Trending December 2023 # Top 4 Use Cases Of Ai In Fashion In 2023 # Suggested January 2024 # Top 13 Popular

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Like every other sector, AI is also changing the fashion industry by offering solutions to various challenges. The global market for AI in the fashion sector was reported at $270M in 2023 and is projected to grow to $4.4B by 2027.

This article explores top AI use cases in the fashion industry to help business leaders in the sector learn where AI can be implemented in their businesses.

AI for design

Most companies in the fashion sector rely on clothing designs made manually. However, creative AI can be an effective way to take over in situations like the pandemic when people can not work. AI-enabled tools can create clothing designs by using data such as images from the brand’s previous offerings or from other designers, data regarding customers’ tastes (color and style choices), and current fashion trends.

Watch this video to see how the London college of fashion, amongst other institutions, is researching to find new ways to use AI for fashion design and production:

While extensive research is being done in this area, limited real-world applications of AI-enabled fashion designing can be observed, and all of the ones that exist are based on human-in-the-loop (HITL) models.

For example, the German fashion platform Zalando and Google created project Muze, which uses machine learning to create fashion designs. The model gathers data regarding customers’ favorite textures, colors, and style preferences by asking a series of questions to create clothing designs.

The project created 40,424 fashion designs within the first month.

However, some found the designs created by the model strange and unwearable (See the image below).

But, with generative AI growing and improving at the speed of light, the design will soon be practical and considerable.

Improved production

Currently, the apparel manufacturing sector mostly relies on manual production processes with questionable working conditions for the workers. However, AI-enabled solutions are changing these trends by enabling automation in the apparel production sector.

AI can help overcome these ethical challenges by enabling automation. For instance, robotics can help automate risky or error-prone tasks in a manufacturing facility to decrease workload and improve worker safety. Companies like Sewbo and Softwear are revolutionizing clothing production by developing automated garment-producing machinery.

Moreover, computer vision enabled with AI also has various applications in fashion production, including efficient quality assurance and predictive maintenance of equipment which reduces the downtime of the machines and ensures operational continuity.

For more on AI training data collection, feel free to download our free whitepaper:

Trend forecasting

Fashion trend forecasting is the process of predicting possible future fashion trends. Traditionally, fashion trend forecasters combine their fashion knowledge, intuition, and historical data to predict possible fashion trends. However, measuring the accuracy of trend forecasts is difficult, and you can not know how accurate they are.

In the current digital era, AI is being used to accurately predict fashion trends using different types of data. For instance, the fashion tech company Heuritech developed an AI-enabled service to predict fashion trends by analyzing millions of social media images.

Watch the video to learn more:

Trend prediction can also be used to reduce wastage in the fashion and clothing sector by designing clothes people would actually want to wear. More accurate predictions can lead to leaner production and distribution cycles and less waste.

Improved fashion retail

AI-enabled technologies are widely used in fashion retail. The applications include:

Intelligent automation of repetitive back office tasks such as invoice creation can be automated.

AI-enabled computer vision systems can enable inventory management automation, retail theft prevention, cashierless automated stores, etc.

RPA also has various applications in retail, including improved customer relationship management and marketing operations.

Watch how H&M, one of the largest fashion retailers in the world, leverages AI to improve its operations:

Top 12 Sap Conversational Ai Use Cases & Applications In 2023

Business leaders are exploring conversational AI adoption in different departments due to its ability to facilitate processes and handle time consuming issues. SAP software which allows connection and collaboration across business departments can benefit from conversational AI capabilities.

The aim of implementing conversational AI in SAP is to make the user’s experience easier and simplify business interactions. Chatbot implementation in SAP enables better customer service, quick on-demand insights about business resources and data, facilitate issue solving, and simplify notification and alerts.

SAP Conversational AI solutions, which implement natural language processing, combine:

Using digital assistants to guide users through SAP processes

A chatbot building platform to build and test chatbots for different business purposes.

Conversational AI SAP solutions

System Applications and Products (SAP) is an Enterprise Resource Planning (ERP) software. SAP consists of business-specific models that allow users to collect and share data across business departments.

SAP digital assistant

SAP digital assistants leverage machine learning algorithms to allow the chatbot to learn from practice and become more contextually aware. Using a digital assistant within an SAP software enables:

Smooth collaboration across SAP modules (also called applications or models) by

providing faster access to information across business departments

Answering FAQs

Task automation by customizable workflows. Users can create custom chatbot intents to perform specific tasks

Better decision-making by providing operational insights

Personalized recommendations according to user behavior

Based on the user’s role/position, an SAP digital assistant can display reports, news, or alerts, as well as schedule meetings and invite different users to participate in a process.

Chatbot building platform

Chatbot building platforms allow users with minimal or no coding experience to build, train, and deploy chatbots. Nonetheless, in order to build a chatbot, the user must grasp the concepts of skills and intents which we explained in our article Intent Recognition in Chatbots in 2023.

SAP Conversational AI provides a platform to build or adapt end-to-end chatbots and integrate them with SAP ecosystem. The user can also use the platform to create chatbots from scratch to automate specific tasks in customer support, IT service, or purchasing. However, in order to build a chatbot, the user must grasp the concepts of skills and intents.

Leveraging SAP chatbot creating platform enables:

Faster chatbot creation, training, and deployment

Chatbot connection to multiple SAP solutions, external communication channels, or back-end systems.

Analysis of customers’ and employees’ communications to further improve users’ experience.

What are some conversational AI use cases in SAP?

Conversational AI can be implemented in the following SAP modules. We provide in-depth examples of their usage and briefly mention the business use cases these bots can serve. For more information on specific chatbot use cases, please refer to our articles on chatbot usecases by industry and by department.

IT department First level IT support

Many IT issues, that SAP users face, can be solved by simple solutions found in tutorials or SAP Help documents. However, users need the assistance of an IT professional to provide these documents and guidelines. Digital assistants in SAP systems can handle these simple tasks.

For example, MOD Pizza, an American fast casual pizza restaurant chain, used SAP CoPilot digital assistant to facilitate IT support processes. When a SAP system user requires IT support, the digital assistant provides links to tutorials or SAP Help documentations. The digital assistant can also direct the user to a live IT agent and send them information about the context of the problem, such as screenshots to facilitate problem-fixing.

Source: SAP user experience community

HR department

HR employees handle an enormous amount of employee information to which they may require access on the spot. An HR chatbot can retrieve employee information and visualize the data using SAP features. With this functionality, an HR chatbot can be used to answer:

FAQ on HR policies Complete HR requests like reserving Paid Time Off (PTO) Guide users through onboarding

For example, Nestlé, the world’s largest food & beverage company, used SAP conversational AI platform to create an HR chatbot. The HR chatbot provides self-service access to HR department data, such as headcount or full-time versus part-time hire ratios, in a secure and consistent approach.

source: SAP conversational ai tutorial

Customer service

SAP users in customer support department need access to different documents and information when responding to customer inquiries. Implementing a chatbot in customer support enables:

Management of multiple inquiries at the same time Provide accurate responses about products and services

For example, Groupe Mutuel, a Swiss insurance company used SAP conversational AI platform to develop a chatbot that can:

Respond to customer’s inquiries 24/7, in French and German through the company website

Enable end-to-end process integration and self-service scenarios for insurance and health plan members

For more on chatbots uses in healthcare, feel free to read our article Chatbot Applications / Use Cases in Healthcare in 2023

Sales & marketing

The sales and marketing departments can leverage SAP to measure marketing campaign results, and automate different marketing processes, such as email responses after cold calls.

Conversational AI in SAP marketing models can:

Serve as SDR

Sales development representatives (SDR) are the ones who initially speak to customers and book a product demo in B2B context. The demo would be run by a more experienced sales rep.

Chatbots can handle these tasks. they can converse with customers, provide product/service information such as pictures, videos, or links and schedule appointments for demos or trials.

Serve as sales reps

In B2C e-commerce, bots can even book tickets and flights.

For example, Expedia, an online travel shopping company, utilizes a Facebook messenger chatbot to offer customers a 24/7 agents who can book and manage trips, as well as provide COVID-19 updates about travel restrictions and airport openings.

source: expedia facebook messenger

Supply chain management

SAP models for supply chain management (SCM) have the following features:

Ability to collect data about different supply chain resources such as warehouses, inventories, shipments, and storage places.

Ability to connect suppliers, customers, manufacturers, business partners and retailers in one platform

Include different planning applications related to Advanced Planning and Optimization APO

Include applications for supply chain networking, supply chain planning and coordination, and supply chain execution.

Chatbots in supply management SAP models can:

Provide instant and accurate data about SCM resources Process supply chain employees’ requests

Chatbots can process requests based on supply chain data, such as tracking numbers and customer ID

Manage orders

Chatbots can directly collect new orders from customers, manage old/cancelled/delayed orders, and automatically update the supply chain database.

What to expect in the future?

According to our chatbot / conversational stats, 31% of executives said that virtual assistants have the largest impact on their business. Additionally, 75-90% of queries is projected to be handled by chatbots by 2023. This data suggests that SAP software will depend on conversational AI heavily across multiple SAP business models in the future.

Furthermore, implementing RPA along with conversational AI into SAP applications can drive the automation process in enterprise resource planning to the point where the user will only has to ask the chatbot what the next step is, and have the RPA bot take care of it.

For more on RPA in SAP, feel free to read our article Top 5 RPA Use Cases / Application Domains in SAP in 2023

For more on conversational AI

To learn how conversational AI and chatbots work, read our articles about natural language understanding, and top 10 voice recognition applications and use cases

For more on conversational AI successes, failures and market, feel free to read the following articles:

For a comprehensive guide on voice AI and chatbots:

If you think your business can benefit from conversational AI, let our data-driven list of chatbot vendors and chatbot platforms can show you which vendors you can start talking to.

And if you have questions about how chatbots can help your business, we can help:

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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10 Gan Use Cases In 2023

Some highly technical use cases, such as modeling probabilistic distributions or sampling from an arbitrary distribution, may be better suited for other types of generative AI models like Variational Autoencoders (VAEs) or Generative Stochastic Networks (GSNs).

However, most of the popular generative AI applications under use are performed by GAN. In this article, we will explain 10 GAN use cases.

Top 10 GAN Use Cases 1- Image generation

setting

subject

style

location

Figure 1: Generated image of “a running avocado in the style of Magritte”

Source: DALL-E

2- Image to image translation

GANs creates fake images from input images by transforming the external features, such as its color, medium, or form, while preserving its internal components (see Figure 2). This can be used as a general image editing method.

Figure 2: An example of facial attribute manipulation

Source: “FAE-GAN: facial attribute editing with multi-scale attention normalization”

3- Semantic image to photo translation

Figure 3. An example of semantic image to photo translation.

Source: “Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs”

4- Super resolution

GANs can improve video and image quality (see Figure 4). It restores old images and movies by upgrading them to 4K resolution or higher, generating 60 frames per second rather than 23 or less, removing noise, and adding color.

Figure 4: GAN-based restoration of images.

Source: “Towards Real-World Blind Face Restoration With Generative Facial Prior”

5- Video prediction

understand the temporal and spatial elements of a video

generate the next sequence based on that understanding (as shown in the Figure 5)

differentiate between probable and non-probable sequences

Figure 5. Prediction results for an action test split. a: Input, b: Ground Truth, c: FutureGAN.

Source: “FutureGAN: Anticipating the Future Frames of Video Sequences Using Spatio-Temporal 3D Convolutions in Progressively Growing GANs”

6- Text-to-speech conversion

Text-to-speech conversion technology has various commercial applications, including:

Education

Marketing

Podcasting

Advertising

For instance, an educator can turn their lecture notes into audio format to make them more engaging, and this same approach can be used to create educational resources for those with visual impairments.

7- Style transfer

GANs can be used to transfer style from one image to another, such as generating a painting in the style of Vincent van Gogh from a photograph of a landscape (see Figure 6).

Figure 6. The cycleGAN generates designs in the style of different artists and artistic genres, such as Monet, van Gogh, Cezanne and Ukiyo-e.

8- 3D object generation

GAN-based shape generation allows for the creation of shapes that more closely resemble the original source. Also, it is possible to generate and modify detailed shapes to achieve the desired result. See the GANs-generated 3D objects in Figure 7 below.

Figure 7. Shapes synthesized by 3D-GAN.

Source: ”Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling”

The video below shows this process of object generation.

9- Video generation 10- Text generation

With the large language models, generative AI based on GAN model has a range of applications in text generation, including:

Articles

Blog posts

Product descriptions

These AI-generated texts can be used for a variety of purposes, such as:

Social media content

Advertising

Research

Communication.

In addition, it can be used to summarize written content, making it a useful tool for quickly digesting and synthesizing large amounts of information.

If you have questions about GAN or need help in finding vendors, feel free to reach out:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Chatgpt Education Use Cases, Benefits & Challenges In 2023

As it suddenly became popular on the internet and social media, teachers and lecturers across many educational levels are afraid of ChatGPT‘s possible negative impacts on academic integrity. They fear that the chatbot is going to increase plagiarism and spread academic dishonesty.

For academics in the Rochester University, ChatGPT and other similar AI tools are in the “yellow” category of higher education online educational tools, meaning students should be careful while using them.

OpenAI states that they are working on reducing such risks by developing a watermarking method to detect the outputs of the bot.

Whether this will be achieved successfully or not, it is a fact that ChatGPT is changing the traditional learning process and forcing everyone to rethink and remodel education besides other generative AI technologies. In this article, we gathered ChatGPT education use cases for teachers and students, and also explained its benefits and challenges to education.

Top 7 ChatGPT Education Use Cases For Teachers 1- Content creation

ChatGPT is a valuable tool to teach students effectively and engagingly. It can help teachers generate ideas for lesson plans, activities, and projects that align with specific learning objectives and curricular standards.

Also, teachers can use ChatGPT to create and curate educational materials, such as presentations, worksheets, quizzes, and other resources tailored to their students’ needs (Figure 1).

Figure 1. ChatGPT creates a quiz for a class

2- Grammar and writing check

Another use case of ChatGPT for teachers involves leveraging the AI’s natural language understanding capabilities to assist in evaluating and improving the quality of written work. This can be applied in various ways:

Proofreading and editing: Teachers can use ChatGPT to quickly review and correct spelling, grammar, punctuation, and syntax errors in their own written materials, such as lesson plans, handouts, or email communications.

Providing feedback to students: ChatGPT can assist teachers in reviewing student essays, reports, or other written assignments, identifying areas that need improvement and providing specific feedback on grammar, sentence structure, and word choice.

Teaching writing skills: Teachers can use ChatGPT to generate examples and explanations of grammar rules, punctuation usage, and other writing conventions, helping students improve their writing skills.

3- Grading

ChatGPT can assist teachers in reviewing and grading student essays by analyzing the content, structure, and coherence of the writing. The AI can provide feedback on grammar, spelling, punctuation, and syntax, as well as assess the quality of the argument or analysis presented. However, it is important to not rely merely on ChatGPT while grading. Rather, teachers can use ChatGPT for determining the rubric for grading.

4- Designing syllabus outline

Teachers can incorporate ChatGPT by leveraging the AI’s capabilities to help create, organize, and structure course content in a coherent and effective manner (Figure 2). ChatGPT can help in:

Preparing course objectives and goals

Topic generation

Lesson planning

Identifying and curating relevant resources and materials for the course

Figure 2. ChatGPT creates a course design on cellular biology

For Students 5- Help with homework

Answering questions: Students can ask ChatGPT questions about specific topics or concepts they are struggling with, and the AI can provide explanations, examples, or resources to help clarify their understanding (Figure 3).

Problem-solving: ChatGPT can increase the problem solving skills of students by guiding them through the steps of solving problems in subjects like mathematics, physics, or chemistry, helping them understand the underlying concepts and methods.

Concept reinforcement: ChatGPT can provide additional examples, analogies, and explanations to reinforce students’ understanding of complex concepts or topics they are studying.

Improving writing skills: Students can use ChatGPT to get suggestions for improving their essays, reports, or other written assignments, including feedback on grammar, sentence structure, word choice, etc. They can use the tool to write essays, craft the first draft of their written assignments, which can save time to improve the fine details and overall quality of the work.

Figure 3. ChatGPT explains some key concepts for a high school student

6- Research

ChatGPT can support and streamline the research process for various academic projects, assignments, or personal interests (Figure 4). It can facilitate many steps in a research process such as:

Topic selection

Background information about the topic

Identifying relevant resources

Organizing research

Citation assistance

Figure 4. ChatGPT provides creative ideas for a term project

7- Language learning

ChatGPT can be a valuable tool for language learning, offering translations, grammar explanations, vocabulary practice, and conversation simulations to help students practice and improve their language skills. Also, it can help students to schedule a program for improving their language skills (Figure 5).

Benefits of ChatGPT for Education Speed

Artificial intelligence tools like ChatGPT can process and generate information quickly, which can save time and increase efficiency for both teachers and students. It creates a valuable time for students to focus on other tasks by allowing the automation of instant feedback and quick access to information.

By assisting with tasks like

grading

lesson planning

content creation

ChatGPT can save time for teachers, allowing them to focus more on direct instruction and student support.

Availability

ChatGPT provides a readily available source of information and guidance, making it easier for students and teachers to access support when needed, regardless of time or location.

Personalized learning

ChatGPT can help create tailored learning experiences for students based on their individual needs, interests, and skill levels, allowing for more targeted instruction and improved learning outcomes.

Challenges of ChatGPT for Education

Although the chatbot has many capabilities, it also has some limitations and challenges. The video below shows how ChatGPT can fail in some educational tasks.

Here are some other potential challenges of ChatGPT for education:

Accuracy and reliability problems

ChatGPT may sometimes provide inaccurate or incomplete information, which could lead to misunderstandings or confusion for students and teachers.

Possible biases

AI technologies like ChatGPT can inherit biases from the data they are trained on, which may result in biased or unrepresentative content generation that could impact teaching and learning negatively.

Risk of decrease in original and critical thinking skills

The convenience and speed of ChatGPT might lead to an over-reliance on AI-generated content and reduce critical thinking, problem-solving, and creativity in the educational process.

If you have questions or need help in finding vendors, we can help:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Codium Ai: Pricing, Features And Use Cases

An AI-powered tool that analyzes programs and generates meaningful trivial and non-trivial tests for developers to write bug-free codes.

About Codium AI

Codium AI is the first ever AI platform that interacts with developers to generate tests and explain the code. It saves developers from spending hours developing test cases, allowing them to focus on writing efficient programs.

Codium AI Features

Codium AI delivers several incredible features, making it one of the top-picks of developers. Some popular features offered by this tool are as follows:

It shows a real-time view of the output as you modify the code.

It saves developers time by generating meaningful test cases faster.

The tests check the code’s functionality to ensure it is of high quality.

It only analyzes the code that is to be processed.

It has an active community with members all over the globe. You can share your work with the community or interact with them to troubleshoot issues.

It allows users to download test cases as an extension for automation.

Codium encrypts your data to ensure 100% privacy and safety.

Codium AI Use Case – Real-World Applications

Codium AI is an essential tool in the coding industry. Some applications of this tool include the following:

Software developers can use Codium AI to write programs faster.

Quality Assurance Testers can use Codium AI to detect bugs within a program.

Companies can use Codium AI to ensure their software works properly.

Codium AI Pricing

FAQs

Does Codium AI understand all programming languages?

Unfortunately, Codium AI supports a few programming languages. It is a relatively new platform, so the developers are adding more programming languages to it. Currently, you can use it for JavaScript, Python, and TypeScript. Java will be added to the platform in the coming months.

How will my code in Codium AI remain safe?

Codium AI is an SOC2 certified platform. It ensures 100% privacy and security for your information. All your data will be encrypted before being stored on the system. Also, it only processes the necessary code, not your entire program.

Is Codium AI 100% right?

Codium AI uses artificial intelligence and machine learning algorithms to analyze the code and generate tests. The platform is programmed with powerful technologies, but nothing is perfect. So, you cannot rely on the Codium AI suggestions entirely. You must verify the test generated by the platform. If you find a bug, you can report it to the team and help them improve the tool.

Is Codium AI free?

Yes, Codium AI is available for free right now. You can start using it by creating an account on its official website. However, the company has announced that it will launch a paid plan for enterprises in the future. Until then, you can use it for free.

Who is the founder of Codium AI? Who is on its team?

Itamar Friedman and Dedy Kredo are the founders and CEOs of Codium AI. The tool is managed by a thriving and mission-oriented team of people from various tech giants, including Shopify, LinkedIn, Vine Ventures, Synk, and Espagon.

Codium AI is a powerful AI tool for developers, software companies, and businesses looking to create bug-free code faster. It saves the time of coders and produces high-quality programs, resulting in reliable and efficient software.

4.7/5 – (1348 votes)

Graphql Vs. Rest In 2023: Top 4 Advantages & Disadvantages

Since the release of GraphQL in 2023, there have been comparisons between GraphQL and REST due to their similar end results and GraphQL’s innovative approach. In some instances, GraphQL is even seen as a direct alternative or has been used in conjunction with REST.

Despite GraphQL’s innovative approach and acclaimed potential, in 2023, only 19% of companies used GraphQL, while 82% used REST.

While REST is far more widely used than GraphQL at the moment, industry leaders and big companies such as Facebook, Airbnb, and Github are adopting GraphQL.

GraphQL vs. REST

Figure 1: Representation of the GraphQL process

GraphQL is an open-source query and manipulation language for APIs developed by Facebook in 2012. Contrary to REST architecture, GraphQL is not an API specification; it’s a runtime for fulfilling queries with existing data. Backbend GraphQL provides a type system that describes a schema for data; in return, this gives front-end API consumers the ability to request the exact data they need.

Figure 2: Representation of REST API

1. Specificity

GraphQL can provide customers with the exact data they need. One of the most common problems with traditional REST APIs is that they tend to cause overfecthing, obtaining more information than needed. A REST query will extract all the data from a specific resource, while GraphQL will only get what is dictated in a query (see Figure 3).

Figure 3: Rest API query vs. GraphQL query

Source: Medium.

2. Performance

GraphQL can process customized queries which contribute to enhanced performance. Processing customized queries reduce the number of API calls.

Contrary to REST, GraphQL has a single endpoint, it is much more predictable, and there is a lower chance of unnecessary API calls. Research shows that mitigating GraphQL from REST increases performance by 66%

3. Flexibility

GraphQL allows its user to integrate multiple systems, and it can fetch data from existing systems. This allows GraphQL to be utilized without needing to uninstall existing infrastructures, and it can work with existing API management tools.

4. Less effort to implement 1. Single endpoint bottleneck

While a single endpoint is one of the strengths of GraphqL, it can become a bottleneck in certain circumstances. HTTP’s built-in cache function in REST APIs produces faster results than GraphQL in almost every scenario. This is because REST APIs’ multiple endpoints allow them to use HTTP caching to avoid reloading resources. GraphQL’s single endpoint pushes the user to rely on an additional library.

2. Security

REST’s vast popularity and authentication methods make it a better option for security reasons than GraphQL. While REST has built-in HTTP authentication methods, GraphQL does not provide a specific process to ensure security. The user must figure out their own security methods, whether authentication or authorization. However, users can overcome this issue with a well-planned security plan for using GraphQL.

3. Complexity 4. Cost

One of the significant drawbacks to using GraphQL is that it is more difficult to specify the API rate limit than REST. This creates the risk of the cost of queries being unexpectedly large, leading to computation, resource, and infrastructure overload.

To overcome such risks, the user must calculate a query’s cost before executing it. However, calculating GraphQL’s queries is challenging due to its nested structure. Thus, it is best to use a machine-learning approach to estimate.

If you want to explore specific software, feel free to check our data-driven list of testing tools and data-driven test automation tools vendor list. If you have other questions, we can help:

He received his bachelor’s degree in Political Science and Public Administration from Bilkent University and he received his master’s degree in International Politics from KU Leuven .

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