Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
I cannot tell you how excited I am by this paper. Wow. Today you will see more incredible
images generated by an AI. However, not from OpenAI, but Google! Just a few months ago,
OpenAI’s image generator AI called DALL-E 2 took the world by storm.
You could name almost anything, Cat Napoleon, a teddy bear on a skateboard on Times Square,
a basketball player dunking as an explosion of a nebula, and it was able to create an
appropriate image for it. However, there was one interesting thing about it. What do you
think the prompt for this must have been? Hmm. Not easy, right? Well, it was “A sign that says
deep learning”. Oh yes, this was one of the failure cases. Please remember this.
Now, we always say that in research, do not look at where we are, always look
at where we will be two more papers down the line. However, we didn’t even make it
two more papers down the line. What’s more, we barely made two months down the line,
and here it is: this is Google Research’s incredible image generator AI, Imagen.
This technique also looks at millions of image and text description pairs,
and learns what people mean when they say this is a guitar, or this is a panda.
But, the magic happens here. Oh yes, it also learned how to combine these concepts together,
and how a panda would play a guitar. The frets and the strings seem a little wacky,
but what do we know, this is not human engineering. This is panda engineering. Or a robot
engineering something for a panda. We are living crazy times indeed.
So, what is different here? Why do we need another image generator AI?
Well, let’s pop the hood, and look inside. Oh yes. Two things that will immediately make a difference
come to mind. One, this architecture is simpler. Two, it learns on longer text descriptions,
and hopefully that also means that it can generate text better. Let’s have a look.
What? Are you seeing what I am seeing? That is not just some text, that
is a beautiful piece of text, exactly what we were looking for. Absolutely amazing.
And, these not the only advantages, you know, I am a light transport researcher by trade, and I
promised to myself that I’ll try not to flip out. But…hold on to your papers, and… holy mother of
papers. Look at that. It can also generate beautiful refractive objects. That duck is
truly a sight to behold. My goodness. Now I will note that DALL-E 2 was also pretty good at this.
And, if we plug in DeepMind’s new Flamingo language model, would you look at that!
Is this really happening? Yes, that is an AI commenting on a different AI’s work.
What a time to be alive! We will have a look at this paper too in the near future,
make sure to subscribe and hit the bell icon, you really don’t want to miss it when it comes.
And, you know what, let’s test it some more against OpenAI’s amazing DALL-E 2. See how
they stack up against each other. The first prompt will be a couple of glasses sitting on the table.
Well, with Google’s Imagen, oh my, these ones are amazing, once again, proper refractive objects,
loving it. And, what about DALL-E 2? There is one with the glasses that with an interesting framing,
but I see both reflections and refractions, apart from the framing, I am liking this. And
the rest…well, yes, those are glasses sitting on the table. But when we say a couple of glasses,
we probably mean these and not these. But that’s really interesting, two AIs that have a linguistic
battle here. Imagine showing this to someone just ten years ago. See how they would react.
Loving it. Also, I bet that in the future, these AIs will work like brands and products today,
where people will have strong opinions as to which ones they prefer.
The analog warmth of Imagen, or the three-year warranty on DALL-E 4?
And wait, you are all experienced Fellow Scholars here, so you also wish to see the
two tested against each other a little more rigorously. And we’ll do exactly that. The
paper checks the new technique against previous results mathematically. Or, we can ask a bunch
of humans which one they prefer. And, wow. The new technique passes with flying colors on both.
And once again, DALL-E 2 appeared just about 2 months ago, and now, a new followup paper from
Google that tests really well against it. This is not two more papers down the line.
Not even two more years down the line. This is just two more months down the line.
And a year before, we had DALL-E 1, and see how much of a difference OpenAI made
in just a year. Now I am almost certain that this paper has been in the works for a while,
and they added the comparisons against DALL-E 2 at the end. But still, a followup paper this
quickly? The pace of progress in AI research is absolutely incredible. What a time to be alive!
So, does this get your mind going? What else would you
use this new technique for? Let me know in the comments below!
Thanks for watching and for your generous support, and I'll see you next time!