This blog will help you come off as a pseudo expert anytime AI comes up in conversation (which is all the time nowadays)
Param Gopalasamy
OneTrust Editorial Team, CIPP/E, CIPP/US, CIPM
November 3, 2023
In a digital tapestry, vibrant and infinite, where data weaves and dances, there exists a maestro known as generative AI. With strokes bold and subtle, it crafts, creates, and conjures images, sounds, and words that echo with the rhythm of invention.
Ugh. That statement was written by generative AI, and it clearly doesn’t mind talking itself up. The art analogy wasn’t bad though – we’ll stick with that throughout the article.
Generative AI is like a digital artist. It doesn't merely analyze or process; it creates. With data as its paint and algorithms as its brushes, this form of artificial intelligence generates new content, which sets it apart from its predecessor, predictive AI, by a significant margin. We no longer just get the weather forecast; we can get a detailed summary of what outfits we should wear each week based on the weather.
In this blog, we’ll talk about some of the technical building blocks of generative AI, dive a bit deeper into its different applications, the effect this is having on the creative process across industries, and what the future holds.
Get ready to learn way more about AI than you ever thought you’d need to know.
As we previously discussed, AI has been around in our lives for some time. Its previous avatar was predictive AI – which was widely used across industries and use cases. The name is a bit self-explanatory, which is … you guessed it, predicted things. In a nutshell, you give a predictive AI system a bunch of data, you’ll get a prediction on what’s going to happen next. Think stock markets, financial targets, the weather, even Netflix’s recommendations for you.
However, despite its prowess, Predictive AI’s canvas was only half-painted (bringing that art analogy back). It could analyze and predict, but when it came to creation, its hands were tied. Predictive AI could tell you the probability of rain tomorrow but couldn’t paint you a picture of the storm. And as someone who has interacted with ChatGPT quite a bit, let me tell you, generative AI has no problem painting a very verbose picture.
And so, last year, Generative AI made its entrance. Unlike its predictive counterpart, Generative AI doesn’t just analyze; it imagines and creates. It takes the baton from predictive models and runs forward, generating new data that is coherent and relevant (sometimes). There’s still some work to be done on the coherent and relevant part, as Google Bard’s public mishap was a clear sign to the world that we’re not quite there yet.
This transition from predictive to generative AI represents a leap from understanding the world to adding something new to it. While Predictive AI might inform a doctor about a patient's likelihood of having a disease, Generative AI could assist in creating personalized treatment plans or even simulate the progression of the disease under different conditions.
Use cases at this level of significance are what’s driving the mass excitement (some may say hysteria) around Generative AI. Given the possibility of having an accurate AI system that can provide relevant, timely solutions to seemingly any problem – it's not too hard to see why the hype train has been rolling.
This is the section where you can really differentiate yourself from standard “AI hype train person” to “Wow, they really know what they’re talking about!”.
If you want to know how generative AI models really work, there are three primary elements involved:
Imagine you walk into an art studio (the generative AI system) with an idea or a request. It's like telling an artist, "Hey, I want something that looks like a sunset over a city." Let's see how the team of artists and storytellers – the GANs, VAEs, and Transformers – work together from that prompt:
Sketching with GANs
Your idea first goes to the GANs. As the name suggests, the Generative Adversarial Network consists of two entities:
After a few rounds of sketching and checking, the GANs come up with a detailed and beautiful sketch of the sunset scene.
Imagine coming up with some copy or a picture and having an annoying proofreader immediately correct you afterwards multiple times. That’s what this stage is (no wonder the potential of AI is said to be more than humans).
Sculpting with VAEs
Now that we have a sketch, the VAEs step in. They interpret this sketch and think about all the different ways it can be enhanced. Maybe they imagine 3D buildings popping out or the light of the sunset casting realistic shadows. Using their knowledge of data, they add depth and dimension to the scene, turning the flat sketch into something more lifelike.
This would be like an architect or sculptor taking a look at a sketch and coming up with ideas of how to bring it to life.
Storytelling with Transformers
With our image getting richer, the Transformers jump in. They want to add a narrative or a backstory. Why is the sunset important? Who lives in those buildings? Think of any marketing professional you know – and this step suddenly makes a lot more sense.
They craft a description or a story: "As the sun dipped below the horizon, the city's lights began to twinkle. Each window told a story, from the baker ending his day to a writer just beginning her novel under the warm glow of a lamp."
This final step is what adds context and the human-like element to generative AI.
These three elements together turn a simple prompt into cohesive instructions, descriptions, images, or more – as each layer continues to add depth to the AI system’s response.
Now in the business world, the term generative AI has reached “Payphone” by Maroon 5 levels of being overused and overplayed at this point. But as with “Payphone” (which was an absolute banger), it’s not without reason. Here are just a few use cases across different departments that are already starting to use generative AI effectively.
Again, these are just a few of the applications in the business world that can currently be powered by AI. Presentations, social media, workplace productivity tools, and CRMs are also just a few more ways AI can take over our world.
A big concern when it comes to generative AI is the privacy and ethical side of things.
These are just a few of the very real concerns that surface when it comes to generative AI in the realm of privacy and ethics. The three principles below can help you start to prioritize privacy when introducing AI solutions into your organization.
For the canvas of your AI systems to have a place in your organization, they need to rest on an easel of ethical and privacy frameworks (this may be the last art analogy of the article).
Using the following ethical AI guidelines will help you address the primary ethical concerns that an AI system raises:
Just to make things easier for you in conversation, here’s a list of “AI don’ts” for you to spout off whenever the opportunity presents itself.
So now that we’ve covered generative AI today, its building blocks, and concerns around it, hopefully, you’re a bit more qualified to hold a 5-minute discussion at any dinner table and come off as at least the second-most intelligent person there.
Time for a shameless plug: If you actually want to learn more about AI governance beyond sounding mildly informed over a meal – be sure to check out OneTrust AI Governance.
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