Use cases of Artificial Intelligence in Manufacturing industry

June 21, 2022

Manufacturers can now increase throughput, and speed research and development, thanks to AI and Machine Learning. According to a recent poll, 60% of manufacturers are adopting AI to enhance product quality, increase supply chain speed and visibility, and optimize inventory management. 

Many internal obstacles in the manufacturing industry have been overcome by AI, ranging from a lack of experience to decision-making complexity, integration concerns, and information overload. Businesses may fundamentally revolutionize their processes by implementing AI in their manufacturing plants. 

Let's look at how AI is assisting organizations in the manufacturing industry to achieve their business goals.

Why is artificial intelligence crucial in the manufacturing industry?

Manufacturing generates a lot of analytical data that machines and AI algorithms can easily process. This data contains a lot of variables as hundreds of factors influence the manufacturing process, and while humans find it difficult to examine them, Artificial Intelligence models can accurately forecast the impact of individual variables in such complicated circumstances. 

Artificial intelligence is crucial in the manufacturing industry as it improves the preservation and quality of the goods/products manufactured.  

Below are the few basic requirements of the manufacturing industry which show why there is a high need for AI models to increase the efficiency:

 

  • In the industry revenue volatility is high. 
  • Continuous cost-cutting efforts are to be made.
  • Production schedules are short.
  • A regular need for inspections and regulations.
  • High flexibility, and
  • Efficient manufacturing capacity and supply chain systems are needed.

What are the advantages of artificial intelligence in manufacturing?

Added benefits of using AI in the manufacturing industry include making data-driven quick choices, fast improved manufacturing results, improved process efficiency, reduced operational costs, more scalability, and easier product development. Furthermore, because AI is strong at interpreting and translating natural language, it will be easier for workers and managers to interact with software. Users of software, for example, generally prefer to search for items rather than browse a lengthy menu. AI allows the software to understand the user's intentions, making the system more spontaneous, resulting in better output and fewer mistakes.

Some of the most prevalent use cases of AI in the manufacturing industry-

  1. Management of Inventory: Inventory management inefficiency can result in considerable cost overruns for a manufacturing organization. Traditional demand forecasting methods (ARIMA, exponential smoothing, etc.) used by engineers in manufacturing facilities produce less accurate findings than AI-powered demand forecasting systems. Manufacturers may maintain their order records and add/delete new inventory using AI technologies. In order to manage stocks based on demand and supply, AI is crucial. AI & ML systems that are strong at demand forecasting and supply planning might help enhance inventory planning operations. These technologies help firms better manage inventory levels, reducing the likelihood of cash-in-stock and out-of-stock events.

  1. Predictive Maintenance: A single equipment failure may dramatically affect the entire production process, resulting in increased downtime and expenses. As a result, thorough and timely machinery maintenance is critical. Unfortunately, this is frequently neglected until a severe failure occurs. By analyzing sensor data, manufacturers use AI technology to predict possible downtime and accidents. Manufacturers can use AI systems to predict when or whether functioning equipment will break, allowing maintenance and repair to be arranged ahead of time. Manufacturers can increase productivity while lowering the cost of equipment failure thanks to AI-powered predictive maintenance.

  1. Designing: In addition to easing the maintenance using predictive analytics, AI may assist companies to create and design products/items. Here's how it works: generative design algorithms are fed design goals by a designer or engineer and these algorithms then generate design to emulate an engineer's approach to design. Designers or engineers enter design criteria into generative design software (such as materials, size, weight, strength, production processes, and cost limits), and the software generates all conceivable outcomes based on those factors, and produces design alternatives by exploring all conceivable variations of a solution. Finally, machine learning is used to test and enhance each iteration. Manufacturers may swiftly produce hundreds of design choices for a single product using this technology.

  1. Robotics: Industrial robots, often known as manufacturing robots, automate repetitive operations, reducing or eliminating human error, and allowing humans to focus on more productive aspects of the operation. There are many applications of robots in a manufacturing plant. Assembly, welding, painting, product inspection, picking and putting, die casting, drilling, glass manufacturing, and grinding are some of the applications. Another robotics use is cobots, which employ machine vision to operate securely alongside human workers to do tasks that cannot be entirely mechanized. 

  1. Assembly line integration and optimization: Many pieces of manufacturing equipment now send massive amounts of data to the cloud. Unfortunately, this data is often compartmentalized and does not interact well. Obtaining a comprehensive view of your organization necessitates the use of many dashboards and the assistance of a subject matter expert. You may assure that you're receiving a God-like vision of the business by designing an integrated app that draws data from the breadth of the IoT-connected devices you utilize.

Furthermore, by incorporating Artificial Intelligence into your IoT environment, you may automate a range of processes. Supervisors, for example, are notified when equipment operators exhibit indications of weariness. When a piece of equipment fails, the system can initiate contingency planning or other rearrangement actions automatically.

Artificial Intelligence's Future in Manufacturing Industries

It is quite likely that the Manufacturing Industry will see an empowering development with the application of AI, provided organizations can maintain inventories lean and minimize costs. 

Detecting flaws in the manufacturing process, reducing downtime by implementing predictive maintenance, Responding to changes in demand real time across the supply chain, examining whether delicates such as microchips were manufactured flawlessly, reducing the cost of single-run or small-batch items, allowing for more personalization, shifting monotonous duties to automation to improve employee happiness - are the advantages that AI will offer to the manufacturing industry in the coming future. 

Having said that, the manufacturing industry must prepare for well-organized manufacturing facilities in which the supply chain, design team, production line, and quality control are all tightly integrated into an intelligent engine that generates valuable knowledge insights.

Conclusion-

As you can see, artificial intelligence is becoming a part of commercial solutions across the manufacturing industry's whole value chain. These AI solutions provide firms with immediate chances to deploy and execute on top and bottom-line goals, as well as lead them towards more sophisticated use cases in the future.

Combining AI/ML with other technologies like sensors, robots, and human inputs would enhance operations considerably and lead to new kinds of innovation and productivity in the business. While your business may lack the requisite skill sets, don't allow that to stop you from investing in commercial AI/ML solutions to get you started.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.

 

The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:

Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online

Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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