How Artificial Intelligence is shaping the food industry?

May 16, 2022

Food processing and handling is the most important sector out of the various industries in the world. This sector is subsidized by the greatest employment as human labor is critical to the effective execution of food product production and packaging. But in contrast to this, food companies are failing to sustain the demand-supply cycle and are weak in food safety as a result of human participation. 

To address these challenges in the food industry, industrial automation is the ideal answer. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms are at the heart of automation. Food manufacturing and distribution procedures may be handled more efficiently and effectively by adopting an AI-based system.

But when most people see the use of AI in kitchens manned with robots capable of cooking, they think that AI will eventually replace all human employees. This is a gross exaggeration. While this isn't wholly science fiction (there are cafés where robots conduct minimal meal preparation), it isn't the most prevalent manner that AI is transforming the restaurant industry. 

It is not hard to feel astonished at how many possibilities AI has for optimizing, automating, and improving the food business.

Product classification and packaging

One of the time-consuming and tiresome jobs for production units in the food processing business is the appropriate ordering and packaging of food. As a result, such a time-consuming activity may be done by AI-based systems, reducing the possibility of error and substantially increasing the industry's output rate.

Following this, the majority of product sorting and packing operations are now done by an automated system. Industries realized benefits from employing such AI-based intelligent decision-making systems. These systems provide faster production rates, higher quality products, and lower labor costs.

AI-based intelligent decision-making systems use a number of tools and methodologies, such as laser-technology-based systems, X-ray-based systems, high-resolution cameras,  and infrared spectroscopy. At the input channel, these techniques and technologies are utilized to assess every element of food goods whereas conventional systems were only able to distinguish between excellent and bad items based on their look.

AI helping in the Management of the Demand-Supply Chain

As long as food companies are concerned about food safety rules, they must be more transparent about the journey of food items in the supply chain system. AI is used to monitor each stage of the process. It handles everything from pricing to inventory management. It also handles predicting and tracking the course of belongings from where they are grown to where clients acquire them. Systems powered by AI, offer transportation booking, billing, and inventory management. These systems also promote discipline by preventing the acquiring of a large number of items, which would result in material degradation.

Revenue Prediction Using AI & ML

The quality of food and services provided by the owners is critical for an operating business such as a restaurant or food outlet. Aside from food and services, forecasting restaurant sales output is also an important aspect of the business. The owner of a food chain or restaurant must develop a solid business plan for their future operations in order to enhance business development and profit. Using Artificial intelligence multiple fitting algorithms can be employed to construct a sales prediction. 

In the food sector, establishing an appropriate fitting algorithm for sales forecasts, such as five months' or 14 months' sales prediction, takes a significant amount of time and effort. In this day and age of data, it is feasible to obtain sales forecasts at the touch of a button. AI and ML leverage this data to enable the discovery of the most appropriate algorithm for a certain business and easy algorithm deployment inside the same company with zero chances of any fault.

Use of AI in Self-Ordering Systems

Customers prefer point-of-sale (or self-service) systems, particularly at well-established restaurants. These systems use AI to help clients purchase by providing precise information about the flavors or spices used, their preferences, and even freshly added things. Every restaurant that employs automated systems is now using these technologies. Point-of-sale technology has aided restaurants in dealing with issues such as staff shortages, client engagement, and incorrect orders.

Food vending machines using AI in their applications

After deciding on its menu and marketing approach, a food-selling company must establish a dependable system for supplying its services to the public online. This system can be an online site that offers rapid ordering and suggestions, or it can be a mobile application that has additional benefits such as incorporating artificial intelligence (AI)-based systems. 

It is a good idea to add one's food-selling company to these AI-based systems, because of the rising number of food-based e-commerce apps. This will enable the businesses to have the greatest functions of these e-commerce apps without spending additional money, building them for themselves. 

AI may also aid in the development of an automated customer-service sector, allowing the company to conduct administrative activities such as consumer grievance redressal, assigning personnel, and preparing reports more effectively.

Artificial Intelligence to Manage Food Waste

Deploying Artificial Intelligence (AI) and Machine Learning (ML) technologies assist in waste management, expanding operations and keeping relevant in the changing market environment of the food business. 

By lowering food waste by 2030, AI can tackle this challenge and create USD 127 billion in potential, beginning with regenerative agriculture techniques. Currently, it appears that the field of AI in the food and beverage business is dominated by innovative start-ups and tech firm collaborations building AI and machine learning algorithms to address specific difficulties. 

The big firms utilize artificial intelligence to differentiate between different forms of food waste. Smart scales, AI-guided intelligent cameras and other technologies are being used to reduce food waste and check food quality. To recognise the thrown food, the system is programmed using AI and machine learning techniques.

Cleaning in an Innovative Way

Clean-in-place (CIP) is an efficient and effective method of cleaning equipment, although it consumes a lot of water. are working on a device that employs. Many researchers are using ultrasonic and UV sensors to deliver feedback that may be used to cut water use.

The ultrasonic sensors are attached to the exterior of pipes and equipment, whereas the UV system is installed within the top of a tank and includes UV lamps and a camera. Both technologies monitor fouling on a surface and train artificial intelligence models to detect when all fouling has been eliminated. 

Conclusion

While artificial intelligence is having a significant influence on the food and beverage business, it is still in its infancy. Due to the expenses associated with their deployment, such technologies are now mostly utilized by major manufacturers. And while many less tech-savvy food enterprise owners may find AI daunting, the simple fact is that technology has earned its place in the food industry's future. AI is here to stay, and with it come several advantages for independent eateries that embrace the trend.

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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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