Generative AI: What Is It, Tools, Models, Applications and Use Cases

What is Generative AI: Exploring Examples, Use Cases, and Models

It has functions including AI, publishing, full multitrack editing, transcription, and screen recording. GitHub Copilot, in partnership with GitHub and OpenAI, created Copilot, a code completion Artificial Intelligence tool. There are plenty of examples of chatbots, for example, providing incorrect information or simply making things up to fill the gaps. While the results from generative AI can be intriguing and entertaining, it would be unwise, certainly in the short term, to rely on the information or content they create.

For example, an educator can convert their lecture notes into audio materials to make them more attractive, and the same method can also be helpful to create educational materials for visually impaired people. Aside from removing the expense of voice artists and equipment, TTS also provides companies with many options in terms of language and vocal repertoire. Generative AI uses various methods to create new content based on the existing content. A GAN consists of a generator and a discriminator that creates new data and ensures that it is realistic. GAN-based method allows you to create a high-resolution version of an image through Super-Resolution GANs.

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These early efforts focused on developing programs capable of reasoning, problem-solving, and learning from experience. Generative AI can convert X-rays and CT scans into more realistic images, which can be helpful for diagnosis. For example, by using GANs (Generative Adversarial Networks) to perform sketches-to-photo translation, doctors can get a clearer, more detailed view of the inside of a patient’s body.

Examples of generative AI include ChatGPT, DALL-E, Google Bard, Midjourney, Adobe Firefly, and Stable Diffusion. Our goal was neither to tell you AI is an amazing solution, nor to “debunk” anything. Instead, we wanted to do a broad, detailed analysis of generative AI for testing, to push the conversation, Yakov Livshits research, and application in the field a half-inch forward. Despite the rhetoric Brooks logic seems to apply today, simple english descriptions of software sent into AI do fall short. What we might be able to achieve, though, is to identify a series of small, rapid victories in utilizing AI for testing.

DALL-E 2

On top of it, the primary goal of generative AI focuses on creating digital models that resemble physical objects in texture, size, and shape. 3D modeling technology has been a powerful tool for transforming different industries, such as entertainment, product design, and architecture. Some of the best generative AI examples in content generation use cases point at ChatGPT, Jasper Chat, and Google Bard. The advanced language models have set new milestones in the field of content generation.

And if a business or field involves code, words, images or sound, there is likely a place for generative AI. Looking ahead, some experts believe this technology could become just as foundational to everyday life as the cloud, smartphones and the internet itself. Regardless of the approach, generative AI models must be evaluated after each iteration to determine how closely their generated data matches the training data.

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.

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Many companies such as NVIDIA, Cohere, and Microsoft have a goal to support the continued growth and development of generative AI models with services and tools to help solve these issues. These products and platforms abstract away the complexities of setting up the models and running them at scale. Firefly can create high-quality images and stunning text effects from just textual inputs. Whenever there is user input/prompt, the generator will generate new data, and the discriminator will analyze it for authenticity. Feedback from the discriminator enables algorithms to adjust the generator parameters and refine the output. The generative AI technology can help automate software programming tasks using LSTM (Long Short-Term Memory) network, which generates new code based on existing code.

Right now, an AI text generator tends to only be good at generating text, while an AI art generator is only really good at generating images. In 2020, OpenAI released Jukebox, a neural network that generates music (including “rudimentary singing”) as raw audio in a variety of genres and styles. A series of other AI music generators have followed, including one created by Google called MusicLM, and the creations are continuing to improve.

As the name suggests, video prediction is nothing but predicting the anomalies in the video. Image-to-image or image-to-photo translation is a method of converting a semantic sketch or image into a realistic one. This is done exactly through the same generator and discriminator in the GAN model. Generative AI is a thrilling new technology that has unlimited possibilities and can change our
lives and work. In the past, AI has been the domain of engineers, data scientists, and experts.

• It uses a conversational chat interface to interact with users and fine-tune outputs.
• For example, companies can produce curated content for customers, such as music playlists, book recommendations, and more.
• The top generative AI examples in video creation and editing could offer solutions for translating your imagination into reality.
• In the US, 37% of professionals in the marketing and advertising sector have used generative AI in their jobs.

If you don’t know how the AI came to a conclusion, you cannot reason about why it might be wrong. Generative AI can be used to provide personalized sales coaching to individual Yakov Livshits sales reps, based on their performance data and learning style. This can help sales teams to improve their skills and performance, and increase sales productivity.

By using AI to enhance the resolution of these materials, they can be brought up to modern standards and be more engaging for students who are used to high-quality media. ChatGPT and other similar tools can analyze test results and provide a summary, including the number of passed/failed tests, test coverage, and potential issues. Tools like ChatGPT can convert natural language descriptions into test automation scripts.