Google's Most Powerful Gen AI Tool Just Dropped....But No One Noticed

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Google's Most Powerful Gen AI Tool Just Dropped.But no one noticed

Transcript

00:00Google has just rolled out its latest text-to-image AI model,

00:06Image in 3, making it accessible to

00:08all users through their ImageFX platform.

00:11Alongside this release,

00:13they've published an in-depth research paper

00:15that delves into the technology behind it.

00:17This move represents a major step forward,

00:20expanding access to a tool that was

00:22previously available only to a select group of users.

00:25All right, so Image in 3 is a text-to-image model.

00:28It can generate images at a default resolution

00:30of 1024 by 1024 pixels,

00:33which is already pretty high quality,

00:34but what really sets it apart is that

00:36you can upscale those images

00:38up to eight times that resolution.

00:41So, if you're working on something

00:42that needs a huge, detailed image,

00:45like a billboard or a high-res print,

00:47you've got the flexibility to do that

00:49without losing any quality.

00:50That's something that not every model out there can offer,

00:53and it's a big plus for anyone working in designer media.

00:56Now, the secret actually lies in the data it was trained on.

01:00Google didn't just use any old data set.

01:02They went through a multi-stage filtering process

01:04to ensure that only the highest quality images

01:07and captions made it into the training set.

01:10This involved removing unsafe, violent,

01:12or low-quality images, which is crucial,

01:14because you don't want the model learning from bad examples.

01:17They also filtered out any AI-generated images

01:20to avoid the model picking up on the quirks

01:22or biases that might come from those.

01:24They also used something called deduplication pipelines.

01:28This means they removed images

01:30that were too similar to each other.

01:32Why?

01:32Because if the model sees the same kind of image

01:35over and over again, it might start to overfit.

01:38That is, it might get too good at generating

01:41just that kind of image and struggle with others.

01:43By reducing repetition in the training data,

01:46Google ensured that Imogen 3

01:48could generate a wider variety of images,

01:50making it more versatile.

01:51Another interesting aspect is how they handled captions.

01:55Each image in the training set

01:56wasn't just paired with a human-written caption.

01:59They also used synthetic captions

02:01generated by other AI models.

02:03This was done to maximize the variety and diversity

02:05in the language that the model learned.

02:07Different models were used

02:08to generate these synthetic captions,

02:10and various prompts were employed

02:11to make sure the language was as rich

02:13and varied as possible.

02:14This is important because it helps the model

02:16understand different ways people

02:18might describe the same scene.

02:20All right, so how does Imogen 3

02:21stack up against other models out there?

02:23Google didn't just make big claims.

02:25They actually put Imogen 3 head-to-head

02:28with some of the best models out there,

02:30including DALL-E3, Mid-Journey V6, and Stable Diffusion 3.

02:35They ran extensive evaluations,

02:36both with human raters and automated metrics,

02:38to see how Imogen 3 performed.

02:40In the human evaluations, they looked at a few key areas,

02:44overall preference, prompt image alignment,

02:46visual appeal, detailed prompt image alignment,

02:48and numerical reasoning.

02:49Let's break these down a bit.

02:51First, overall preference.

02:52This is where they ask people to look at images

02:54generated by different models

02:56and choose which one they like best.

02:58They did this with a few different sets of prompts,

03:01including one called Gene AI Bench,

03:03which consists of 1,600 prompts

03:05collected from professional designers.

03:07On this benchmark, Imogen 3 was the clear winner.

03:11It wasn't just a little bit better.

03:12It was significantly preferred over the other models.

03:15Then there's prompt image alignment.

03:17This measures how accurately

03:19the image matches the text prompt,

03:21ignoring any flaws or differences in style.

03:23Here again, Imogen 3 came out on top,

03:25especially when the prompts were more detailed or complex.

03:29For example, when they used prompts

03:30from a set called Doe CCI,

03:32which includes very detailed descriptions,

03:34Imogen 3 showed a significant lead over the competition.

03:37It had a gap of plus 114 LO points

03:40and a 63% win rate against the second best model.

03:44That's a pretty big deal

03:45because it shows that Imogen 3

03:47is not just good at generating pretty pictures.

03:49It's also really good at sticking to the specifics

03:52of what you ask for.

03:54Visual appeal is another area where Imogen 3 did well,

03:57though this is where Mid Journey V6

03:59actually edged it out slightly.

04:02Visual appeal is all about how good the image looks,

04:04regardless of whether it matches the prompt perfectly.

04:07So while Imogen 3 was close,

04:09if you're all about that eye candy factor,

04:12Mid Journey might still have a slight edge,

04:14but make no mistake.

04:16Imogen 3 is still right up there.

04:17And for a lot of people,

04:18the difference might not even be noticeable.

04:20Now, let's talk about numerical reasoning.

04:22This is where things get really interesting.

04:23Numerical reasoning involves generating

04:25the correct number of objects when the prompt specifies it.

04:28So if the prompt says five apples,

04:31the model needs to generate exactly five apples.

04:33This might sound simple,

04:34but it's actually pretty challenging for these models.

04:37Imogen 3 performed the best in this area

04:39with an accuracy of 58.6%.

04:42It was especially strong when generating images

04:45with between two and five objects,

04:46which is where a lot of models tend to struggle.

04:48To give you an idea of how challenging this is,

04:51let's look at some more numbers.

04:52Imogen 3 was the most accurate model

04:55when generating images with exactly one object,

04:57but its accuracy dropped a bit

04:59as the number of objects increased

05:01by about 51.6 percentage points

05:03between one and five objects.

05:05Still, it outperformed other models like DALI 3

05:08and Stable Diffusion 3 in this task,

05:10which highlights just how good it is

05:12at handling these tricky prompts.

05:14And it's not just humans

05:15who think Imogen 3 is top-notch.

05:17Google also used automated evaluation metrics

05:20to measure how well the images match the prompts

05:23and how good they looked overall.

05:24They used metrics like CLIP, FIQUIS Score, and FD Dyno,

05:28which are all designed to judge the quality

05:30of the generated images.

05:32Interestingly, CLIP, which is a popular metric,

05:35didn't always agree with the human evaluations,

05:37but VQ-ASCORE did,

05:39and it consistently ranked Imogen 3 at the top,

05:42especially when it came to more complex prompts.

05:44So why should you care about all this?

05:46Well, if you're someone who works with images,

05:48whether you're a designer, a marketer,

05:50or even just someone who likes to create content for fun,

05:53having a tool like Imogen 3 could be a huge asset.

05:56It's not just about getting a nice picture,

05:58it's about getting exactly what you need

06:00down to the smallest detail

06:02without compromising on quality.

06:03Whether you're creating something for a website,

06:06a social media campaign, or even a large print project,

06:08Imogen 3 gives you the flexibility and precision

06:11to get it just right.

06:12But let's not forget,

06:13it's not just about creating high-quality images.

06:16Google has put a lot of effort

06:18into making sure this model is also safe

06:21and responsible to use.

06:22However, they've had their fair share of challenges

06:25with this in the past.

06:26You might remember when one of Google's previous models

06:28caused quite a stir.

06:30Someone asked it to generate an image of the pope,

06:32and it ended up creating an image of a black pope.

06:35Now, this might seem harmless at first glance,

06:37but when you think about it,

06:38there's never been a black pope in history.

06:40That's a pretty big factual inaccuracy.

06:43Another time, someone asked the model

06:45to generate an image of Vikings,

06:47and it produced Vikings who looked African and Asian.

06:50Again, this doesn't align with historical facts.

06:52Vikings were Scandinavian, not African or Asian.

06:55These kinds of errors made it clear

06:56that while trying to be inclusive and politically correct,

06:59the model was pushing an agenda

07:01that sometimes led to results that were simply inaccurate

07:04and historically misleading.

07:06These incidents sparked a lot of debate.

07:08There's a fine line between creating a model

07:11that's inclusive and one that distorts reality.

07:13While it's crucial to avoid harmful or offensive content,

07:16it's just as important

07:18that the model remains factually accurate.

07:20After all, if the images it generates

07:22aren't grounded in reality,

07:23it loses its effectiveness and frankly, its usefulness.

07:26If a model starts producing images

07:28that don't reflect historical facts or cultural realities,

07:31it's not doing anyone any favors.

07:33It ends up being more of a tool for pushing an agenda

07:36rather than a reliable factual generator.

07:38Now, with Imogen 3,

07:40Google seems to be aware of these pitfalls.

07:42They've evaluated how often the model

07:44produces diverse outputs,

07:46especially when the prompts are asking for generic people.

07:49They've used classifiers to measure the perceived gender,

07:52age, and skin tone of the people in the generated images.

07:56The goal here was to ensure that the model

07:58didn't fall into the trap

08:00of producing the same type of person over and over again,

08:03which would indicate a lack of diversity in its outputs.

08:06And from what they've found,

08:08Imogen 3 is more balanced than its predecessors.

08:11It's generating a wider variety of appearances,

08:13reducing the risk of producing homogeneous outputs.

08:16They also did something called red teaming,

08:18which is essentially stress testing the model

08:20to see if it would produce any harmful or biased content

08:23when put under pressure.

08:25This involves deliberately trying to push the model

08:27to see where it might fail,

08:29where it might generate something inappropriate or offensive.

08:32The idea is to find these weaknesses

08:35before the model is released to the public.

08:37The good news is that Imogen 3 passed these tests

08:40without generating anything dangerous

08:42or factually incorrect.

08:43However, recognizing that internal testing

08:45might not catch everything,

08:47Google also brought in external experts from various fields,

08:50academia, civil society, and industry

08:53to put the model through its paces.

08:56These experts were given free reign

08:57to test the model in any way they saw fit.

09:00Their feedback was crucial in making further improvements.

09:03This kind of transparency and willingness

09:05to invite external scrutiny is essential.

09:08It helps build trust in the technology

09:10and ensures that it's not just Google

09:12saying the model is safe and responsible,

09:14but independent voices as well.

09:16In the end, while it's important

09:18that a model like Imogen 3 is safe to use

09:20and doesn't produce harmful content,

09:22it's equally important that it doesn't stray

09:24from factual accuracy.

09:25If it can strike the right balance,

09:27being inclusive without pushing a politically correct agenda

09:31at the expense of truth,

09:32it'll not only be a powerful tool

09:34from a technical perspective,

09:35but also one of the most reliable

09:37and effective image-generating models out there.

09:40All right, if you found this interesting,

09:42make sure to hit that like button,

09:44subscribe, and stay tuned for more AI insights.

09:48Let me know in the comments

09:49what you think about Imogen 3 and how you might use it.

09:52Thanks for watching, and I'll catch you in the next one.

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