Trending December 2023 # Top 13 Use Cases / Applications Of Ai In Manufacturing In 2023 # Suggested January 2024 # Top 13 Popular

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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)

quality (27%)

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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Top 6 Use Cases Of Generative Ai In Education

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

Their skills 

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: 

Reading lists

Study guides 

Discussion questions 

Flashcards

Summaries. 

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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Applications Of Artificial Intelligence: 13 Ai Examples

Here are some real-Life Use Cases in Different Sectors

E-commerce Application:

Artificial Intelligence (AI) allows retailers to upgrade their customer experience on and off their web pages. It can be directed to make more informed decisions using customer and business data. It helps you to predict business trends and offers owners strategies/tips for developing their businesses.

The following are AI applications in the E-commerce sector:

Personal Shopping: Artificial intelligence technology uses design recommendation engines to improve customer interaction.

For example, AI will use customers’ data to display all needed products/items. It helps you to improve customer-seller relationships and builds trust towards brand/services.

Virtual Assistants: Natural language processing can make human-personal conversation as sound as possible. It provides real-time engagement between customers and AI assistants.

For example, the assistant would suggest tips to customers to buy the right product in a live talk.

Note:

Real-time engagement means face-to-face conversation.

NLP- Natural language processing is the ability of the machine to understand human language.

Fraud Detection: Fraud detection is another important area of application for AI. It helps you to reduce the possibility of credit card fraud. It works by accounting latest activities, trends, and behavior when tracking customers’ transactions.

For example, sales security increases in peak periods of fraud and vice-versa. Customers prefer to invest their money in products/services with high-rated customer reviews. AI can easily detect this behavior, allowing users to receive authentic service.

Education Application

AI is also used widely in the education sector to prioritize administration work. This allows teachers to concentrate more on students.

The following are essential AI applications in Education sector:

Automation of Administrative Tasks: AI has helped teachers and tutors to increase productivity.

For example, automated private messages allow students to grade homework, save time in communicating with Guardians, and manage multiple courses simultaneously.

Smart Content Creation: Adaptive intelligence can digitize content like conferences, video lectures, and textbook guides.

For example, making Audiobooks for students to listen to and preparing a flexible lesson plan.

The use of intelligent voice assistants: It allows students to access supplementary learning material and receive support from them. It also reduces the printing expenses of temporary handbooks. It provides quick solutions to frequently asked issues, unlike delayed responses of teachers.

Personalize Learning: AI technology offers Hyper-personalization techniques to track students’ behavior for self-improvement. It also includes habits and plans students need to adopt for better grades.

Everyday Use Application

There are many applications of AI in our daily lives. It helps us to read emails, get driving directions, and find the best movie or music recommendations. It can also unlock electronic devices in the simplest of ways. Face ID is biometric, which uses AI to unlock the smartphone.

The following are AI applications in everyday use.

Automation On Vehicles: Automobile companies are using AI to teach computers to think and act like humans. It contributes to detecting and driving through obstacles.

For example, self-driving cars or autonomous vehicles work on the same concept.

AI In Spam Filters: AI in mail systems works by detecting and sending suspicious emails into trash/spam folders. It helps you save time by filtering out irrelevant emails and removes the virus-infected email which might delete your data.

For example, Gmail has achieved a filtration capacity of about 99.9 percent.

AI In Face Recognition: Facial recognition is a commonly used AI application in businesses. Modern Gadgets like Phones, Laptops, and PCs use facial recognition technology to detect and identify users.

AI Recommendation System: These systems work on providing feedback from user data. This application is widely used in every industry.

For example, the LinkedIn platform suggests you add relevant people to your friend list. Platforms like YouTube and Facebook use AI recommendation systems to deliver personalized information.

Navigation Application

Artificial Intelligence is also helpful in navigating directions. AI-based GPS signals are used to ensure the safety of military units. These satellite-generated signals help you to track their position, timing equipment, and navigation.

Here are the important AI applications in the Navigation sector:

Road Mapping: GPS technology can give users accurate, timely, and thorough information to improve safety. A typical example of road mapping is Uber and other logistics firms that use AI for operational efficiency and optimizing routes. Google Maps also uses AI to calculate traffic and construction to find the quickest route to your location.

For Example, Google Maps offers directions based on the shortest path from Berlin to Potsdam. The areas are highlighted in the colored form to represent the intensity of traffic. The dark color indicates max traffic, whereas the light shade is for minimum traffic.

AI In Airline Flights: AI technology has widely contributed to plane operations. A survey in 2023 recorded that pilot-operated only 7% of planes, while AI managed the rest. Based on AI, special jetliners that work without a pilot have been manufactured.

Robotics Applications

Ai applications are used in the robotic industry. AI-powered robots plan their journey based on obstructions in their path. AI helps robotics technology to increase machine intelligence in different scenarios.

For example, it allows the robot to understand logistical and physical data patterns to respond accordingly.

The following are AI applications in the Robotic sector.

AI-Based Household Robots: Amazon’s Astro bot is an example of an AI-powered domestic robot. As this AI-powered robot ensures home security while moving around the house. It also sends an alert to capture an image of an unknown person inside the house.

AI-Based Manufacturing Robots: AI-based manufacturing robots have the potential to be the most transformative. For example, robots can make a BMW car engine from small components.

Healthcare Applications

Artificial intelligence has many uses in the healthcare industry. For example, AI technology can detect chronic illnesses early by analyzing lab and other medical data. It employs a combination of historical analysis and medical knowledge for new medications.

Here are the use of AI applications in the health sector:

AI-Supported Medical Image Analyses: Artificial intelligence helps clinics examine body scans. This allows radiologists and cardiologists to find details to treat critical patients. AI also helps avoid potential errors in reading electronic health records (EHRs), so more exact diagnoses.

Gaming Application

Artificial intelligence applications have gained decent popularity in the gaming industry. AI can generate intelligent humans like NPC to interact with players. It can also help to predict human behavior, which can be used for better game design and testing. The goal of AI in Gaming is to improve the player’s experience.

The following are AI applications in the Gaming sector:

Alien Isolation: Alien Isolation games, which were launched in 2014, are AI-based games. The game employs two Artificial Intelligence systems to interact with players. The ‘Director AI’ locates your whereabouts while ‘Alien AI’ continuously guards players.

Domestic Application

Innovative home technology, an AI-based system, is widely used in domestic applications. These applications include household applications, home safety, and security lighting.

AI can connect IoT devices to enhance processing and learning skills. These skills can predict human behavior in return. AI-powered smart home gadgets interact to collect data to help in learning human habits. This gathered information can forecast users’ habits and establish situational awareness.

The following are AI applications in the Domestic sector.

Alexa, Google Assistant, And Siri: AI controls smart devices with the voice control feature. It includes Alexa, Siri, and Google Assistant. Voice commands can be used to control Advance home security systems. Moreover, researchers modified voice recognition technology to add value to voice control devices.

Home Automation Systems: Home automation means the automatic control of devices in your home. It allows owners to set alarm systems, control Bluetooth speakers and security cameras, and detect harmful gases.

Finance Applications

The benefits for the financial industry include personal money, business finance, and consumer finance. AI’s evolved technology can help to improve a wide range of financial services. These services include venture capital, customer service, and the making of trading algorithms.

The following are AI applications in the finance sector.

For example, “Capital ones Eno” is an early AI system in personal finance.

Consumer Finance: Consumer finance means the money given to an individual for household or personal use. This finance division needs safe security in the transaction process.

Therefore, AI is the most helpful application from its security point of view. It deals with preventing fraud and controlling cyber-attacks. JPMorgan is one known bank that is utilizing AI in consumer finance processes.

Patterns and Anomalies pattern:

Ai is the best tool for verifying similarity and deviation in data. It uses Mi and cognitive approaches to study patterns in data. It also serves to search for the connections between that data.

AI can examine and identify abnormalities in patterns that humans would otherwise Ignore. The goal is whether the sets of data point fits in the given pattern. If the data doesn’t fit, then it is an anomaly.

Social Media Applications

AI can use data from social media audiences to generate revenues. The following are AI applications in the Social Media sector.

Facebook: Facebook uses AI-based, Deep text technology for language conversion. That allows Facebook to better interpret discussions utilizing this technology. It can be used to translate postings from multiple languages automatically.

Agriculture Applications

The Use of AI are emerging technology in the agriculture field. It helps to improve accuracy and harvest quality, known as precision agriculture.

The use of AI can bring sustainability to the agriculture sector.

For example, it can yield healthier crops, control pest attacks, and measure soil conductivity and PH.

The following are AI applications in the agriculture sector.

AI In Weather Forecast: Farmers can analyze weather forecasts in detail using artificial intelligence. That would help them create the best planting schedule and type of crop to be grown.

AI Eliminating Soil Deficiencies:  An AI-based app like plantix uses an algorithm to trace lacking in plants and soils. It also includes the examination of deficiencies in the soil and the elimination of plant pests. AI also provides recommendations and tips for healthy plant growth.

Marketing Applications

Artificial intelligence (AI) applications are also critical in marketing. It uses the newest data-driven strategies to produce sales. For example, it can track sales data for a certain period to suggest strategies for the near future. The use of AI in marketing applications allows owners to collect and analyze huge amounts of data in a short time.

The following are AI applications in the marketing sector.

Content Marketing: AI can assist with content marketing in a way consistent with the brand’s style and voice. It can manage routine tasks such as campaigns, reports, and performance.

Online Shopping application

The current e-commerce market is competitive and saturated, so it needs to be faster and smarter to succeed. Consider the construction of a website. For example, an AI website builder can design the website for you within minutes, unlike time-consuming manual web design.

The following are AI applications for online shopping.

Inventory Management: AI’s predictive analytics are making a massive impact on inventory management. For example, it can keep inventory up to date, shelves stocked, and keep track of everything, which is difficult to do manually.

It can also carry out predictive analysis on current and future elements in market demand. This act will bring all elements needed in the future for a successful business.

Is Artificial Intelligence limiting human application?

AI replaces most repetitive tasks and other duties with robots. Human interference decreases, which will present a significant challenge to employment standards. Many firms aim to replace the least skilled employees with AI robots that can more efficiently do similar jobs. However, every firm cannot afford an AI machine because of its creation, maintenance, and repair expenses.

However, it still lacks some meaningful qualities that demand human intervention. So, it is the AI-Human partnership that can bring a brighter future. To explore this further, consider checking out some of the best AI chatbots, which are excellent examples of this partnership in action.

Why is AI Used?

Following are some essential reasons for using AI:

Automation: AI can put repetitive tasks on automation that was earlier performed manually so that it is finished in less time.

Accuracy: AI can be trained to be made more accurate than humans. It can extract and interpret data to provide better decisions for medical tasks. For example, it can pinpoint the locations in the body where cancerous cells are developing.

Enhancement: AI makes products/services more effective by creating an exciting experience for end-users. That includes optimizing conversation bots or communicating better product recommendations.

Top 12 Use Cases Of Rpa In Procurement Process In 2023

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 policy

3. 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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Top 10 Ai Chip Makers Of 2023: In

As the figure above illustrates, the number of parameters (consequently the width and depth) of the neural networks increase, which indicates greater model size. To derive meaningful results from existing deep learning models, organizations require increased computing power and memory bandwidth. 

Powerful general-purpose chips (such as CPUs) cannot support such sophisticated deep learning models. Therefore, AI chips that enable parallel computing capabilities are increasingly in demand, and according to McKinsey, this trend will continue.

However, even Intel, which has numerous world-class engineers and a strong research background, needed three years of work to develop its own AI chip. Therefore, for most companies, buying chips or platforms that run on a purpose built AI chip, from these vendors is the only way to develop powerful deep learning models. This article will introduce 10 AI chip vendors to help companies choose the right one.

Who are the leading AI chip producers?

1. Nvidia

Source: Google

Nvidia has been producing graphics processing units (GPUs) for the gaming sector since 1990s. The PlayStation 3 and Xbox both use Nvidia graphics arrays. The company also makes AI chips such as Volta, Xavier, and Tesla. Thanks to the generative AI boom, NVIDIA had excellent results in Q2 2023 and reached a trillion in valuation.

NVIDIA’s chipsets are designed to solve business problems in various industries. Xavier, for example, is the basis for an autonomous driving solution, while Volta is aimed at data centers. DGX™ A100 is the flagship AI chip of Nvidia which is also designed for data centers. Product integrates 8 GPUs and up to 640GB GPU memory. Nvidia Grace is the new AI chip model that the company released for the HPC market in 2023.

2. Intel

Intel is one of the largest players in the market and has a long history of technology development. In 2023, Intel became the first AI chip company in the world to break the $1 billion sales barrier. 

Intel’s Xeon processors, which are appropriate for a variety of jobs, including processing in data centers, have had an impact on the company’s commercial success. In comparison to earlier generations, the third generation Xeon platinum series has up to 40 cores and 1.6 times greater memory bandwidth, and 2.66 times greater memory capacity compared to previous generation.

Gaudi is the neural network training accelerator  from Intel. This product is able to scale as models get larger and has a relatively low total cost of ownership. For inferencing, Intel has the Goya, optimized for throughput and latency. 

Intel® NCS2 is the latest AI chip from Intel and was developed specifically for deep learning. 

3. Google Alphabet

Google Cloud TPU is the purpose-built machine learning accelerator chip that powers Google products like Translate, Photos, Search, Assistant, and Gmail. It can be used via the Google Cloud implementation. Edge TPU, another accelerator chip from Google Alphabet, is smaller than a one-cent coin and is designed for edge devices such as smartphones, tablets, and IoT devices.

4. Advanced Micro Devices (AMD)

AMD is a chip manufacturer that has CPU, GPU and AI accelerator products. For instance, AMD’s Alveo U50 data center accelerator card has 50 billion transistors. Accelerator can run 10 million embedding datasets and perform graph algorithms in milliseconds.

IBM launched its “neuromorphic chip” TrueNorth AI in 2014. TrueNorth contains 5.4 billion transistors, 1 million neurons, and 256 million synapses, so it can efficiently perform deep network inference and deliver high-quality data interpretation.

In April 2023, IBM launched its new hardware, “IBM Telum Processor”. Its development process took three years and is aimed to improve efficiency of use of large datasets.  According to IBM, it is suitable to use Telum processors for missions such as preventing fraud immediately due to its improved processor core and memory compared to previous AI chips of the company.

The below video further introduces Telum Processor:

Who are the leading AI chip startups?

We would also like to introduce some startups in the AI chip industry whose names we may hear more often in the near future. Even though these companies were founded only recently, they have already raised millions of dollars.

Source: Statista and Reuters

6. SambaNova Systems

SambaNova Systems was founded in 2023 with the goal of developing high-performance, high-precision hardware-software systems. The company has developed the SN10 processor chip and raised more than $1.1 billion in funding.

It is important to note that, rather than selling its AI processors, SambaNova Systems builds data centers and leases them to the businesses. Product as service approach of SabaNova Systems might enhance product stewardship and force them to produce more durable products since they own it through the lifecycle. AIMultiple evaluates product as service approach as one of the circular economy best practices. 

7. Cerebras Systems

Cerebras Systems was founded in 2023. In April 2023, the company announced its new AI chip model, Cerebras WSE-2, which has 850,000 cores and 2.6 trillion transistors. Undoubtedly, the WSE-2 is a big improvement over the WSE-1, which has 1.2 trillion transistors and 400,000 processing cores. 

Celebra’s system works with many pharmaceutical companies such as AstraZeneca and GlaxoSmithKline because the effective technology of WSE-1 accelerates genetic and genomic research and shortens the time for drug discovery.

8. Graphcore

Graphcore is a British company founded in 2023. The company announced its flagship AI chip as IPU-POD256. Graphcore has already been funded with around $700 million.

Company has strategic partnerships between data storage corporations like DDN, Pure Storage and Vast Data. Graphcore works with research institutes around the globe like Oxford-Man Institute of Quantitative Finance, University of Bristol and Berkeley University of California are other reputable research organizations that use Graphcore’s AI chips.

9. Groq

Groq has been founded by former Google employees. The company represents a new model for AI chip architecture that aims to make it easier for companies to adopt their systems. The startup has already raised around $350 million and produced its first models such as GroqChip™ Processor, GroqCard™ Accelerator, etc.

On the first of March, Groq acquired Maxeler, which has high performance computing (HPC) solutions for financial services applications.

10. Mythic

Mythic was founded in 2012. It developed products such as M1076 AMP, MM1076 key card, etc., and has already raised about $150 million in funding.

You can also check our sortable list of companies working on AI chips.

You might enjoy reading our articles on TinyML and accelerated computing.

If you have questions about how AI hardware can help your business, we can help:

This article was drafted by former AIMultiple industry analyst Görkem Gençer.

References

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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7 Use Cases Of Chatgpt In Marketing For 2023

The share of artificial intelligence in the marketing industry is rapidly increasing (see Figure 1). However, the use of relatively new tools, such as generative AI and, in particular, ChatGPT in marketing is not widely known. 

Figure 1. Market value of artificial intelligence (AI) in marketing worldwide from 2023 to 2028

In this article, we will explain 7 use cases of ChatGPT to help digital marketers have an effective marketing strategy. 

1- Content creation

Content creation, text generation in specific, using ChatGPT can be a powerful tool for marketing. These AI-generated texts can be used for a variety of purposes other than generating ideas, such as:

Contents generated by ChatGPT can be integrated with other marketing strategies and channels like:

Creating various contents for digital marketing campaigns

Preparing posts for social media platforms

Generating personalized, attractive and persuasive emails for email marketing.

For more on the use cases of generative AI in copywriting, check our comprehensive article.

2- Personalized customer experience

ChatGPT with its natural language processing (NLP) can generate personalized content for your customers based on their preferences, past behavior, and demographics. This can help you create targeted content that resonates with your audience, which can lead to higher engagement and conversion rates.

3- Audience research

Audience research involves gathering data and insights about your target audience to better understand their interests, preferences, behaviors, and needs. This information can help you create more effective marketing strategies, including content creation, ad targeting, and product development.

ChatGPT can be used to analyze customer data such as: 

Search queries

Social media interactions

Past purchases to identify patterns and trends in customer behavior. 

By analyzing this data, ChatGPT can help you identify your target audience’s preferences, interests, and pain points, which can inform your marketing messaging, content, and product development.

4- SEO optimization

ChatGPT can be a valuable tool for SEO in marketing. SEO, or search engine optimization, involves optimizing your website and content to rank higher in search engine results pages (SERPs) for relevant keywords and phrases. Here are some ways that ChatGPT can help with SEO:

Generate attractive topic ideas for content marketing

Make keyword research

Find the right and attractive titles

Group search intent

Create content structure

Generate meta descriptions

Figure 3. ChatGPT SEO-friendly title suggestions for contents in B2B marketing

5- Writing product descriptions

Product descriptions are a crucial part of marketing, as they provide potential customers with information about the features, benefits, and value of a product. ChatGPT can help create compelling and informative product descriptions that resonate with your target audience.

6- Chatbot for customer support

ChatGPT can be integrated into a chatbot to provide instant and personalized customer support. Chatbots can help customers with frequently asked questions, provide technical support, and even troubleshoot issues. Chatbots in marketing can help: 

Improve customer satisfaction

Reduce response times

Decrease the workload of customer service representatives.

7- Creating customer surveys

Surveys are an effective way to gather feedback and insights from customers, which can help marketers improve their products, services, and marketing strategies. Here are some ways that ChatGPT can help with creating customer surveys:

Question generation

Organizing survey structure

Making surveys multilingual with its translation ability

Survey analysis

If you have questions or need help in finding vendors, please contact:

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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