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Generative AI Explained: Gartner’s Top Strategic Technology Trend for 2022

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The term Artificial Intelligence (AI) was coined for the first-time way back in 1956 by John McCarthy. Since then, it has come a long way. In this digital era, AI isn’t just a tech buzzword. The field of AI is fast-moving, and new breakthroughs are frequently made. Using algorithms, AI programs can process and analyze huge amounts of data at breakneck speed in a way that would be difficult (if not impossible) for humans in a single lifetime.

From the favourite apps to the digital tools such as chatbots that assist us in our queries, we have a brush with AI in our daily lives even though we may not realize its presence. However, AI will have a bigger role to play in the future than merely beng present in the daily use apps or customer friendly bots. Generative AI is set to build on the progress that basic AI has already made.

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Also Read: Artificial intelligence: Its benefits and challenges

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By 2025, generative AI will account for 10% of all data produced, up from less than 1% today. (Source- Gartner)

What is Generative AI?

Generative AI correlates to the programs that allow machines to use elements such as audio files, text, and images to produce new plausible content. In other words, it allows computers to abstract the underlying pattern related to the input, and then use that to generate similar content. MIT describes generative AI as one of the most promising advances in the world of AI in the past decade.

There are several applications of Generative AI such as,

  • Image-to-image Conversion: It translates an image to another. For example: black and white photographs to color, day photos to night photos, a photo to an artistic painting or satellite photos to google maps views.
  • Text-to-image Translation: It produces realistic photographs from textual descriptions of simple objects like birds and flowers.

(Example of Textual Descriptions and GAN-Generated Photographs of BirdsTaken from StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, 2016.)

  • Photos-to-emojis: It changes real photos to emojis or small cartoon faces.

(Example of Celebrity Photographs and GAN-Generated Emojis. Source- machinelearningmastery.com)

  • Face Aging: It generates older version of faces from a young photo.

(Source- Medium.com)

  • Image Processing: You can bank on this technology to upscale image processing capabilities- upgrading low-resolution images to high-resolution ones.
  • Film Restoration: Generative AI enhances old movies by upscaling them to 4K and beyond. It removes noise, adds colors, and makes it sharp.
  • Audio Synthesis: Generative AI can render any computer-generated voice into one that truly sounds like human voice.

Key benefits of Generative AI include:

  • Identity Protection: Generative AI is helping maintain the anonymity of individuals through avatars. For instance, it has been used to protect the identity of interviewees in news reports about the persecution of LGBTQ people in Russia.
  • Fraud Detection: Automating fraud detection processes has helped identify illegal and suspicious activities. AI is detecting illicit transactions using predefined algorithms and rules.
  • Trend Analysis: AI and ML techniques help to deep-dive into the data to study trends that are beyond traditional mathematical analysis.
  • Healthcare: Generative AI can be employed for rendering prosthetic limbs, organic molecules, and other items from scratch when actuated through 3D printing, CRISPR, and other technologies. It can also enable early identification of potential malignancy to more effective treatment plans. 

The challenges

One of the key challenges with Generative AI is that just like any other technology, it can also be used for fraudulent purposes such as scamming people and creating fake news. It can also lead to privacy/security related issues with its ability to create fake images. Another challenge is that Generative AI algorithms require massive amount of training data to perform tasks as they only combine what they know in different ways. Thus, it can generate an unexpected outcome at times.

Wrapping up

Despite the challenges, Generative AI is set to disrupt more industries than we can imagine. It is finding applications in crucial fields such as healthcare and defense. As the technology evolves, it will find more advanced applications.

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