How AI Vision is Changing Public Safety for the Better

As we build a better society, one of the most important measures of progress is public safety. Companies around the world are designing innovative technologies to detect and prevent crimes. With AI, it has now become easier to leverage historical data to identify patterns and predict locations where crimes are most likely to happen in order to focus all energies into making the most vulnerable areas safe.

Moreover, with technologies like facial recognition and validation, it has become possible to solve crimes with reduced time and effort. More and more improvements in law enforcement can be seen with the implementation of data-based technologies, especially Artificial Intelligence.

But what’s in store for the future of law enforcement?

Vision Intelligence For Public Safety

Vision intelligence has highly practical applications in surveillance and public safety. With the ability to detect patterns in visual feed – images or videos, it becomes easier to track and identify items, patterns, people, and behaviours to trigger an alarm or warn the responsible security personnel in real-time of impending threats.

Some of the vision intelligence applications that are most useful in public safety are:

1. Facial Recognition and Validation

Facial recognition is currently a relatively commonplace technology where most smartphones are now locked/unlocked with it. However, it’s application in surveillance and public safety is more profound, and paramount. With AI-based visual intelligence, facial recognition can not only identify people behind a disguise but also read emotions and expressions to predict suspicious behaviour if and when a person is intending to commit a crime.
Moreover, with facial validation, it becomes possible to detect and prevent bypassing of facial recognition through identity theft or deep fakes.

 

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 2. Behavior Detection

When trained to identify a set pattern of behaviour that people are required to follow, an AI-based camera can detect anomalies from the set pattern and provide alerts or notifications. This can be implemented in controlled public places such as prisons, mental asylums, etc. where a set pattern of behaviour is expected from individuals and any anomaly can be a sign of trouble.

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3. Skeletal Construction

Certain movements can be identified in individuals that indicate criminal intent. In surveillance, with video cameras ingrained with vision intelligence, one can identify if there’s a fight that breaks out in an alleyway and whether it’s just a quarrel or it has serious repercussions. This insight could potentially enable the surveillance to make the right decision at the right time to prevent escalation of the situation and possibly prevent a crime from happening.

AI Vision

In addition to all this, the applications of vision intelligence are becoming more pronounced and imperative as we advance in technology and operations.

We, at Cogniphi, are building effective vision intelligence solutions that are outcome-driven and significantly improves the existing cameras in an area to drive insights and ensure improved surveillance and safety.

About Cogniphi

Cogniphi is a technology company that focuses on building next-generation vision intelligence solutions that are outcome-driven and seamlessly integrates into the existing infrastructure. Cogniphi’s AI Vision is a platform that’s built to improve operational efficiency at every level of an organization, across industries and sectors.

If you’re wondering how Cogniphi’s AI Vision can help you transform your business, get in touch with us for a free demo!

Why AI Vision is Crucial in Quality Inspection

The Fourth Industrial Revolution or Industry 4.0 is here – it’s blurring the lines between the physical, digital, and biological worlds. With a fusion of progressive technology like AI, robotics, the Internet of Things, 3D printing, and more, it’s changing the way the world functions.

We are witnessing the inception of full automation across sectors – manufacturing, retail, pharma, medicine, and more. Data has become instrumental to making this happen and Artificial Intelligence is what’s driving it. As we progress into a more capable, more exciting future, it’s imperative that the quality of production, in all sectors, becomes better and better – it’s called progress for a reason.

AI has been providing the inputs for digital transformation but without the capability to understand context. The AI cameras currently used by most businesses require a human to look at the footage to add context in the detection and have a very low accuracy which makes them ineffective. Without context, there’s little value that these cameras can provide.

The machines that businesses and organizations have setup into their premises to monitor data, especially cameras, only look at the 1s and 0s of the data that’s presented to them – they lack the clarity of context that denies them the opportunity to be precise, accurate, and error-free.

And that’s about to change.

In the last few years, we’ve taken huge leaps in vision intelligence. This prominent technology enables cameras to look at visual feed as a whole – complete in its context. This empowers them to identify, detect, or distinguish between objects with better clarity, precision, and accuracy.

Current advantages of vision intelligence in quality control

With the recent advancement in vision intelligence, we have already seen the benefits in quality control like:

  1. identifying damaged/faulty goods during production
  2. enforcing PPE for workers
  3. checking on vacant shelves in supermarkets
  4. Identifying and eliminating repetitive tasks

Why AI Vision will become crucial for quality inspection

Quality control is fast becoming automated in a lot of companies across sectors.

Such improvements in the process not only improves the productivity of a process but also leads to more efficient production leading to better returns for businesses with a higher production at reduced costs.

For instance, a camera overlooking a production line must have a person sitting behind it, ensuring that no product is damaged or defective. Now, this person could easily miss a few defective products owing to fatigue which could affect the later production. With an AI-enabled camera, the defective products can be instantly and consistently flagged and pulled out of the production line, saving wastage any further in the process.

These intelligent cameras can not only detect imperfections but also enable geometric inspection, packaging control, product classification, and more.

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AI Vision

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Some of the undeniable advantages of vision intelligence in quality inspection include:

  1. Being precise and accurate in reporting and flagging inefficiencies
  2. Accelerating the production speed through seamless flow of repetitive tasks
  3. Reducing the downtime of a process by eliminating breaks or rest
  4. Lowering the operational costs by cutting down on manual labor and saving on wastage

Cogniphi is accelerating the future of vision intelligence with Cogniphi’s AI Vision by enabling leaders in the manufacturing with specific cognitive intelligence to solve problems. We take a very objective approach to solving business problems – analyzing the challenge to find out the reasons behind any inefficiencies and reaching the root of the cause and solving it from within to ensure that the return on investment is profound.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. It believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.

 

Start-up vs SME – Recognise the Difference and Get Hired Faster

So, you are looking for a job at a start-up, are you? Well, then it will do you good to understand the difference between a Start-up and just another small business, and also appreciate the nuances behind the recruitment strategies of a Start-up. Get a few tips too at the end of it all.

Why is a startup not just another SME (small medium enterprise)?

 

The typical SME is a business concern started primarily for earning a profit. It typically runs on its own steam and is focused on business at hand, how to increase revenues and turnover, how to cut leakages and increase profits, and how to increase efficiency. Strategies are based on market requirements and number of customers.

A start-up, on the other hand, has a different mindset altogether. It’s trying to create a new world with an idea or a succession of ideas. The focus is on innovation, creating value out of the idea, value for the promoters, for the employees, for the investment partners and shareholders, for its collaborators and partners, and for its customers.

And it is not any incremental value jump that a start-up is aiming at. The start-up strongly believes that the world will gain exponentially when the idea is fully baked. And the founder is fully aware that the baking will require tons of dedication and a committed way of life devoted to the idea.

The act of sourcing talent for a Start-up

Start-ups differ most from SMEs in the personnel front. Since it generally embodies a new business idea it requires multiple hands and minds working to develop it. The usual start-up is one where a number of heads do their thing simultaneously. That is also one reason why Building the Team is the most important task at the beginning of the Start-up’s life cycle.

Building a talent pipeline is critical for the company during these early days. And, to this end, creating a compelling employer brand and projecting a modern corporate culture help to yield top candidates. A start-up HR has the opportunity to craft the company story and build processes from scratch. Start-ups are also known to leverage unique sourcing strategies, particularly if they have support of large private equity partners or venture capitalists.

The recruiter for a start-up is generally on the look-out for versatile people since, in a small office, all hands on deck is a daily event. Hence, someone with a varied skill-set and who can be a team player will be in great demand. In the start-up environment one certainly gets to know everyone else well and definitely needs to learn to work with everyone quickly.

Tips for the Start-up Job applicant

Tip 1 is that the Recruiter is actually on a sales mission. He is not just trying to match CVs with profiles, but also to project the company in the right manner. He is more likely to be looking for flashes of brilliant thinking, drive and a positive attitude, and for people who fit in with the company’s culture.

Tip 2 is to demonstrate that you possess adequate communication skills and ease of working with others in a team. If you have genuine stories of how you have solved problems in business or real life, nothing like it.

Tip 3 is to be ready to make a pitch. The start-up recruiter is bound to ask the candidate to make his own sales pitch to determine the extent of passion and ideas in him, which often also reveals if he is a right fit or not. Convey your passion, motivation and convictions precisely. It’s very likely that the pitch can make it swing either way.

Tip 4 is to be Aware and Visible. Awareness is about the company, its product or mission, its culture and the industry in general. Visibility is about yourself, your LinkedIn profile and your online presence. Your professional connections and endorsements of your skills by others will count very much.

Tip 5 is to remember to be respectfully curious. You are interviewing the company as much as they are interviewing you, but do not make assumptions. Ask questions with a genuine interest to know more.

And, finally, Don’t pretend to be someone you’re Not.

How Predictive Insights Can Help You Reduce Operational Waste

Predictive analytics refers to a category of advanced analytics that can predict future outcomes with the help of historical data as well as data mining techniques, statistical modeling, and machine learning. Businesses use predictive analytics to detect patterns in their data to recognize opportunities or threats.

As interesting as this might sound, there is a vast ocean of unchartered territories and unclaimed knowledge that takes possibilities of predictive insights beyond tomorrow. Learn more about how companies can benefit from streamlining their operations to reduce waste with the help of this ingenious technology.

Ready to dive in? Let’s get right into it!

What is meant by Predictive Insights?

Predictive analytics is generally correlated with data science and big data. Businesses at present have heaps of data across equipment log files, transactional databases, video, media images, sensors, or various sources of data. To achieve actionable insights using this data, analysts and scientists take the aid of machine learning and deep learning algorithms to determine patterns for predictions regarding future trends and events.

These can include nonlinear and linear regression, support vector systems, neural networks, and decision trees. The findings obtained using predictive analytics can then be applied to prescriptive analytics with the goal of driving operations depending upon these predictive insights.

The History and Present Milestones of Predictive Analytics

Even though predictive analytics has been used by businesses for decades, the time to exploit it correctly has just begun. An increasing number of companies are adopting the use of predictive analytics as a way to boost their bottom line and reap the benefits of a competitive edge.

The question that arises is – why is it starting to peak now?

There are several answers to this question:

  1.  Increasing quantity and types of data, or maybe, an increasing interest to utilize data to extract actionable insights.
  2. Quicker and more economical computers are available today with huge computing power.
  3. User-friendly software.
  4. More challenging market scenarios present the requirement for competitive segregation of products and services.

With the rise of interactive and user-friendly software in all markets and industries, predictive analytics is not just a domain for mathematicians or statisticians anymore. This niche is now free of complexities and can be used by business analysts or business experts with ease.

What Makes Predictive Analytics Vital to Reducing Operational Waste?

Companies are rapidly adopting predictive analytics to sort out their existing problems and reveal better opportunities. The common benefits of predictive insights to operations include:

– Fraud Detection: Gathering various methods of analytics can boost the detection of trends and patterns to prevent criminal conduct. With cybersecurity being at the forefront of businesses’ main concerns, the help of high-quality behavioral analytics reviews all the transactions and processes undertaken with suppliers and quality control in real-time. This helps them to find any irregularities that could point towards fraud, advanced threats, or zero-day vulnerabilities.

–  Improving Production Strategies: Predictive analytics are employed to understand the demand and supply chain of their production process. By monitoring their suppliers’ inflow of raw material, they can place orders well in advance, avoiding idle time or resorting to last-minute supplies that compromise quality.

– Efficient Production Operations: Many businesses employ predictive models to project inventory and control their resources. Predictive analytics helps businesses to maximize their operational efficiency and reduce rejects or sub-standard production by figuring out discrepancies in processes or equipment much before these breakdowns occur.

– Risk Reduction: The integrity and competency of the shop-floor staff matter a lot in manufacturing. With predictive analytics and insights, one can look up the reasons for the continual reject causes in their production jobs and batches. If human error is responsible for these wastes, they can be promptly addressed. If the insights rule out human error, it also gives manufacturers the chance to improve accountability, thereby lowering the risk of equipment malfunction or external factors like raw material quality.

How are Predictive Insights Used in the Manufacturing Operations

In the manufacturing industry, it’s crucial to find the factors that cause poor quality and failure in production processes. It is also important to optimize elements, services, and logistics. One of the most successful manufacturers that use predictive analytics to improve their grasp of warranty claims is Lenovo; their step forward led them to reduce 10 to 15 percent in warranty charges. Another benefit of using predictive analytics in manufacturing is that monitoring the equipment performance and yield quality can help track inconsistencies much before the quality checks fail.

Optimizing complex production networks

Predictive maintenance studies are devised to boost the operations and profitability of each production machine and process. Additionally, PPH maximization can streamline the interaction of the processes and machines used. Including all the processes, be it from the purchase of raw materials to the production process and sales, this superior technique of modeling exponentially improves the revenue in complex production operations and supply chains.

Unlike manual planning, advanced analytics generally considers about a thousand different variables and 10,000 constraints to assist the manufacturers to understand what to buy, what to manufacture, and how to manufacture in order to generate great profits throughout the year.

Achieving abundant profits with less

Just like predictive maintenance helps to enhance the uptime of any given asset, predictive analysis can improve its productivity and OEE. Even minute improvement in the overall operational efficiency (OEE) can dramatically boost profits before interest and tax (PBIT). This is achieved by enhancing the production output, material costs, and throughput to boost the profitability of every process involved.

Reduction in downtime in a busy environment

With the help of advanced analytics, manufacturers can boil down the causes of their equipment breaking down and control the input metrics so that they can fix an issue before the breakdown occurs or be prepared to quickly fix issues in order to minimize total downtime. Predictive maintenance can help to increase the equipment lifespan by 20 to 40 percent and reduce downtime by 30 to 50 percent.

Operations are linked closely with the metrics of production, reject (waste), and downtime, which can now be monitored and controlled better with the aid of predictive analytics. By projecting the future demands, manufacturers can be prepared for market shifts, optimize their processes and improve the overall efficiency of their sites using this sophisticated technology. We hope that this blog gave you valuable insight into the world of predictive intelligence and its link to operational efficiency.

What Is AI Vision and Why Is It Here to Stay?

Artificial Intelligence is one of the most misunderstood and overused buzzwords of our generation. We hear so many organizations bragging about their use of AI without really understanding what they are doing with it. In this blog post, our experts share how Artificial Intelligence is used in Cameras to improve business operations. Also, learn why it will become the future of how we do business.

Computer Vision is the application of Artificial Intelligence (“AI”) through a visual medium, like a live camera feed or an image. It essentially aims to replicate human perception and visual cognition. You must have already encountered computer vision without even realizing it, for example, the face-lock on your phone screen, to some level, is a simple application of computer vision or the Instagram or Snapchat filters that alter your hairstyle or turn your face into an animal on the screen, are all made possible with Computer Vision.

Any device that can comprehend a visual feed like a human being is a computer vision system, however, Computers have the ability to process more than humans can. Think about yourself reading these words. It’s likely that you’re focusing on one word at a time while you are reading, and recognizing the words around these words without fully processing them. Using Computer Vision, a computer would be able to see and read all the words on the page at the same time, drawing immediate meaning from the entire page

This principle also applies in a CCTV control room. Normally you would have people monitoring the live camera feeds on multiple screens, but how many live feeds can one person accurately monitor at a time? That’s where Computer Vision shines. It’s able to continuously and accurately monitor the feeds from all cameras simultaneously.

Automation integrated with AI Vision enables the workforce to overcome typical challenges of humans, like fatigue. It provides unseen insights that humans find difficult to access or comprehend in real-time. While human perception has its own share of limitations, a camera can gather every second of visual data from your premises that can be further analyzed to identify areas typically missed by humans.

Computer vision is the most natural next step in machine evolution. The purpose of AI is not to replace humans but to assist them. For that to happen, it needs to command similar cognitive abilities with enhanced capabilities. However, the underlying science behind AI Vision is not as complex or overwhelming as it might appear. Here’s a brief understanding of how AI Vision functions:

Capture Metadata from Camera Feeds

Content feed from a camera is analyzed by trained models to recognize objects, their actions, specific characteristics, and interactions in space and time. Typically, in a retail store, a camera equipped with Cogniphi’s AI Vision can detect shoppers, analyze footfall, recognize customers with face recognition, and evaluate customer expressions like anger with emotion recognition. Similarly in manufacturing, AI Vision can do myriad tasks such as identifying quality for inspection, counting warehouse inventory, etc.

Pattern Recognition and Anomaly Detection

Computer Vision derives relevant insights from unstructured data through contexts and occurrences of patterns along with their co-relations. In a retail store, AI Vision can analyze shopper dwell time, shopper interaction with products, alert anomalies such as misplaced objects in the wrong area, water spillage, and identifying reasons for inventory shrinkage, etc.

Recommendations and Predictive Analytics

Smart Vision equipped with actionable insights provides recommendations in the form of real-time alerts, analytics, and also integrates with existing business systems.

Cogniphi’s AI Vision is a pioneer in providing hyper-local computer vision solutions to retailers and manufacturers. Different components of the solution cater to different aspects of visual data and analytics to

– detect objects and their interaction in space and time.

– detect human body parts, their movement, interactions with real-world objects, their transformations, and other associated patterns.

– detect human faces and recognize people in 1:1 (Verification) and 1:N (Identification) mode.

– detect attention, emotions, and expressions on the faces

– identify and detect textures of objects.

– extract data from several documents and categorize data to train the model to interpret and analyze this data.

AI Vision is Here to Stay

Vision Intelligence technology is a cost-effective upgrade to existing data feeding cameras. On top of being a non-disruptive installation, Cogniphi’s AI Vision can help an enterprise reduce human error and experience overall increased productivity.

The Grand View Research states the growing valuation of the global computer vision market is expected to go up from $11 billion in 2020 to reach $19 billion by 2027. Manufacturing, Energy, Retail, Transportation, and Healthcare are the industries identified as best positioned to capitalize on this technology in the coming years.

The 2020 McKinsey Global Survey on AI has concluded that 50% of companies have adopted AI in one or other business functions and that the majority of use cases are aimed at optimizing operations or at product development or at customer service improvement. As companies increasingly look at AI to solve real-world challenges that depend a great deal on visual inputs, computer vision will have a huge role to play in achieving these objectives.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. It believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.

Reimagining Brick & Mortar Retail: How AI Vision can help

As the world recovers from the disastrous global pandemic, we are witnessing the revival and renewal of brick-and-mortar retail stores. eCommerce giants such as Amazon, are investing in innovative physical stores, which is reassuring for the traditional brick-and-mortar retail format.

For physical stores to remain competitive against these online behemoths, it is imperative that they stay up to date with the latest technologies. Modern technology can revolutionize the customer shopping experience and the efficiency of retailers’ operations. For example, Amazon stores are contactless, which means shoppers can walk in, select products, and walk out without stopping to pay. Customers are immediately charged for their goods as they leave the store. While this is convenient, an upgrade to this extent is not practical for most retailers, especially those outside the grocery style format, where customer service is key.

While innovation at Amazon’s scale is cost-prohibitive and inaccessible, there are other, more cost-effective, methods that most retailers can benefit immensely from currently.

Cogniphi’s AI Vision technology upgrades the retailer’s existing security cameras, enabling these cameras to understand what they see and send actionable insights to staff for action. These cameras become intelligent eyes that collect data 24/7 and improve store operations, revenue generation, efficiency, and safety.

The use cases of the data that could be collected using AI Vision are endless. Here are a few questions to demonstrate how beneficial this technology can be:

    • How does a retailer know when a product is out of stock on the shelf?
    • How long does it take a retailer to replenish that stock or place an order?
    • How does a retailer know what kind of customer is purchasing that product?
    • How does a retailer know how long it took a customer to decide on a product?
    • How does a retailer gather this information across all their stores?
    • How does the head office group know that the individual store is correctly displaying the products at all times?

These questions go deeper and deeper to demonstrate the depth there is for improvement in the retail space.

Enhancing existing security cameras with AI means that a camera can see that stock levels are low, can check the system to see if there is stock available in the stockroom, and can send a notification to staff to replenish immediately. This is a far greater outcome than the current model of waiting hours or until the next day for the product to be replenished, causing the store to miss out on sales.

Furthermore, the AI cameras can collect data on how many shoppers were interested in that product, provide information on how long it took the shopper to decide on that product or detect if a product was stolen and trigger a response for staff or security. This data is all anonymized so no shopper is identified.

These are only a few applications of many, that Cogniphi’s AI Vision can achieve. Below we explore applications that Cogniphi has executed at retailers around the world.

Heat Maps

Retail heat maps can help understand individual shop functionality and identify customer behavior at and around aisles. Retail heat map technology uses real-time imaging to detect movement and assign colors to each floor area based on traffic volume, frequency of visits, or dwell time in those areas to understand customer activity, test new merchandising strategies, and experiment with layouts.

The heat maps can be filtered by different metrics and by different customers, for example, by demographics, or by whether a shopper is alone, is a couple or a part of a group. This data is captured at a statistical population level so retailers can now make decisions off population-size data which includes all the available data sets as opposed to traditionally limited sample sizes.

The data can be used to re-design a store layout, product layout, and optimise category positioning by understanding which areas in a store have high traffic and by who, to achieve growth in basket size and value of purchase.

AI-based Loss Prevention

With AI Vision, theft can be prevented by identifying concealment of products, products that are not scanned at checkouts or products that are incorrectly scanned at checkouts. The cameras detect suspicious activity and behavior in real-time, giving retail stores enough time to respond proactively before the product leaves the store, as opposed to reactively spending time searching through footage to find evidence after the product is long gone.

Some retailers have opted to integrate notifications into point-of-sale devices for staff at checkouts, who can stop the offender before they leave or make sure products are scanned correctly. In other stores, a notification is immediately sent to security personnel with an image of the offender. An added advantage of AI Vision is that each incident is recorded and cataloged for later reference, flexible to your requirements.

AI Vision can also prevent staff from stealing by tracking each product across the store in real-time. Reducing such instances in the store can significantly reduce the losses faced by retailers on a regular basis.

Shopper Analytics

Conventional sensor-based, customer analytics apps can detect in-store traffic in limited detail and without contextual information, for example, they cannot tell the difference between a staff member and a shopper, so the data is always inaccurate. They also do not understand when a shopper is shopping in a group so when calculating store conversion rate, the data shows a lower conversion as not all members of a group, for example, a family will be expected to make a purchase. Cogniphi’s AI Vision can help retailers analyze and observe buyer behavior very closely by providing contextual data about shoppers.

Use cases include:

    • Notifying staff if a customer has not been approached for assistance within 30 seconds of entering the store.
    • Notifying staff if a customer is displaying expressions of confusion, frustration or is upset.
    • Collecting data on how many shoppers come into the store each day and ensuring accurate staff resource planning to meet demand.
    • Collecting data on localized shopper demographics for a more personalized product range and marketing messages.
    • Analyzing queue length and alerting staff to open more checkouts or close checkouts.
    • Providing accurate conversion rates and statistics.

Stock Management

AI Vision can monitor shelves and aisles to check if any of the shelves are out of stock or out of place.  This ensures timely adjustments so no customer misses out on purchasing products. It also ensures products are ordered on time so there are no out of stocks.

This data can be collected across all stores and shared with suppliers to hold them accountable for out of stocks or so that retailers can increase their negotiating power by proving they are fulfilling their trading terms and conditions.

Compliance Management

For stores that have multiple locations, there are many requirements imposed on them by their centralized head office. The challenge for head offices to manage is: how do they ensure all their stores are consistently meeting the many compliance conditions imposed on them?

Conditions include (among many):

    • Correct staff uniform
    • Opening and closing on time
    • Correct display of marketing materials and point of sale
    • Adherence to planogram requirements
    • Appropriate cleanliness and cleaning method
    • No out of stocks on the shelf

Using Cogniphi’s AI Vision, each store can receive an accurate rating out of 100%, calculated in real-time, on how they are performing against these KPIs. This will prioritize the efforts of Area Managers whose role is to ensure each store in their area is up to standard. When a store starts to drop its compliance, immediate notifications can be sent to managers to obtain remedies.

Occupational Health and Safety (OH&S)

Retail stores have many risk factors that can endanger the safety of staff and shoppers. In some stores, there are heavy machines that require trained operators. In other stores, a slippery surface could cause an individual to trip and hurt themselves.

Cogniphi’s AI Vision can monitor a store 24/7 for hazards that can impose an OH&S risk. If a spill or slippery surface is detected. Staff will be notified immediately to clean the area. If the process is for staff to wear protective clothing, like a mask, any breach of this will result in a notification to a supervisor and the overall OH&S rating of the store will decrease. 

Looking at a new dawn in retail

Cogniphi’s AI Vision is a plug-and-play solution that layers AI on top of retailers’ existing video/CCTV infrastructure.  It is pre-loaded with functionality features that enable deployment with faster returns.

The solution is highly flexible with retailers globally, progressively adding more features to their cameras every year. We find that typically retailers will tackle 1 to 3 problems in their first year, and after they see results and a positive return on investment, they add more features.

Cogniphi empowers companies with tools, people, and hyper-local solutions to rapidly innovate and adapt in a hyper-competitive business landscape. If your business is interested in finding out more, please see our website for more information [Click here] or reach out to our team, and one of our friendly consultants will be in touch with you [Reach us].

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. Cogniphi believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.

How AI Vision Will Transform the Future of Manufacturing

A decade ago the manufacturing industry saw a rapid digitization due to the inception of Industry 4.0. Although terms like ‘smart machines’ and “digital revolution” have contributed plenty in mystifying the fourth industrial revolution, the fact of the matter isn’t as beautiful as it sounds.

The manufacturing industry is still ripe with challenges that hinder the production or efficiency of the output. This is certainly true for companies who have either not yet realized the importance of data in the manufacturing process or have tried and failed to gain any valuable insights for the same.

Many companies in the sector at present face challenges in production loss, inventory management, and material wastage, significantly influencing the general yield and productivity of the manufacturing process.

Companies try and fail as they miss out on the most crucial underlying issue – focusing on the “what” of the problem rather than the “why

For instance, a misalignment of factory equipment may not be visible to the manual workforce on the outside. The staff might keep operating it nonetheless at reduced efficiency. This will not only reduce the quality of output but may also pose the risk of aggravating the issue.

That’s where vision intelligence can help.

How Cogniphi’s AI Vision Transforms Manufacturing

AI Vision revolutionizes manufacturing processes with the real-time identification of anomalies and identification of defective production early on in the process, avoiding wastage of resource and time.

This transformative change not only improves the quality of output but also enhances the productivity of the manufacturing process whilst simultaneously reducing the cost of production. With AI Vision, manufacturers can draw more value from their processes without the need to accentuate capital expenditure.

Vision AI isn’t a replacement for your existing surveillance or quality check systems. It’s rather an enhancement to the whole infrastructure, filling the “context hole” in the traditional MES systems.

Not just that, Vision AI also enables manufacturers with predictive and analytical capabilities to enhance day-to-day operations. It helps in predicting faults, breakdowns, and empowers manufacturers with predictive maintenance to eliminate outages or shutdowns saving as much as 20% of loss of time.

All of this takes place with Vision AI in a non-intrusive manner, enabling the production to stay on track while real-time insights are drawn and acted upon.

Moreover, with AI Vision, the factory floors can be administered for compliance adherence, texture anomaly detection, or machine usage. With early detection and proactive detection, manufacturers can easily flag and eliminate compliance breaches early on, avoiding any downstream rejections or stoppages.

The quality of the products can be supervised throughout the production process to ensure it’s on par with the industry standards, eliminating any chance of wastage in the final output.

Looking Ahead: AI Vision & The Future of Manufacturing

There’s very little doubt to the fact that the future of manufacturing will be shaped by AI Vision. Despite the prominence of automation in manufacturing, a lot of work on the assembly line is still carried out manually which might not be necessary as innovation brings enhanced machines to the forefront. The new-age industrial robots will see an upgrade with vision intelligence integrated into their core systems for enhanced attention to detail.

A complete manufacturing unit that’s void of any human presence might still be a distant reality as human supervision is still a need for manufacturing. Vision Intelligence will help the supervisors monitor the production processes with real-time insights on any false positives/negatives, red flags, anomalies, or inefficiencies and inconsistencies. It will make the existing processes ready for the future.

Conclusion

Continuous improvement in the manufacturing process demands visibility which isn’t ideal in traditional surveillance systems. There is a crippling need to upgrade these systems with vision intelligence in order to gather real-time, non-intrusive insights for a proactive production process. Implementing AI Vision will help companies gain vision data, inferences, prediction, productivity, and live view in a simple, comprehensive, and concise dashboard to enable proactive action and continuous improvement.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. It believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.

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

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

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!