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How AI Powers The Food Processing Industry

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How AI Powers The Food Processing Industry

Can computers understand food? Can they smell aromas or taste flavours? Well, with Artificial Intelligence (AI) taking the world by storm, the food industry is not outside the purview of AI’s midas touch. In fact, AI is expected to spur the industry on to the path of growth and expansion.

According to some sources, AI in the food and beverages market is expected to register a CAGR of 28.64 percent, during the forecast period 2018-2023. According to others, today, the Food Product and Handling industry is capped at a whopping $100 billion and will continue to grow at a CAGR of 5% at least till 2021.

Here are ways in which AI is fostering the highest standards of processing and handling of food products across the world.

Sorting

One of the most important tasks in a food-processing unit is sorting. Sorting fresh produce by size, colour and quality is the first thing to be carried out and it is time consuming. For instance, sorting potatoes by size and colour will determine whether a food giant will get French fries, hash browns or chips made out of them. Herein comes the role of AI powered machines. Companies like TOMRA Sorting Food have developed sensor-based optical sorting solutions with machine learning capabilities that use cameras and near-infrared sensors to “view food in the same way that consumers do” and sort it based on that perception, says a report. This results in fewer hours spent on manual sorting, higher yields, less wastage and better quality of prepared food.

Managing Supply Chain

With newer food safety regulations being introduced ever so often and a need for transparency growing by the day, it has become imperative for food and beverage companies to put in place robust supply chain management. There are several ways in which this is being done including food safety monitoring and testing of product at every stage of the supply chain and accurate forecasting to manage pricing and inventory.

Personal Hygiene Maintenance

Maintenance of personal hygiene for everyone entering and exiting a food-processing unit is of utmost importance. In 2017, tech company Kankan signed a big deal to provide AI-powered solution for improvement of personal hygiene among workers of food processing units in China. It uses face recognition technology to detect if workers are violating rules that ensure they wear masks and caps to maintain proper hygiene at work. According to Kankan this technology is accurate by over 95 per cent.

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Cleaning processing equipment

This process is time consuming and essential to the supply chain. However, researchers are using AI to come up with better technology to reduce time taken and resources spent on cleaning equipment. For instance, researchers at the University of Nottingham have been developing a system that uses AI to reduce and cut down cleaning time and resources by 20-40 per cent. The system known as self-optimising-clean-in-place uses ultrasonic sensing and optical fluorescence imaging to detect food residue and microbial debris in equipment and facilitate cleaning of the same.

Thus, the importance of AI in various sectors of the economy cannot be stressed enough. For more on how AI powers the IT industry, read DexLab Analytics’ blog here and to know more on how AI powers space exploration read its blog here. DexLab Analytics is a premier institute offering artificial intelligence certification in Delhi NCR.

 


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Top Programming Languages That AI Engineers Can Choose From in 2020

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Top Programming Languages That AI Engineers Can Choose From in 2020

Artificial Intelligence, the science of making computers function with the intelligence of the human brain sans the intervention of human beings, is the biggest find of the century. It is powering everything from our personal email to space exploration.

It is, thus, imperative to discuss the very platforms that make AI a reality. Computer programming languages too are fast evolving and there is no one such language that fits the needs of an AI engineer comprehensively. So we shall examine the plus points of the most popular programming languages to choose from this year.

  1. Python

Python is an easy-to-learn programming language that helps AI novices enter the world of programming easily. Python not only has an excellent repository of libraries and a strong community support on the Internet, it is also extremely flexible as a programming language. Platform independence and extensive frameworks that are most needed for Deep Learning and Machine Learning are advantages Python boasts of. Some of its most widely used libraries are Tensor Flow, Scikit-Learn, PyTorch, Keras, SparkMLlib, MXNet and Theano.

  1. Java

Java, widely held to be the best programming language in the world, has two decades worth of testimonials by AI engineers to back the claim. It is highly user friendly and flexible in nature, equipped with excellent platform independence. It is therefore widely sought after for developing AI models. It has some very strong libraries like TensorFlow, Deep Java library, Kubeflow, Open NLP among others.

  1. R

R, created in 1995, is currently maintained by the R Development Core Team. It is the implementation of S programming language. It helps AI engineers develop statistical software and data analysis. What makes it robust, as a language for developers, is the fact that it facilitates crunching of large numbers. In this regard it scores over python. Moreover, says a report, with R you can work on various paradigms of programming such as functional programming, vector computation, and object-oriented programming.

  1. Prolog

Short for Logic Programming, this language is especially suited to build AI models on and develop NLP packages with. For instance, Prolog is used to build chatbots very effectively. Eliza, the first ever chatbot, was built with the assistance of Prolog. “Prolog offers two approaches for implementing AI that has been in practice for a long time and is well-known among data scientists and researchers: The Symbolic Approach that includes rule-based expert systems, theorem provers and constraint-based approaches and the Statistical Approach that includes neural nets, data mining and machine learning.”

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There are many more languages to choose from. Lisp, Julia and Haskell are some of these strong and worthy languages AI engineers can choose to use besides the ones listed above. Every programming language has its own merits and demerits. It is upto the AI engineer to choose wisely after conducting a thorough research and doing due diligence. Dexlab Analytics, a premier artificial intelligence training institute in Gurgaon, suggests the use of Python and R for building AI models.

 


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How AI is Transforming The IT Industry

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How AI is Transforming The IT Industry

Artificial Intelligence, the science of making computers function with human-like intelligence, has taken the world by storm. It has transformed the biggest of businesses and industries, from healthcare to agriculture to space exploration. Artificial Intelligence has already become the biggest find of the century.

Information Technology, related to all things computers, software and data transmission, cannot be untouched by artificial intelligence. AI has already brought several advantages to the IT sector, a subject that we are going to examine in this blog brought to you by DexLab Analytics, a premier artificial intelligence training institute in Gurgaon.

Secure Systems

Security of data is of prime importance in today’s world where data is the new oil. Both government and private organisations are therefore striving to better protect the tons of data they are privy to. Through the use of algorithms, AI can provide the necessary security and help create a layered secure system. Not only that, it can also help detect security breaches and potential threats. According to a report, AI and Machine Learning have become crucial to data security in the IT industry. 

Productivity Enhancement

In the IT industry, the most important thing developers are expected to perfect is programming. However, they face numerous challenges in the course of their stints in IT companies with problems like bugs in code and erroneous code marring their goals. AI can be used to solve this problem in that a series of algorithms can be used to aid programmers write better and bug-free code. By judging the structure of the code, AI powered systems can provide suggestions that can improve productivity and help save time in the production process.

Automating Backend Processes

AI is a great enabler of automation, work that can be accomplished without or with minimal human intervention. With the use of deep learning techniques, AI can go a long way in helping automate back end processes in IT companies. This will not only help save costs but also increase accuracy and reduce human effort. “AI enabled methods improve over time as the algorithms adjust to enhance productivity and learn from mistakes,” says a report.

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Application Deployment

During software development, deployment control involves various stages and this means that the software versioning control is advantageous and crucial to the deployment stage. AI is known for its predictive powers and capabilities. It can thus be used for predicting problems during the versioning stage. This eases the entire chain of processes because programmers and developers do not have to wait till the last stage to know about hiccups or improve the application’s processes.

Server Optimization

In IT offices and workplaces, computer servers are more often than not loaded with requests in the millions. They in turn have to open those many corresponding web pages, a process that can make them slow and unresponsive. AI, as a service, can help solve this problem by optimising the host server to improve customer service and enhance operations. As the demand for IT increases across business sectors, AI will be increasingly used to integrate staffing demands and provide a seamless integration of current business functions with technological ones.

—EOM—

 


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Using Deep Learning To Track Tropical Cyclones: A Study

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Using Deep Learning To Track Tropical Cyclones: A Study

The severe cyclonic storm Nisarga approached the Maharashtra coast around Alibagh in Raigadh with “a sustained wind speed of 100-110 kmph” on June 3, 2020. Then it made landfall at Alibagh at around noontime. Landfall simply means that the storm, after having intensified over the ocean, has moved on to land.

Though the storm mellowed down in intensity as it approached the Maharashtra coast, government bodies took all precautions and evacuation work was done in advance on the basis of forecasts done by meteorologists and scientists.

To save lives and property, it is imperative to predict cyclones and the intensity with which they will strike. Deep Learning, a branch of artificial Intelligence, is helping scientists make breakthroughs in the science of forecasting cyclones.

Image Source: outlookindia.com

Existing Storm Forecast Models

Most conventional dynamical models make accurate short term predictions but they are computationally demanding and “current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing”, says a report.

A tropical cyclone forecast involves the prediction of several interrelated features like track, intensity, rainfall, storm surge etc. The development of current hurricane and cyclone forecasts have advanced over the years but they are largely statistical in nature. The main limitation of this method is the complexity and non-linearity of atmospheric systems.

Deep Learning Models

Recurrent Neural Networks in deep learning models have been, of late, used to study increasingly complicated systems instead of the traditional methods of forecasting because they promise more accuracy. RNNs are a class of artificial neural networks where the modification of weights allows the model to learn intricate dynamic temporal behaviours, says another report.

An RNN with the capability of modelling complex non-linear temporal relationships of a hurricane or a cyclone could increase the accuracy of predicting future cyclonic path forecasts.

Machine Learning

Generally speaking, there are two methods or approaches to detecting extreme weather events like tropical cyclones – the data driven method which includes machine learning and the model driven approach which includes numerical simulation.

“The model-driven approach has the limitation that the prediction error increases with lead time because numerical models are inherently dependent on initial values. On the other hand, machine learning, as a data-driven approach, requires a large amount of high-quality training data,” says a report.

High quality data is easy to procure given the large amounts of data generated from weather stations on a daily basis the world over. So the machine learning method is easier to work and generate results from.

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Conclusion

So what was difficult to do, that is find suitable metrics to study and detect the path of tropical cyclones earlier, has now become easier to do and scientists have been able to achieve accuracy in their predictions through the use of neural networks and artificial intelligence in general. For more on the subject, do read our blog here and here. Dexlab Analytics is a premier Deep Learning training institute in Delhi.

 


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How Artificial Intelligence Powers Earthquake Prediction

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How Artificial Intelligence Powers Earthquake Prediction

Artificial Intelligence is the key to the future of weather forecasting, a fact well known. But did you know it is also powering earthquake prediction the world over? Yes. Artificial Intelligence techniques like machine learning are gradually being enlisted in forecasting seismic activity.

While earthquake prediction has not yet become an exact science, efforts are on to make improvements and make forecasts reliable. For this, AI powered neural networks, the same technology behind the success of driverless cars and digital assistants, is being used to enhance research based on seismic data.

Neural Networks

A report says that, “Scientists say seismic data is remarkably similar to the audio data that companies like Google and Amazon use in training neural networks to recognize spoken commands on coffee-table digital assistants like Alexa.”

When it comes to studying earthquakes, it is the computer, a fast and able machine, looking for patterns in mountains of data rather than relying on the weary eyes of a scientist. Also, instead of a sequence of words, what the computer is studying is a sequence of ground-motion measurements.

Studying Aftershocks
Image Source: cbs8.com

Studying Aftershocks

Scientists in the US have experimented with neural networks to accelerate earthquake analysis and the speed at which they were producing results and studies was 500 times faster than they could in the past. Also, AI is not only useful in studying earthquakes but it is being used in forecasting earthquake aftershocks as well.

In fact, researchers say it is a time of great scientific advancement, so much so, that “technology can do as well as — or better than — human experts”.

Artificial Intelligence
Image Source: smithsonianmag.com

Artificial Intelligence

Geophysicist Paul Johnson’s team in the US has been studying earthquakes for quite some time now and it has made advancements in “using pattern-finding algorithms similar to those behind recent advances in image and speech recognition and other forms of artificial intelligence, (where) he and his collaborators successfully predicted temblors in a model laboratory system — a feat that has since been duplicated by researchers in Europe”, says a report.

Now Mr Johnson’s team has published a paper wherein artificial intelligence has been used to study slow slip earthquakes in the Pacific Northwest. While advancements are being made in the field of studying slow slip earthquakes, it is the bigger and more potent ones that really need to be studied. But they are rare. So the question remains – Will Machine Learning be able to analyse a small data set and predict with confidence the next big earthquake?

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Machine Learning 

Researchers claim “that their (machine learning) algorithms won’t actually need to train on catastrophic earthquakes to predict them.” Studies conducted recently suggest “seismic patterns before small earthquakes are statistically similar to those of their larger counterparts”. So, a computer trained on hundreds and thousands of those small temblors might be able enough to predict the big ones.

For more on artificial intelligence, and its varied applications, do peruse the DexLab Analytics website today. DexLab Analytics is a premier institute in India offering Machine Learning courses in Delhi.

 


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How AI Powers Space Missions Like Those of SpaceX’s– A Study

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How AI Powers Space Missions Like Those of SpaceX’s– A Study

On May 30, 2020 a SpaceX Falcon 9 rocket carrying Crew Dragon was launched at 3:22 p.m. US Eastern Time from the Kennedy Space Center, this being the first time a space mission was launched by NASA since it decommissioned its ageing and unsafe Space Shuttle fleet in 2011. The rocket was successful in deploying the vehicle into orbit and safely returning to Earth.

The mission

Aboard the Crew Dragon are astronauts Bob Behnken and Doug Hurley who are to be launched into the International Space Station. The mission marks the first time a private company has pulled off a crewed mission into low Earth orbit, a report said. The Crew Dragon and its self-landing, reusable Falcon 9 rocket is owned by SpaceX, who’s founder and CEO is Elon Musk. NASA just rents the spacecraft and the rocket at a cost of around $55 million per passenger, said another report.

AI in SpaceX’s mission

But did you know this historic mission is powered by the cutting edge technology of artificial intelligence? Yes! A sophisticated AI autopilot steers the cone-shaped Crew Dragon that is on its way to the ISS. Once the Crew Dragon reaches within 60 feet of the space station, the astronauts will maneuver the vehicle to the ISS and remain in space for weeks on end, depending on when they are called back. In 2018 too, a SpaceX rocket flew into space with the first robot powered by artificial intelligence.

Image Source: businessinsider.in

AI in Indian space missions

India too has been generating indigenous AI technology to power its space missions. Take for example its Chandrayaan-2 mission that was launched in July 2019. Scientists integrated AI technology with Chandryaan-2’s rover – Pragyan. A report said, the Indian Space Research Organisation delivered Pragyan – a solar-powered robotic vehicle that was to explore the lunar surface on six wheels. Moreover, “the artificial intelligence algorithm could also help the rover detect traces of water and other minerals on the lunar surface.”

How AI powers space exploration

AI helps analyze the huge amounts of data emanating from space exploration and this helps advance space exploration with each passing day. Moreover, AI is making it possible for rovers currently roving the atmosphere of Mars to take decisions independent of the mission. The NASA Curiosity rover can dodge obstacles on its route by itself and determine the best route possible. Data received from space is mainly in the form of images that are studied through machine learning techniques at the NASA Frontier Development Lab that has roped is the services of tech giants like IBM and Microsoft.

Infact, machine learning is helping in solar storm damage detection, atmosphere measurement, and determining the ‘space weather’ of a given planet through the magnetosphere and atmosphere measurement. Reports say the same technology is used in resource discovery in space. Moreover, AI applications “can optimize planetary tracking systems, enable smart data transmission, and nullify the risk of human error (by using predictive maintenance),” said this report.

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So artificial intelligence is finding advanced applications is all sectors of the economy and it is clearly becoming indispensable to our lives. In India, institutes are trying hard to inculcate the science of artificial intelligence in the Indian workforce, an effort that is directly resulting in the founding of a great many challenging courses like artificial intelligence certification in Delhi NCR.

 


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How AI is Transforming e-Commerce

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How AI is Transforming e-Commerce

AI is transforming our world in more ways than one with the advent of self-checkout cash registers to security checks at airports. However, in this essay, we look at how AI is transforming the world of e-Commerce.

Already, tech giants are investing heavily in AI and machine learning systems to power their startups and new sources of business. A report, infact, shows that “a recent study by Business Insider suggests that as much as 85% of customer interactions will be managed without a human by as soon as 2020.”

Here is a short list of ways in which businesses are using AI to better understand their customers, generate new leads and provide an enhanced customer experience.

Customer-centric search

Companies like Twiggle are using NLP to “narrow, contextualise and ultimately improve search results for online shoppers.” This is being done to prevent customers from abandoning searches due to a high volume of irrelevant search results showing up on screens.

Also, with enhanced visual capabilities of AI powered software, “AI is enabling shoppers to discover complementary products (by) size, colour, shape, fabric or even brand”.

Retargeting customers

Many “businesses are overloaded with unmanageable customer data that they do little or nothing with. This is an incredible goldmine of intelligence that could be used to enhance the sales cycle.”

As AI develops, it can detect customer dwell time on e-Commerce platforms and strategize marketing offers for customers. “In other words, omni-channel retailers are starting to make progress in their ability to remarket to customers.”

More efficient sales process

Customers are spending more and more time on television and social media. So, if you “want to tailor your problem-solving solutions and create a strong sales message that reaches consumers at the right time on the right platform, then integrating AI into your CRM is the way to go.”

Many AI systems enable NLP and voice input systems like Siri or Alexa that allow them to answer customer queries, solve problems and also identify new arenas of opportunities for the sales team.

Personalization across multiple devices

Personalization is the crux of e-Commerce, like you must have experienced on platforms like Amazon. However, with each passing day, AI systems are becoming more sophisticated with deeper levels of personalization penetrating the ecommerce world.

“Whether it is a mobile application, the website, or an email campaign, the AI engine is continuously monitoring all devices and channels to create a universal customer view. This unified customer view enables e-Commerce retailers to deliver a seamless customer experience across all platforms.”

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Chatbots

Moving from mass-market sales to individualized marketing, many e-Commerce retailers are becoming more sophisticated with their AI capabilities “in capturing attention, and one approach widely developing is known as ‘conversational commerce’.”

“In the e-Commerce world, this is the confluence of visual, vocal, written and predictive capabilities. ”Chatbots are one such simulation system that goes a long way in building customer service relations. To read more about chatbots, do go through our previous blog on the subject.

DexLab Analytics, a premiere artificial intelligence training institute in Gurgaon, trains professionals in the latest technological advancements available.

 


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How AI is Assisting The Science of Weather Prediction in Times of Cyclones Like Amphan

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How AI is Assisting The Science of Weather Prediction in Times of Cyclones Like Amphan

The Amphan super cyclone last week ravaged West Bengal in India and parts of Bangladesh, killing scores of people, damaging houses, uprooting trees and electricity poles and flooding large swathes of land. The category 2 storm, with wind speeds over 150 km per hour was predicted several days earlier and this helped the concerned governments evacuate thousands of people before it struck.

Thus, we can hardly emphasise the importance of weather forecasting and prediction in today’s world, especially when climate change is at it severest. By some estimates, for instance, in 2016 there were nearly 800 weather related disaster events, thrice the number recoded in 1980. And even if all countries adhere to the Paris climate pledges, by 2100, it is likely that average global temperatures will be higher than pre industrial times by 3 degrees.

Artificial Intelligence

It is therefore imperative for us to study how Artificial Intelligence and its many branches like machine learning, deep learning and neural networks are helping predict weather events. For instance, researchers at Rice University have developed a deep learning model that can predict heat waves and winter storms, i.e. extreme weather conditions. The model was trained by studying hundreds of maps that showed surface temperatures and air pressures. After training, the model was used to read maps it had never seen before and it predicted weather conditions with upto 85 percent accuracy.

Machine Learning

Similarly, Microsoft is investing huge sums of money in ‘AI for Earth’, its flagship project committed to developing machine learning models to predict weather conditions. It has given grants to Columbia University professors to study the pattern of tree distribution in storm affected areas of America by processing thousands of images. In turn, the prediction model studies how much Carbon Dioxide decomposed vegetation is emitting into the atmosphere resulting in global warming.

Deep Neural Networks

Deep neural networks (DNNs) are gradually replacing physics based models of weather forecasting that have been in use for decades now. DNNs are being used to supplant parameterization of physical schemes in the traditional weather forecasting model in the US. DNNs help save on computational complexity in the forecasting process and help in scalability without compromising the model’s prediction accuracy. Humidity, wind velocity, temperatures and much more can be studied and predicted using DNNs.

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In India

Last year the Ministry of Earth Sciences organised an event wherein officials said artificial intelligence will be used to predict extreme weather conditions in India. In an article, it was reported that a top ministry official said, “Society needs information about extreme weather events at least 7 days in advance which we are yet to achieve. We are working to provide impact based forecast to society that will tell people about how it will impact their lives. To achieve this, we are going to use artificial intelligence and machine learning to help in improving our understanding of weather and climate phenomena and their forecasting.”

Thus, it is no surprise that more and more institutes in India are gearing themselves for the AI revolution in the country. DexLab Analytics is one such artificial intelligence training institute in Gurgaon to look out for.

 


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How AI is Powering Manufacturing in 2020

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How AI is Powering Manufacturing in 2020

The world has seen a transformation in its economic activities since the coronavirus pandemic broke out. Economies have come to a grinding halt and manufacturing has dipped. Now what nations need is resilience and strength to carry on production in all sectors. What they are most depending on is the power of Artificial Intelligence to enhance the manufacturing process and help save money and drive down costs.

Here are some examples of how AI is powering the manufacturing sector in 2020.

  • AI is being used to transform machinery maintenance and quality in manufacturing operations today, according to Capgemini.
  • Caterpillar’s Marine Division is using machine learning to analyze data on how often its shipping equipment should be cleaned helping it save thousands of dollars.
  • The BMW Group is using AI to study manufacturing component images in and spot deviations from the standard production procedure in real-time.

In fact, a study shows that in the four earlier global economic downturns companies using AI were actually successful in increasing both sales and profit margins. Companies are all striving to utilize human experience, insights and AI techniques to give manufacturing a fillip in these times of a crisis.

Manufacturing using AI in real-time

Real-time monitoring of the manufacturing process is advantageous because it translates to sorting out production bottlenecks, tracking scrap rates and meeting customer deadlines among other things. The huge cache of data used can be utilized to build machine learning models.

Supervised and unsupervised machine learning algorithms can study multiple production shifts’ real-time data within seconds and predict processes, products, and workflow patterns that were not known before. A report  suggests 29% of AI implementations in manufacturing are for maintaining machinery and production assets.

Detecting Outages

It was found that the most popular use of AI in manufacturing is predicting when equipment are likely to fail and suggesting optimal times to conduct maintenance. Companies like General Motors analyze images of its robots from cameras mounted above to spot anomalies and possible failures in the production line and thus preempt outages.

Optimizing Design

General Motors uses AI algorithms to give and produce optimized product design. General Motors can achieve the goal of rapid prototyping with the help of AI and ML algorithms. Designers provide definitions of the functional needs, raw materials, manufacturing methods and other constraints and the company along with AutoDesk has customized Dreamcatcher to optimize for weight and other vital criterion. In this way, AI comes together with human endeavor to produce a-class product designs that cost lesser.

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Inconsistencies

Nokia has begun using a video application that takes the help of machine learning to alert an assembly operator if there are inconsistencies in the production process in one of its factories in Oulu, Finland. It alerts a machine operator about inconsistencies in the production of electronic items and this helps preempt poor production process and helps the company save on a lot of money and capital.

There are many other production processes AI is helping revolutionize. Only time will tell how much of AI will power the manufacturing sector. But this technological advancement is surely making an impact on economies worldwide. Meanwhile, for more details, do peruse the DexLab Analytics website. DexLab Analytics is a premiere machine learning institute in Gurgaon.

 


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