The progress of civilization boils down to people becoming more technologically savvy over the millennia. In the early 21st century, scientists and engineers saw to it that the machines they develop are also smart, ushering in the concept of artificial intelligence (AI). Being able to do things that only humans were capable of not long ago, robots and machines powered by AI penetrated various fields, including business, education, healthcare, entertainment, etc.
Today, AI know-how has developed into several related technologies (such as blockchain, machine learning, natural language processing, the Internet of Things) that find broad applications across many digitally-powered domains. This roster is getting ever longer as new high-tech innovations push their way in. Generative AI is one of such phenomenon that is growing in popularity in the tech industry.
What is generative AI? It is disruptive software that can create new content pieces by analyzing tons of existing data (be it text, audio files, video clips, or images). How can it do that? Generative AI tools go through a slew of input information, identify the pattern it is based on, and generate—hence the name—output containing similar content.
A more sophisticated variety of generative AI goes further than just monitoring a real-life environment to produce content. It can also leverage mathematical emulation and process unknown patterns revealed through it. Conventionally, such mechanisms rely on the application of stress testing and sensitivity analysis.
No matter what type of generative AI we speak of, it employs three operational techniques to do its job.
Why plural? Because there are two neural networks that participate in content generation. The generator neural network’s task is to create new content that resembles the input data. The discriminator neural network is used to differentiate the source and the generated content to determine which is closer to the original.
During the generation cycle, these two operate in alternation. The generator attempts to produce more and more realistic data, and the discriminator learns to discern between fake and real data better and better.
Their responsibility is to understand the input (typically, text or images), classify it according to the pre-set criteria, and generate similar input relying on huge databases. LaMDA, GPT-3, Wu-Dao, and other tools of this kind are able to gauge the relevance and significance of input pieces, thus imitating the cognitive attention characteristic of humans.
Although these two solutions are called encoders, only one of them actually automatically translates input data into compressed code. The second acts as a decoder, transforming information into a digestible format again. Such two-step data processing helps to save on storage facilities since the downsized data takes much less memory space.
Now that you understand the meaning of generative AI, let’s see what benefits generative AI use cases can promise to businesses, private persons, and non-commercial organizations.
There are several advantages generative AI applications yield.
Generative AI can create avatars, thus concealing the real appearance of people who don’t want to disclose their identities for any reason while being interviewed or working online.
Machines aren’t astute enough yet to fathom some abstract concepts they can encounter in the real or simulated world, and generative AI is instrumental in tackling this challenge.
The shuttle pattern of operation utilized in self-learning GANs helps to get high-quality images, video, or audio, even if the input content is far from perfect.
Generative AI-fueled tools can detect malicious or at least suspicious activities in no time and prevent all kinds of damage to a business or a person.
Reinforcement ML is based on rewarding desired actions and punishing unwanted ones. However, detecting where a certain step belongs is oftentimes biased. Generative AI techniques can remove or at least considerably curtail this bias.
Despite the evident boons, generative AI applications can harbor some annoying pitfalls.
While implementing the best practices of generative AI, make sure you realize possible bottlenecks and misconceptions related to them.
You can’t count on generative AI algorithms to function properly unless they rely on a solid amount of input content.
This software can work wonders but only within limits imposed by the training data. It can’t create any new text or images out of the blue.
GAN’s mechanisms are still unstable and hard to control, being prone to generate a totally unanticipated result.
Generative AI use cases seen in the healthcare and financial sectors should be monitored very closely to forestall any money-related or sensitive data leakages of individuals and businesses.
There is no state-of-the-art know-how that wrongdoers can’t put to their evil uses, and generative AI is not an exception where fraudulent scams of various kinds can be involved.
Once you learn to handle these challenges, you can enjoy the bright vistas the application of this technology heralds.
As experts of Gartner estimate, the proportion of digital data produced by generative AI will grow tenfold by 2025, which is a spectacular spike in comparison with the 1% it accounts for today. What are the sectors where the application of this technology is going to reign supreme?
In this field, generative AI performs a dual function. First of all, it can upgrade patient treatment. Second, it can strengthen patients’ data privacy. The former of the generative AI use cases is more frequent by far when fake, underrepresented data is generated to train and develop the model. For instance, GANs can supply various angles of an X-ray image to visualize possible tumor growth outcomes. Or, it can detect malignant developments by comparing the image of healthy organs from the databank with the affected one. The second way of using the technology focuses on data de-identification, helping to secure the reversal process, which is far from being totally penetration-proof.
A related domain is a pharmacology, where generative AI can be instrumental in drug discovery. The know-how is able to create molecular structures of drugs employed in curing certain maladies. The treatment of new diseases can also be significantly facilitated when this technology performs a quick database search of compounds to be used for this purpose. This automated procedure is much faster than when it is done manually. Generally, by 2025, Gartner expects half of drug development initiatives to rely on generative AI.
Many old movies and classic Disney cartoons belong to the treasury of global culture, but their quality often fails the imperatives of our time. Generative AI can upscale them to 4k and even more, generate 60 frames per second instead of the conventional 23, remove noise, and transform black-and-white into color.
Together with cinema, the video game industry is another entertainment realm that relies on moving images, and generative AI can lend a helping hand as well. Software developers’ efforts can be lightened, and development duration considerably reduced when AI algorithms generate 3D models utilized in computer games. Such models can be absolutely new or stem from 2D images previously entered into it. And 2D pictures can also be generated by the technology to find further use in specific game and cartoon genres, for instance, anime.
What is done with dynamic images can be applied to static ones with greater efficiency.
A more serious use case is related to the identification of people who are missing or wanted and whose appearance may have undergone some changes. In this case, growing a beard or mustache, dyeing hair, or another facial expression caught by the camera will be no obstacle for generative AI recognition mechanisms.
Non-fungible tokens are all the rage in the digitally-driven world of today, whose sales topped $25 billion last year. NFT art occupies a prominent place in the niche, with cartoons, memes, and paintings carrying the day. Generative AI tools are second-to-none means of producing such art pieces that can bring torrents of cash into the coffers of their creators.
Generative AI is not only about pictures. The realm of sound can also benefit from its application. In cinematography and video gaming, this know-how can be employed to produce foley elements, ambient sounds, voiceovers, and other audio effects that make up an essential part of a movie or a video game that audiences enjoy so much.
Another audio use case is related to computer-generated music. Neural networks that generative AI relies on can imitate the workings of the human brain, producing music pieces. In 2016, the first AI-created song was released by Google’s Magenta, and we are likely to see more music pieces (maybe even symphonies and operas) composed by robots.
The sphere of retail and commerce can also find the employment of generative AI highly advantageous. While interacting with goods, people reveal emotions and give evaluations of both the product they bought and the services the sales organization provided. AI algorithms can be trained to analyze consumer-generated texts, speech samples, and facial expressions that give a clue to the understanding of the attitude of clients to the item in question.
Other types of generative AI mechanisms can monitor the web activity of online customers and analyze user data to gauge how satisfying the UX is or how successful an advertisement or the entire marketing strategy was. Such data can be further leveraged in client segmentation to identify various customer groups and map out targeted promotional campaigns, thus augmenting upselling and cross-selling opportunities.
It would be strange indeed if cutting-edge technology ushered by Industry 4.0 couldn’t be used by professionals in the high-tech domain. The same is true of generative AI, which software developers apply to automate manual coding. Humans “explain” to a specifically-honed solution what they want to obtain, and the machine churns out the requested programs in necessary quantities.
Another type of generative AI caters to the needs of citizen developers and laymen who don’t have sufficient expertise in coding to build apps or various solutions without even knowing programming languages. According to Gartner, within two years, such solutions are expected to come as a part of the suite of 50% of development platforms.
In both cases, the development speed of new software products is drastically enhanced, which can be a game-changer in the swiftly progressing business world of today.
Generative AI is an innovative technology that enables smart machines to produce entirely new content after processing the input textual, audio, and visual data. Leveraging three specialized techniques, this know-how paves the way for disruptive future trends in various fields. NIX United can help you develop a high-end generative AI-powered solution to increase the efficiency of your business and augment revenue.
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