to all of us to shape its trajectory
and ensure that it leads to a better future for all.
Well said.
And as we racked up this deep dive,
I want to encourage all of you listening
to continue exploring the fascinating world of AI
and to engage in these thoughtful discussions about its
potential and its implications.
The future of AI is being written right now.
It is.
And your voice matters.
Absolutely.
Get involved.
Stay informed.
That's a great message to end on.
Thank you for joining us on this deep dive
into the world of AIMV2 and multimodal AI.
We hope you found it informative and thought provoking,
and perhaps even a little bit mind blowing.
Until next time, keep exploring, keep questioning,
and keep learning.
See you next time.