Author: SuperAdmin

Careers in AI and ML – What the job seeker ought to know

Posted on by SuperAdmin

It doesn’t require any great perception to know that the field of Artificial Intelligence and Machine Learning presents a very lucrative employment opportunity these days. Expectedly, many of today’s youth are striving to pursue a career in this field. As innovations continue and AI-ML strengthens itself as a technology that can change society more than any other, the jobs being created in the sector are bound to grow multi-fold.

Qualities and Education

The best news for AI job seekers is that AI programming capabilities can be self-taught. And many different types of skill sets can come in handy. However, formal qualifications and specific skill sets are still sought for very much by recruiters. Having said that, let us take a look at what the specific capabilities and educational fields are that will interest the recruiter.

Abilities to innovate, think out of the box and solve problems are the top qualities hirers look for. At Cogniphi we also give value to a candidate’s habit of being in touch with the latest innovations in the IT industry, and good communications and other soft skills. Soft skills are as important as math and computing skills because AI/ML engineers work as part of multi-functional teams, and leadership and teamwork skills are needed to work effectively with colleagues and customers.

Indians do not have to be instructed to pursue formal education, because that is part of the general culture. And, it always helps. For AI jobs, HR recruiters now mainly target a few preferred areas of study like Robotics, Engineering, Physics, Mathematics, Statistics, Computer/Cognitive/Neural Sciences, Coding and Programming. Digital Humanities is also in demand as they teach students to collaborate more. Certifications will of course jump start careers.

Career Paths

Experts in select fields will always be snapped up by employers. But, for the new entrant, remember that it is the Software developer job that is the most common route to establishing a career in AI/ML. There will be countless opportunities for the developer who specializes in specific AI programming like training cameras or drones to understand what they see, or teaching smart online assistants to interact more intelligently, or improving the output from manufacturing robots.

The other obvious jobs like Data analyst, Software designers, Architects, Testing engineer, Algorithm specialist, Business analyst, Hardware specialist and so on will of course continue to grow in demand. Getting a head start and carving your path early will be a good advice to take.

Salaries

The supply-demand gap for trained AI/ML professionals will only grow, for sure. The trend of Machine Learning engineers earning much more than the average software engineer will also continue.

Algorithmic coding and ML skills carry a premium, which is why salaries in AI and ML jobs are higher compared to other profiles. Of course, just possessing those skills don’t mean that you are the doyen of the AI world and you are eligible for fancy salaries.

Final pay scales always depend,without doubt, on experience, education background, interview performance and most of all, Attitude. This last parameter carries more weight than you may imagine.

Which Industries?

Although recent innovations like Alexa and driver less cars are what have brought AI/ML to the limelight, it has been a great career choice for some time now, mainly because the technology has been adopted across most industries and businesses.

The need for trained professionals is ever growing and there are several viable and unique jobs for the taking. From transportation to retail to health to manufacturing to security to entertainment to finance and a host of other sectors, the demand is near universal now.

Get Started

If you have strong computer and programming skills but are new to the AI field that you want to be a part of, take mathematics and ML courses, and get as much hands on training as you can. Get some exposure to general business knowledge also.

If you are already a programmer, you can focus on getting an understanding of algorithms and straight away go into coding.

If you are a data scientist wanting to take the leap into ML, first determine what you want to do. If you feel you are good at preparing data and also have sound business knowledge and communication skills, the best way forward is to acquire hard-core programming skills and get proficient at model building and visualization. There will be several areas of work in which you can decide to specialize.

Two things to remember in general:  One, there is no better learning than hands-on, and two, AI education can never stop at any stage of your career.

Combating Food Waste using AI and Computer Vision

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One of the biggest ongoing tragedies in our lifetime is the fact that millions of tons of food (estimated at 1.3 billion tons globally, annual) end up in the trash bins every year. It is not a geography-specific tragedy. Food waste happens across the world, its immensity undiminished whether it be Europe, America, Africa or Asia. The problem is so acute that the United Nations has put a target of curbing food waste by 50% by 2030.

Waste happens across the entire value chain, from farm to processing centres to distribution outlets to households to kitchens and to tables. Studies have shown that whereas kitchens and households are the areas contributing the most to food waste, it is the production and processing phases that account for 30% of the food being destroyed.

Artificial Intelligence a valuable tool

AI has been revolutionizing the food industry and has made it possible for companies to drastically reduce their food waste. Thanks to AI, these companies have become SMART through these two routes: Forecasting Demand more accurately to improve planning and production processes, and Detecting and Analysing Food waste Data for superior decision-making.

Better planning through AI-based Demand forecasts

Several data-based algorithms are already in use to improve production planning and process-efficiency, particularly to control excess production and aesthetic quality fluctuations that may depend on the raw material used. AI is also extensively used to forecast requirements, which has helped tremendously in reducing waste of perishable food like vegetables and fruits that spoil quickly or meat and other products that have an expiry period. It is now a proven fact that AI and ML methods are most effective in those functions that are predictable and controllable.

Production processes based on accurate forecasts and analysis of customer behavior obtained from such an IT eco-system help in optimizing inventory levels, expiry dates and time to market. The end result is less waste, less price fluctuations and more profits. As food companies and AI developers work together more, innovation and remarkable outcomes are bound to be plentiful.


Computer Vision for analyzing Kitchen Waste

Data of food that is thrown away is the most important information which can help in fighting food waste. Since manual collection of this data is not practical on a long term basis, cameras trained by AI software to identify and classify discarded food will play a crucial role in this battle against food waste in kitchens, hotels, restaurants and hospitals.

Waste bins are literally turned into AI-enabled bins that detect and record the discards. Information on weight and cost of discarded waste can also be captured by incorporating AI-powered scales. By helping kitchen management and policy makers to improve food preparation methods, revise menu card options and make better buying decisions, insights provided by the data can be the core for building critical applications to drive waste reduction.

The food industry is also working towards establishing a pool of AI algorithms that have been successfully implemented, so that more data gets shared across multiple users. This also helps the AI model to be trained on a continuing basis. It is gratifying that some of the top food corporates have pledged to share details of their successful programs that have enabled them to fight food waste, so that others can also benefit from them.

The great debate on limiting AI usage

Posted on by SuperAdmin

Artificial Intelligence now pervades almost all walks of human life. In many areas, what was once thought to be fictional is today commonplace. Companies and governments are routinely deploying AI.

Widespread usage of AI, which is essentially machine intelligence replacing or aiding human intelligence, will naturally create new risks. It is no surprise that there has been a lot of debate about regulating its use in several activities, including the use of AI in law enforcement, which is perceived as a risk to privacy and fundamental rights.

The European Union (EU) proposes to prohibit use of Facial recognition technologies by law enforcement for the purpose of surveillance. Live face detection will be banned in public space, unless the “situations involve the search for potential victims of crime, including missing children; certain threats to the life or physical safety of natural persons or of a terrorist attack.”

Other applications that may manipulate people into causing self-harm or harm to others will be completely banned. A few months ago there was a news report that a chatbot built on GPT-3 (Generative Pre-trained Transformer 3, a language model that uses deep learning to produce human-like text) had advised one fake patient to kill himself when he reported he had suicidal tendencies.

Huge fines will become applicable for anyone dabbling with even AI-generated videos that look remarkably real, unless they are clearly labelled as computer-generated.

EU has become the first body to outline draft rules on regulating AI. Before long many others will follow suit. In India there are no laws currently in vogue relating to AI or ML as the Government’s intent right now is in promotion of AI and its applications. But, even as existing policy encourages rapid development of AI for economic growth and social good, the limitations and risks of data-driven decisions and the societal and ethical concerns in AI deployment will surely be considered by policy makers.

The Human Element

The ultimate aim of AI research, as in any technology advance, is to improve lives. However, fortunately or unfortunately, AI will never be a substitute for human philosophy and intellectuality. Machines are unlikely to ever gain an understanding of humanity, and our innate emotions and motives. The human touch will always be missing – empathy, love or any other emotion. Instilling AI with human–compatible values will be a major challenge.

It is widely expected that, within a decade, automation will replace a variety of current jobs. We may also assume that this new industrial revolution will engender a new workforce that is able to navigate and take control of this data-dominated world. Nevertheless, socio-economic disruptions are bound to erupt.

Steve Shwartz, author of the book “Evil Robots, Killer Computers, and Other Myths: The Truth about AI and the Future of Humanity” says that the notion of AI taking jobs is a myth. “Today’s AI systems are only capable of learning functions “that relate a set of inputs to a set of outputs,” he says. “Rather than replace jobs, AI is replacing tasks — especially repetitive, data-oriented analyses are candidates for automation by AI systems”.

AI will be beneficial only if it is developed with sustainable economic development and human security in mind, and not centred around perfectionism and maximum productivity. How much AI must be regulated to favour ethics and human security over institutional efficiency is a vital question at this juncture.

The debate rages on!

Computer Vision can make a major impact in Hospitality business

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The hotel & travel industry is clearly in need of redemption. Considerable change and disruption are forecast, and only the most adaptable will survive. Rapid change through AI and technology may be the need of the hour.

Artificial intelligence has enabled innovations and new opportunities to enhance customer service, and to boost customer retention, as well as to improve operational efficiency for many a business. Computer Vision, the new field of AI (also known as Vision Intelligence or AIVI) that trains computers to interpret and understand data from visual images and videos and then react to what they see, could well be the biggest ray of hope for the Hotel and Travel industry.

Several companies in the hospitality sector are gearing up to leverage the use of digital technologies to develop their overall business and enhance customer experiences.

Security

By making sense of visual data in the same way as humans do, AIVI removes the need for constant eye balling of multiple TV monitors, and thus enhances video surveillance by detecting intrusions and unauthorized activities, and alerting the security mechanism. Physical monitoring of parking areas, restaurants, bars, pool decks, and perimeter boundaries becomes possible without human intervention since the computer receiving the camera images have been taught to recognize patterns, and identify and alert when there are anomalies.

Cameras are also easily taught these days to detect weapons and firearms in camera footage, and programmed to send alerts to the security team, which no longer has to keep monitoring the camera feeds. This reduces reaction time and might even stop violence before it starts. Detection of suspicious packages or bags lying unattended also helps improve overall security.

Surveillance thus assumes a whole new meaning with the adoption of Computer Vision.

Customer Experience

Computer Vision also helps to provide personalised service and support, for example by recognizing VIPs as they arrive for check in so that they can be prioritized for enhanced service. There is nothing a guest loves more than being given Customised and Proactive service. This is particularly true in luxury hotels.

At the other end of the spectrum AIVI also helps to alert presence of any undesirable person or known criminal. Face recognition is now a fairly well established technology that it can manage even staff access to restricted areas, thus ensuring guest and employee security.

Age recognition through Vision Intelligence also helps in controlling, say, sale of alcohol to minors without need for identification. Such measures aid in reducing load on Security and other personnel and free them for focusing on more critical tasks.

Monitoring and managing Covid protocols in crowded areas like a bar or pool-side also results in customers feeling safe and cared for. Random increases in guest traffic in certain service areas are also captured and alerted to management for any staff reallocation. Patterns of guest movement inside the hotel premises can also be collated to aid staff formations.

Kitchen efficiency

Improving kitchen efficiency and reducing food waste is a recent application based on computer vision that might well revolutionize food management in the not too distant future. It is based on capturing and processing food waste data non-intrusively by using cameras powered by Vision Intelligence directly over the bins, without human intervention and without affecting the operations.

Food waste data is considered as the most vital input in cost cutting as well as in reducing carbon emission. With more images being processed VI gets smarter each day and learns to give smart outputs without human errors, which eventually helps the kitchen management to take informed decisions.

Potential of AI in Hospitality

In a business sector where margins are tight, embracing the potential of AI is a step in the right direction. A McKinsey report on the global economy two years back had said that companies which ignore AI might actually see a major drop in cash flow. This could be very true of the hospitality sector more than any other. Vision AI is at a stage that it can play a transitional role and presents an opportunity for this sector to improve efficiency and widen margins.

Why Business Process Transformation is the heart of AI projects

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All visionaries and thought leaders agree on one thing, and that is Cognitive Automation will enable faster and more accurate decision making in a world in which the speed and complexity of business are growing exponentially.

But how do companies and businesses benefit from all these powerful technologies to win in a competitive world?

There is a saying attributed to Gartner that Transformation is a business problem, not a technology problem. Artificial Intelligence and Technology cannot create business value or drive change in a vacuum. The most important thing is to study existing processes and practices, what the roles and profiles of people are in different jobs, and determine how to change manual processes to improve the outcome of complex jobs.

Insight is everything

Building AI algorithms are just a small part of the total solution. Creating insights and using those insights to set new benchmarks is the real challenge. This is why you need to have both the business and technical arms of your company to work together to enable real process change.

Decoding the business problem is the primary task in framing an AI solution. Is AI the best way forward? You can decide that only by rummaging through data about the business domain and by understanding interactions between the customers and the products or services handled by the business. Once you have enough data to analyse, use that data to decide whether or not the AI approach is the right way to solve a problem.

AI algorithms, which will of course improve the process of how work gets done, will follow only then. What these algorithms do is mimic the cognitive processing power of a human and automates data extraction. They are the breakthrough components that help speed up operations and at lower cost, but typically are however only 10% of the solution.

Adopting technology to make those algorithms create value is the next step. This essentially is the process of integrating AI into the IT architecture and operations, whereby the AI components function within that ecosystem comprising workflow, security, control mechanisms, and so on. You have now achieved tech advancement but only added a further 20% to the solution.

Changing familiar processes and systems already in operation is 70% of the work. It is only this that will finally impact the business and create value. While AI algorithms and technology are important elements, these capabilities needed to be paired with process transformation to make the solution work. Several changes may have to be made before genuine benefits accrue. This could involve organisational changes, staff redeployments, product segmentation, and plenty more, depending on the nature of business and the market catered to. Technology companies that have helped clients create business value using AI know that Business Process Transformation is what matters.

An engineering degree isn’t a must for a career in AI

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Elon Musk Tweeted last year that his company is looking for AI developers and said that it does not really matter if applicants have any real degree, all they have to do is pass a “hardcore coding test”. While the SpaceX and Tesla CEO’s Tweet might have been bizarre for some people, it wasn’t entirely surprising. Over the past few years, there has been a growing consensus in the AI tech community that college degrees, especially engineering degrees, might be increasingly irrelevant for those looking to apply for jobs in the Artificial Intelligence space. Why is this happening and how is it possible to enter the world of AI without an engineering background?

A career in AI is mainly characterised by the use of sophisticated computer software and programs, automation, and robotics. And while all these three areas of study do converge in a conventional engineering degree, it is equally possible to acquire these skills outside a university. Those working AI opine that if an individual has a strong hold of math and statistics that is coupled with coding skills and knowledge of programming languages such as Python and R,and a good understanding of AI and it’s ability, then there is nothing stopping them from working in the field. Google and Apple have acknowledged that formal college degrees are no longer a prerequisite for applicants. This is mainly because companies that drive innovation understand that the value of people who are passionate self-starters and are willing to learn on the job.

Having said this, there will always be those who are worried about not having the backing of a formal engineering degree in order to get noticed by potential employers and recruiters. While this may be a valid concern, there are many ways to work around this problem of lack of experience or a formal degree. Personal projects that showcase basic machine learning skills and meet proper coding standardsare one way of doing this. More often than not, recruiters are always on the lookout for candidates who are self-starters and a personal project involving problem-solving is a great way of showcasing a proactive attitude. In addition to this, participating in Hackathons, coding challenges, and working on open source projects are a few other ways in which candidates can strengthen their recruitment potential even without an engineering background.

Many of the leading recruiters in the AI space give a great deal more priority to skillset than the nature of degree a candidate has. The key for them is to identify those who have excellent problem-solving skills in ,a real passion for technology, teamwork and dedication to work towards long term goals. In short, the problem-solver and hard worker is the real engineer even if he does not have a degree. A distinctive mark of such companies is often their interview process that focusses on foundations, coding skills and potential of the candidate to contribute to their overall business objectives. Forward-looking companies handpick their team of passionate tech enthusiasts and put them under the guidance of team leaders working on quality projects in multiple business sectors. That inevitably enables informal learning and building of AI skills and consequently enrich their career portfolio.

Career Artificial Intelligence Machine Learning Computer Vision Cogniphi

AI is destined to affect every industry in the near future, making it one of the most sought-after areas to build a career in. According to a Forbes Magazine article that recently sourced data from LinkedIn about hiring in the field of AI: artificial intelligence specialty hiring has increased by 74 percent in the past four years and these figures are only set to increase. There is also appreciation that what is taught in a generic college programme may be just a fraction of the specialized knowledge, awareness and skill that an AI professional will gather on the job. Although an engineering degree is viewed by AI talent hunters as a foundation for good technical education, the real challenge for them is how to nurture the degree holder and turn him into an AI technologist. Hence, it really is the best juncture to step into this field, and not having a formal engineering background is not an impediment to having a thriving career in AI.

 

 

 

Your surveillance system might be the chink in your armour. Make it fool-proof with AIVI

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Dozens of digital eyes peer at us today – guarding us from the moment we step out of our homes, and sometimes even before. We have surveillance cameras watching over our gates, our roads, and our offices, and even overseeing us in shops, malls, and ATMs.

Between Chennai, Hyderabad, and Delhi being among the top ten cities in the world with the highest number of CCTV cameras per square kilometre, and video surveillance buttressing Indian investigative agencies as they crack violent cases – these cameras have already proven themselves to be indispensable. It is, therefore, imperative to have cameras surveying your company’s premises, and to use them as the bedrock of your business’ security structure.

But these digital eyes are only as good as their human counterparts monitoring a feed. CCTVs’ watchful presence might prove to be a powerful deterrent, but their passivity results in complete reliance on external forces for intervention. These days, law enforcement too depends on captured footage to solve crime.

Things are changing, however, with AIVI (Artificial Intelligence Vision) – the tool that effectively marries computer vision algorithms with data-driven learning of Artificial Intelligence. Installing AIVI technology amplifies a company’s video surveillance system, transforming billions of hours of footage from an overwhelming wave of monotony into an organized database to apprehend perpetrators.

Here are six ways in which computer vision can help your company reduce security risks:

Identification

Cameras with AIVI are significantly more adept at recognising and labelling objects – whether human, vehicle, or weapon. Installing this technology is an easy investment, especially considering most facilities are equipped with CCTV cameras.

This intelligence elevates an average camera, making it accurately identify a human presence even in the case of a partial capture. This process can even be widened to study people’s proportions and gaits, and can be trained to identify people based on these traits – even during less than ideal circumstances.

Facial recognition

The specificity that computer vision-powered cameras allow for, based in the technology’s pattern-recognition techniques, enables them to verify the identity of individuals based on extremely distinct features – like biometric identification through iris and retina scans. By extension, this technology allows for digitising all company documents since signatories can be corroborated through this highly precise process. These modes of biometric authorisation simplify and solidify security processes, and consequently reduce the scope for fraudulent activities.

Future forward

With the possibility of analysing movements and recognizing suspicious behavior in real time, and immediately notify authorities. Something as minute as a deceitful glance can be captured. From ringing alarms exactly when an item is shoplifted, to alerting authorities, vision intelligence turns CCTV cameras into a proactive part of cracking crime. Computer vision can also identify contextually dangerous behavior – such as a worker showing signs of fatigue and dozing off while operating high-risk equipment.

Outside of security concerns, these AIVI capabilities can track behavior to provide detailed insights into what stakeholders and business partners respond to positively and negatively on company premises. Just tap into the data AIVI gathers on your organisation’s performance indicators – from customer satisfaction to client retention rates.

Omnipresence: Computer vision can monitor all visitors on the premises, and alert security teams in a fraction of a second if an attempted robbery or security breach is occurring. The response – such as locking down the area – can be immediate. This promptness, along with hyper-specific discernment, enables security teams to share access privileges on an individual level, and allows them to even track miscreants through vision intelligence.

Furthermore, critical assets can be surveilled in real time – whether they are highly valued technologies or cash. AIVI’s collection of historical data and pattern-learning capabilities can even offer a guide to where human resources need to be focused to strengthen the company’s security structure. And in case the crime has already occurred, security personnel can rely on the database to sift out relevant information within seconds – whether that detail is a man in a blue shirt, or a license plate. Computer vision – through a technique called “generative adversarial networks” – even offers the ability to enhance and recreate images, providing security teams more valuable visual details around critical incidents.

Safety measures: The midst of a raging pandemic, AIVI-enabled cameras can ensure that employees and workers socially distance on the premises. This technology can also alert workers who are dangerously close to hazardous equipment, or toxic substances.

Computer vision’s deep learning faculties can also understand manufacturing and standard operating procedures, and make sure materials and processes are being handled safely on the factory floor.

Similarly, AIVI can track statutory compliance and make sure the company adheres to rules and regulations including the Factories Act, Shops and Establishment Act, and even Sexual Harassment of Women at Workplace (Prevention, Prohibition, and Redressal) Act.

Quick training: Newly hired employees can receive access privileges based on their scope of work. AIVI’s expanding database can also serve as an exhaustive resource when it comes to training workers on standard operating procedures, and areas that need the focus of increased human resources – for instance, deploying more security personnel at risk-points.

Computer vision capabilities don’t replace human resources, rather they enhance and augment manned security systems. AIVI’s surveillance offerings can predict critical security instances before they occur, prevent them from happening through a real time warning system, and protect you and your company’s valuable assets. This is an irrefutably reliable way to make your security systems proactive and fortified.

Roadmap for Vision Intelligence and why more industries will embrace Vision AI

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Over the past ten years, Artificial Intelligence or AI technology has hurtled towards unimaginable advancements. And even though most people remain unaware of what AI tech such as Computer Vision (CV) or Vision Intelligence entail, chances are that they’ve already used it. AI has ubiquitously entered all our lives; it is helping us to drive smarter, unlock our phones faster, shop better, and soon, it will be a part of almost every aspect of our lives.

What is CV or Vision Intelligence and why will we see more of it in the years to come?

As human beings, we have the amazing ability to sense our surroundings. With the help of our eyesight and cognitive capabilities we can visualize what is around us and make decisions based on what we see. Computers on the other hand, aren’t able to do this automatically. CV or Vision Intelligence is thus a subset of AI that enables computers to see, identify, and interpret visual data as humans would. The process is complex and requires vision algorithms and applications for the computer to learn. However, once the process is complete, computers can see, interpret, and analyse visual data much better and faster than any human ever could. In addition to being more efficient, Vision Intelligence is also an extremely malleable technology. From automobiles to agriculture, Vision AI can be tailored to meet the requirements of all sorts of industries and its uses are wide-ranging. Below are examples of how Vision AI is being customised to help a whole host of industries.

Manufacturing

Vision Intelligence has been a revolutionizing force in the manufacturing space. From smart factory floors to quality control and accident prevention, Vision AI can help with almost every manufacturing process. In a modern factory setup, automated production lines are fitted with multiple moving machines such as conveyor belts and robotics units. For seamless production to continue, none of these systems can afford a breakdown. However, more often than not, stoppages do happen and they hamper production. Here is where Vision AI steps in. Armed with AI-based vision, CCTV cameras can analyse and diagnose every minor defect in a production line and issue real-time updates in case of machine failure or other problems. For example, if a conveyor belt is stuck due to improper material alignment, CV will preemptively flag the issue and notify the shift manager. Or, if a worker is standing too close to a vat of dangerous chemicals, AI-based CV systems can issue a red alert, thereby avoiding an accident. These are just a few examples of how AI has been a game-changer in manufacturing.

Retail

Retail is another sphere in which Vision Intelligence is creating ripples. For too long now, retail stores and supermarkets have faced a host of issues such as inventory mismanagement, revenue loss, and theft. Vision Intelligence has a solution for all of this. With the help of CCTV systems, and cameras placed on shelves and other crucial points, images of products and customers are captured, processed, and analysed to help retailers draw actionable insights. Vision-based tech and Deep Learning algorithms thus help generate insights like the effect of product placement on sales and customer shopping patterns in order to create more effective and personalised shopping experiences. AI Vision-powered cameras can also help to detect theft or incidents of sweethearting which is a form of theft where cashiers or checkout counter employees can give away merchandise to a “sweetheart” customer such as a family member or friend.

Health

In healthcare, Vision AI has the potential to save lives. Technologies like Automated Pathogen Detection combine the power of AI and automation to help test samples of human tissue, sputum etc. in a faster and more accurate manner. Meanwhile, there are several other AI-based tools that are being developed to analyse three-dimensional radiological images – a process that could potentially speed up diagnoses and suggest much-more effective treatments for patients.

The above three industries are just a few of the examples out of a vast pool of sectors that Vision Intelligence is making a splash in. In the years to come, Vision Intelligence or Computer Vision will grow in its reach and capabilities, and more and more industries will realise its multifaceted potential.

Vision Intelligence could be a permanent feature in the post-pandemic retail store

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We are a year into the novel coronavirus pandemic and it is now evident that shopping will never be the same experience again. Beneath its glitzy exterior, shopping at its very heart is a collective experience; it is a way for us to socially interact with friends, family or even strangers. And even though the pandemic has robbed us of this banal pleasure and pushed consumers towards online shopping, brick and mortar stores are here to stay, in fact, according to Euromonitor International – an English strategic market research firm – 83% of all products purchased globally in 2022 will still be bought in-store. However, social distancing is also a reality that isn’t going away anytime soon and customers are going to want safe shopping experiences. It is here that Artificial Intelligence (AI) holds vital answers and solutions.

Vision AI in Retail

Today, digital transformation in retail is all about connecting technologies and converting data into valuable insights so as to improve customer safety and experience. A distinct subset of AI’s Deep Learning capabilities known as Computer Vision or Vision Intelligence is leading the way in this sphere.

Vision-enabled AI is a relatively new field of computer science that integrates Video data, Computer Vision and Machine learning, and trains computers to replicate activities and identify patterns. Vision Intelligence (sometimes rudimentarily referred to as Video Analytics) works by interacting with the surroundings, helping the computer ‘sense’ and ‘recognize’ the live environment and ‘learn’ from the memory of past experiences by extracting patterns in visual signals. It provides the best contextual information to help one speed up business operations since it literally turns camera images into actionable insights and helps retailers be situation-aware real time.

During the pandemic, Vision Intelligence has opened the door for retailers to have their brick and mortar stores fully compliant with Covid-19 safety rules and regulations.

Safety guidelines adherence 

AI-trained CCTV video analytics can be used to ensure social distancing is maintained. Essentially, ceiling-mounted cameras armed with computer vision use Deep Learning AI models to analyse videos and issue real-time alerts about people gathering in large groups or customers not wearing masks, etc. Vision Intelligence can also help store managers allocate personnel based on an analysis of customer inflow patterns; vis-a-vis footfalls, buying patterns, etc. Additionally, features such as heat maps automatically check temperatures of all customers, while issuing real-time alerts in case of anomalies.

Contactless shopping

Shopping in a post-pandemic world is going to focus on contactless technology. Anxious consumers worried about contracting pathogens from a point of sales (POS) will want to view and buy products with little to no physical contact. With the help of augmented reality apps, consumers can see display renderings of products and ingredients while maintaining social distancing. And no-contact checkout technology that uses AI tech can automatically detect when products are taken from or returned to a shelf, thus keeping track of a virtual cart. A contactless purchase means that a customer can simply exit a store, receive an e-receipt, and pay online. This reduces close-contact interaction and helps store staff to focus on other aspects of customer service and management.

In addition to compliance, Vision AI can additionally help retailers gain better insights into customer preferences and create more personalized shopping experiences. Vision AI studies consumer interactions and non-intrusively collects data that will help in determining, developing and implementing tailor-made solutions. For example, features like dwell time monitoring and facial emotion tracking, it is possible to address customer frustrations in real time by eliminating slow billing, predicting queue build up, identifying and assessing inappropriate staff behaviour, and resolving inadequate staff availability in certain areas. Advanced pattern recognition algorithms are also now available to help tracking aisle behaviour of the shopper, which also helps to understand shopper interests, tastes and preferences, as well as improve product placement.

All over the world, whether it is small boutiques or multinational supermarkets, retailers are looking to create convenient, personalised, enjoyable, and most importantly safe shopping experiences. The need for socially distanced shopping has accelerated the need for stores to adapt to digitisation. Computer Vision or Vision Intelligence seamlessly offers retailers the chance to improve their business while adhering to global safety guidelines in a post-pandemic world.

An introduction by Cogniphi VP, Seshagiri Sriram, to Dockers and how it simplifies a developer’s life

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This is the first in a series of articles on docker. In this article, we aim to introduce the reader to the concepts of containers and some of the benefits they offer.

Sub-sequent articles will focus on deep dive into the usage and technologies underlying Docker and its eco-system. 

What is Docker?

Docker is a platform that uses containerization technology. It is used to package an application and all its dependencies together inside containers.  By doing so, the application works in any environment (at least in theory) – One way to look at it is to think of it as an easy way to ship “production ready” applications with all dependencies packed in.

WHAT IS A VIRTUAL MACHINE (VM)?

A virtual Machine (VM) is a file. This file is called an image. However, the file has an interesting property – with the right tools, the file acts as if it was an actual physical computer. What this means is that you can have multiple virtual machines running inside one physical machine – this saves cost of provisioning a physical computer and associated costs – licenses, maintenance etc.

A VM can then be thought of like a special program – a program that runs an operating system or part of it. A VM is said to be sandboxed – tech geek speak for saying that it is isolated from the host operating system. This means that it can be used for several uses including but not limited to

  1. Testing Other operating systems (including Beta Releases)
  2. Accessing data that cannot be accessed normally (typically virus infected data),
  3. Performing OS level backups
  4. Running programs that were not meant for the host Operating system itself.

More than one VM can be run simultaneously on the same Physical computer. This is done by means of a special software called a hypervisor. Each VM provides a set of virtual hardware – basically each VM shares resources with others saving costs in physical hardware and associated maintenance costs – people, power, cooling – among others.

Figure 1: Core Virtualization
Fig 1.a – MS-DOS (Yes the old version running on Windows 10 Machine)
Fig 1-b – Windows 3.11 (yes old version of Windows on Windows 10)

So Why do we need a Container?

Running multiple virtual machines on same machine take a long time to boot up. In addition, these may cause performance issues. Management is another issue that is not simple when running multiple VMs.

If you have seen figure 1-a and Figure 1-b and think it’s easy (well actually it is ?), setup is really messy and very non-intuitive – setup time takes a long time and some complicated process to follow to get it working right.

Think of a Container as a virtualization at the OS level.

Figure 2: Containers

Some of the advantage of containers over VMs are:

– These tend to be more light weight,

– boot up faster,

– can be managed better (auto removal when done for example).

These advantages are shown graphically below:


Ref: https://www.slideshare.net/EdurekaIN/getting-started-with-docker-docker-tutorial-docker-training-edureka

– Containers allow you to run more applications on a physical machine than VMs. When resources are a constraint, containers may be a better choice.

– Containers allow on to create portable and consistent operating environments – Development, Staging, Production. This consistency helps in reducing development and deployment cost, besides making it easier to monitor systems to ensure higher level of availability to the end customer.

Docker Terms and Terminologies

Docker Image

A docker image is a read only template used to create container. THIS IS IMPORTANT – READ ONLY TEMPLATE.  These images are either built by you, or readily available from Docker Hub or any other repository.

Docker Container

A Docker container is an instantiation of one or more image. It contains everything that is needed to run the application – from the OS to the network to libraries to the actual app. This is the actual “running” instance.

Docker Daemon

The docker daemon is the core of Docker. It works together with the Docker CLI (command Line Interface). Think of it as a service that runs on the host Operating System (and yes, it runs now natively on Windows 10 upwards – Older Versions of Windows runs a stripped down version called Docker Desktop).

Docker Architecture – a 10000 feet view


Ref: https://www.slideshare.net/EdurekaIN/getting-started-with-docker-docker-tutorial-docker-training-edureka

We have the client (usually the Docker CLI). Note that since the Daemon has a REST API, you could write your own clients in Java or C# to call the Docker Daemon.

The docker host is the server where the Docker Daemon is running. The client and host do not have to be on same machine – that’s the key takeaway.

Finally, we have the registry where the images are stored. (and in Cogniphi, we have our own registry hosted in Azure, but it could very well be a standard image pulled from Docker Hub. And for those technically inclined, the registry is an actual docker container itself).

So what happens when you issue a command like below (In next articles, we will discuss all the commands and the meaning of various parameters)

docker run ubuntu /bin/bash

The client makes a request for an image called ubuntu. If one is found, well and good. If not, it pulls down the image from the registry, Next it starts a container from the image and runs /bin/bash. When the command finishes execution, the container is automatically stopped.

Why is this so important?

When you want to go to market quickly, you do not want to spend a lot of time setting up infrastructure. Docker comes with a large number of pre-built images – that you can deploy in a matter of minutes.

For example, you want to set up Apache HTTPD. On a windows world, you would need to first find the relevant Apache HTTPD Binary, download/unzip it, configure it, run it – a set of activities that can easily take up to 1 day. With docker it becomes a single liner

docker run httpd

While the above example is over simplified, it still can be started in a few minutes not days. You want to setup mysql? Again a single one liner.

The speed to market and deployment is one of the key selling points. You would agree that spending time on plumbing is not worth anybody’s time.

You want to make sure that your application runs on Ubuntu 18 as well as Ubuntu 20. It’s a simple matter of spinning 2 docker images – one with Ubuntu 18 and one with ubuntu 20 and having your application run on both images. Note that this significantly reduces testing time – since containers and dockers are light weight, these can be run in parallel. Again the end result is that your products and projects are more rapidly available to market.

So what do we get beyond ready to market and scalability? Docker today is as secure or insecure as the underlying OS. In the olden days, Docker was considered insecure because of “run as root” – this is no longer true. In next series of articles we will cover the hows and whys of securing and managing a docker infrastructure – briefly covering on topics like Docker Swarm and Kubernetes and then seeing how the whole ecosystem looks like.

If this sounds interesting, sign up for an account at hub.docker.com (account creation is free – repositories are not – so be careful). If you do not want to do so, you can head over to https://labs.play-with-docker.com/ – it provides you a cloud based playground to learn more about docker. Have fun Dockering !

Source – The images used in the blog are from a course previously designed and published by the author himself.