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

A prompt can be anything from text and images to music and video, and even new chemical compounds for use in drug development. In this way, generative AI has the potential to revolutionize a wide range of industries and applications. Generative AI models use a complex computing process known as deep learning to analyze common patterns and arrangements in large sets of data and then use this information genrative ai to create new, convincing outputs. The models do this by incorporating machine learning techniques known as neural networks, which are loosely inspired by the way the human brain processes and interprets information and then learns from it over time. It’s able to produce text and images, spanning blog posts, program code, poetry, and artwork (and even winning competitions, controversially).

DALL-E can also edit images, whether by making changes within an image (known in the software as Inpainting) or extending an image beyond its original proportions or boundaries (referred to as Outpainting). In this work Durk Kingma and Tim Salimans introduce a flexible and computationally scalable method for improving the accuracy of variational inference. In particular, most VAEs have so far been trained using crude approximate posteriors, where every latent variable is independent. Recent extensions have addressed this problem by conditioning each latent variable on the others before it in a chain, but this is computationally inefficient due to the introduced sequential dependencies. Generative Adversarial Networks are a relatively new model (introduced only two years ago) and we expect to see more rapid progress in further improving the stability of these models during training.

ChatGPT tutorial: ChatGPT – A guide on how to use OpenAI’s new ChatGPT

Users can enter a descriptive prompt into DALL-E and receive a detailed image only seconds later. For example, prompts can range from “a simple sunset” to “a watercolor-style fall sunset landscape featuring purples and oranges.” Both prompts would result in very different outputs. Other use cases involve using images to report on the state of crops in the field and using satellite data to predict future weather patterns. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole.

The use of synthetic data generated by AI has the potential to overcome the challenges that the banking industry is facing, particularly in the context of data privacy. Synthetic data can be used to create shareable data in place of customer data that cannot be shared due to privacy concerns and data protection laws. Further, synthetic customer data are ideal for training ML models to assist banks determine whether a customer is eligible for a credit or mortgage loan, and how much can be offered. Meanwhile, the way the workforce interacts with applications will change as applications become conversational, proactive and interactive, requiring a redesigned user experience. In the near term, generative AI models will move beyond responding to natural language queries and begin suggesting things you didn’t ask for.

ML for the business today

This way, generative AI models can actually bring versatile use cases – breaking the old-known myths that “AI is dumb”. As a result, companies can stand up to applications and realize their benefits much faster. But also, you want to go straight to consumers on a one-to-one segment, “one type of marketing” basis. You want the ability for these technologies to create more compelling, individualized marketing that creates value. This is just an example of how executives are thinking about their own learning journey along this dimension of generative AI, which I wouldn’t have heard a year ago from clients.

While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more genrative ai than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs are performed. Bard, created by Google, is a version of language models for dialogue applications (LMDA) that’s optimized and trained on data from sources open to the public.

Google Docs has a feature that attempts to automatically augment text with AI generated content. These are very useful examples, so I’ll call them passive AI – analyzing the existing data and generating output and helping to make decisions or even making them automatically. Based on text, voice analysis, image analysis, web activity and other data, the algorithms decide what the opinion is of the person towards the products and quality of services. The upscale examples include photography of a woman from 64 x 64 input to 1024 x 1024 output. The process helps restore old images and movies and upscale them to 4K and more. Previously, integrating AI meant investing in training, improving, and keeping the performance under inspection.

Industry and society will also build better tools for tracking the provenance of information to create more trustworthy AI. Generative AI often starts with a prompt that lets a user or data source submit a starting query or data set to guide content generation. At a high level, attention refers to the mathematical description of how things (e.g., words) relate to, complement and modify each other. The breakthrough technique could also discover relationships, or hidden orders, between other things buried in the data that humans might have been unaware of because they were too complicated to express or discern. An LLM generates each word of its response by looking at all the text that came before it and predicting a word that is relatively likely to come next based on patterns it recognizes from its training data.

Tome is a revolutionary generative AI solution that takes the hassle out of creating presentations. By providing a simple prompt, users can instantly generate captivating slides for product presentations, sales pitches, training sessions, client proposals, and more. Bloomreach is a cloud-based software for the travel industry that personalizes customer touch-points, drives business growth, and supports different providers. It helps identify frequent travelers, create personalized experiences, and gain valuable customer insights. Geneticists are learning to understand gene expression — how specific genes and combinations of genes get turned on and off — and what genes do when they’re active.

In 2021, the release of DALL-E, a transformer-based pixel generative model, followed by Midjourney and Stable Diffusion marked the emergence of practical high-quality artificial intelligence art from natural language prompts. Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content. Microsoft’s decision to implement GPT into Bing drove Google to rush to market a public-facing chatbot, Google Bard, built on a lightweight version of its LaMDA family of large language models. Google suffered a significant loss in stock price following Bard’s rushed debut after the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system. Meanwhile, Microsoft and ChatGPT implementations also lost face in their early outings due to inaccurate results and erratic behavior.

The better I am at leveraging my own data and insights into the model, the more it can be a competitive advantage. You can actually use these systems to augment human contact center representatives as virtual experts. If you can take a corpus of your corporate data and hook it up to a large language model, you can query it.

• Generative AI offers better quality results through self-learning from all datasets.
• Training GANs for the purpose of fraud detection, by utilizing it with a training set of fraudulent transactions, helps identify underrepresented transactions.
• Traditional AI, on the other hand, has focused on detecting patterns, making decisions, honing analytics, classifying data and detecting fraud.
• For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly.
• To achieve realistic outcomes, the discriminators serve as a trainer who accentuates, tones, and/or modulates the voice.
• Data and extracting valuable information from it has become critical for successful business operations and planning.

Both relate to the field of artificial intelligence, but the former is a subtype of the latter. Engaging GenAI now will set up any HR team to deliver services in ways that the function is only starting to explore. Strive to use this evolving technology to guide the entire business forward—and keep employees satisfied and committed. Most Hype Cycles have a few emerging technologies that end up being rated low or moderately beneficial; all of the technologies in the AI Hype Cycle were rated high or transformative.