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How AI is Changing The Way You Conduct Business?

How AI is Changing The Way You Conduct Business?

In the pages of sci-fi, we find a world that is completely driven by AI, but, we no longer have to wish for a dystopian world to become a reality to experience AI firsthand. Our world as we know it is gradually being reshaped by the powerful presence of AI. From your Smartphone to virtual assistants, AI is taking slow but firm strides which is evident from the high demand for courses like artificial intelligence certification in delhi ncr.

So, when everything is being impacted by AI, why should your business lag behind? You would be surprised to learn the incredible changes AI could bring to the table.

Be it managing administrative tasks or, ensuring data security, AI could streamline operations and add efficiency to every task that needs to be performed to ensure zero error. Here is how you can make the difference
 

  • Implement AI to smoothen your HR practices: Managing your employees and recruiting new ones are essential tasks for any organization. Your HR department can be more efficient with their tasks at hand with the help of machine learning. Dealing with scores of applications while recruiting new faces can be a mundane task. But, now processing applications and sorting out the ones that match the criterion gets easier and so does the entire hiring process. Throw in chatbots in the interview process to ensure that you select the right candidates.
  • Lessen errors during manufacturing: During the manufacturing process, oftentimes  one or, two faulty products end up spoiling the image of the brand and most importantly you end up spending time, money, and personnel for return and refund procedure. To err is human but, with AI coming into play, any kind of glitch could easily be detected and prevented at an early stage. This would ensure that only quality products are ending up in the showrooms.
  • Customize marketing strategies: It is so hard to guess what your customers want and even more difficult to chalk out a marketing plan around that guesswork. However, advanced analytics could help you keep a track of consumer behavior because it is purely based on data. So, now that you have eliminated the guesswork, you can come up with a marketing strategy that actually works to entice customers.
  • Provide customer service round the clock: While running your business you must have noticed how difficult and essential it is to stay in touch with constant queries. People who are buying from you might have umpteen number of queries and you need a big team to handle that. However, despite your best efforts, some queries go unanswered, this might create a negative impact on the customers. So, why not take the help of chatbots who can handle all the primary queries round the clock and provide customer satisfaction? Critical queries could be handed over to a support team with specialized knowledge.

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You can do more with AI-powered tools and systems to make your business grow. The world is moving with AI towards a brighter future and, it is time for you to welcome and embrace its power.

 


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AI’s Contribution to Photography: An Assessment

AI’s Contribution to Photography: An Assessment

The next time you are buying a smart phone or a digital camera, instead of concentrating on the lens specifications, try to find out what the manufacturer has to say about artificial intelligence. Because the nature of photography has changed for good in a world fast recalibrating to keep pace with the exigencies of advanced computing. In this article we will examine how artificial intelligence has transformed the way the world looks at photography now.

Smart Devices

Photography, the idea of sharing with others what we can see through a lens, has been a long developed art with teachers and mentors passing down acquired skills to protégés and juniors for years now. However, with the advancement of AI’s uses in the photography industry, things have changed tremendously.

Take for instance Apple’s A11 Bionic neural engine chip that powers the latest generation of iPhones. The chip is fitted with AI technology that assists in image and face recognition, AR applications and more. Google Pixel then came out with its high-tech hardware chip designed for dedicated image enhancement and image processing.

The Chinese smartphone, Huawei’s P20 Pro, features four cameras. Besides achieving the highest DxO Mark score to date, the Huawei P20 Pro is packed with AI features, such as real-time image scene recognition, meaning it can discern 500 scenarios in 19 categories, such as animals, landscapes, as well as an advanced night mode, where the AI assists in processing noisy photos, making them almost perfect, says a report.

So it has become the norm for smart devices to have inbuilt AI powered hardware that help enhance the process of photography unlike traditional cameras. Manufacturers are concentrating on image capture and real-time processing because it is a market differentiator.

Processing professional photographs

Professional photography needs to be processed and cannot be used in the RAW format. But the procedure has been enhanced by AI technology lately. For instance, recently, PetaPixel released a research paper that talked about how extremely underexposed images can be retrieved through techniques wherein AI is applied to the digital images. This technology can be used in high-end security cameras as well.

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Photo optimization

Photo optimisation is what AI has been able to take to the next level. A team of AI developers at Skylum is working on technology that will allow smartphone images to be expanded and printed with very high resolution and sharpness. This technology will help consumers lagging behind with older smart phones and old technology to optimise photos taken years ago. Other companies are trying to build technology that will compress RAW images up to 10 times the original heavy files without loss of data. 

It might seem like AI is intruding into the art space of photography, especially for professionals who have spent years honing the art of taking and editing photographs. But for the common user, AI powered technology is a boon and this technology is being sought by the best tech companies across the world. In India, artificial intelligence certifications in Delhi NCR are springing up to cater to a growing clientele that wants to join the AI revolution.

 


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5 Crucial Subsets of Artificial Intelligence

5 Crucial Subsets of Artificial Intelligence

As simply as can be said, artificial intelligence is a machine’s ability to replicate human intelligence and accept new inputs and perform tasks on them like human beings, learning from experience. The term was coined in the 1950s but it has today come to significantly become popular in relation to large data sets and new advanced algorithms.

AI has become the most revolutionary advancement in computing science and it is powering all sectors of the economy from banking to healthcare and agriculture today. There are many sciences branching out of artificial intelligence like machine learning, deep learning, neural networks, computer vision and robotics. Let us learn a little about each of these.

Machine Learning

Machine Learning, a crucial subset of artificial intelligence, is the machine’s ability to learn from experience with no need for human intervention explicitly. It is the most widely used form of AI in the market today. Machine Learning refers to the computer programs that are fed data, learn from them and use this experience to take intelligent decisions. Machine Learning is used in analysis, fraud detection and GPS based predictions to name a few.  

Neural Networks

Neural networks are a bunch of algorithms modelled after the neural networks that make up the human brain. They are designed to absorb and assimilate and interpret sensory data through labelling or clustering row input. The patterns they sense and interpret are in the form of numerical data, a format all text, images or even sounds must be translated into for a computer to understand. They help label, cluster and classify data based on similarities in the input fed.  

Deep Learning

Deep Learning is a technique of machine learning that uses neural networks to learn up the way humans do – by example. And it does them accurately. It is the science behind driverless cars that can distinguish a lamppost from a person. Deep learning requires a large amount of labelled data sets to be able to work and effective and substantial computing power. Deep Learning finds its applications in aerospace technology, healthcare and driverless locomotives industry among others.

Robotics

A robot is a machine capable of sensing and interpreting and interacting with its environment. Robots have become much smarter and intuitive, thanks to artificial intelligence. Robotics is an interdisciplinary field of science and engineering that is powered by a consolidated science of mechanical engineering, electrical engineering, computer science, and algorithms. Robots are used in automobile manufacturing and used to move objects in space or related fields.

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Computer Vision

Computer Vision is the field of study that seeks to enable computers to “see” virtually like the human eyes do. A computer learns by labelling or classifying various objects, albeit much faster than human beings. Its goal is image classification and recognition. The Internet is inundated with pictures and photographs. In order to search for these images, the computer system needs to know what is in them. This is where the technology of computer visions comes in.

So you see how vast the scope of artificial intelligence really is. It is a science unto itself. To learn more about the science, professionals are increasingly joining artificial intelligence training institutes across the world. DexLab Analytics, the institute that brought this article to you, is a premiere artificial intelligence training institute in Gurgaon.

 


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Libraries In The Era of Artificial Intelligence

Libraries In The Era of Artificial Intelligence

Artificial Intelligence has entered our homes and our workplaces in more ways than one. From our email services to smart vacuum cleaners and more, AI has made life easier and smoother for us. It is no surprise then that libraries, the most crucial resources we have for research, are embracing the powers of AI to streamline the vast repository of material housed by them. Here is a list of applications of AI in the library ecosystem the world over.

Expert Systems

Expert Systems are knowledge based computer systems that play a role as intelligence interfaces for providing access to a database or knowledge system. Libraries can use Expert Systems to facilitate reference services to users and members. For instance, ES provide recommendations to researches looking up a particular question.

Pointer is a very successful application in the area of reference work. It is not a knowledge board system but a computer assisted reference program. Tools like Plexus used widely in public libraries facilitate information retrieval about subject areas, reference books and more. ES are also widely used in cataloguing, indexing and classification of material in libraries across the world.

NLP

Natural Language Processing, a very crucial aspect of Artificial Intelligence, might seem like it is meant for speaking into machines and expecting them to process our words and translate them into textual matter. But NLP has more to it than just this.

Clever low-level natural language processing techniques can permit the use of free-text queries in large information retrieval systems; however, until semantic and pragmatic processing are feasible, difficult problems remain inadequately matching the true subject content of queries with that of document surrogates and documents themselves, says a report.

Robotics

As libraries continue to provide a vast cache of reference material and digital resources, they also continue to receive large amounts of printed material. This combined pressure of providing digital and printed resources to members has led to a major space constraint in libraries across the world. The goal of the Comprehensive Access to Printed Material (CAPM) is to build a robotic, on-demand and batch-scanning system that will allow for real time browsing of printed material through a web interface, says a report. After a user activates the CAPM system, it will initiate a robot to retrieve the requested item and provide it to the person making a request.

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However, despite these advantages and more, very few libraries are properly adopting technological advancements in a digital era. “So far, AI’s potential has remained largely untapped among research libraries. A recent Ex Libris survey revealed that while nearly 80 per cent of research librarians are exploring the use of AI and machine learning, only about 5 per cent are currently leveraging the technology,” says a report.

The reasons behind this trend are budgetary problems and the fear of making the post of a librarian obsolete among others. Irrespective of these reasons, it can be safe to assume that AI is the future of the library system. For more on this, do peruse the DexLab Analytics website today. Dexlab Analytics is premier institute offering natural language processing course in Gurgaon.


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How AI Powers The Food Processing Industry

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

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

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

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

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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