The article enlists the 10 problems that have been solved with the application of Generative AI

Generative AI has become a new buzzword. Generative AI refers to "programs that can use existing content like text, audio files, or images to create new plausible content". Basically, it enables computers to learn the underlying pattern related to the input, and then use that to generate similar content. According to a report, 30% of manufacturers will use generative AI to increase product development efficiency by 2027. Generative AI in 2022 has many applications which have appeared as a saviour in different sectors. Applications of Generative AI allow producing novel and realistic visual, textual, and animated content within minutes. According to Gartner, by 2025, the percentage of data developed by Generative AI will amount to 10% of all generated data. In this article, you will see some complex problems solved by Generative AI.

Here are listed 10 complex problems solved by Generative AI 

Content Creation

Quality content creation is the key element in the success of any organization. Content is very essential for marketing also. An important part of content creation involves the use of existing data to generate brand-new images, videos, text, or audio files. Machine learning and generative AI have made this function possible in a seamless manner by detecting underlying patterns within a given piece of content to create new data. There are multiple ways in which AI applications create content from existing content which saves money and time.

Creating Realistic Dubbed Foreign Films and Series

The onslaught of COVID-19 has made movement over the last two years exceedingly challenging. This has directly impacted the ability of people to go to movie theatres for entertainment. As result, the demand for OTT and streaming platforms has increased globally more frequently than before. This means that the concept of dubbing films and series for foreign audiences has become more prominent on OTT platforms. But the problem associated with dubbed films and series has been the dissonance between facial expression, lip movement, and the local dialogue being spoken. As an application, deepfakes solves this problem by manipulating images or videos using AI and computer vision.     

Healthcare 

Applications of generative AI are being widely used in the Healthcare industry for treating patients in the most effective manner. Generative AI is very helpful in the early detection of potential malica which can be defined as "a specific intent by the defendant to cause substantial bodily injury or harm to the claimant", allowing them to develop an effective treatment.

Generative AI for Security

Generative models learn the distribution of data from a sample dataset and can then generate new data instances. Recent advances in deep learning have brought forth improvements in generative model architectures. Recently researchers have found that 

applying generative AI models with machine learning, in helping in identifying attacks. Generative Adversarial Networks (GAN), an area of machine learning, is a new method to protect the system from attacks and build safer systems. GAN can learn to generate new samples from the input data set, compare them with the labeled real-world data, and decide whether they are realistic or fake.

Creating Useful data from old ones

Photos and videos preserved for decades and centuries can also be given touch-ups with generative AI and deepfakes to upscale them to 4K-quality media and beyond. Generative AI also allows studios to generate videos that have 60 frames per second instead of less than 30 fps. Also, its ability to remove noise from old media files is one of the complex problems solved by Generative AI. The application of Generative AI makes them incredibly clear and sharp in terms of color and contrast. 

Image Generation

Previously image generation was considered the toughest task and it was time-consuming also. Previously professional artists were being hired by companies to create image content for their use which was deeply cutting their pockets. But now, generative AI can transform text into images and generate realistic images based on a setting, subject, style, or location that the user specifies. Therefore, it is possible to generate the needed visual material in a quick and simple manner. 

Robotics Control

Generative AI ensures higher quality outputs by self-learning from every set of data. It also minimizes the risks associated with a project and trains machine learning algorithms to be less biased. Additionally, it permits robots to comprehend more abstract concepts – both in the real world and in simulations. It guides the motion of the robot and allows for matching the target locations of the features in significantly fewer steps than state-of-the-art visual servoing methods.

Text Generation

Generative AI had also solved the ongoing complexity of text generation. A branch of generative AI, GANs offer alternatives to the deficiencies of state-of-the-art ML algorithms. GANs are presently being trained to be useful in text generation as well, despite their initial use for visual purposes. Creating dialogues, headlines, or ads through generative AI is commonly used in marketing, gaming, and communication industries. These tools can be used in live chat boxes for real-time conversations with customers or to create product descriptions, articles, and social media content.

Music Generation

Previously music generation was considered the work of highly qualified professionals in the music field. But now Generative AI is also purposeful in music production. Music-generation tools can be used to generate novel musical materials for advertisements or other creative purposes. In this context, although, there remains an important obstacle to overcome, namely copyright infringement caused by the inclusion of copyrighted artwork in training data.

Image Resolution Increase (Super-Resolution)

Generative AI has solved the problem of image resolution. Various applications of Generative AI are being used to create new content based on the existing content. Generative Adversarial Networks (GANs) are one of these methods. A GAN consists of a generator and a discriminator that creates new data and ensures that it is realistic. The GAN-based method allows you to create a high-resolution version of an image through Super-Resolution GANs. This method is useful for producing high-quality versions of archival material and medical materials that are uneconomical to save in high-resolution format. Another use case is for surveillance purposes.