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Jul 24, 2024 · Applications of generative AI. Generative artificial intelligence has applications in diverse industries such as health care, manufacturing, software development, financial services, media and entertainment, and advertising and marketing. Let’s examine some of the different ways professionals in these industries apply generative AI to their ...
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1. Video Generation
Real-life example: OpenAI’s Sora video generator is being marketed to Hollywood Studios.2
2. Video Prediction
Real-life example: Lucid Dream Network enhanced its video production by utilizing Pictory’s script-to-video tool, which offered pre-built templates and smooth integration of music and visuals. This innovation helped the company boost its productivity by 350% and amplified its social media reach and engagement by 500%. A GAN-based video prediction system: 1. Comprehends both temporal and spatial elements of a video 2. Generates the next sequence based on that knowledge (See the figure below) 3...
3. Image Generation
Real-life example: Coca-Cola, working with OpenAI and Bain & Company, launched the “Create Real Magic” platform. By utilizing OpenAI’s GPT-4 and DALL-E models for creative generation, this project allowed users to create custom artwork using iconic Coca-Cola imagery, such as the contour bottle and Santa Claus. With generative AI, users can transform text into images and generate realistic images based on a setting, subject, style, or location that they specify. Therefore, it is possible to ge...
4. Semantic Image-to-Photo Translation
Based on a semantic image or sketch, it is possible to produce a realistic version of an image. Due to its facilitative role in making diagnoses, this application is useful for the healthcare sector. Figure 3: Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs.4
5. Image-to-Image Conversion
It involves transforming the external elements of an image, such as its color, medium, or form, while preserving its constitutive elements. One example of such a conversion would be turning a daylight image into a nighttime image. This type of conversion can also be used for manipulating the fundamental attributes of an image (such as a face, see the figure below), colorize them, or change their style.
Real-life example: Twilio enhanced its voice synthesis capabilities through collaboration with Amazon Polly, a cloud-based text-to-speech service. This partnership introduced more than 50 voices across 25 languages to Twilio’s platform and provided developers with new APIs for more advanced speech synthesis control in their voice applications.
11. Idea Generation
LLM output may not be suitable to be published due to issues with hallucination, copyrights etc. However, idea generation is possibly the most common use case for text generation. Working with machines in ideation allows users to quickly scan the solution space. It is surprising to get a machine’s help in becoming more creative as a human. This is possibly because generative AI’s capabilities are quite different (e.g. more flexible, less reliable) than how we typically think about machines’ c...
12. Text Generation
Researchers appealed to GANs to offer alternatives to the deficiencies of the state-of-the-art ML algorithms. GANs are currently being trained to be useful in text generationas well, despite their initial use for visual purposes. Creating dialogues, headlines, or ads through generative AI is commonly used in marketing, gaming, and communication industries. These tools can be used in live chat boxes for real-time conversations with customers or to create product descriptions, articles, and soc...
13. Personalized content creation
It can be used to generate personalized content for individuals based on their personal preferences, interests, or memories. This content could be in the form of text, images, music, or other media, and could be used for: 1. Social media posts 2. Blog articles 3. Product recommendations Personal content creation with generative AI has the potential to provide highly customized and relevant content.
15. Code generation
Real-life example:Amazon has introduced Amazon Q, an AI-powered tool that significantly reduces the time and effort required to update foundational software like Java. This tool automates code transformations, reducing the upgrade time from 50 developer-days to just a few hours, and saving an estimated 4,500 developer-years of work. In six months, Amazon modernized over half of its Java systems, enhancing security and cutting infrastructure costs, resulting in $260 million in annual efficienc...
16. Code completion
One of the most straightforward uses of generative AI for coding is to suggest code completions as developers type. This can save time and reduce errors, especially for repetitive or tedious tasks.
17. Code review
Generative AI can also be used to make the quality checks of the existing code and optimize it either by suggesting improvements or by generating alternative implementations that are more efficient or easier to read.
26. Conversational AI
Another use case of generative AI involves generating responses to user input in the form of natural language. This type is commonly used in chatbots and virtual assistants, which are designed to provide information, answer questions, or perform tasks for users through conversational interfaces such as chat windows or voice assistants. ChatGPT is a popular example for conversational AI. It offers a highly informative and integrated conversation to users, like philosophical discussions. For an...
27. Data Synthesis
Generative AI can produce synthetic datathat mirrors real-world statistics without relying on actual data points, useful for model training, data privacy, and NLP tasks.
28. Data visualization
Some generative models like ChatGPT can perform data visualization which is useful for many areas. It can be used to load datasets, perform transformations, and analyze data using Python librarieslike pandas, numpy, and matplotlib. You can ask ChatGPT Code Interpreter to perform certain analysis tasks and it will write and execute the appropriate Python code. Also, you can ask the model to visualize your data in a preferred format. Figure 9: Data analysis with ChatGPT code interpreter. Learn...
31. Streamlined drug discovery and development
Real-life example: LeewayHertz11 develops custom AI agents and copilots to streamline drug discoveryand helping companies save time and resources across various stages: 1. Target identification: Analyzes biological data to identify and prioritize promising drug targets. 2. Lead optimization: Screens chemical libraries, generates new molecules, and optimizes molecular properties. 3. Preclinical evaluation: Predicts drug behavior and potential interactions, ensuring safety and effectiveness. 4....
32. Personalized medicine
Real-life example:AI4BetterHearts is a global initiative led by the Novartis Foundation, Microsoft AI for Health, and partners to improve cardiovascular health by uniting and analyzing heart health data. The collaboration aims to break down data silos and leverage machine learning to transform health systems from reactive to preventive care. Partnering with Harvard’s Health Systems Innovation Lab, the initiative examines health system performance across 80 countries, with insights complementi...
33. Improved medical imaging
By combining the power of machine learning with medical imaging technologies, such as CT and MRI scans, generative AI algorithms can accelerate precision in medical imaging with improved results.
35. Personalized lessons
By leveraging generative AI for education, personalized lesson plans can provide students with the most effective and tailored education possible. These plans are crafted by analyzing student data such as their past performance, skillset, and any feedback they may have given regarding curriculum content. This helps ensure that each student, especially those with disabilities, is receiving an individualized experience designed to maximize success.
36. Course design
From designing syllabi and assessments to personalizing course material based on students’ individual needs, generative AI can help make teaching more efficient and effective. Furthermore, when combined with virtual realitytechnology, it can also create realistic simulations that will further engage learners in the process.
37. Content creation for courses
Generative AI allows rapid creation of diverse teaching materials, including quizzes and concept reviews. This would help educators quickly generate unique content. Also, AI can generate scripts for video lectures or podcasts, streamlining multimedia content creation for online courses (see the figure below). Figure 11: An example of AI-generated course content from NOLEJ.
41. Creative designing for fashion designers
Real-life example:ClothingGAN is an AI tool designed to generate creative garment designs. By leveraging GitHub’s resources, the platform showcases how generative AI is transforming the fashion industry by allowing designers to produce innovative and unique designs quickly and efficiently. Generative AI is a valuable tool that can bring new life to fashion designs. From creating innovative styles to refining and optimizing existing looks, the technology helps designers keep up with the latest...
42. Turning sketches into color images
Utilizing Generative AI, the fashion industry can save both precious time and resources by quickly transforming sketches into vibrant pictures. This technology allows designers and artists to experience their creations in real-time with minimal effort while also providing them more opportunity to experiment without hindrance.
43. Generating representative fashion models
By leveraging generative AI to create a variety of fashion models, fashion companies can better serve their diverse customer base and accurately display their products in a more authentic manner. They can use such models for virtual try-on options for customers or 3D-rendering of a garment.
45. Fraud detection
Real-life example: Stripe integrated OpenAI’s GPT-4 to improve their ability to detect malicious activities and understand user needs by analyzing vast amounts of data for more tailored and accurate responses to customer inquiries. By analyzing the syntax of Discord posts, GPT-4 flags suspicious accounts for Stripe’s fraud team to investigate. GPT-4 also scans inbound communications to identify coordinated malicious activity to support Stripe’s ability to manage fraud. The integration of GPT-...
46. Risk management
By leveraging GANs, it is possible to compute value-at-risk estimationsthat display the potential amount of loss in certain periods or build economic scenarios for forecasting financial markets. Moreover, GANs aids in understanding volatility by generating new and assumption-free situations founded on historical data trends.
47. Generating user-friendly explanations for loan denial
Decision makers and loan applicants need to understand the explanations of AI-based decisions, including why the loan applications were denied. A conditional GAN is a useful tool to create applicant-friendly denial explanations as in the figure below. AI-generated loan decline explanations. Figure 14: Generating User-Friendly Explanations for Loan Denials Using Generative Adversarial Networks.17
Sep 24, 2024 · Best Buy is using Gemini to launch a generative AI-powered virtual assistant this summer that can troubleshoot product issues, reschedule order deliveries, manage Geek Squad subscriptions, and more; in-store and digital customer-service associates are also gaining gen-AI tools to better serve customers anywhere they need help.
Oct 14, 2024 · By utilizing generative AI models, applications can automate the video creation process and create stunning AI videos from scratch using text descriptions. You need to simply add some texts describing the kind of video you wish to generate and generative AI will instantly process your request and transform your texts into captivating videos efficiently.
Mar 17, 2024 · Now let‘s look at the top use cases of these generative AI techniques across industries and functions. General Applications of Generative AI 1. Media and Content Creation. Generative AI excels at creating realistic media assets and marketing content at scale. Image Generation: Create original images from text prompts. Useful for social media ...
Sep 12, 2024 · Applications of Generative AI. Here are some of the leading generative AI applications: Language: Generative AI excels in the domain of text by leveraging large language models (LLMs). These models are used for a wide range of tasks, including essay writing, code generation, translation, and even analyzing genetic sequences. Audio:
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Generative AI is playing a transformative role in the production processes, democratizing creativity and empowering individuals to generate a wide range of content, including images, videos, articles, and music. 📹 🎶. Let's delve into some tangible examples of how generative AI is reshaping the media landscape:
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