Bài viết 1: 20 Examples of Generative AI Applications Across Industries \n \n Generative artificial intelligence (AI) is a trend just beginning its journey to the mainstream. Gartner projects that by 2026, over 100 million people will use generative AI to help them complete their work [1]. McKinsey looked at 63 different uses for generative AI and concluded that, if they were all implemented, the technology could add $2.6 trillion to $4.4 trillion worth of value to the global economy [2].

In this article, you’ll learn 20 examples of generative AI applications in various industries and how to start using generative AI for your organization.

What is generative AI?
Generative AI is artificial intelligence designed to create unique text or image results in response to user prompts. The technology uses machine learning to return an output based on the user’s prompt. AI engineers train the technology using large data sets, which the model consults when determining the best possible answer to a prompt. Another way to look at generative AI is as a form of predictive artificial intelligence. Based on the information provided, generative AI will predict which words and in which order will give the best answer to the user's prompts.

You can use generative AI to create new written, visual, or audio content, summarize complex data, generate code, assist with repetitive tasks, or make customer service more personalized.

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Examples of generative AI
Examples of generative artificial intelligence that you may have heard of include Google’s Bard, ChatGPT, or DALL-E from OpenAI.


ChatGPT or DALL-E: Generative artificial intelligence created by OpenAI, a Microsoft-backed, profit-capped company with the mission to develop artificial intelligence to serve humankind


Google Bard: Google’s generative AI with integrations to Google products like Google Lens and Gmail, operating with a language model called PaLM-2 that was trained on the largest data set out of all generative AI models available at the time of its release


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 field.

Health care and pharmaceuticals
Generative artificial intelligence has applications for all parts of the health care and pharmaceutical industry, from discovering and developing new life-saving medicine to personalizing treatment plans for individual patients to creating predictive images for charting disease progression. Some of the possibilities for generational AI in health care include:


Enhancing medical images: Generative AI can augment medical images like X-rays or MRIs, synthesize images, reconstruct images, or create reports about images. This technology can even generate new images to demonstrate how a disease may progress in time.


Discovering new drugs: Researchers can use generative artificial intelligence via a related field called generative design to research and develop new medicines. Gartner projects that 30 percent of the new drugs created by researchers in 2025 will use generative design principles [1].


Simplify tasks with patient notes and information: Healthcare professionals keep and take notes about patient medical care. Generational AI can build patient information summaries, create transcripts of verbally recorded notes, or find essential details in medical records more effectively than human efforts.


Personalized treatment: Generative AI can consider a large amount of patient information, including medical images and genetic testing, to deliver a customized treatment plan tailored to the patient's needs.


Advertising and marketing
Generative artificial intelligence offers many solutions to professionals working in advertising and marketing, such as generating text and images needed for marketing or finding new ways to interact with customers. Here are some examples of generative AI applications in advertising and marketing:


Generate marketing text and images: Generative AI can help marketing professionals create consistent, on-brand text and images to use in marketing campaigns. This technology also offers translation tools to spread your marketing message into new territories. Gartner predicts that marketing professionals will use generative AI to create 30 percent of outbound marketing materials by 2025 [1].


Generate personalized recommendations: Generative AI helps create powerful recommendation engines to help customers discover new products they might like. With generative AI, this process is more interactive for customers.


Create product descriptions: Beyond flashy advertising campaigns, generative artificial intelligence can help with tedious or time-consuming content requirements like creating product descriptions.


Enhance search engine optimization: SEO professionals can use generative AI for tasks like image tags or page titles or to create content drafts. You could also use a tool like ChatGPT or Bard to recommend changes you could make to content to improve SEO ranking.


Manufacturing
In manufacturing, professionals can use generative AI to look for ways to improve efficiency, anticipate maintenance needs before they cause problems, help engineers create better designs faster, and create a more resilient supply chain. Let’s explore these potential manufacturing solutions:


Accelerating the design process: Using generative AI, engineers and project managers can work through the design process much faster by generating design ideas and asking the AI to assess ideas based on the constraints of the project.


Provide smart maintenance solutions for equipment: Maintenance professionals can use generative AI to track the performance of heavy equipment based on historical data, potentially alerting them to trouble before the machine malfunctions. Generative AI can also recommend routine maintenance schedules.


Improve supply chain: You could use generative AI to track down the cause of problems in the supply chain by speaking conversationally with the technology to sort through a vast amount of transactional or product data. Generative AI can also help generate delivery schedules or recommendations for suppliers.


Software development
For a software development team, generative AI can provide tools to create and optimize code faster and with less experience using programming languages. A few examples of the applications of generative AI in software development include:

 

Generating code: Software developers can create, optimize, and auto-complete code with generative AI. Generative AI can create code blocks by comparing them to a library of similar information. It can also predict the rest of the code a developer begins to type, much like how auto-complete works while texting on a smartphone.


Translate programming languages: Generative AI can be a tool for developers to interact with software without needing a programming language. The generative AI would act as a translator.


Automate testing: Developers can improve their automated testing processes using generative AI to highlight potential problems and execute testing sequences faster than other AI methods. Generative AI can learn the logic of the software and how users will interact with it and create test cases to demonstrate various user scenarios.


Financial services
According to McKinsey, generative AI could add $200 billion to $340 billion of value to the banking industry annually [2]. Some of the applications of generative AI in the financial services industry include artificial intelligence investment strategies, drafting documentation and monitoring regulatory changes, and using generative AI as an interpreter to facilitate communications between clients and investors.


Create investment strategies: Generative AI can recommend the best investments according to your or your client’s goals. This technology can find and execute trades much faster than human investors and can do so within the parameters you set for the kind of transaction you want.


Communicate and educate clients and investors: Financial services professionals sometimes need to communicate complex information to clients and colleagues. Generational AI can provide hyperpersonalized customer service without adding more customer service professionals.


Quickly draft documentation and monitor regulation: Generative AI can monitor regulatory activity, keep you informed of any changes, and create drafts of documents such as investment research or insurance policies.


Media and entertainment
Media and entertainment could embrace generative AI in several ways, considering the industry primarily engages in the same task as the tech: generating unique content. Generative AI can help create and edit visual content, create short highlight videos of sporting events, and make working with content management systems easier.


Create audio and visual content: Generative AI can create new video content from scratch. This tech can also help you make visual content faster by creating visual effects, adding graphics, or streamlining editing.


Generate highlights for sports and events: When it comes to sporting and live events, gen AI can create highlight reels instantly and allow fans to create their own custom highlights. For example, fans could generate highlights of a particular play or a tournament series.


Manage tags for better content management: Generative AI can tag and index extensive media libraries, making locating the files you need at any time easier. Similar to our manufacturing example above, generative AI allows using conversational language to find the information or media you’re looking for in a complex media library.


How to find solutions with generative AI
If you’re interested in bringing generative AI to your company, you can approach the technology in two ways. First, you can use existing models and learn to engineer prompts to your needs. Or, you can customize solutions to fit your business processes.


You can use existing generative AI tools like ChatGPT. In this scenario, you’ll focus on learning how to write prompts that get the best answer possible from the technology. For example, you might identify who your audience is and the appropriate tone of the piece to help the application deliver the correct results.


You can integrate custom solutions from an enterprise-level company or build your own generative AI tools. While it won’t be feasible or practical for many companies to create their own generative AI solutions, many gen AI companies offer solutions you can tailor to your business needs. Generative models will vary on features, cost, and security or privacy standards.


Bài viết 2: 50 Useful Generative AI Examples in 2024 \n \n Generative AI has become a hot topic in the past few months, and for good reason — it is changing the world right before our eyes. 

The AI revolution is impacting every sector, including healthcare, education, finance, agriculture, and construction, with new AI solutions emerging daily. 

In this article, we will explore 50 practical applications of generative AI across different industries. 

To demonstrate the real impact of AI, we have also integrated real-world generative AI examples that are already leaving a profound imprint on people's work processes. 

Are you ready to dive right in? Let’s go!

But wait - what exactly is generative AI? 🤖
Generative AI refers to a form of artificial intelligence that prioritizes the creation of original data rather than solely processing and organizing pre-existing data. By utilizing large language models, it has the ability to generate diverse outputs, including unique written content, images, videos, and music.

Generative AI examples in healthcare
The world of healthcare certainly has its fair share of challenges, doesn't it?

We're talking about rising healthcare costs, a fragmented system, struggles with health information exchange and interoperability, and limited access to care, just to name a few.

However, AI presents an opportunity to address these challenges.

While it is not a magical solution, generative AI can be leveraged in various ways to make a meaningful impact. 

Here are some examples:

#1 Conversational AI apps for patients
👉 Example: Ada 

Ada is a doctor-developed symptom assessment app that offers medical guidance in multiple languages. Optimized with the expertise of human doctors, Ada utilizes AI to support improved health outcomes and deliver exceptional clinical excellence.

image

                Generative AI applications in healthcare span from conversational AI chatbots to medical education. In the image, you can see Ada, a conversational healthcare app designed for individuals.
              
#2 AI applications for early detection of certain diseases
👉 Example: SkinVision app 

SkinVision is an app for early detection of skin cancer. With its regulated medical service, AI technology, and expert input, it teaches users to self-examine, understand risks, and address immediate concerns.

#3 AI for accessibility
👉 Example: Virtual volunteer / Be My Eyes

It’s an AI app designed for visually impaired individuals that harnesses the power of GPT-4 to convert images into text instantly. Users can send images through the app for immediate identification, interpretation, and conversational visual assistance. 

#4 AI for patient interactions and support
👉 Example: Hyro

This conversational AI is designed specifically for health systems to enhance patient engagement and address staffing challenges. With HIPAA-compliant conversational AI, users can automate common interactions, scale operations, and overcome staffing shortages.

#5 AI for medical product development and design
👉 Example: Uizard 

Uizard leverages AI for quickly and easily prototyping various digital products, such as apps and landing pages. With its intuitive interface, it greatly simplifies the once manual design process.

#6 AI-generated media for enhanced medical training and simulation
👉 Example: PEDAL 

PEDAL is an AI-driven platform that helps with better decision making in oncology. With a biobank of 150,000 tumor samples across 137 cancer types, the platform predicts drug responses with unmatched precision.

Generative AI examples in education
So, there's a class of 20+ students sitting in a classroom. 

Some of them are visual learners, while others prefer reading. Some are introverted and freeze when put in the spotlight, and others need extra help with biology. It's quite a diverse group of people, and they have a diverse range of needs, don't you think?

There's no doubt that education today faces many challenges, including unequal access, outdated methods, and the need for personalized learning. Fortunately, with the advent of AI, the solutions seem closer each day. 

image

                AI video is an emerging form of media that holds great potential for educational purposes. With Synthesia, you can create videos with realistic AI avatars in 120+ languages by simply typing in text.
              
Here's how we can use generative AI in education:

#7 AI apps for personalized learning experiences
👉 Example: Knowji 

Knowji is an AI-driven app that enhances vocabulary acquisition for learners of all ages. With captivating content and a state-of-the-art spaced repetition algorithm, this tool ensures the long-lasting retention of words.

#8 AI apps for innovative learning approaches
👉 Example: Hello History

Hello History is an app that offers a unique approach to learning history. Users can engage in conversations with historical personalities, which makes the study of history much more engaging and interactive.

#9 AI generators for creating more engaging training materials
‍👉 Example: Synthesia 

Synthesia is an AI video generator that creates videos from text. And not just any videos, but videos with real human presenters - AI avatars. It’s a great online tool that helps educators effortlessly transform their text-based documents into an engaging video training featuring a human face, establishing a deeper connection with the viewers.

Here’s how it works:

PlaySynthesia STUDIO product demo
#10 AI solutions for assessment, grading, and giving feedback to students
👉 Example: Gradescope 

Gradescope is an AI-powered tool that simplifies assessment grading for teachers. It efficiently grades both digital and paper-based assignments, providing quick and accurate results. Additionally, Gradescope offers valuable insights into students' knowledge levels across various subjects.

#11 AI summarization tools
👉 Example: Genei 

Genei leverages AI to accelerate research by automating time-consuming tasks. This app instantly summarizes PDFs and websites, saving students and researchers a significant amount of time. Additionally, Genei can provide concise and summarized responses to questions based on relevant resources.

#12 AI learning companions and personal tutors for individualized support
👉 Example: Duolingo Max 

Duolingo Max is a conversational AI for learning languages that leverages GPT-4. Learners can choose from two innovative features: Explain My Answer and Roleplay. These additions provide a deeper learning experience alongside the existing benefits of Duolingo. 

Generative AI examples in tourism and hospitality
How many hours did you spend planning your last trip? ✈️

Let me guess - quite a lot. 

While some people enjoy it, the majority find it to be a time-consuming and unenjoyable task.

Browsing through numerous pages, searching for locations, finding suitable restaurants, and dealing with car rentals, all while trying to organize a schedule around it, can be incredibly exhausting.

Fortunately, generative AI is poised to provide different solutions. 

While there are already some applications available, we anticipate a significant surge in development in the coming years. 

Here are some concrete use cases of generative AI in the tourism sector:

#13 AI apps for streamlining reservations and itineraries
👉 Example: Tripnotes.ai 

Tripnotes is a data-powered travel planner that simplifies, well… trip planning. Users can paste their travel inspiration from text messages, social media, or blogs, and the app automatically saves and researches each mentioned place leveraging generative AI.

#14 AI search
👉 Example: Microsoft Bing 

Microsoft Bing is an advanced search engine that incorporates cutting-edge AI technology. With its web, video, image, and map search functionalities, Bing offers a comprehensive search experience, and also includes real-time chat and co-creation features.

#15 AI business solutions for the travel industry
👉 Example: Bloomreach

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.

#16 AI for marketing
👉 Example: Runway 

Marketing tourist destinations and services requires a significant amount of multimedia content, with video being the most popular format at the moment. Fortunately, AI can assist with video editing. One example is Runway, which offers over 30 integrated AI tools to facilitate smooth and accessible video editing for everyone, regardless of their previous knowledge and video editing skills.

#17 AI chatbots for customer service and support
👉 Example: ChatBot  

ChatBot is an AI customer support tool that improves service by streamlining processes and offering support across various channels and languages. It leverages large language models to enhance the user experience with visual explanations and interactive forms.

#18 Virtual guides for personalized guided tours
👉 Example: P.A.D.D.Y 

P.A.D.D.Y. is an AI-powered tour guide created by a group of tour guides in Ireland. This multifaceted AI brings character to the experience of Ireland, tailoring it to individual interests and preferences.

Generative AI examples in marketing and advertising
From the Mad Men era to the age of the internet, social media, and hyperconnectedness, marketing has undergone a remarkable transformation. 

Traditional methods have been replaced by digital strategies, personalized messaging, and interactive experiences that businesses must navigate in order to connect and resonate with their target audiences.

The constantly evolving landscape demands an abundance of localized, niche, and relatable content – and this is where generative AI can play a crucial role:

#19 AI for content creation (text, images, video, audio…)
👉 Example: AI video generators 

Generative AI enables innovative methods of content creation. One such example is AI video generation. What used to be a physical process (cameras, actors, studios…) has now transitioned into a fully digital realm, making video creation convenient and accessible to all.

#20 AI for content repurposing
👉 Example: Jasper Campaigns   

Omnichannel marketing is at the core of today's marketing. This feature by Jasper enables users to create end-to-end marketing campaigns in their brand's tone and voice with a single brief. Based on the brief, the AI can generate as many assets as needed, including emails, case studies, Facebook and Google ads, press releases, and more.

image

                One generative AI example that can be used in marketing is Jasper Campaigns. With just a single brief, this AI can generate content tailored for various communication channels.
              
#21 AI for personalized marketing strategies
👉 Example: RAD AI 

RAD AI merges data-driven insights and authentic content to assist marketing teams in crafting impactful campaigns. By analyzing past performance and formulating effective strategies, it aims to establish genuine and emotional connections with the target audience across various marketing channels.

#22 Generative AI for content localization 
👉 Example: Lokalise AI  

Lokalise AI is an automated localization and translation platform designed for various applications, including web apps, customer service, documents, mobile apps, games, and marketing assets. With its advanced features like contextual translation, alternative variants, rephrasing, and concise adaptations, it enables seamless communication with global audiences in different languages.

Generative AI examples in finance and business
AI is also transforming the finance and banking sectors, making them more user-friendly than ever before. 

AI is providing a significant upgrade to banking operations by automating tasks that were previously performed manually, resulting in more efficient processes.

With the help of generative AI, we are witnessing exciting communication possibilities, including new and improved ways to present information, highly useful real-time AI assistants, and streamlined content optimization processes.

#23 AI solutions for business owners
👉 Example: Yooz 

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.

#24 AI for content optimization
👉 Example: AdCreative.ai 

AdCreative.ai is a generative AI app that quickly generates conversion-focused ad creatives and social media posts. With the ability to specify the target audience and platform, it selects the ideal message aligned with specific business goals.

#25 AI for personal finance
👉 Example: Cleo

Cleo, an AI money app designed for individuals, evolutionizes how people manage their financial lives. With a simple chat interface, Cleo assists users in saving money, budgeting effectively, and gaining financial knowledge.

#26 AI chatbots and virtual assistants for enhanced customer service
👉 Example: Boost.ai 

Boost.ai is an AI-powered conversation builder that delivers accurate responses to customers using advanced natural language processing and your customized training inputs. It seamlessly operates across various platforms, including websites, Slack channels, Zendesk, and Teams.

#27 AI-generated presentations
👉 Example: Tome 

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.

Generative AI examples in media and entertainment
In the dynamic landscape of media and entertainment, a clear trend is unfolding: a continuous evolution towards more immersive and interactive content. 

As our attention spans diminish, innovative content formats are surfacing to captivate audiences, such as concise tweets, engaging TikToks, and creative reels.

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:

#28 AI-based content personalization
👉 Example: BuzzFeed’s Infinity Quizzes 

One example of how media outlets can utilize generative AI for their content is BuzzFeed. In February 2023, they launched their first "Infinity Quizzes," which create personalized quizzes for users based on a few inputs.

#29 AI solutions for more immersive user experiences
👉 Example: My AI on Snapchat

Snapchat has recently introduced My AI, an AI chatbot that can answer users' questions and engage in conversations. Whether it's answering trivia questions, offering gift advice, providing trip planning assistance, or suggesting dinner options, My AI offers a personalized experience driven by AI.

#30 AI apps for scalable content creation
👉 Example: Canva

Canva is a design platform that offers AI-powered solutions for content creation. Through its AI capabilities, Canva streamlines the process of creating visual content by providing features for resizing, image and video editing, generating AI text to speech avatars, and converting text to images.

#31 AI for ideating different solutions, formats, concepts… 
👉 Example: Sudowrite

Sudowrite is an interactive AI writing assistant that offers valuable features like rewriting paragraphs in various styles, creative brainstorming, and character generation. Developed by writers, it provides an enjoyable user experience and produces remarkably human-like stories.

#32 AI-generated art
👉 Example: Midjourney

Midjourney is a cutting-edge image generator that transforms text descriptions into captivating images. With its advanced capabilities in generating intricate compositions, realistic edits, and incorporating diverse details, it is pushing the boundaries of visual art creation.

Want to know more about how generative AI is going to transform the media industry?

Check out this article: 

Generative AI examples in retail
Generative AI is redefining the way we shop and interact with brands, bringing convenience and efficiency to consumers while empowering retailers to deliver targeted marketing campaigns, optimize pricing strategies, and gain valuable insights into consumer behavior.

The possibilities that generative AI offers in the retail sector are unprecedented, and here are some of the notable examples:

#33 AI-generated product images
👉 Example: Lalaland

Lalaland transforms product creation for the fashion industry by eliminating the need for physical samples. Users can effortlessly select a model/avatar, apply their design, and generate the final image. The app provides diverse plans with options for various body sizes, hairstyles, body shapes, custom poses, and more.

#34 AI-generated mockups
👉 Example: Dall-E 

Dall-E is an AI image generator that creates images based on text descriptions. This means that a process that previously required a physical product can now be replaced by generative AI. It can generate hyper realistic images and mockups that are literally impossible to distinguish from actual photographs.

#35 AI-generated product descriptions
👉 Example: Copy.ai 

Catering to the diverse needs of marketers, this AI text generator proves to be a valuable asset for crafting various types of product descriptions. Whether it's for emails, product pages, Instagram, or ads, it covers a wide range of writing requirements.

#36 AI-powered customer service chatbots 
👉 Example: Conversica 

Conversica is an AI-powered solution that automates customer follow-ups and drives meaningful engagements. It seamlessly integrates with multiple tools commonly used in retail, such as Hubspot, Marketo and Salesforce.

#37 AI-supported search results for enhanced shopping experiences
👉 Example: Bard 

Bard, a conversational AI chatbot created by Google, is changing the shopping experience thanks to its interactive user interface. Available in three languages and accessible in over 180 countries and territories, Bard engages in natural conversations and fetches information from the web to assist users in making informed purchasing decisions.

Generative AI examples in manufacturing
Gone are the days of traditional manufacturing as we knew it. Today, the manufacturing industry is a vibrant and rapidly evolving landscape, where technological advancements and streamlined processes are revolutionizing production. 

One such advancement is generative AI, which brings forth multiple benefits. Here's how it can be used:

#38 AI-driven product development and design 
👉 Example: Midjourney 

Midjourney is an AI image generator that can create realistic images based on detailed text inputs. Manufacturers can utilize it to generate prototypes, quick mockups, and visualizations without the necessity of physical samples.

#39 AI apps for enhanced training and simulation
👉 Example: 3D simulation by Protostar.ai 

This AI app leverages extensive data collected from diverse sensors and sources to construct a digital replica of a facility or factory. By utilizing real-world information, it can create simulations that provide predictive insights into product performance and process outcomes.  

#40 AI for customer interactions and support 
👉 Example: Tidio 

Tidio is a customer support AI software that empowers small and medium-sized organizations with real-time chat, personalized recommendations, and task automation. It’s easy to set up and their basic plan is free to use.

#41 AI solutions for finding answers to complex issues
👉 Example: Wizdom.ai

Wizdom is an AI solution that analyzes vast amounts of data from the global research ecosystem to offer valuable insights for decision-making. With its comprehensive approach, it empowers users to make informed decisions and stay at the forefront of advancements in their field.

#42 AI apps for streamlined communications
👉 Example: Hyperwrite 

HyperWrite is a user-friendly online platform and Chrome extension that assists with copywriting, enabling users to refine their writing and enhance productivity. It can write different types of text, from emails to social media posts to long articles.

Generative AI examples in construction and real estate sector
The construction and real estate sector has experienced a substantial transformation in recent years. 

Through the integration of advanced technologies such as modeling, drones, and prefabrication methods, the industry has transitioned from traditional manual processes to a more efficient and digitally-driven approach. This shift has facilitated enhanced project management, cost control, and accelerated construction timelines.

However, the transformation does not end there - generative AI is another technology poised to make a tremendous impact in this field.

#43 AI for rendering
👉 Example: Vizcom 

It enables designers and architects to swiftly create and render designs with a multitude of options, including color, material, finish, and part-specific modifications. The result is faster and more versatile design iterations than ever before and thus better user experience for clients.

#44 AI chatbots
👉 Example: ChatGPT

ChatGPT is a state-of-the-art AI chatbot that utilizes natural language processing to generate human-like conversations. Users can participate in interactive dialogues, asking questions, seeking additional information, or even requesting alternative responses. Although ChatGPT's knowledge is based on data available until 2021, its exceptional accuracy is truly remarkable.

#45 AI design solutions
👉 Example: Maket.ai  

Maket is an AI tool that empowers architects, designers, builders, contractors, and developers in the residential industry. Its core feature is automated floorplan generation. Additionally, Maket assists users in navigating zoning codes and offers a wide range of styles to explore.

image

                Generative AI has numerous applications in construction and real estate. One example is Maket.ai, which helps architects and designers generate various visual styles and floor plans. 
              
#46 AI-powered communication and marketing solutions for the industry
👉 Example: NeuralText 

NeuralText is a versatile generative AI app equipped with three powerful features: Paragraph Generator, Content Outline, and Product Description. It also goes beyond content creation by assisting with SEO optimization.

Generative AI examples in agriculture
Despite the revolution in the agricultural industry, it continues to face challenges such as climate change, resource scarcity, market volatility, and labor shortages, to name just a few.

However, by harnessing advanced technologies, including the integration of generative AI, farmers can overcome these hurdles and achieve optimized crop production, efficient resource management, and sustainable practices.

#47 AI for agricultural education
👉 Example: Farmer.chat  

Farmer.CHAT is an AI-based farmer advisory service that connects governments and farmers for real-time communication. It provides data-driven insights and decision-making tools to optimize crop management, reduce waste, and increase yields. With a combination of documents, videos, and vetted data sources, Farmer.CHAT delivers actionable recommendations to farmers in India, Ethiopia, and Kenya.

#48 AI for marketing and content generation
👉 Example: AdCreative.ai  

This generative AI app can be used to create compelling ad creatives as well as organic social media posts. It’s very easy to use - based on target audience and platform preferences, the AI algorithm generates visuals and text in minutes.

#49 AI for streamlining business operations
👉 Example: Zia 

Zia is an AI-powered virtual assistant that provides a comprehensive suite of business support services. Zia helps users with many business-related tasks, including data gathering, insightful analytics, email translation, and proficient writing assistance.

#50 AI for decision support in farming
👉 Example: Semantic Scholar 

Semantic Scholar is an invaluable resource for researchers seeking expedited access to emerging scientific knowledge. With a comprehensive index of over 2 million academic research papers, this AI-powered application swiftly extracts key insights, enabling users to stay abreast of the latest trends in their respective fields.

Conclusion
In conclusion, it is evident that the generative AI landscape is flourishing with a wide range of tools catering to diverse industries. 

With new tools emerging daily, we will continue to monitor and expand our list to stay up-to-date in this dynamic realm of AI. \n \n Bài viết 3: Gartner Experts Answer the Top Generative AI Questions for Your Enterprise
Generative AI isn’t just a technology or a business case — it is a key part of a society in which people and machines work together. \n What is generative AI?
Generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs.  

Generative AI uses a number of techniques that continue to evolve. Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. 

Today, generative AI most commonly creates content in response to natural language requests — it doesn’t require knowledge of or entering code — but the enterprise use cases are numerous and include innovations in drug and chip design and material science development. (Also see “What are some practical uses of generative AI?”)

Download Now: A Workbook for Planning Your GenAI Strategy

The Journey to Generative AI
What’s behind the sudden hype about generative AI?
Gartner has tracked generative AI on its Hype Cycle™ for Artificial Intelligence since 2020 (also, generative AI was among our Top Strategic Technology Trends for 2022), and the technology has moved from the Innovation Trigger phase to the Peak of Inflated Expectations. But generative AI only hit mainstream headlines in late 2022 with the launch of ChatGPT, a chatbot capable of very human-seeming interactions.

ChatGPT, launched by OpenAI, became wildly popular overnight and galvanized public attention. (OpenAI’s DALL·E 2 tool similarly generates images from text in a related generative AI innovation.)

Gartner sees generative AI becoming a general-purpose technology with an impact similar to that of the steam engine, electricity and the internet. The hype will subside as the reality of implementation sets in, but the impact of generative AI will grow as people and enterprises discover more innovative applications for the technology in daily work and life.

What are the benefits and applications of generative AI?
Foundation models, including generative pretrained transformers (which drives ChatGPT), are among the AI architecture innovations that can be used to automate, augment humans or machines, and autonomously execute business and IT processes. 

The benefits of generative AI include faster product development, enhanced customer experience and improved employee productivity, but the specifics depend on the use case. End users should be realistic about the value they are looking to achieve, especially when using a service as is, which has major limitations. Generative AI creates artifacts that can be inaccurate or biased, making human validation essential and potentially limiting the time it saves workers. Gartner recommends connecting use cases to KPIs to ensure that any project either improves operational efficiency or creates net new revenue or better experiences.

In a recent Gartner webinar poll of more than 2,500 executives, 38% indicated that customer experience and retention is the primary purpose of their generative AI investments. This was followed by revenue growth (26%), cost optimization (17%) and business continuity (7%).

Primary Focus of Generative AI Initiatives
What are the risks of generative AI?
The risks associated with generative AI are significant and rapidly evolving. A wide array of threat actors have already used the technology to create “deep fakes” or copies of products, and generate artifacts to support increasingly complex scams.

ChatGPT and other tools like it are trained on large amounts of publicly available data. They are not designed to be compliant with General Data Protection Regulation (GDPR) and other copyright laws, so it’s imperative to pay close attention to your enterprises’ uses of the platforms. 

Oversight risks to monitor include:

Lack of transparency. Generative AI and ChatGPT models are unpredictable, and not even the companies behind them always understand everything about how they work.

Accuracy. Generative AI systems sometimes produce inaccurate and fabricated answers. Assess all outputs for accuracy, appropriateness and actual usefulness before relying on or publicly distributing information. 

Bias. You need policies or controls in place to detect biased outputs and deal with them in a manner consistent with company policy and any relevant legal requirements.

Intellectual property (IP) and copyright. There are currently no verifiable data governance and protection assurances regarding confidential enterprise information. Users should assume that any data or queries they enter into the ChatGPT and its competitors will become public information, and we advise enterprises to put in place controls to avoid inadvertently exposing IP. 

Cybersecurity and fraud. Enterprises must prepare for malicious actors’ use of generative AI systems for cyber and fraud attacks, such as those that use deep fakes for social engineering of personnel, and ensure mitigating controls are put in place. Confer with your cyber-insurance provider to verify the degree to which your existing policy covers AI-related breaches.

Sustainability. Generative AI uses significant amounts of electricity. Choose vendors that reduce power consumption and leverage high-quality renewable energy to mitigate the impact on your sustainability goals.

Gartner also recommends considering the following questions:

Who defines responsible use of generative AI, especially as cultural norms evolve and social engineering approaches vary across geographies? Who ensures compliance? What are the consequences for irresponsible use?​

In the event something goes wrong, how can individuals take action?

How do users give and remove consent (opt in or opt out)? What can be learned from the privacy debate?

Will using generative AI help or hurt trust in your organization — and institutions overall?

How can we ensure that content creators and owners keep control of their IP and are compensated fairly? What should new economic models look like? 

Who will ensure proper functioning throughout the entire life cycle, and how will they do so? Do boards need an AI ethics lead, for example?

Finally, it’s important to continually monitor regulatory developments and litigation regarding generative AI. China and Singapore have already put in place new regulations regarding the use of generative AI, while Italy temporarily. The U.S., Canada, India, the U.K. and the EU are currently shaping their regulatory environments.

Also see, “What are the best practices for using generative AI?” and “Should I craft a usage policy for generative AI?”

What are some practical uses of generative AI today?
The field of generative AI will progress rapidly in both scientific discovery and technology commercialization, but use cases are emerging quickly in creative content, content improvement, synthetic data, generative engineering and generative design. 

In-use, high-level practical applications today include the following.

Written content augmentation and creation: Producing a “draft” output of text in a desired style and length

Question answering and discovery: Enabling users to locate answers to input, based on data and prompt information

Tone: Text manipulation, to soften language or professionalize text

Summarization: Offering shortened versions of conversations, articles, emails and webpages

Simplification: Breaking down titles, creating outlines and extracting key content

Classification of content for specific use cases: Sorting by sentiment, topic, etc.

Chatbot performance improvement: Bettering “sentity” extraction, whole-conversation sentiment classification and generation of journey flows from general descriptions

Software coding: Code generation, translation, explanation and verification

Emerging use cases with long-term impacts include:

Creating medical images that show the future development of a disease​

Synthetic data helping augment scarce data, mitigate bias, preserve data privacy and simulate future scenarios

Applications proactively suggesting additional actions to users and providing them with information

Legacy code modernization

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How will generative AI contribute business value?
Generative AI provides new and disruptive opportunities to increase revenue, reduce costs, improve productivity and better manage risk. In the near future, it will become a competitive advantage and differentiator. 

Gartner splits the opportunities into three categories.

Revenue opportunities
Product development: Generative AI will enable enterprises to create new products more quickly. These may include new drugs, less toxic household cleaners, novel flavors and fragrances, new alloys, and faster and better diagnoses.

New revenue channels: Gartner research shows that enterprises with greater levels of AI maturity will gain greater benefits to their revenue.

Cost and productivity opportunities
Worker augmentation: Generative AI can augment workers’ ability to draft and edit text, images and other media. It can also summarize, simplify and classify content; generate, translate and verify software code; and improve chatbot performance. At this stage, the technology is highly proficient at creating a wide range of artifacts quickly and at scale.

Long-term talent optimization: Employees will be distinguished by their ability to conceive, execute and refine ideas, projects, processes, services and relationships in partnership with AI. This symbiotic relationship will accelerate time to proficiency and greatly extend the range and competency of workers across the board.

Process improvement: Generative AI can derive real, in-context value from vast stores of content, which until now may have gone largely unexploited. This will change workflows.

Risk opportunities
Risk mitigation: Generative AI’s ability to analyze and provide broader and deeper visibility of data, such as customer transactions and potentially faulty software code, enhances pattern recognition and the ability to identify potential risks to the enterprise more quickly. 

Sustainability: Generative AI may help enterprises comply with sustainability regulations, mitigate the risk of stranded assets, and embed sustainability into decision making, product design and processes.

Which industries are most impacted by generative AI?
Generative AI will affect the pharmaceutical, manufacturing, media, architecture, interior design, engineering, automotive, aerospace, defense, medical, electronics and energy industries by augmenting core processes with AI models. It will impact marketing, design, corporate communications, and training and software engineering by augmenting the supporting processes that span many organizations. For example:

We believe that by 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today. Generative AI looks promising for the pharmaceutical industry, given the opportunity to reduce costs and time in drug discovery.

We predict that by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, up from less than 2% in 2022. Text generators like GPT-3 can already be used to create marketing copy and personalized advertising.

In the manufacturing, automotive, aerospace and defense industries, generative design can create designs optimized to meet specific goals and constraints, such as performance, materials and manufacturing methods. This accelerates the design process by producing an array of potential solutions for engineers to explore.

What are the best practices for using generative AI?
Technologies that provide AI trust and transparency will become an important complement to generative AI solutions.​ Also, executive leaders should follow this guidance for ethical use of LLMs and other generative AI models:

Start inside. Before using generative AI to create customer- or other external-facing content, test extensively with internal stakeholders and employee use cases. You don’t want hallucinations to harm your business.

Prize transparency. Be forthcoming with people, whether they be staff, customers or citizens, about the fact that they are interacting with a machine by clearly labeling any conversation multiple times throughout.

Do your due diligence. Set up processes and guardrails to track biases and other issues of trustworthiness. Do so by validating results and continually testing for the model going off course.

Address privacy and security concerns. Ensure that sensitive data is neither input nor derived. Confirm with the model provider that this data won’t be used for machine learning beyond your organization.

Take it slow. Keep functionality in beta for an extended period of time. This helps temper expectations for perfect results.
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Should I craft a usage policy for generative AI?
Your workforce is likely already using generative AI, either on an experimental basis or to support their job-related tasks. To avoid “shadow” usage and a false sense of compliance, Gartner recommends crafting a usage policy rather than enacting an outright ban. 

Keep the policy simple — it can be as streamlined as three don’ts and two do’s if using ChatGPT or other off-the-shelf model:

Don’t input any personally identifiable information.

Don’t input any sensitive information.

Don’t input any company IP.

Do turn off history if using external tools (like ChatGPT) that enable that choice.

Do closely monitor outputs, which are subject to sometimes subtle but meaningful hallucinations, factual errors and biased or inappropriate statements.

If the company is using its own instance of a large language model, the privacy concerns that inform limiting inputs go away. However, the need to keep a close eye on outputs remains.

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How will generative AI impact the future of work?
In business, many people are content creators of some kind. Generative AI will significantly alter their jobs, whether it be by creating text, images, hardware designs, music, video or something else. In response, workers will need to become content editors, which requires a different set of skills than content creation.

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. For example, your request for a data-driven bar chart might be answered with alternative graphics the model suspects you could use. In theory at least, this will increase worker productivity, but it also challenges conventional thinking about the need for humans to take the lead on developing strategy.

The net change in the workforce will vary dramatically depending on such factors as industry, location, size and offerings of the enterprise.

Where should I start with generative AI?
Many enterprises have generative AI pilots for code generation, text generation or visual design underway. To establish a pilot, you can take one of three routes:

Off-the-shelf. Use an existing foundational model directly by inputting prompts. You might, for example, ask the model to create a job description for a software engineer or suggest alternative subject lines for marketing emails.

Prompt engineering. Program and connect software to and leverage a foundational model. This technique, which is the most common of the three, allows you to use public services while protecting IP and leveraging private data to create more precise, specific and useful responses. Building an HR benefits chatbot that answers employee questions about company-specific policies is an example of prompt engineering.

Custom. Building a new foundational model goes beyond the reach of most companies, but it’s possible to tune a model. This involves adding a layer or proprietary data in a way that significantly alters the way the foundational model behaves. While costly, customizing a model offers the highest level of flexibility.
What do I need to buy to enable generative AI?
The costs for generative AI will range from negligible to many millions depending on the use case, scale and requirements of the company. Small and midsize enterprises may derive significant business value from the free versions of public, openly hosted applications, such as ChatGPT, or by paying low subscription fees. For example, OpenAI is currently $20 per user per month. However, free and low-cost options come with minimal protection of enterprise data and associated output risks.

Larger enterprises and those that desire greater analysis or use of their own enterprise data with higher levels of security and IP and privacy protections will need to invest in a range of custom services. This can include building licensed, customizable and proprietary models with data and machine learning platforms, and will require working with vendors and partners. In this instance, costs can be in the millions of dollars.

It’s also worth noting that generative AI capabilities will increasingly be built into the software products you likely use everyday, like Bing, Office 365, Microsoft 365 Copilot and Google Workspace. This is effectively a “free” tier, though vendors will ultimately pass on costs to customers as part of bundled incremental price increases to their products.

What does Gartner predict for the future of generative AI use?
Generative AI is primed to make an increasingly strong impact on enterprises over the next five years. Gartner predicts that:

By 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020.

By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.

By 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps. 

By 2026, over 100 million humans will engage robocolleagues to contribute to their work.

By 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop. This is not happening at all today.

Who are the major tech providers in the generative AI market?
The Generative AI marketplace is on fire. Beyond the big platform players, there are many hundreds of specialty providers funded by ample venture capital and a wave of new open-source models and capabilities. Enterprise application providers, such as Salesforce and SAP, are building LLM capabilities into their platforms. Organizations like Microsoft, Google, Amazon Web Services (AWS) and IBM have invested hundreds of millions of dollars and massive compute power to build the foundational models on which services like ChatGPT and others depend. 

Gartner considers the current major players to be as follows:

Google has two large language models, Palm, a multimodal model, and Bard, a pure language model. They are embedding their generative AI technology into their suite of workplace applications, which will immediately get it in the hands of millions of people.

Microsoft and OpenAI are marching in lockstep. Like Google, Microsoft is embedding generative AI technology into its products, but it has the first-mover advantage and buzz of ChatGPT on its side.

Amazon has partnered with Hugging Face, which has a number of LLMs available on an open-source basis, to build solutions. Amazon also has Bedrock, which provides access to generative AI on the cloud via AWS, and has announced plans for Titan, a set of two AI models that create text and improve searches and personalization.

IBM has multiple foundation models and a strong ability to fine-tune both its and third-party models by injecting data and retraining and employing the model.
Is this the start of artificial general intelligence (AGI)?
It depends whom you ask. 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.

The likely path is the evolution of machine intelligence that mimics human intelligence but is ultimately aimed at helping humans solve complex problems. This will require governance, new regulation and the participation of a wide swath of society.

Bài viết 3: Top Generative AI Industry Applications: An In-Depth Look \n \n The overall AI landscape took a significant turn with the arrival of powerful generative AI models, resulting in the mainstream adoption of automation. Consequently, generative AI has captured the attention of numerous organizations, prompting questions about its transformative capabilities, and more importantly, real-world use cases.

So, what are the most important generative AI applications today? How does this new-age technology operate? In this blog, we aim to answer these critical questions and provide a comprehensive overview of the applications of generative AI, its benefits, the reasons behind its rapidly-growing popularity, and more.

Let’s get started! Understanding generative AI
The advent of prominent generative AI tools like ChatGPT and Midjourney has prompted many to better understand what generative AI is. Moreover, generative AI applications and tools are empowering both organizations and individuals to automate tedious tasks, make better decisions, and streamline operations for maximum efficiency. Here’s a deeper look into generative AI, its benefits, models, known risks, and popular examples.

1. What is generative AI and how does it work?
Generative AI is a subset of AI that uses machine learning techniques like semi-supervised or unsupervised learning algorithms to create digital content like images, audio, videos, codes, or texts. This is done through training, where algorithms are supplied with large datasets of output/input examples to obtain patterns from the input that result in conclusions about the desired output.

Today, generative AI applications primarily involve generative AI models being trained to create content as responses to natural language requests. This doesn’t require any experience or knowledge of coding. In a nutshell, generative AI begins with prompts that could be texts, images, designs, audio, or any other input that the specific AI system can process. AI algorithms then return new content in response to the provided prompts.

2. Why is generative AI quickly gaining popularity?
The early versions of generative AI involved submitting data through APIs or other complicated processes. Developers often had to learn the ins and outs of specialized tools and write applications via Python or other languages.

However, late 2022 witnessed a surge in generative AI’s popularity, with the arrival of ChatGPT. One of the most prominent generative AI applications, OpenAI’s ChatGPT is a chatbot capable of highly human-like interactions. ChatGPT paved the way for the wide adoption of generative AI tools, where an increasing number of people and organizations started using these tools for various needs, from writing essays to transforming business operations.

Currently, Hootsuite reports 100 million+ Americans will use generative AI by 2024, and the number is predicted to reach 116.9 million by 2025. This raging popularity of generative AI is primarily due to the vast benefits it offers. Generative AI applications are designed to enhance customer experiences, expedite product development, boost employee productivity, deploy customized and innovative content, and more.

3. The known risks of generative AI
As beneficial as generative AI is, it also comes with major limitations due to the technology being in its nascent years. Here are some of the most known risks of implementing generative AI:

Lack of transparency - Generative AI models can be unpredictable in their responses, and those using such models may not always understand how they or applications based on these models operate. This lack of transparency makes generative AI applications a bit troublesome to work with.
Biased responses - When using applications of generative AI or generative AI models, it’s important to have controls or policies in place to detect biased responses to ensure their appropriate usage. For instance, if a business is using a generative AI platform, it must have controls to detect biased outputs to let employees deal with them in a manner aligned with the business policies.
Inaccuracy - Generative AI models, still in the early stage of development, often produce fabricated and inaccurate responses. Hence, when implementing them for generative AI use cases, one must assess responses for appropriateness, usefulness, and accuracy before relying on them or distributing the information.
Privacy concerns - Generative AI models may also inadvertently memorize and recreate content around private or sensitive information from their training data. This can compromise individual security and privacy and is a major concern for enterprises implementing generative AI applications.
Intellectual property (IP) issues - Generative AI models can also be trained on proprietary or copyrighted data. If so, there could be ethical and legal concerns regarding the ownership and usage of the generated content.
4. The most popular examples of generative AI
While generative AI has its drawbacks, its benefits and future potential far outweigh them. Some of the recent generative AI applications have proven how this new-age technology can help with innovation and creativity, indicating usability for both businesses and individuals. Some of the popular examples of generative AI are the following applications:

ChatGPT- One of the major drivers behind the worldwide popularity of generative AI applications, ChatGPT is a chatbot built by the Microsoft-backed AI research organization, OpenAI. This AI-powered chatbot took the world by storm, thanks to its human-like responses, starting with OpenAI’s GPT 3.5 implementation. Now, GPT-4 has been released, offering a more seamless interface with better AI capabilities for more accurate responses. ChatGPT’s massive popularity earned it Microsoft’s investment - a significant one - and the tech giant even incorporated GPT into its Bing browser.
DALL.E - One of the first generative AI tools to be widely adopted, DALL.E is another OpenAI creation, built through GPT implementation. DALL.E is a multimodal AI application, trained on a vast amount of images and their text descriptions, that can identify connections across various media like text, audio, and vision. In this case, DALL.E connects words to visual elements, meaning it can generate images from user prompts.
Bard- Google has also been an early leader in facilitating transformer AI models for various types of content, including processing language. However, Google never released a public interface for AI models, until Microsoft used GPT in Bing, which prompted Google to also launch its own chatbot, Google Bard. Bard’s initial run was devastating due to overall AI platforms’ erratic behavior and inaccurate responses. However, Google has since released a new Bard version, built on PaLM 2 (Google’s most advanced LLM) which allows the chatbot to have higher efficiency and offer more visual responses to prompts.
Midjourney- Midjourney is another prominent example of generative AI that generates images from natural language prompts. While Midjourney is one of several machine learning-based image generators to have emerged recently, it has quickly become one of the most preferred generative AI applications alongside DALL.E. One of the biggest reasons behind this is Midjourney’s ability to generate high-quality images from simple text prompts, allowing lesser-experienced users to easily access excellent images for digital use.
Key generative AI applications
While chatbots like ChatGPT and Google Bard have quickly risen in popularity, there are other generative AI use cases that are becoming prominent. Here are some of the most significant applications of generative AI that are being widely implemented today.

Key_Generative_AI_Applications

1. Image generation and manipulation
One of the most common use cases of generative AI is image generation, which is typically text-to-image conversion. Here, users can enter a textual prompt describing what type of image they want, and the AI tool will process the input to generate realistic images. When using such generative AI applications, users can specify subjects, styles, settings, locations, or objects to generate the exact images as per their requirements.

Apart from text-to-image AI applications that generate realistic images or 3D models, there are tools that facilitate image enhancement and manipulation, letting users modify existing images. Some of the major functions such tools can perform are:

Semantic image-to-image translation - Creating realistic versions of an image based on semantic photos or sketches.
Image completion - Generating missing portions of an image, such as filling in backgrounds with objects, people, or other elements. AI tools with this capability can also fix torn photographs or fill in missing pixels.
Image super-resolution - Enhancing the resolution of images without any pixel-tear or other aspects that can cause loss of detail.
Image manipulation - Altering or modifying existing images. For example, users can transform an external element of an image, like its color, lighting, form, or style, while maintaining its original elements.
2. Software and coding
Generative AI applications have already begun transforming the software development and coding landscape through innovative solutions that streamline coding. Hence, software and coding have quickly become one of the most prominent use cases of generative AI, as its applications hold the potential to improve code quality, enhance productivity, and even spark new software innovation avenues.

Here’s how applications of generative AI are impacting software and coding:

Code generation - One of generative AI’s most prominent applications in software development is code generation. This involves training AI models on vast repositories of existing code, allowing them to generate code functions, snippets, or even entire programs based on prompted requirements. Code generation through generative AI applications proves invaluable in accelerating software development by automating repetitive coding tasks, and letting developers focus on problem-solving and higher-level design.
Code completion - Generative AI can also boost coding efficiency by offering intelligent code completion and suggestions. IDEs (integrated development environments) can leverage generative AI models to predict future code lines that developers may write based on the context, expediting the coding process and reducing the possibility of error.
Natural language interfaces for coding - Generative AI also enables natural language interfaces to code, allowing a developer to interact with software systems through human language instead of programming languages. Many organizations implement this through generative AI applications to bridge the gap between domain experts and developers. In turn, this helps to save resources on hiring experts to tackle software systems, by simply letting developers do it.
Automated testing - Generative AI-powered tools can automate test case and scenario generation which is generally quite time-consuming during a software development lifecycle. Such tools analyze code and its probable execution paths to generate comprehensive test suits, thus enhancing code coverage and allowing developers to identify potential bottlenecks early on.
3. Video creation
Generative AI applications also simplify video production through highly flexible and efficient features that generate high-quality video content. Using generative AI models, applications can automate tedious tasks like video compositions, and animations, adding special effects, editing video snippets, etc. Like image generation, generative AI tools for video production can create videos from scratch, which can be used for enhancing video resolution, video manipulation, and completion.

Video generation AI tools can also perform:

Video style transfers - AI video tools with this feature can generate new videos that follow the same style as another reference image or video.
Video predictions - AI tools with this capability can predict the next frames in a video, using generative AI models. Such tools understand a video’s spatial and temporal elements, producing future sequences based on that data.
Besides video generation, generative AI applications are also helpful for 3D shape generation, where they’re used to build 3D models and shapes through generative models. AI tools achieve this through techniques like autoregressive models, GANs (generative adversarial networks), and VAEs (variational autoencoders). This is especially helpful when creating highly-detailed shapes which may not be possible when manually creating a 3D image.

4. Audio generation
Another one of the widely implemented generative AI use cases is audio generation, where generative AI is used to expedite the process of creating audio. There are three major use cases under this category, which are:

TTS generators - GAN-based TTS (text-to-speech) generators can generate realistic speech audio from a user’s textual prompts. TTS AI tools use extensive text and speech data to train machine learning models, which can then be tweaked to create high-quality audio from text. Moreover, such tools are often used in applications like speech-based interfaces, speech-enabled devices, and assistive technologies.
Creating music - Making music has proven to be one of the most common generative AI applications today. Generative AI models can easily produce new music pieces and generate complete audio by learning the styles and patterns of the music a user inputs.
STS conversions - STS (speech-to-speech) conversions involve generative AI creating new speech or voices via existing audio files, which is commonly implemented in audio-related AI applications. STS conversions have become massively popular in the gaming and filming industries, where professionals use AI tools with STS conversion capabilities to seamlessly create voiceovers.
5. Text generation and summarization
ChatGPT is one of the best examples of text-generative AI tools that creates and summarizes textual content from user prompts. Such tools utilize generative AI models and are trained on large data sets to generate updated and authentic content. Listed below are some of the most common use cases of generative AI applications used for text generation and summarization:

Content creation - Generative AI models are extremely helpful in creating various types of written content, from blogs to marketing posts and social media copies. Plus, generative AI applications like ChatGPT also speed up the writing process by generating ideas, quotes, content outlines, etc.
Language translation - AI developers can also fine-tune generative AI models for translation tasks, where the models can analyze texts in one language and provide accurate translations in another.
Virtual assistants and chatbots - Generative AI powers virtual assistants and chatbots, letting them generate contextually relevant and natural responses in real-time user conversations. Creating chatbots like ChatGPT has become one of the biggest generative AI use cases. Such chatbots enhance user engagement and help businesses offer personalized assistance.
Content aggregation - In addition to text creation, generative AI tools can automatically summarize bulk texts like research papers, news articles, blogs, and lengthy emails to help users get a concise overview of the content. This also includes document summarization that helps businesses streamline document-related tasks using generative AI models.
Automatic report generation - In business intelligence and data analysis, generative AI can help summarize complex datasets and generate detailed reports. This simplifies decision-making and allows concerned stakeholders to better understand trends, patterns, and insights.
6. Organizational collaboration
The latest advancements in generative AI applications have also led to businesses achieving better team collaborations. Personal productivity tools like word processing and email can now be augmented via automation to boost the accuracy and efficiency of users, i.e., organization members.

An excellent example of generative AI’s collaboration enhancement capabilities is Microsoft implementing GPT-3.5 in Teams Premium, which uses AI to enhance meeting recordings. It automatically divides a recording into sections, generates titles, and adds personalized markers for better reference.

Another notable example is the wildly popular startup, Jasper.ai. This generative AI-powered can be used to automate tedious writing tasks, as its powerful automation capabilities allow it to generate complete texts for various purposes, from job descriptions to marketing copies, and more.

7. Chatbot performance improvement
While chatbots are one of the most prominent generative AI applications, the technology also contributes to enhancing chatbot performance and abilities. In turn, this helps to facilitate more engaging and effective interactions between chatbots and users, which is primarily possible through generative models and NLP (natural language processing).

Here’s how generative AI is currently implemented for chatbot performance improvement:

NLU enhancement - Generative AI models help enhance a chatbot’s natural language understanding (NLU). Training AI models on vast amounts of text data enables them to learn intricate language patterns, context, and nuances. This allows chatbots to better understand user inputs, accurately extract intent, and determine entities.
Human-like response generation - One of the biggest benefits of generative AI implementation is allowing chatbots to generate human-like text. This has also become one of the most common generative AI applications, where it’s used to train a chatbot on a diverse range of conversations to learn how a human expresses themself. In turn, this helps the chatbot generate natural, conversational, and tailored responses.
Handling open-ended prompts - Traditional rule-based chatbots often struggle with unfamiliar topics or open-ended user queries. Generative AI empowers chatbots to better handle such user inputs, even those they aren’t specifically programmed for. This elevated flexibility is achieved through training AI models on vast conversational data, enabling a chatbot to generate plausible responses to a wider range of queries.
User profiling - One of the most transformative generative AI applications has been implementing this technology to facilitate chatbots creating user profiles. Using generative AI, chatbots can analyze past conversations to understand user preferences and establish a user profile based on them. This helps chatbots to tailor responses and recommendations to users, offering a highly personalized experience and better user engagement.
8. Enterprise search
Lastly, one of the most recent generative AI use cases has been the enterprise implementation of this technology for streamlined search. Using generative AI, organizations can access information faster, as such AI models can be trained to securely read through all organizational documentation, like contracts, research reports, business trend analysis, and so on. Moreover, developers can train generative AI models to automatically highlight the important sections of a document and allow enterprise members to quickly access the information they need.

To sum up
Generative AI has shifted far away from being a mere advanced tech concept. Today, developers and organizations are actively implementing this technology to create generative AI applications 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.

Turing’s generative AI development services are driven by in-depth expertise and continuous innovation that help us offer tailored solutions. Our team of AI experts leverages vast industry experience to ensure business transformation by harnessing the true potential of generative AI, aligned with customer needs.

1000+ fast-scaling startups and Fortune 500 companies have trusted Turing for their engineering needs and business transformation, and so can you. Talk to an expert today and get tailored solutions designed for rapid business transformation. \n Bài viết 5: Top Applications of Generative AI for Individuals and Businesses \n \n Generative artificial intelligence (GenAI) is not just a passing trend; it’s a transformative tool with vast practical applications. From creating new product designs to optimizing business processes and even creating entire virtual worlds, its potential is boundless.

The impact of generative AI solutions on the AI landscape is profound and extensive, and we’re only beginning to uncover its full potential. Gartner predicts that by 2026, over 100 million people will rely on this technology to streamline their work processes. McKinsey’s analysis also suggests that its widespread implementation could contribute a staggering $2.6 trillion to $4.4 trillion to the global economy.

Curious to learn more about GenAI? Keep reading to discover all the basics – from the definition and mechanisms to applications of generative AI and more.

What Is Generative AI?
Generative AI is a subset of artificial intelligence that harnesses machine learning methods like unsupervised learning algorithms to create new and unique content. In response to your prompts, you can create images, videos, music, speech, synthetic data, and software code. For example, in unsupervised learning, the model learns from datasets without labeled outputs, autonomously identifying patterns and structures. In the end, GenAI’s ultimate goal is to employ these models to analyze input data and produce novel content based on it.

Chances are you’ve come across at least one of the most prominent generative AI tools today, including ChatGPT, Google Gemini, Microsoft Copilot, and DALL-E.

How Does Generative AI Work?
Generative AI uses machine learning techniques, particularly a subset called deep learning. Basically, deep learning utilizes artificial neural networks, which mimic the structure and function of biological neural networks found in animals’ brains.

What sets deep learning apart is its capability to learn semi-supervised and unsupervised. This means that deep learning models can sift through vast amounts of unlabeled data with minimal human oversight. For instance, the model dissects and analyzes data independently in unsupervised learning, seeking patterns without predefined instructions. These foundational models serve as the groundwork for generative AI applications.

Via deep learning, generative AI models spot patterns within human-generated content, learn, and reproduce them.

Among the most common generative AI models are generative adversarial networks (GANs), transformer-based models, variational autoencoders (VAEs), and large language models (LLMs).

Generative AI offers a wide range of applications, from creating videos and audio to crafting text and writing code. Its versatility benefits both individuals and businesses across various domains.

General Generative AI Applications
General Generative AI Applications

Visual Content Applications
1. Image Generation and Enhancement
Generative AI solutions predominantly operate through text-to-image mechanisms. Users input descriptive text, specifying subjects, styles, objects, or locations, and the tool creates realistic images matching those descriptions.

Beyond text-to-image tools, there are options for image enhancement, offering functionalities like:

Image completion: Generative AI tools can fill in missing parts of images, like creating realistic backgrounds or fixing torn photographs.
Semantic image-to-photo translation: This involves generating photorealistic versions of images based on sketches or semantic descriptions.
Image manipulation: Users can modify existing images, altering elements like style, lighting, color, or form while retaining the original essence.
Image super-resolution: These solutions can increase the resolution of images without losing detail, improving the quality of images like those captured by CCTV cameras.
2. Video Creation
Generative AI models can also help streamline video production by providing efficient and flexible tools to generate high-quality video content. It automates time-consuming tasks like composing videos, adding special effects, and creating animations. Similar to image generation, these AI tools can create videos from scratch, manipulate existing videos, enhance video resolution, and complete incomplete videos.

Other advanced features of GenAI when it comes to video creation are:

Video prediction: This involves predicting future frames in a video, such as the movement of objects or characters. Once generative models understand a video’s temporal and spatial elements, they will be able to produce the next sequences based on that information and distinguish between probable and improbable sequences.
Video style transfer: AI video generators can create a new video that mimics the style of another video or a reference image, allowing for creative and consistent visual presentations.
3. 3D Shape Generation
Generative AI capabilities also excel at creating intricate 3D shapes and models. It uses various techniques, such as VAEs, GANs, autoregressive models, and neural implicit fields.

These AI systems are particularly useful for generating detailed shapes that would be difficult to produce manually. Moreover, they enhance the efficiency and performance of 3D-based tasks, including 3D printing, 3D scanning, and virtual reality applications.

Audio-Related AI Applications
1. Creating Music
You may not notice, but the generative AI model is a powerful tool for producing new music compositions. By analyzing patterns and styles from existing music, these genAI-based tools can yield new pieces suitable for different creative endeavors like advertisements. However, pay attention to copyright infringement since it can be a big issue when copyrighted music is used in the training data.

2. Text-to-Speech (TTS) Generators
Text-to-speech generators, often based on GANs, can transform written text into realistic speech audio. Basically, they use discriminators to refine the voice output, ensuring it sounds natural and expressive. Plus, being trained on extensive speech and text datasets, TTS models can then produce high-quality speech. These solutions are widely used in speech-enabled devices, voice-based interfaces, and assistive technologies, making them highly versatile.

Some TTS models allow us to export human-like speech audio depending on the nuance of the sentence. Then, they will translate the audio into other languages, too.

3. Speech-to-Speech (STS) Conversion
Generative AI is able to convert one speech style to another using existing audio files. This capability proves especially beneficial in the gaming and film industries, where professionals can quickly and efficiently create voiceovers.

Just by leveraging STS conversion, they can produce a variety of voices without needing multiple voice actors, thereby saving great time and resources.

4. Speech-to-Text Models
Have you ever told Siri to make a call automatically or ask Alexa for the latest weather updates? It’s about the magic of speech-to-text technology. These models enable users to input their voice to make requests, and then they provide responses instantly. Some common STT applications include GPS systems for spoken direction guidance, public announcements at airports or railway stations, or telecommunication for reading out text messages.

Text-based Applications
Generative AI platforms like ChatGPT have gained immense popularity since their launch. These platforms excel at content creation in various formats. You can write articles, website pages, and dialogues, summarize content, translate text to other languages, and even complete a sentence. Gemini, in particular, takes the text generation application to the next level by providing multiple versions of an article or email for you to choose from.

Apart from new content creation, text-generative AI tools are adept at handling numerous language-related tasks. They can answer questions, complete unfinished texts, categorize text into different groups, rephrase and enhance content, and engage in human-like conversations on a wide range of topics.

In general, here are some key areas where generative AI models for text generation can be leveraged:

Creative writing: Generative AI can craft fictional pieces such as stories, song lyrics, or poems, making it a valuable tool for writers and artists seeking inspiration or assistance.
Conversational agents: These models are used to develop virtual assistants and chatbots capable of automatically responding to user inputs and maintaining natural, fluid conversations.
Language translation: Generative AI models can quickly and accurately translate text from one language to another, making communication across language barriers much easier.
Marketing and advertising: In marketing, generative AI can create product descriptions, ad copy, social media posts, and catchy slogans, enhancing the efficiency and creativity of marketing campaigns.
Code-based Applications of Generative AI
Generative AI use cases also extend to software development when it assists in code generation processes, thus reducing developers’ workload and streamlining the software creation process.

Overall, these models are capable of the following:

Code completion: Generative AI models are great at analyzing the context of code snippets to suggest the next line of code, facilitating seamless code completion.
Code generation: Leveraging natural language processing capabilities, generative AI models can understand text prompts and translate them into executable code. No matter the programming languages you require, it can generate code without hassles.
Test case generation: Generative AI can create comprehensive test cases to evaluate software functionality, ensuring the software performs as expected and meets quality standards.
Automated bug fixing: Developers can input code into generative AI models like GPT, which then identifies and fixes bugs automatically, reducing manual debugging efforts.
Model integration: Using generative AI, developers can seamlessly integrate machine learning models into their software based on specific requirements, such as neural networks or decision trees, enabling rapid implementation and deployment.
Industry-specific Generative AI Applications
Industry-specific Generative AI Applications

Generative Artificial Intelligence finds its footing in diverse sectors like healthcare, marketing, and finance.

Let’s see how professionals in these industries harness the power of generative AI in their respective fields.

1. Healthcare Applications
Generative AI is expected to revolutionize the healthcare and pharmaceutical industries, offering a host of solutions from drug discovery to personalized patient treatment plans and predictive disease imaging.

Here’s a closer look at its potential applications in healthcare:

Enhancing medical images: Generative AI can improve medical imaging, from augmenting X-rays and MRIs to synthesizing and reconstructing images. Predictive models can even generate images to illustrate disease progression over time.
Discovering new drugs: Researchers apply generative AI algorithms, often through generative design, to explore and develop innovative medicines. According to Gartner, by 2025, around 30% of newly developed drugs will make use of generative design principles.
Streamlining patient documentation: Generative AI is also a big help since it can streamline documentation tasks by summarizing patient information, transcribing verbal notes, and extracting important details from medical records with greater efficiency than manual efforts.
Personalized treatment plans: Generative AI analyzes vast patient data, including medical images and genetic testing results, to tailor treatment plans to individual patient needs, optimizing healthcare outcomes.
Telehealth and remote patient monitoring: In the age of telehealth, generative AI supports remote patient monitoring by analyzing real-time health data from wearables and other devices. This enables healthcare professionals to monitor patients’ well-being remotely and intervene promptly when necessary, ensuring seamless continuity of care.
2. Advertising and Marketing
Generative AI and natural language processing provide numerous innovative solutions for professionals in advertising and marketing, enhancing how they create relevant content and interact with customers:

Generating marketing text and images: Generative AI assists marketing professionals in crafting consistent, on-brand text and images for campaigns. This technology also provides language translation tools to expand marketing messages into new markets. Gartner predicts that generative AI will be used to create 30% of outbound marketing materials by 2025.
Producing personalized recommendations: Generative AI fuels powerful recommendation engines, helping customers discover products tailored to their preferences. This interactive process enhances customer engagement and satisfaction.
Creating product descriptions: Generative AI alleviates the burden of creating new product descriptions, providing businesses with efficient solutions to make engaging and informative content for countless products.
Enhancing search engine optimization: SEO specialists use generative AI for optimizing image tags, crafting page titles, and generating content drafts. Tools like ChatGPT or Google Gemini can even recommend changes to improve SEO rankings, streamlining their optimization process.
3. Finance and banking applications
Generative AI has the potential to transform the finance and banking industries, potentially adding $200 billion to $340 billion of value annually, according to McKinsey. Indeed, Fintech companies and banks can adopt generative AI solutions to automate repetitive tasks, boost productivity, and make more informed decisions.

Here are some key potential applications:

Real-time fraud detection: Generative AI can inspect vast amounts of transaction data to detect and intercept fraudulent activities. By identifying patterns and anomalies, it can effectively prevent fraud before it affects customers.
Customized banking experiences: Via customer data visualization, generative AI can offer personalized financial advice, product recommendations, and tailored services, helping improve customer satisfaction and engagement in the banking sector.
Generative AI for credit scoring: GenAI models can also analyze various data points, such as income, employment history, and credit history, to accurately predict an individual’s or entity’s creditworthiness, improving the credit scoring process.
Risk management and fraud detection: Generative AI aids in managing credit, market, and operational risks by examining historical data to identify patterns and anticipate future risks, enhancing overall risk management strategies.
Robotic process automation: Generative AI automates repetitive tasks like data entry and compliance checks, ultimately boosting efficiency and reducing operational costs for financial institutions.
Unlock the true potential of generative AI with Neurond
Generative AI tools are reshaping our world in the near future, driving innovation across diverse industries and applications. From the creative fields of art and music to the precision-focused sectors of healthcare and finance, generative AI’s versatility expands the boundaries of what we can achieve. This technology is not just about automating tasks; it serves as a powerful catalyst for innovation, enabling us to solve complex problems and devise once-unimaginable solutions.

At Neurond, our generative AI consultant services are fueled by deep expertise and relentless innovation, allowing us to deliver customized solutions to organizations around the world. Our team of AI experts leverages extensive industry experience to drive business transformation, harnessing the full potential of generative AI and natural language processing in alignment with customer needs.

Contact us today to explore how generative AI can propel your business forward with rapid transformation!

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