Combating Food Waste using AI and Computer Vision

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

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

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

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

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.

Automated Pathogen Detection is set to transform pathology and here is what you should know about it

52-year-old Rakesh Singh walks into a primary health care center in rural Rajasthan with what seems to be a suspected case of Tuberculosis. After hours of waiting in line, his sputum sample is collected and sent to a lab for a sputum smear microscopy. Days go by but Rakesh’s results turn out to be inconclusive, and his diagnosis and line of treatment are subsequently incorrect. Although Rakesh’s case is fictional, the reality of TB in India isn’t. According to the WHO, India recorded about 2.69 million cases of TB in 2018 and the country’s caseload is the highest in the world.

What if there was a way to make TB diagnosis better, faster and error-free? What if there was a way to fine tune the process of sputum smear microscopies? Here is where Artificial Intelligence (AI) and Automated Pathogen Detection hold some answers.

Automated pathogen detection might sound like futuristic words out of a medical lexicon, but thanks to advances in AI, it is fast becoming a reality in laboratories across the world. In essence, Automated Pathogen Detection is a process that combines the power of AI and automation to help test samples of human tissue, sputum etc. in a faster and more accurate manner by eliminating the need for manual human labour. Take for example the process of a Sputum Smear Microscopy (SSM), which is still the primary method for diagnosis of pulmonary tuberculosis in developing countries like India. For an SSM, sputum collected from a patient’s lung is placed on a slide and stained to highlight the bacteria which are then counted by hand. The process of counting thousands of tiny strains is extremely tedious, manual, and time-consuming.

Automated pathogen detection that is aided by advanced artificial intelligence offers a revolutionary solution to this. Vision-enabled AI software can help analyze microscope output that is fed from digital cameras as video. The video is then converted into a series of images and the bacterial load is identified and counted from these images. Aided by AI neural networks and a workflow that is augmented by vision intelligence, the possibility of human error is completely weeded out and samples can be tested 24×7 at a much faster rate. Tuberculosis is just one of the myriad examples; AI-augmented workflow is now also beginning to play a pivotal role in cancer diagnoses wherein tissue biopsy samples can be analyzed more pertinently and effectively, leading to potentially life-saving diagnosis. And in the years to come, AI-aided workflow will find more applications in diagnostic pathology.

One of the main reasons why technologies like Automated Pathogen Detection are finding a stronger foothold in medicine is because they help tackle the decades-long challenges posed by traditional pathology. Medical professionals have been sounding the alarm about scarcity of pathologists and the issues with physical storage of slides in diagnostic pathology for many years now. India, for example, has a load of nearly 40 million sputum samples that are collected annually, and the volume is only set to increase year-on-year. Proliferation of AI-based technology could mean that images of slides don’t need to be physically stored and instead, they can then be digitally archived and even printed in a report. AI-augmented workflow is also largely operator-independent and requires very little human intervention. This could mean that low and middle income countries like India that have a shortage of skilled pathologists need not lose out on high quality and accurate diagnosis. AI-augmented workflow that is empowered by vision intelligence has the potential to address these and a host of other challenges in medicine.

Apart from being a game changer in diagnostic medicine, Automated Pathogen Detection is also the perfect embodiment of the promises that new-age tech, AI, and automation hold. In an article developed by the World Economic Forum, it was envisioned that in the Fifth Industrial Revolution, humans and machines will dance together! This of course is metaphorical, but it perfectly encapsulates the essence of technology such as Automated Pathogen Detection that is not meant to replace pathologists but instead support them and help them make rapid and accurate decisions that can save lives.

SLIP, TRIP, FALL – Four-letter problems with a 4-letter solution

One of the leading causes of workplace injuries is STF – an abbreviation for the three dreaded words, namely Slip, Trip, and Fall. In Australia, there are more deaths from Slips, Trips, and Falls than there are from fires. In the USA, more than 2000 people need emergency medical care after a slip and fall accident every day, the medical bills for which can often run into astronomical figures of USD 30,000 per case. On an average, 11 working days are also lost as a result of slip and fall injuries. Hence, it is no wonder that insurance claims for incidents involving STF run into billions.

Most STF cases are caused by a lack of active monitoring and shortcomings in safety practices. In fact, negligence is identified as the main reason for STFs and proving it is the easiest route for an accident victim to claim compensation. It is now acknowledged that Slip and Fall accidents are a public health problem because they are so common and costly.

Many slip and fall accidents are preventable and several nations have guidelines for employers to keep workplaces safe and minimize the chances of accidents. If businesses and individuals take the initiative to keep their property safe for customers, other guests, and employees, then they can take preemptive action and prevent these accidents before they happen.

This is where AIVI (Vision enabled Artificial Intelligence) can play a leading role.  AIVI is a technology platform developed by AI experts Cogniphi; it enables an easy and practical solution that can help to continuously track, monitor, and send out real-time alerts whenever there are any shortcomings in safety practices at work places. Be it a poorly lit corner or a slippery surface or a poorly maintained walkway and badly stacked goods, AIVI technology can detect these problems and flag them before disaster strikes.

The AIVI Artificial Intelligence software, which harnesses the power of Computer Vision and Data-driven Learning, works with existing or newly installed camera hardware to detect anomalies in a series of existing conditions and practices followed at retail outlets, factory floors, gas stations, hospitals, nightclubs, or any other workplace. Through its Machine Learning capability, AIVI filters approved conditions and keeps updating itself so as to fine tune its algorithms for pattern recognition and become a literal third eye that warns you of inadequacies in real-time. Solutions deployed can also be taught to learn new patterns and anomalies, and adapt to varying needs as well as build predictive systems.

Even in cases where a Slip, Trip, and Fall does happen in a situation monitored by an AI-enabled video, the instant detection of a Fall can be rapidly relayed to the authorities concerned and illicit a quick response instead of delayed medical care. Timely handling of an STF injury can lead to lesser damage for the person and company.

Talk to Cogniphi and get a further feel of how Vision Intelligence can predict and prevent STF accidents and save your business immense loss caused by Negligence.

Industry 5.0: Vision Intelligence in the new-age Smart Factory

Our world today is in the midst of its fifth industrial revolution. It is an era that is pushing the boundaries of science and technology to harness its best possible potential for the benefit of mankind. To have a deeper understanding of what Industry 5.0 is all about and how it is transforming our lives today, we need to delve into what constituted its predecessor – Industry 4.0. The fourth industrial revolution was all about introducing the basics of automation to the world and applying it heavily in the manufacturing space. 4.0 essentially brought together robots and other interconnected devices to execute repetitive and routine tasks that are best done by machines. Industry 4.0, like most other industrial revolutions, was a giant leap for human innovation, but it also brought to the fore, fears about machines replacing humans and this gave rise to a lot of negative sentiments that led to robots and technology being cast as the enemy. Industry 5.0 is dispelling all such notions and showing us how man and robot are not rivals and in fact can work together as partners.

Industry 5.0 takes the founding pillars of 4.0 – automation and efficiency – and adds a human touch to it via artificial intelligence and smart machines. And if Industry 4.0 was all about by automation, then Industry 5.0 will be about a sort of synergy and harmony between humans and machines. Industry 5.0 is constantly demonstrating to us that pairing humans and machines to further utilize human brain power and creativity is the way to go in the future. Take for example Cobots or collaborative robots that are specially designed to share space with humans. They are one of the best examples of Industry 5.0 because they are designed to integrate with humans; a good example of this would be surgery cobots or co-pilot cobots that assist humans to perform highly specialized tasks during surgery and flying respectively.

Another fascinating and remarkable leap made by Industry 5.0 is vision intelligence. At its core, vision intelligence is a subset of artificial intelligence that works towards making computers and machines visually enabled – it very literally is the process of giving machines the very human ability to see. Through vision intelligence, machines can be given the ability to see and process visuals the same way humans do. Computers don’t subjectively react to visuals the way humans do and hence lack decision making capabilities. However, through programming a photo recognition software or cobots and robots, machines can be taught to mimic ­­­human qualities and thus enable us to live enhanced lives.

 

Vision Intelligence and the Smart Factory

 

A pertinent example of vision intelligence’s uses would be its applications on a factory floor. Manufacturers today have the ability to run smart factory floors with the vast applications of vision intelligence technology. CCTV cameras can be programmed to do much more than just capture moving grainy images, instead, they can be programmed to perform cognitive functions, for e.g., segregating damaged goods from good food produce. Picture hundreds of ears of corn moving on a conveyor belt as workers sort the good ones from the bad as fast as humanly possible. Now imagine a vision-enabled machine aiding human workers to spot the poor-quality corn ears through their AI enabled vision technology. Aiding in quality checks of corn produce is just one of the myriad examples of vision intelligence applications in factories.

CCTV infrastructures can be further adapted to build intelligence into factory designs. A surveillance system at a chemical factory for example can be taught to gauge distance between a worker and a vat of dangerous chemicals, thereby sounding a real-time warning alarm and reducing the risk of industrial accidents. Similarly, vision AI tech can be useful at construction sites where each and every process can be monitored real time and chances of mishaps are thereby reduced.

The human decision-making process is steeped in context and analysis. Our brains interpret visuals, contextualize the situation and make a prediction or decision based on a number of variables. Up until now, machines only had the capacity to perform repetitive pre-programmed tasks because they lacked the ability to see and process visuals. However, with vision intelligence, machines can now observe human patterns and make predictive decisions by learning from the big data they collect, thereby becoming almost-apprentices to workers in factories.

In a factory setup, vision intelligence is thus a game-changing development that can be used to streamline complex processes and aid human beings to perform better.

Ushering in a new era of Healthcare with Vision AI

The global pandemic has forced us to rethink our existing healthcare system and has created a need to harness advances in technology. Vision-enabled Artificial Intelligence (AI) that combines Computer Vision and Machine Learning (ML) has the proven technologies and potential to improve patient care and hospital efficiencies.

With increasing disease complexities, rising expenses and shortcomings in infrastructure, the healthcare sector needs a panacea for development and growth. By deploying Vision AI, with little addition to existing infrastructure, hospitals and clinics can bring about a system of continuous quality improvement and make healthcare more accessible and inclusive.

The primary areas of Healthcare that are leveraging cutting-edge advances like Vision AI quicker than any other are research, diagnostics, health monitoring, treatment, patient outcomes, Covid protocol monitoring, and facilities management. Here’s a brief look at how.

 

Research, Diagnostics, Health Monitoring and Treatment

Vision-enabled AI, by developing patterns and correlations in events and data, paves the way for research discoveries that can be life-saving, and also help in error-free and speedy diagnosis that leads to precise and enhanced treatment.

 

What ML does is it provides, by identifying certain critical patterns and signals that the human mind might miss, an extended arm to the doctor to fine tune his interpretation of available medical data. Further, advanced video analytics, by providing facial analysis and subtle clues about a patient’s behaviour, can often enhance the physician’s own expertise to get an accurate understanding of what a person is actually experiencing, and ensuring that nothing goes unnoticed.

 

Elevated Patient Satisfaction

 

 

AI-driven innovations hold great potential in connecting better with patients by delivering more personalized care and streamlined services. By tracking nursing care to needy patients, patient mobility, tendency to wander from the bed zone, discomfort, injury-prone situations and unusual behavior, Vision AI is already playing a critical remote control role in the vital areas of Patient Safety and Patient Satisfaction. On the advanced technology front it is not far away that Vision-based patterns and insights on patient distress (through face expressions) will help detect instances of Shock or Cardiac so that critical medical attention can reach him in time.

 

Adherence to Safety protocols

The pandemic is driving changes in hospital safety and this is where the application of Vision AI can be effectively implemented straight away. Compliance monitoring in health centres is now automated and remotely controlled through practical applications that account for the importance of touch-free, contact-less in-patient care.

 

Vision AI, for instance, helps in tracking glove, gown, and mask utilization, and in analysing utilization of hand sanitizers/hand hygiene. These applications are now available to continuously check patients, hospital staff, vendors and visitors for all contamination protocols to ensure compliance throughout critical areas. It can detect and alert, in real time, patient flow and crowding of waiting rooms and corridors such that compliance protocols are not violated.

Quicker Turn around

At a time when hospital occupancy is at an abnormal high, making room for more patients has also become a top priority. In large facilities operational efficiency jumps multi fold by automating room assignment, tracking room turnover step-by-step, and detecting the true cause of delays.Vision Intelligence can easily be integrated with existing facilities management systems to remotely monitor patient discharge, room cleaning and readiness so as to reduce turn-around time to the minimum and optimize patient flow.

 

The global COVID-19 pandemic has opened our eyes to the need of better support to our hospitals and essential frontline workers who risk their lives to keep us healthy and safe. As AI is increasingly becoming a part of our daily lives, it is time we harness it to build a smarter and more connected healthcare system that benefits all of us, every day.

 

 

 

 

 

 

How Vision Intelligence can improve Business Outcomes

From deep reinforcement learning to wavelet powered deep networks, explore how Cogniphi’s AIVI (Artificial Intelligence Vision) is taking this challenge head-on and transforming businesses with new level of efficiencies.

AIVI is a cutting-edge hybrid system that brings computer vision and artificial intelligence together into one powerful tool. It is propelled by data driven learning, feedback-based supervised learning and advanced computer vision algorithms.

Harnessing the outcomes, AIVI is able to enhance functions in processes in a range of industries, such as these:

Healthcare – Next generation tech-enabled solutions, redefining the health system and hospital operations through AIVI. From prognostic prediction to disease detection to patient experience, reinvent medical technology and healthcare to face new challenges posed by COVID. Automate pathogeny detections, enable vision-based tracking of nursing care to critical patients, monitor crucial assets, as well as derive new insights into pathogen and patient behavior.

Retail – Cutting-edge insights and SMART data-points around customers’ retail behaviour. Digitise operations, in-store learning and customer perception patterns using technology which can influence margins and keep bringing shoppers back for an awesome experience.

Manufacturing – Technology that enables predictability, improves intelligent design and reduces wastage. AIVI has revealed an immediate 18% spike in efficiency improvement and upto 23% loss reduction than the traditional MES system in factory operations.

Surveillance – Where a sensitive digital eye meets an efficient digital brain. Transform surveillance systems to the next level by garnering insights to predict, prevent and protect valuable assets. Amplify the effectiveness of home security systems, office security systems, theft prevention and more through smart surveillance.

AIVI relies on complex spatial and time-bound patterns to detect anomalies. It filters approved behaviours and not only provides robust pattern detection but also exposes capability to build predictive systems from the metadata (colour, feature, contour, texture) of the detected objects and their features.

Cogniphi’s AI engines, with self-learning and contextual computing capabilities, enable quick prototyping, testing, and product optimisation and development to deliver transformational outcomes that will delight the user. The best part is that solutions deployed can be taught to learn new patterns and anomalies on-the-go to adapt to varying needs.

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