Generative AI: Key Information and examples

Artificial intelligence isn’t a new concept today. Its implementation has brought enormous benefits to many industries. Year by year, AI is developing, which means that newer and newer technologies are being discovered. Generative artificial intelligence is one of the most visible and powerful techniques of AI emerging on the market. According to Gartner, it is one of the top 5 strategic technology trends for 2022.

What is generative AI, and what are its benefits and challenges? How can your company grow with AI software consulting?

What is generative AI?

Generative AI is an innovative technology that creates completely new data sets from the available text, images, audio files, and movies. In other words, computers detect a certain pattern related to the output and then produce similar content. GAI can support many activities, such as software code development, drug formulation development, and targeted marketing. It can also be used for fraud, extortion, political disinformation, or identity spoofing.

Generative AI models

GENERATIVE ADVERSARIAL NETWORKS

GAN acts as an algorithmic architecture using two neural networks (a generator and a discriminator). Both networks will oppose each other to generate synthetic and new data instances by traversing real data. You can use it to generate video, voice recognition and generate images. The potential of GANs can serve both good and bad. They will distribute the data and mimic each other. Their performance will be remarkable in all areas such as speech, music, images, and text.

VARIATIONAL AUTO-ENCODERS

Autoencoders consist of two elements, i.e. encoder and decoder. The encoder transforms the input data into a representation. The decoder, on the other hand, transforms the data from representation into output data.

TRANSFORMERS

Examples of transformers can be GPT-3, LaMDA, or Wu-Dao. These models:

  • Understand text or images
  • Learn selected classification tasks
  • Can generate text and images from huge amounts of data

What are the challenges of generative artificial intelligence

Generative AI has great potential and contribution to many industries. However, the use of this technology also comes with many challenges. For example:

  • Unexpected results:

Some generative AI models, such as GANs, may produce unexpected results because it is not easy to control their behavior.

  • Security issues:

Since generative AI is capable of generating fake images and photos, it can lead to an increase in fraud and identity theft.

  • Generating pseudo images:

To perform tasks, generative AI algorithms require a large amount of training data. However, GANs cannot generate entirely new text or images. Using different methods, they only combine what they know.

Generative AI models: Examples

HEALTHCARE

Generative AI allows for better and faster identification of potential ailments or diseases. The goal is to create an effective treatment method in the early stages of the disease.

For example, the GAN model is used to calculate different angles of the X-ray image to locate tumor expansion.

IMAGE PROCESSING

Generative AI improves the quality of the images. Generative AI tools transform a poor-quality, low-resolution image into a clearer, more detailed image. For example, they can scale up the image from a 64 * 64 input to 1024 * 1024. This makes it possible to upscale old images and movies to 4K resolution. It also helps you turn black-and-white movies into color.

MOVIE RESTORATION

We have already mentioned this in the previous point. Generative AI allows you to improve the quality of old movies. It can create color from black and white productions. In addition, it generates a higher number of frames per second (instead of 23, it generates 60) and makes the image better quality and sharper.

SOFTWARE DEVELOPMENT

Generative AI also has a significant impact on the software development sector. All thanks to automatic coding. IT professionals can develop a solution that gives AI models the context of what they need.

In comparison to a manual coder, a model-based tool such as GENIO can significantly increase a developer’s productivity. This tool allows non-coders to create business applications tailored to their needs and reduce the company’s dependence on the IT department.

DETECTING FRAUD AND IDENTITY PROTECTION

Many companies use automatic fraud detection. With AI, they can accurately and quickly locate suspicious and malicious activities. AI now detects illegal transactions through preset algorithms and rules, which makes it easier to identify thefts.

In addition, with the help of generative AI, people can maintain privacy through the use of avatars instead of images. Aside from that, companies select impartial recruitment practices and conduct unbiased research.

Conclusion – the future of generative AI

The contribution of generative AI can be seen in many implementations: from image processing and film restoration to healthcare. It will undoubtedly have a significant impact on other industries in the future. By enabling machines to do more than just replace manual labor and take on creative tasks, we’re likely to see a wider range of generative AI use cases. 

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