Generative Artificial Intelligence: A Knock on the Door of Machine Intelligence

The intelligence of computational devices is a term that has been constantly redefined since the starting of the 20th century and still, we have not been able to define it properly.
Whereas finally, we have been able to define what a computational device is.

Artificial intelligence has even built-in pathways to our daily life, earlier limited to institutions who had computational devices of the size of whole rooms, but these days chances are that you are already relying on artificial intelligence at every moment, from your apps to the tools that auto-detect the spam messages, and from giving you recommendations to binge-watch new shows every day.

But there’s much more to AI than translation tools and predicting your shopping patterns.

Generative artificial intelligence and deep learning are the new revolutions in the field of Artificial Intelligence and they hold the potential to totally outgrow the current limitations of Artificial Intelligence, and it would let us make a major step towards Artificial Intelligence closing down on Human Intelligence.

Generative Artificial Intelligence (AI): What it is and Where it’s Going

Despite how terrified or excited you may feel at the thought of a deep machine learning program that allows a computer to become a master level chess player capable of defeating the human titleholder in a measly four hours, AI is already transforming everything from graphic design to content creation, namely many of the areas essential to designing comprehensive marketing campaigns and events.

Using algorithms, artificial intelligence programs can process and analyze massive amounts of data at breakneck speed in a way that would be impossible for humans in a single lifetime.

Generative artificial intelligence refers to programs that make it possible for machines to use things like text, audio files and images to create content.

Until recently, attempts made by machines — even really smart machines — to perform basic human tasks like drawing a sketch or writing a few lines of prose have produced results that have ranged from the comical to the downright creepy.

And although much of the news surrounding the imminent robot takeover has been greatly exaggerated (for now), generative artificial intelligence is essentially making the canvas and toolkit available to creative professionals and marketers across the board much larger and varied than ever before.

The new deep learning programs have become much more sophisticated and successful in producing human-like results in four key areas:

Generating Quasi-Life Like Images and Models

The best example being of images of a human face that has actually been rendered by what is known as a generative adversarial network. It looks like a real picture of a human face, but it is actually a compilation of a series of data sets taken from numerous images of human faces. It’s a computer model of model. Confused enough?

None of these people exist in real life, you may really think that you have seen these people before.

Do checkout https://generated.photos/ , every company needs a face for their campaign, but the catch is you have to pay the people.
Generated changes the whole scenario by generating faces which don’t exist and thus you won’t face any copyright issues. Cool right?

Language Translation

If you plan international events or branding campaigns on a global scale, the concept of a multilingual robot has probably been something of a pipe dream. While the robots have still not caught up to the humans in language translation abilities, deep learning researchers and program developers have found a new technique (known as sequence to sequence) that is improving the results in common programs like Google Translate and customer service chatbots.

Image Understanding

Machines are getting a lot better at accurately recognizing objects in an image thanks to sophisticated deep learning algorithms. Suppose you have a database of millions of images, but you or your client have a very specific idea about the handful of images that would make for the perfect logo or infographic for a campaign. Generative AI programs that utilize image understanding can not only shave off value time (and ultimately money) off the design process, they may help to deliver more accurate and targeted results.

The model was just given the text and it produced the following images

Music Generation

Many of us are bad at music but how incredible can it be that you just put in some random words or random tunes and what comes out is a master piece.
Amazon DeepComposer is all about this and we will be using it at the end of this article

How a noise was translated into music.

What’s next? Generative AI Tools for Branding and Events

On a practical level, one of AI’s most valuable contributions to the event marketing industry is the ability to use and optimize all the data quickly and efficiently. Events and campaigns can be more targeted and granular than ever before thanks to the ability to know your audience and customers at a deeper level.

Astonishing GAN Applications

So does this mean you can fire your design and marketing team and outsource your agency’s events and campaigns to Siri and her army of increasingly intelligent and creative friends?

Not just yet.

But as AI programs become more sophisticated, intuitive and prevalent, they do offer a number of unique and exciting opportunities to leverage the work your organization is already doing and to offer your clients and audience a more dynamic and enriched experience.

Creating Music using GANs

Now that you know a little about GANs , let’s compose some music with AWS DeepComposer models. We’ll begin this demonstration by listening to a sample input and a sample output, then we’ll explore DeepComposer’s music studio, and we’ll end by generating a composition with a 4 part accompaniment.
Here attached is a video tutorial by Udacity:

1. To get to the main AWS DeepComposer console, navigate to AWS DeepComposer. Make sure you are in the US East-1 region.
2. Once there, click on Get started
3. In the left hand menu, select Music studio to navigate to the DeepComposer music studio
4. To generate music you can use a virtual keyboard or the physical AWS DeepComposer keyboard. For this lab, we’ll use the virtual keyboard.
5. To view sample melody options, select the drop-down arrow next to the Input
6. Select Twinkle, Twinkle, Little Star
7. Next, choose a model to apply to the melody by clicking Select model
8. From the sample models, choose Rock and then click Select model
9. Next, select Generate composition. The model will take the 1 track melody and create a multitrack composition (in this case, it created 4 tracks)
10. Click play to hear the output

I guess AI-generated music would have been a pretty fun experience, I hope you were able to learn something new and got to know how amazing things are becoming every passing day.
If you enjoyed the article do give a clap!

Meanwhile, if you are interested in learning about GANs, do refer to this article:

https://analyticsindiamag.com/10-free-resources-to-learn-gan-in-2020/