Top Generative AI Applications & Use Cases of 2023

Businesses across diverse industries can leverage this technology to improve efficiency, save resources, and optimize performance. Drawing inspiration from these use cases is the first step toward integrating AI within a company and achieving superior results across various aspects of daily operations. For instance, generative AI can be used to examine machine sensor data and forecast when a failure is most likely to occur. This enables equipment manufacturers to plan maintenance and repairs in advance, cutting downtime and enhancing overall equipment performance.

• It’s an AI app designed for visually impaired individuals that harnesses the power of GPT-4 to convert images into text instantly.
• The company showcased 10 AI projects during the conference, such as Project Clever Composites, which simplifies the tedious process of compositing images into backgrounds.
• With a combination of documents, videos, and vetted data sources, Farmer.CHAT delivers actionable recommendations to farmers in India, Ethiopia, and Kenya.

In addition, generative AI models also rely on training data for learning about the rules and patterns in natural language. After training, the generative AI models could generate new text that features a similar style and tone as the input data. In the media industry, combining machine learning techniques with marketing techniques can lead to improved content generation. Predictive targeting is an example of a marketing technique that utilizes both AI and machine learning, foreseeing a customer’s next decision by analyzing their old data and behavior patterns. Synthesia is another example of a well-known generative AI company that implements new synthetic media technology for visual content creation, and it does so by using minimum skills and cost. By utilizing generative AI, both marketing and advertising industries can produce more personalized content, they can better understand the market and consumer behavior, and come up with more efficient campaigns.

Experience Generative AI at the NVIDIA AI Playground

From drug discovery and development to improved medication plans and personalized treatment recommendations, algorithms can assist medical specialists in aiding patients and finding new ways to cure diseases. If this technology had been as advanced a few years ago, who knows how the COVID-19 pandemic outbreak would have been handled? Art and business intersect in many ways, and AI can also provide support in this area.

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. 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. 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.

Neural networks, which form the basis of much of the AI and machine learning applications today, flipped the problem around. Designed to mimic how the human brain works, neural networks “learn” the rules from finding patterns in existing data sets. Developed in the 1950s and 1960s, the first neural networks were limited by a lack of computational power and small data sets.

Corporate leaders are using the deep learning model’s capability to canvas large amounts of data and provide logical responses. So if you’re a developer, it’s time to join the AI revolution and start building interesting AI applications! You can try Yakov Livshits out these models in Google Colab or other collaborative data science notebooks. When you’re building applications using large language models, you should be aware of the common pitfalls, such as factually incorrect information and hallucinations.

Generative AI Application in Manufacturing

Furthermore, for pharmaceutical companies, Generative AI can be used to analyze large data sets on drug interactions, side effects, and efficacy, helping in drug discovery and repurposing. The importance of Generative AI in the healthcare industry cannot be overemphasized. Generative AI can assist radiologists in detecting cancer, heart diseases, and neurological disorders by analyzing medical images, such as X-rays, CT scans, and MRIs. This way, diagnoses can be made more accurately and are less likely to be missed or delayed.

On the other hand, GANs work for generative multimedia and visual content from images and text. Conversational AI tools can be trained on a variety of languages, and it can translate messages from one language to another in real-time. By using machine learning algorithms, manufacturers can predict equipment failures and maintain their equipment proactively. These models can be trained on data from the machines themselves, like temperature, vibration, sound, etc. As these models learn this data management, they can generate predictions about potential failures, allowing for preventative maintenance and reducing downtime.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

How Generative AI is Changing Industries

Yooz is an automated AI solution designed to assist accounting and finance leaders in managing invoices. The solution aims to streamline and automate the invoice processing workflow, reducing manual effort and enhancing overall efficiency. AI is providing a significant upgrade to banking operations by automating tasks that were previously performed manually, resulting in more efficient processes.

Generative AI raises ethical considerations such as the potential for deepfake creation, misinformation dissemination, and intellectual property infringement. Businesses using generative AI should establish clear guidelines and policies regarding the responsible use of such technology. Transparency, consent, and privacy should be prioritized to address these ethical concerns and ensure that generative AI is utilized in a responsible and trustworthy manner.

Most would agree that GPT and other transformer implementations are already living up to their name as researchers discover ways to apply them to industry, science, commerce, construction and medicine. Unscreen revolutionizes video editing by employing AI to automatically remove backgrounds from videos. With capabilities such as background removal, object painting, and subtitle addition, Runway makes professional-grade video editing accessible to everyone, regardless of their Yakov Livshits technical expertise. Notion AI delivers an enhanced user experience by providing an efficient and smooth workflow, earning the praise and appreciation of its user base. AGI, the ability of machines to match or exceed human intelligence and solve problems they never encountered during training, provokes vigorous debate and a mix of awe and dystopia. AI is certainly becoming more capable and is displaying sometimes surprising emergent behaviors that humans did not program.

As the name suggests, foundation models can be used as a base for AI systems that can perform multiple tasks. Today, developers and organizations are actively implementing this technology to create that lead to business transformation, innovation, growth, and better scalability. From creating and completing videos to expediting coding and enhancing chatbots, the generative AI use cases are continuously expanding. AlphaCode by DeepMind is one of the foremost problem-solving and coding solutions in the generative AI space.

#19 AI for content creation (text, images, video, audio…)

However, with GAN and generative AI, text-to-speech is much more natural and sounds like a human. Tools such as ChatGPT and others have shown their potential to write accurate codes for specific programs. This means professionals no longer have to worry about programs that must be added repeatedly; instead, generative AI can do that job. Image-to-image or image-to-photo translation is a method of converting a semantic sketch or image into a realistic one.

Generative solutions excel in sentiment analysis and extracting valuable insights from large datasets. Moreover, internal IT departments can utilize ChatGPT and other AI-driven solutions to assist them in code creation, issue fixing, and system testing. This approach enables saving resources, accelerating processes, and achieving results that surpass expectations. Given that we live in a digital era, having a generative AI tool that automates software production is one of the best ways to help a business thrive. They can engage with thousands of people simultaneously and even translate messages in real-time, eliminating language barriers. It’s a necessity for businesses looking to expand their reach and consistently provide the highest possible level of customer service.

This is especially beneficial for producing high-quality versions of archival or medical materials that are not cost-effective to save in high-resolution format. The technological landscape is in a constant state of evolution and is leading us toward a new industrial environment where humans are working with intelligent machines. These smart machines are embedded with various cognitive technologies such as artificial intelligence and machine learning. According to Gartner’s 2022 Emerging Technologies and Trends Impact Radar report, generative AI is considered a highly disruptive and rapidly advancing technology. Incredibly, the report predicts that generative AI will be responsible for generating 10% of all data (up from less than 1%) and 20% of test data for consumer applications by 2025.