Generative AI: Complete overview of the techniques and applications

It utilizes several Generative AI models like BERT and Transformer or Autoregressive models. However, Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are two of the most prominent generative AI model types. Adam Wealand’s experience includes marketing, social psychology, artificial intelligence, data visualization, and infusing the voice of the customer into products. Wealand joined Red Hat in July 2021 and previously worked at organizations ranging from small startups to large enterprises.

It creates data like audio, images, text, and code using existing information as an idea. With this, models can easily create deep fakes, reinforce machine learning bias, and share misleading content across platforms. Generative AI is frequently Yakov Livshits utilized in creative sectors, specifically to create art and generate images. These models can be trained on a large number of paintings and later be used to generate new ones with similar features and slight variations in style.

How to Evaluate Generative AI Models?

In the image of a horse, you may want to replace the horse with a zebra while retaining the same background. Image segmentation is the task of identifying pixels belonging to a specific object within an image. Meta AI released Segment Anything Model (SAM) that can be used to segment any image and cut out objects from them. So, if you want to experiment with lightweight, open-source LLMs in your applications, you can try out StableLM. It’s OpenAI’s most powerful AI system with better reasoning capabilities and performance than ChatGPT.

Financially, businesses have a huge incentive to embark on the generative AI train, whether as a provider or users. The figure is predicted to grow 35.6% over the next few years to $51.8 billion in 2028. Moreover, businesses that adopted AI in their workflow have demonstrated up to a 10% increase in revenue. BioGPT from Microsoft is a transformer model you can use Yakov Livshits for biomedical data mining and text generation applications. From generating the itinerary for your upcoming travel plans to drafting cover letters that fit the job description, ChatGPT has become a part of our day-to-day tasks. Bard, a conversational AI chatbot created by Google, is changing the shopping experience thanks to its interactive user interface.

#6 AI-generated media for enhanced medical training and simulation

Teach your staff how to use this technology responsibly and effectively, and you’ll be surprised by how much generative AI is able to improve existing processes and work. However, keep in mind that it is important for your company to establish usage rules ahead of time, especially as it relates to data security and uploading proprietary information into any of these tools. Though some of its features are relatively limited compared to ChatGPT, the tool is quickly growing its capabilities. For example, in late April 2023, Bard was updated to support programming and development requirements like code debugging, generation, and explanation.

• During the past few years, generative artificial intelligence (AI) models have made considerable development, revolutionized several industries, and captured the attention of both researchers and enthusiasts.
• In essence, Generative AI models learn and understand the underlying patterns and structures in the input data.
• Generative AI models can generate thousands of potential scenarios from historical trends and data.
• The app provides diverse plans with options for various body sizes, hairstyles, body shapes, custom poses, and more.
• The first neural networks (a key piece of technology underlying generative AI) that were capable of being trained were invented in 1957 by Frank Rosenblatt, a psychologist at Cornell University.

Vertex AI is a cutting-edge solution that enables easy interaction, customization, and integration of foundational models into applications without extensive machine learning expertise. It provides access to various foundation models through the Model Garden and offers a user-friendly interface, Generative AI Studio, for model tuning. Generative AI technology is revolutionizing content creation by quickly producing animated, textual, and visual material that is both novel and realistic. With a diverse range of applications, generative AI is poised to transform numerous industries, including surveillance, healthcare, marketing, advertising, education, gaming, communication, and podcasting. As a result, generative AI has become one of the most important technological trends of the year.

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.

From generating blog posts to social media captions, this technology assists writers, marketers, and content creators by producing relevant and engaging content. As concerning as this may sound to some, it is as important for us to keep an open mind about it and be mindful that for a generative model to operate, human input is a must; hence we are still in control. As we covered earlier on, in order to produce content, generative artificial intelligence lets machines find the underlying pattern related to the input, and it does so through a few different techniques. Python has emerged as the go-to language for developing generative AI applications thanks to its versatility, vast ecosystem of libraries, and easy integration with popular AI frameworks like PyTorch and TensorFlow.

It’s goal is to “empower imagination through artificial intelligence.” It can produce voice-overs, videos, social media postings, and logos. The branch of artificial intelligence known as “generative AI” is concerned with developing models and algorithms that may generate fresh and unique content. These breakthroughs notwithstanding, we are still in the early days of using generative AI to create readable text and photorealistic stylized graphics. Early implementations have had issues with accuracy and bias, as well as being prone to hallucinations and spitting back weird answers. Still, progress thus far indicates that the inherent capabilities of this type of AI could fundamentally change business.

Elevated customer service with generative AI apps

At the request of the user, the AI model builds your home, office, hotel, or any other property in minutes as per your description of the content you provide in the AI model. This helps the real estate builders and promoters to attract customers to their projects. Video Creation is making up with the trend after the text model takes over the trend. The model gets into the video content as text from the users and it creates a new video based on the previous video models used to train it. The AI content is based on the information the user provides to the tool, the more information the content gets curated and provides the exact thing the user needs.

Check out the full list of Use Cases for Generative AI in the Automotive Industry. Now, when booking a hotel or seeking help, guests can address the bot in their preferred language. These Generative AI use cases change how travelers use technology in the hospitality industry. In this article, we’ve talked a lot about Generative AI enterprise use cases in different areas. You can see how it might be used in banking chatbots, online shopping, airports, making cars, and energy companies. The Namecheap survey found that 40% of respondents use generative AI tools daily, and 10% use them on a monthly basis.

The generative AI medical chatbot helps in providing the right information to the users regarding their disease. Other than this generative AI can be deployed for medical research and other medical simulation purposes. Generative AI can take in any type of content, whether text, image, video, code, etc.

You can rely on the most popular generative AI examples for creating unique and diverse game content. It can help developers in offering engaging content and immersive gameplay experiences. It is also important to note that the emerging applications of generative AI technology would have a noticeable impact on other industries. For example, the manufacturing industry could provide the best generative AI examples for improving product development. Furthermore, the hype around generative AI is also another promising reason to look forward to new trends in generative AI. The following post helps you learn more about the potential of generative AI with a detailed outline of top use cases of generative AI along with examples.

These models learn through trial and error, exploring different actions in an environment and receiving feedback in the form of rewards. DRL models have been applied in game playing, robotics, recommendation systems, and autonomous driving, among other areas, generating sophisticated and goal-oriented actions. As this technology continues to get adopted across multiple industries, there are an increasing number of generative AI applications being implemented and improved. Some of the most prominent practical uses of generative AI include chatbot creation, chatbot improvement, text generation and summarization, gameplay content creation, and video/audio creation.