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Due to the COVID-19 pandemic, the use of digital technologies to enhance education has significantly increased as many students around the world have had to shift to online learning. For example, investment in education for adopting innovative technologies increased from $7 billion to $20 billion during the pandemic as trends suggest. However, digital technologies also have the potential to transform the education experience in other ways beyond just online classes. The application of generative AI in education is an example to this.
Generative AI is a digital technology that can quickly create new and realistic visual, textual, and animated content. In other articles, we investigated its use cases in different sectors, such as healthcare and banking. While other technologies like conversational AI and robotic process automation (RPA) are implemented in education, generative AI is not properly implemented in education. Despite this, it has potential use cases for improving it. This article explains the top 6 potential ways to use generative AI in education.1. Personalized Lessons
Personalized lesson plans are a powerful way to ensure that students receive the most effective education tailored specifically to their needs and interests. These lesson plans can be generated by using AI-powered algorithms to analyze student data, such as:
Their past performance
And any feedback they might have given regarding content
AI-based systems can leverage such information to generate customized curriculum that is more likely to engage each student and help them reach their potential. This can be important for children with learning disabilities or disorders.
For example, Speechify is a generative AI-driven tool. It offers text-to-speech or speech-to-text generations on desktops or on online use.2. Course Design
Generative AI tools can help design and organize course materials, including syllabi, lesson plans, and assessments. They can also personalize course material based on students’ knowledge gaps, skills and learning styles, such as practice problems or interactive exercises.
Generative AI can create simulations and virtual environments once paired with other technologies, such as virtual reality. Consequently, it offers more engagement and interactive courses, improving students’ learning experience.
For example, a generative AI system could create a virtual laboratory setting where students can conduct experiments, observe the results, and make predictions based on their observations.3. Content Creation for Courses
Generative AI can assist in creating new teaching materials, such as questions for quizzes and exercises or explanations and summaries of concepts. This can be especially useful for teachers who need to create a large amount and a variety of content for their classes. By using AI, it is possible to create modified or brand-new content from the original content.
Furthermore, generative AI can facilitate generating additional materials to supplement the main course materials, such as:
Also, AI can generate scripts for video lectures or podcasts, streamlining multimedia content creation for online courses. Image generation is another important ability of generative AI for education. Teachers may want to generate images with specific modifications that respond to particular course needs.
For example, NOLEJ offers an e-learning capsule that is AI generated in only 3 minutes. This capsule provides an interactive video, glossary, practice, and summary for a target topic (see Figure 1 below).
More established companies are using AI to generate content that supports their main products. For instance, Duolingo, a language learning platform, uses GPT-3 to correct French grammar and create items for their English test. The company concludes that with the implementation of GPT-3, second language writing skills of customers are increased.4. Data Privacy Protection for Analytical Models
Using synthetic data, which is created by AI models that have learned from real-world data, can provide anonymity and protect students’ personal information. Synthetic data sets produced by generative models are effective and useful for training other algorithms, while being secure and safe to use.
For more on how generated synthetic data enables data privacy, you can check out these articles:5. Restoring Old Learning Materials
Generative AI can improve the quality of outdated or low-quality learning materials, such as historical documents, photographs, and films. By using AI to enhance the resolution of these materials, they can be brought up to modern standards and be more engaging for students who are used to high-quality media.
These updates can also make it easier for students to read, analyze, and understand the materials, leading to a deeper understanding of the content and, ultimately, better learning outcomes.
Using a version of generative AI, Generative Adversarial Networks (GANs), it is possible to restore low-quality images and remove simple watermarks. In Figure 2 below, you can see a prototype for image restoration via GANs. Such image restoration can be adapted to educational materials. For example, in art and design schools, restoring old images would provide the detection of important details of artworks. Also in history classes and research, scanning and restoring old documents can be facilitated.
Figure 2. Image restoration with GANs. (Source: Towards Data Science)6. Tutoring
Another use case of generative AI is to provide tutoring. Generative AI can be used to create virtual tutoring environments, where students can interact with a virtual tutor and receive real-time feedback and support. This can be especially helpful for students who may not have access to in-person tutoring.
According to academic studies, private tutoring children with severe reading difficulty improved their reading skills by 50% in a year. However, providing tutoring to all students can be a challenge. Generative AI can tackle this issue by creating virtual tutoring environments. In these environments, students can interact with a virtual tutor and receive feedback and support in real-time. This can be especially helpful for students who may not have access to in-person tutoring.
For example, TutorAI is trying to implement this kind of use of generative AI in education. It offers an educational platform that generates interactive content on a variety of topics.
Another generative AI work for teaching purposes can be the implementation of chatbots for tutoring. Chatbot Life’s 2023 chatbot report shows that education is the third biggest industry benefiting from chatbots.
Lately, Chat GPT from OpenAI stormed the internet with its ability to engage in highly personalized conversations and definitive answers. It can answer course-related questions from a variety of domains, and can even write essays on the target topic.
On the other hand, implementing generative AI-based chatbots specified and regulated for educational purposes is a future plan. However, it offers potential uses and benefits:
One potential use would be to provide around-the-clock support to students and their parents, including help with homework.
Generative chatbots can also assist with administrative tasks, such as answering student or parent questions, freeing up time for educators to focus on other tasks, such as grading and lesson planning.
The flexibility and natural feeling of generative chatbots make them useful in educational settings, particularly with elementary and middle school children.Challenges of generative AI in education
Although generative AI has a lot of potential to improve educational practices, it may also pose some potential challenges. These can be shortly listed as:
Biases in educational materials
False or inaccurate information
Abuse of it for self interest
Unemployment risks for some teachers or other education professionals
For a detailed discussion on the ethical challenges of generative AI, you can check our article.For more on generative AI
To explore more about generative AI, you can check our other articles:
Discover the top generative AI tools from our detailed list sorted by category:
If you have questions regarding generative AI, 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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The industrial manufacturing industry is the top adopter of artificial intelligence, with 93 percent of leaders stating their organizations are at least moderately using AI.
Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs, and plan future financial actions to progress on their digital transformation.Why is AI important in the manufacturing industry?
Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.
The same study also reveals that the most popular AI use cases in manufacturing are improving:
maintenance (29% of manufacturing AI use cases)
This popularity is driven by the fact that manufacturing data is a good fit for AI/machine learning. Manufacturing is full of analytical data which is easier for machines to analyze. Hundreds of variables impact the production process and while these are very hard to analyze for humans, machine learning models can easily predict the impact of individual variables in such complex situations. In other industries involving language or emotions, machines are still operating at below human capabilities, slowing down their adoption.
The COVID-19 pandemic also increased the interest of manufacturers in AI applications. As seen on Google Trends graph below, the panic due to lockdowns may have forced manufacturers to shift their focus to artificial intelligence.What are the common AI use cases in manufacturing? 1. Predictive maintenance
Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Thanks to AI-powered predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.2. Generative design
Generative design uses machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers enter parameters of design (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software and the software provides all the possible outcomes that can be created with those parameters. With this method, manufacturers quickly generate thousands of design options for one product.3. Price forecasting of raw material
The extreme price volatility of raw materials has always been a challenge for manufacturers. Businesses have to adapt to the unstable price of raw materials to remain competitive in the market. AI-powered software like can predict materials prices more accurately than humans and it learns from its mistakes.4. Robotics
Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to a negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications of robots in plants vary. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding.
Industrial robots have been in manufacturing plants since the late 1970s. With the addition of artificial intelligence, an industrial robot can monitor its own accuracy and performance, and train itself to get better. Some manufacturing robots are equipped with machine vision that helps the robot achieve precise mobility in complex and random environments.
Cobots are another robotics application that uses machine vision to work safely alongside human workers to complete a task that cannot be fully automated. Feel free to learn more about cobots with our comprehensive guide.5. Edge analytics
Edge analytics provides fast and decentralized insights from data sets collected from sensors on machines. Manufacturers collect and analyze data on edge to reduce time to insight. Edge analytics has three use cases in manufacturing:
Improving production quality and yield
Detecting early signs of deteriorating performance and risk of failure
Tracking worker health and safety by using wearables
To learn more about analytics in manufacturing, feel free to read our in-depth article about the top 10 manufacturing analytics use cases.6. Quality assurance
Quality assurance is the maintenance of a desired level of quality in a service or product. Assembly lines are data-driven, interconnected, and autonomous networks. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI systems can detect the differences from the usual outputs by using machine vision technology since most defects are visible. When an end-product is of lower quality than expected, AI systems trigger an alert to users so that they can react to make adjustments.
You can also check the lists of data annotation and AI/ML tools and services to find the option that best suits your project needs:7. Inventory management
Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning. AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc) engineers use in manufacturing facilities. These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen.8. Process optimization
AI-powered software can help organizations optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate bottlenecks in the organization’s processes. For instance, timely and accurate delivery to a customer is the ultimate goal in the manufacturing industry. However, if the company has several factories in different regions, building a consistent delivery system is difficult.
By using a process mining tool, manufacturers can compare the performance of different regions down to individual process steps, including duration, cost, and the person performing the step. These insights help streamline processes and identify bottlenecks so that manufacturers can take action.
For example, a manufacturer that employed a process mining tool in their procure-to-pay processes decreased deviations and maverick buying worth to $60,000.9. AI-Powered digital twin use cases
A digital twin is a virtual representation of a real-world product or asset. By combining AI techniques with digital twins, manufacturers can improve their understanding of the product and allow businesses to experiment in future actions that may enhance asset performance. There are typically 4 applications of digital twins in manufacturing:10. Product development
Manufacturers can use digital twins before a product’s physical counterpart is manufactured. This application enables businesses to collect data from the virtual twin and improve the original product based on data.11. Design customization
Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design.12. Shop floor performance improvement
A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.13. Logistics optimization
Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process.What are the benefits of AI in manufacturing? Safety
Manufacturing is one of the highest-risk industrial sectors to be working in with more than 3,000 major injuries and nine fatalities occurring each year. The involvement of robots in high-risk jobs can help manufacturers reduce unwanted accidents.Cost Reduction
AI technologies can reduce the operation costs of manufacturers due to several applications:
Leveraging AI technologies can enhance organizations’ analytics capability so that they can use their resources more efficiently, make better forecasts, and reduce inventory costs. Thanks to better analytics capabilities, companies can also switch to predictive maintenance leading to eliminating downtime costs and reducing maintenance costs.
This one is obvious but manufacturers don’t need to pay monthly salaries to robots. However, robots require CAPEX which needs to be weighed against the recurring cost of labor.Faster decision making
Thanks to IoT sensors, manufacturers can collect large volumes of data and switch to real-time analytics. This allows manufacturers to reach insights sooner so that they can make operational, real-time data-driven decisions.24/7 production in dark factories
Factories without any human labor are called dark factories since light may not be necessary for robots to function. This is a relatively new concept with only a few experimental 100% dark factories currently operating. However, dark factories will increase over time with the application of AI and other automation technologies since they have the potential to unleash significant savings, end workplace accidents and expand their production capacity.
Read more on AI applications in different industries:
If you still have questions on how AI revolutionizing the manufacturing industry, don’t hesitate to contact us:
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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Discover the fascinating world of generative AI in education through my captivating blog! In this immersive guide, we’ll explore:
The Magic of Visual Storytelling: Discover how AI can convert ordinary text into remarkable visuals, enriching the learning experience for students.
Mastering Python for Creative AI: Get hands-on with Python to implement powerful text-to-image models like Dreambooth-Stable-Diffusion.
Dive Deep into Cutting-edge Algorithms: Understand the inner workings of state-of-the-art models and their applications in educational settings.
Empower Personalization in Education: Explore how AI can personalize content for each learner, delivering tailored and captivating visual stories.
Prepare for the Future of Learning: Stay ahead of the curve by embracing AI-driven technologies and their potential to revolutionize education.
This article was published as a part of the Data Science Blogathon.Table of Contents Project Description
In this project, we will delve into a deep learning method to produce quality images from textual descriptions, specifically targeting applications within the education sector. This approach offers significant opportunities for enriching learning experiences by providing personalized and captivating visual stories. By leveraging pre-trained models such as Stable Diffusion and GPT-2, we will generate visually appealing images that accurately capture the essence of the provided text inputs, ultimately enhancing educational materials and catering to a variety of learning styles.Problem Statement
The primary objective of this project is to create a deep learning pipeline capable of generating visually engaging and precise images based on textual inputs. The project’s success will be gauged by the quality and accuracy of the images generated in comparison to the given text prompts, showcasing the potential for enriching educational experiences through captivating visuals.Prerequisites
To successfully follow along with this project, you will need the following:
A good understanding of deep learning techniques and concepts
Proficiency in Python programming.
Familiarity with libraries such as OpenCV, Matplotlib, and Transformers.
Basic knowledge of using APIs, specifically the Hugging Face API.
This comprehensive guide provides a detailed end-to-end solution, including code and output harnessing the power of two robust models, Stable Diffusion and GPT-2, to generate visually engaging images from the textual stimulus.
Stable Diffusion is a generative model rooted in the denoising score-matching framework, designed to create visually cohesive and intricate images by emulating a stochastic diffusion process. The model functions by progressively introducing noise to an image and subsequently reversing the process, reconstructing the image from a noisy version to its original form. A deep neural network, known as the denoising score network, guides this reconstruction by learning to predict the gradient of the data distribution’s log-density. The final outcome is the generation of visually engaging images that closely align with the desired output, guided by the input textual prompts.
GPT-2, the Generative Pre-trained Transformer 2, is a sophisticated language model created by OpenAI. It builds on the Transformer architecture and has undergone extensive pre-training on a substantial volume of textual data, empowering it to produce a contextually relevant and coherent text. In our project, GPT-2 is employed to convert the given textual inputs into a format suitable for the Stable Diffusion model, guiding the image generation process. The model’s ability to comprehend and generate contextually fitting text ensures that the resulting images align closely with the input prompts.
Combining these two models’ strengths, we generate visually impressive images that accurately represent the given textual prompts. The fusion of Stable Diffusion’s image generation capabilities and GPT-2’s language understanding allows us to create a powerful and efficient end-to-end solution for generating high-quality images from text.
Step 1: Set up the environment
We begin by installing the required libraries and importing the necessary components for our project. We will use the Diffusers and Transformers libraries for deep learning, OpenCV and Matplotlib for image display and manipulation, and Google Drive for file storage and access.# Install required libraries !pip install --upgrade diffusers transformers -q # Import necessary libraries from pathlib import Path import tqdm import torch import pandas as pd import numpy as np from diffusers import StableDiffusionPipeline from transformers import pipeline, set_seed import matplotlib.pyplot as plt import cv2 from google.colab import drive
Step 2: Access the dataset
We will mount Google Drive to access our dataset and other files in this step. We will load the CSV file containing the textual prompts and image IDs and update the file paths accordingly.# Mount Google Drive drive.mount('/content/drive') # Update file paths data = pd.read_csv('/content/drive/MyDrive/SD/promptsRandom.csv', encoding='ISO-8859-1') prompts = data['prompt'].tolist() ids = data['imgId'].tolist() dir0 = '/content/drive/MyDrive/SD/'
Using OpenCV and Matplotlib, we will display the images from the dataset and print their corresponding textual prompts. This step allows us to familiarize ourselves with the data and ensure it has been loaded correctly.# Display images for i in range(len(data)): img = cv2.imread(dir0 + 'sample/' + ids[i] + '.png') # Include 'sample/' in the path plt.figure(figsize=(2, 2)) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.axis('off') plt.show() print(prompts[i]) print()
Step 4: Configure the deep learning models: We will define a configuration class (CFG) to set up the deep learning models used in the project. This class specifies parameters such as the device used (GPU or CPU), the number of inference steps, and the model IDs for the Stable Diffusion and GPT-2 models.
We will also load the pre-trained models using the Hugging Face API and configure them with the necessary parameters.# Configuration class CFG: device = "cuda" seed = 42 generator = torch.Generator(device).manual_seed(seed) image_gen_steps = 35 image_gen_model_id = "stabilityai/stable-diffusion-2" image_gen_size = (400, 400) image_gen_guidance_scale = 9 prompt_gen_model_id = "gpt2" prompt_dataset_size = 6 prompt_max_length = 12 # Replace with your Hugging Face API token secret_hf_token = "XXXXXXXXXXXX" # Load the pre-trained models image_gen_model = StableDiffusionPipeline.from_pretrained( CFG.image_gen_model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=secret_hf_token, guidance_scale=9 ) image_gen_model = image_gen_model.to(CFG.device) prompt_gen_model = pipeline( model=CFG.prompt_gen_model_id, device=CFG.device, truncation=True, max_length=CFG.prompt_max_length, num_return_sequences=CFG.prompt_dataset_size, seed=CFG.seed, use_auth_token=secret_hf_token )
Step 5: Generate images from prompts: We will create a function called ‘generate_image’ to generate images from textual prompts using the Stable Diffusion model. The function will input the textual prompt and model and generate the corresponding image.
Afterward, we will display the generated images alongside their corresponding textual prompts using Matplotlib.# Generate images function def generate_image(prompt, model): image = model( prompt, num_inference_steps=CFG.image_gen_steps, generator=CFG.generator, guidance_scale=CFG.image_gen_guidance_scale ).images image = image.resize(CFG.image_gen_size) return image # Generate and display images for given prompts for prompt in prompts: generated_image = generate_image(prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') plt.show() print(prompt) print()
Our project also experimented with generating images using custom textual prompts. We used the ‘generate_image’ function with a user-defined prompt to showcase this. In this example, we chose the custom prompt: “The International Space Station orbits gracefully above Earth, its solar panels shimmering”. The code snippet for this is shown below:custom_prompt = "The International Space Station orbits gracefully above Earth, its solar panels shimmering" generated_image = generate_image(custom_prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') plt.show() print(custom_prompt) print()
Let’s create a simple story with five textual prompts, generate images for each, and display them sequentially.
A lonely astronaut floats in space, surrounded by stars.
The astronaut discovers a mysterious, abandoned spaceship.
The astronaut enters the spaceship and finds an alien map.
The astronaut explores the new planet, filled with excitement and wonder.
Now, let’s write the code to generate and display images for each prompt:story_prompts = [ "A lonely astronaut floats in space, surrounded by stars.", "The astronaut discovers a mysterious, abandoned spaceship.", "The astronaut enters the spaceship and finds an alien map.", "The astronaut decides to explore the new planet, filled with excitement and wonder." ] # Generate and display images for each prompt in the story for prompt in story_prompts: generated_image = generate_image(prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') plt.show() print(prompt) print()#import csv
Executing the above code will generate images for each story prompt, displaying them sequentially along with their corresponding textual prompts. This demonstrates the model’s ability to create a visual narrative based on a sequence of textual prompts, showcasing its potential for storytelling and animation.Conclusion
This comprehensive guide explores a deep learning approach to generate visually captivating images from textual prompts. By harnessing the power of pre-trained Stable Diffusion and GPT-2 models, an end-to-end solution is provided in Python, complete with code and outputs. This project demonstrates the vast potential deep learning holds in industries that require custom and unique visuals for various applications like storytelling, which is highly useful for AI in Education.
5 Key Takeaways: Harnessing Generative AI for Visual Storytelling in Education
Importance of Visual Storytelling in Education: The article highlights the significance of visual storytelling in enhancing the learning experience by engaging students, promoting creativity, and improving communication skills.
Python Implementation: The article provides a step-by-step Python guide to help educators and developers harness the power of generative AI models for text-to-image synthesis, making the technology accessible and easy to integrate into educational content.
Potential Applications: The article discusses various applications of generative AI in education, such as creating customized learning materials, generating visual aids for storytelling, and assisting students with special needs, like visual impairments or learning disabilities.
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
Chief Procurement Officers (CPOs) are pessimistic: 66% of them surveyed in 2023 believe that supply chain volatility will persist in 2023.
A remedy is a more robust procurement process that keeps the business ahead of the market and geopolitical dynamics. For instance, paying vendors on time ensures timely delivery of goods which sustains the manufacturing cycle. This at least ensures that whatever supply chain issues the company is facing, it’s not procurement-related.
Robotic process automation (RPA) can assist the procurement department in managing their procurement tasks better. In this article, we will explain the top 12 use cases of RPA in procurement.1. Input identification
RPA’s first procurement application is input identification. RPA can retrieve each product’s input list from the bill of materials (BOM) and store it in a hub.2. Contract management
Robotic process automation can automate contract management. Use cases would include:
Drafting B2B contracts by automatically extracting the vendors’ info and putting it on the draft
Sending notification to the procurement teams whenever a contract is reaching the expiry date
Archiving each contract in each vendor’s dedicated database
Using OCR and NLP to review contracts and ensure the SLA terms comply against company policy3. Purchase request & purchase order submission
Purchase requests and purchase orders are submitted to inform the company’s decision-makers and the vendor of the type and quantity of the needed items.4. Category management
Different departments are in charge of purchasing their own materials. RPA, intelligent automation, and ML tools can identify and assign each product’s category with the correct procurement department and tag them. RPA also reminds procurement staff to approve delivery notices or reschedule production in case of delayed shipments.5. Purchase request approval
RPA in procurement is useful because it can automatically approve routine purchase requests by referring to business rule engines. For example, machine learning algorithm would identify the reorders for commonly used items. The data can then be structured. RPA bots will then place reorders. So as long as the orders meet the procurement strategy, orders for current needs can be approved without human involvement.
And if the order is an exception and needs human intelligence for assessment, the request can be forwarded to the procurement manager for final approval.6. Automated re-ordering
RPA in procurement can monitor the inventory levels on the dashboard and automatically create purchase orders for the reordering products. One of the benefits of automated re-orders is a consistent manufacturing process because the vital intermediary goods will always be in-time for the production cycle.
Automated re-orders would help bypass the need for a human to keep monitoring the inventory levels and fill out purchase orders electronically or otherwise. This ensures that the future needs of the company are tended to.7. Inventory management
Robotic process automation (RPA) and IoT integration enables digital monitoring of inventory levels. This feature allows RPA to create automated reports and inventory audits.
Some businesses, such as restaurants, need to not overload their inventory of perishable produce. So it’s important to have a real-time Especially for businesses that rely on fresh inventory levels, such as restaurants that overload on perishable stuff, it’s important to have a real-time report of what exactly you have right now.
Automating inventory management also means products that stay in the warehouse longer can be recognized and purchased less, enabling smart procurement.8. Three-way matching
Another use case of RPA in procurement is automated three-way matching. The RPA bots can automatically compare purchase requests, with the supplier invoices, and the delivery receipt to confirm that the ordered products are those which should’ve been ordered. Three-way matching also ensures that the goods have been delivered.9. Automated payments
RPA bots can be scheduled to make automated payments after schedule triggers. On-time payments improve supplier relationship management and uphold the business reputation. Moreover, finance APIs allow ROA bots to make payments to the correct vendor in the right amount. That’s because they would exchange the information between the suppliers’ list and the AP automation solution. This reduces the workload of the procurement teams and makes correct, timely payments.10. Supplier onboarding
Same as with employee onboarding, companies can leverage RPA to automate parts of their supplier onboarding. For instance, RPA bots can extract vital information from the suppliers’ websites (such as their references, prices, etc.) and put it in a report.
Moreover, RPA in procurement also means that bots can asses the suppliers through rule-based decisions. For instance, if a company wants to hire an event planner with experience in the pharmaceutical industry, and there are no case studies of that on the vendor’s website or attached to their profile, they can be ranked lowest.
These preliminary assessments can be time-consuming. By having robots completing these tasks, the employees can spend their time on higher value work.11. Price negotiation
After receiving a vendor quote, companies can use RPA bots to automatically negotiate prices through a rules-based framework. So when it comes to approving/rejecting/negotiating a quote, intelligent automation-enabled RPA bots can compare the quoted price against the established threshold. Then following the conditional result (i.e., “if price is X% higher than Y, do Z”) the bots can send their rebuttal.12. Digitized records For more on RPA
To learn more about RPA and its use cases, read:
Download our RPA whitepaper for in-depth look into the topic:
And if you’re ready to invest, we have a data-driven list of RPA vendors prepared.
We can help you in select the best RPA vendor according your needs:
He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.
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Is the JetPack AI Assistant available within WordPress?
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Can I use JetPack AI Assistant to write blog posts for publishing online?
You can use the JetPack AI Assistant to write blog posts for your online blog. It can generate blogs on diverse topics and publish them online. It generates unique, plagiarism-free content that can be used for personal or commercial purposes.
JetPack AI Assistant is a powerful companion for writers and editors. It can rapidly write and edit various types of content within the WordPress editor. The tool is ideal for freelancers, editors, and businesses that want to save time while producing high-quality content.
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Generative AI describes algorithms that can be utilized to create new content
Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content such as audio, code, images, text, simulations, and videos. Recent breakthroughs in the industry could radically change the way we approach content creation. The way we approach content creation could be drastically altered by recent breakthroughs in the field.
Machine learning encompasses generative AI systems, and one such system, ChatGPT, describes its capabilities as follows:What are DALL-E and ChatGPT?
The generative pre-trained transformer (GPT) is receiving a lot of attention right now. It is a cost-free chatbot that can respond to almost any question. It was developed by OpenAI and will be made available to the public for testing in November 2023 and already regarded as the best AI chatbot ever.
Medical imaging analysis and high-resolution weather forecasts are just two examples of the many applications of machine learning that have emerged in recent years. It is abundantly clear that generative AI tools like ChatGPT and DALL-E can alter how a variety of tasks are carried out.What Distinguishes Artificial Intelligence from Machine Learning?
Artificial intelligence is a type of machine learning. Models that can “learn” from data patterns without human guidance are developed through machine learning to develop artificial intelligence. The unmanageably colossal volume and intricacy of information (unmanageable by people, in any case) that is presently being produced has expanded the capability of AI, as well as the requirement for it.How is a Generative AI Model Constructed?
Boldface-name donors have given OpenAI, the company behind ChatGPT, former GPT models, and DALL-E. Meta has released its Make-A-Video product, which is based on generative AI, and DeepMind is a subsidiary of Alphabet, the parent company of Google.
But it’s not just talent. Asking a model to practice using almost anything on the internet will cost you. OpenAI has not disclosed the exact cost but estimates that GPT-3 was trained on about 45 terabytes of text data-about a million square feet of bookshelf space, or a quarter of the entire Library of Congress-valued at several million dollars. These are not resources that your gardening business can use.What Kinds of Outputs Can Be Generated by a Generative AI Model?
You may have noticed that the outputs produced by generative AI models can appear uncanny or indistinguishable from content created by humans. The match between the model and the use case, or input, and the quality of the model as we have seen, ChatGPT’s outputs appear to be superior to those of its predecessors so far determine the outcomes.
On-demand, AI-generated art models like DALL-E can produce strange and beautiful images like a Raphael painting of a child and a Madonna, eating pizza. Other generative artificial intelligence models can deliver code, video, sound, or business reproductions.
However, not all of the outputs are appropriate or accurate. Generative AI outputs are combinations of the data used to train the algorithms that have been carefully calibrated. Since how much information is used to prepare these calculations is so unquestionably huge-as noted, GPT-3 was prepared on 45 terabytes of text information-the models can seem, by all accounts, to be “inventive” while creating yields.What Kinds of Issues Can Be Resolved by A Generative AI Model?
In a matter of seconds, generative AI tools can produce a wide range of credible writing and respond to criticism to make the writing more useful. This has suggestions for a wide assortment of ventures, from IT and programming associations that can profit from the momentary, generally right code produced by computer-based intelligence models to associations needing promoting duplicate.
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