Data, in general, is the lifeblood of assisted Machine learning projects. The more data you have, the more accurate the end-product will be. However, it is not simply enough to have raw data. You need to have this data annotated so that the machine learning algorithm can properly identify the objects in a given image, understand human speech, and many other functionalities. 

Even on the surface, we can see the correlation between correctly annotated data and the success of the project. However, this is also supported by research since according to some estimates, 80% of AI project development time is spent on preparing the data. The reason data annotation is so important is that even the slightest error could prove to be disastrous. There are different types of annotations performed as per project requirements. Image Segmentation is the most pixel perfect type of annotation.

Image Segmentation

It is a pixel level annotation task. The objects are segmented individually and classified as per the class labels. Since it is done at the pixel level the time consumption is more but at the same time gives deep predictions.Image segmentation is the task of partitioning an image into multiple segments. This makes it a whole lot easier to analyze the given image. 

This is quite similar to grouping pixels together on the basis of specific characteristics. Now these characteristics can often lead to different types of image segmentation, which we can divide into the following:

• Semantic Segmentation
• Instance Segmentation

Let’s take a moment to understand these concepts.

Semantic Segmentation

Semantic segmentation treats multiple objects of the same class as a single entity. In semantic segmentation, all objects of the same type are marked using one class label. For example, if the image contains two persons, both of them will be marked with the same label for ex. Person.. It helps the visual perception model to learn with better accuracy for right predictions when used in real-life.

Semantic annotation tells you the presence and shape of objects, but not necessarily the size or shape. For example it can tell you that the image contains bananas but not the number of bananas present.

Instance Segmentation

It is a refined version of Semantic Segmentation. instance segmentation treats multiple objects of the same class as distinct individual objects (or instances).  In instance segmentation similar objects get their own separate labels. For example, if the image contains two persons, both of them will be marked with the different labels like Person1 and person2. Typically, instance segmentation is harder than semantic segmentation.

Instance segmentation takes semantic segmentation to the next level by revealing the presence, shape, size, count, and location of the objects featured in an image. For example, it can tell you that the image contains bananas and the number of bananes present with their specific locations. Instance segmentation is used when information of every pixel is critical and may influence the accuracy of the perception model.

In other words, semantic segmentation treats multiple objects within a single category as one entity. Instance segmentation, on the other hand, identifies individual objects within these categories.

To achieve the highest degree of accuracy, computer vision teams must build a dataset for instance segmentation. Also it completely depends on your use case , that which type of segmentation will accurately train your model.Whether you’re using semantic or instance segmentation, you can perform pixel-wise segmentation, which includes every pixel within the outline of an object, or boundary segmentation, which only considers border coordinates.

Applications:

Sometimes bounding boxes simply aren’t accurate enough.There are several applications for which semantic segmentation is very useful.

1. Medical Images: Automated segmentation of body scans can help doctors to perform diagnostic tests.

2. Autonomous Systems: Autonomous vehicles such as self-driving cars and drones can benefit from automated segmentation. For example, self-driving cars can detect derivable regions.

3. Geographical Image Analysis: Aerial images can be used to segment different types of land. Automated land mapping can also be done.

Concluding Thoughts

The definitive goal of a computer vision project is to develop a deep learning algorithm capable of detecting objects in real-time with high-accuracy. For computers, machines need to learn from hundreds to thousands of labeled or annotated images. So it is important to pick a partner that understands the nuances of these image segmentation techniques. Be sure to do some research and identify those that can simply use computer vision versus those that can drill down to help build a robust data set of images that will take your projects to the next level. 

TagX provides you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Our text, image, audio, and video annotations will give you the courage to scale your AI and ML models. Regardless of your data annotation criteria, our  managed service team is ready to support you in both  deploying and maintaining your AI and ML projects. 

How about trying many garments to see how they look and fit on your body without actually wearing those things before buying?  Shoppers always have a doubt about how a garment will look on their own body, and hence ordering online was a matter of hit-or-miss and that’s why they prefer shopping at physical stores where they can actually try the product. Also in a physical store it’s not always possible to try so many varieties of clothes. So why not stand in front of a camera or mirror and try as many clothes and accessories you want. This is achieved through AI powered virtual fitting rooms. Virtual fitting rooms allow customers to see what they would look like in any item of clothing. Virtual fitting rooms are leaving less space for doubt by offering several benefits to the shoppers.

Virtual mirrors incorporate computer vision and augmented reality technology to allow users to try on different outfits in different sizes and colors without having to change and use the fitting room. A customer scans the code of a clothing item and the virtual mirror displays the image of the person in the outfit.

The lighting and background scene can also be changed to give the shopper a fairly good idea of how they would look in the outfit in various environments without having to actually physically wear it.Virtual mirrors use gesture recognition algorithms to recognize user commands and they also feature a virtual cart. Once a buyer short lists an item for purchase, it is added to the virtual basket for later payment and checkout.

Virtual mirrors and recommendation engines

In retail, virtual mirrors may become the next frontier of personalization and customer experience enhancement. A virtual mirror is a conventional mirror with a display behind the glass. Equipped with computer vision cameras and AR, virtual mirrors can show a range of contextual information, which helps customers connect with the brand better.These are some of the companies doing wonders in virtual fitting Technology 

Zeekit

Zeekit , is revolutionizing the way people browse, share and shop from their mobile devices through the most advanced virtual fitting room app for consumers and retailers.

Using the Zeekit app, consumers can now virtually “try on” fashions before buying online or trying on in-store. After uploading a full-body picture, a shopper taps a product they see online, in print or in store and is able to see how it looks and fits on their actual body. The item can then be mixed and matched with fashions from different retailers in their virtual closet, shared with friends or purchased through a link in the app. Retailers and brands can easily incorporate the Zeekit button in their online, mobile and physical stores to give shoppers the ability to try on their entire catalog of products, virtually.

FindMine

FindMine claims to offer virtual fitting rooms as part of an array of retail marketing solutions that include AI-driven eCommerce, mobile, in-store and personalized email services.. The ML-powered engine behind FindMine’s flagship product provides its users with real-time fashion recommendations based on their current outfit. For example, if a person tries on a hat in the fitting room, the virtual mirror will recognize the hat and recommend a bag, accessories, jewelry, and other items that will complement the look.

To make this happen, a retailer feeds FindMine’s algorithm with the data on each product from their catalog. The system then considers a multitude of items’ characteristics including color, description, price, and gender targets to recommend other relevant products at the store.

Virtusize

Virtusize is a virtual fitting solution that enables online fashion retailers to illustrate size and fit for consumers.Virtusize lets customers compare specific measurements of an item they are looking to buy with a similar item they already own. By displaying and overlaying 2D silhouettes of both garments, the startup says that customers can more accurately compare sizes and, ultimately, choose the item that would fit them best. It’s a compelling pitch and has obvious cost savings over the up front work involved in 3D visualisation of a retailer’s entire catalog.

Benefits of using Virtual Fitting Rooms

In-Store Navigation

These applications claim that studying customer behavior helps retailers understand the various factors that influence the buying decision. Customer behavior could include where in the store they go, the route they take, how much time they spend inside, among others. Understanding the behavior enables store owners to respond to customer needs, and potentially increases the chance of customers buying a product.

Better brand exposure

These days, a brand that uses AR tools motivates many people to try new features and share their experience on social media. This grapevine approach brings tech-friendly companies into the spotlight and provokes faster organic growth. Fancy VR events can also bring more potential customers closer to the brand, especially when shows are impossible to conduct due to lockdowns.

Personalization

In traditional shopping, a customer relies on a shop assistant for personalized support. shop assistants may be pushy, annoying, or over-enthusiastic. Oh yes, one more aspect to consider: recent social distancing rules don’t encourage person-to-person interaction. Luckily, Augmented Reality apps come to the rescue. Now you can visit a smart AR shop that remembers your personal preferences and metrics and is ready to guide you around the area.

Reduction of returns

Statistics show that people return about 30% of all goods they buy online. For offline stores, the number of returns equals 9%. Therefore, most frequent customers tend to buy from a retailer with better return policies and free shipping. It goes without saying that such practices mean extra costs that retailers would prefer to avoid. In this way, AR/VR shopping tools allow customers to examine a product in greater detail and make an informed decision. Thus, it reduces the number of returns and decreases those related costs.

Conclusion

The aim of the AI technologies seems to be to enhance the shopping experience by providing convenience or increasing engagement. As for the retail industry, it’s almost certain that more and more businesses will be gradually switching to AI and AR/VR tools in order to enhance customers’ shopping experience.. These technologies allow everyone to learn more details about products, provide a comfortable and stress-free shopping environment, and help make informed decisions when buying a product.

Shoppers have been known to abandon their shopping carts seeing the long queues at the counters. In spite of the amazing strides the retail industry has seen, what customers’ want is freedom from the endless lines at the checkout counters for a seamless shopping experience.

Checkout-free technology has brought a new experience to the retail environment.The automated system recognizes products and bills the customer accordingly.This means less waiting time for the shopper and a quicker service as compared to conventional shopping checkout lanes.

Using AI-powered cameras and software, computer vision is changing the way people interact with the physical world. Computer vision and AI are ultimately going to have an impact that goes far beyond retail  autonomous driving, manufacturing, offices, gyms  to fundamentally alter and better the way we live.

What is Autonomous Checkout?

The system is making use of a combination of computer vision ,affordable ceiling-based cameras and precise in-store navigation maps to detect the actions performed by each customer entered.

Customers can have their face scanned by a facial recognition software before entering the store or are required to swipe a card before entering. By understanding the interactions of customers and seeing the movement of products it is  enabling a checkout-less experience. Whether in retail locations or worksites, users can grab a selection of items and walk away, while the system takes care of recording the transaction.

Using auto-checkouts in stores is a win-win strategy for both customers and retailers. More staff can be employed to help customers shop, rather than spending the company’s resources on cashiers’ manual labor.A frictionless shopping experience is the driving factor for retailers to strive for cashierless stores. 

How it Works

Session Start

Shopping sessions can start in a variety of ways depending on retailer’s preference. In a standard set up, customers initiate a transaction at an entry gate using a personal QR code from an app. Facial recognition can also be used for identification. Other setups can be configured without an entry gate or even without an app.

Customer Detected

Upon entering the store, strategically placed cameras capture the scene. Deep learning models running on local servers to detect humans in these video feeds.

Anonymous Tracking

When a shopping session is started, customers are assigned a random ID. A central server uses this to track each shopper throughout the store as they pass through from camera to camera.

Product Selection

Using deep learning models trained on product and positioning data from Product Mapper software, the system determines when customers interact with products & whether to add or subtract that item from their cart.

Check-Out

Upon leaving the store  customers are charged via their digital wallet, receiving a receipt via email or text. In other configurations, a POS kiosk may auto-populate the customer’s cart for checkout, allowing use of conventional payment methods such as cash, credit, etc.

Conclusion

 The disruptive potential of AI powered checkout systems is here for the brick-and-mortar stores to adapt to this new shopping experience. Fewer cashiers, reduced checkout lines, and reinvented shopping carts will redefine the customer experience. Amplified by machine learning, image recognition, sensors and deep learning algorithms, frictionless checkout systems are a long lasting technology. Autonomous checkout technology will reduce labor costs, improve customer experience and improve profit margins for retailers.

Clearly, there is a need for autonomous checkout technology from both a shopper and retailer perspective. However, the major point of focus for retailers should always be the customer’s in-store experience and how they can enhance this through the implementation of autonomous checkout. By being able to improve this experience, shoppers will in turn value those brands that are taking extra steps to put the shopper’s needs first.

The success of AI algorithms relies heavily on the quality and volume of the data. Real-world data collection is costly and time-consuming. Furthermore, due to privacy regulations, real-world data cannot be used for research or training in most situations, such as in healthcare and the financial sector. The data’s availability and sensitivity are two other drawbacks.We need massive data sets to power deep learning and artificial intelligence algorithms.

Synthetic Data, a new zone in artificial intelligence frees you from the headaches of manual data acquisition, annotation, and cleaning.  Synthetic data Generation solves the challenge of acquiring certain kinds of data which cannot be collected otherwise. Synthetic data generation will yield the same results as real-world data in a fraction of the time and without sacrificing privacy.

Synthetic data Generation focuses on visual simulations and recreations of real-world environments. It is photorealistic, scalable, and powerful data created with cutting-edge computer graphics and data generation algorithms for training. It’s extremely variable, unbiased, and annotated with absolute accuracy and ground truth, eliminating the bottlenecks that come with manual data collection and annotation.

 

Importance of Synthetic Data

There are a number of advantages to using synthetic data. The most obvious way that the use of synthetic data benefits data science is that it reduces the need to capture data from real-world events, and for this reason it becomes possible to generate data and construct a dataset much more quickly than a dataset dependent on real-world events. This means that large volumes of data can be produced in a short timeframe. This is especially true for events that rarely occur, as if an event rarely happens in the wild, more data can be mocked up from some genuine data samples.

Beyond that, the data can be automatically labeled as it is generated, drastically reducing the amount of time needed to label data. Synthetic data can also be useful to gain training data for edge cases, which are instances that may occur infrequently but are critical for the success of your AI.

Different types of synthetic data

Text

Synthetic data can be artificially-generated text. Today, machine learning models allow the conception of remarkably performant natural language generation systems to build and train a model to generate text.

Media

Synthetic data can also be synthetic video, image, or sound. You artificially render media with properties close-enough to real-life data. This similarity allows using the synthetic media as a drop-in replacement for the original data. It can turn particularly helpful if you need to augment the database of a vision recognition system, for example.

Tabular data

Tabular synthetic data refers to artificially generated data that mimics real-life data stored in tables. It could be anything ranging from a patient database to users’ analytical behavior information or financial logs. Synthetic data can function as a drop-in replacement for any type of behavior, predictive, or transactional analysis.

How Is Synthetic Data Created?

That’s the real fun part. Since synthetic data is generated from scratch, there are basically no limitations to what can be created; it’s like drawing on a white canvas. 

We can’t speak for everyone, but we, at TagX use gaming engines to generate our synthetic data that replaces remote sensing imagery; the same engines used for titles like GTA and Fortnite. The creation process is done in 3D to allow complete control of every element in the environment and the objects populating it.

Another important thing to understand about synthetic data generation is this: the more you invest in it, the better the results you’ll get in algorithm training. We invest a lot in appearance and randomization, two elements we found have a very positive impact on training results. The closer synthetic data resembles real data – with all its imperfections! – and offers a wide variety of structures, environments, scenarios and inherent randomized nature, the better the learning process will be. 

Synthetic Data Generation by TagX

TagX focuses to accelerate the AI development process by generating data synthetically to fulfil every data requirement uniquely. TagX has the ability to provide synthetically generated data is pixel-perfect, automatically annotated or labeled and ready to be used as ground truth as well as train data for instant segmentation.

Sentiment analysis models focus on polarity (positive, negative, neutral) but also on feelings and emotions (angry, happy, sad, etc), urgency (urgent, not urgent) and even intentions (interested v. not interested).

Sentiment analysis and natural language processing can reveal opportunities to improve customer experiences, reduce employee turnover, build better products, and more. 

You can analyze text on different levels of detail, and the detail level depends on your goals. For example, you may define an average emotional tone of a group of reviews to know what percentage of customers liked your new clothing collection. If you need to know what visitors like or dislike about a specific garment and why, or whether they compare it with similar items by other brands, you’ll need to analyze each review sentence with a focus on specific aspects and use or specific keywords.

Why is Sentiment Analysis important?

Automated Sentiment Analysis is essential for properly understanding and quantifying the opinions expressed in the text. With large amounts of data, understanding the feedback in any meaningful way becomes time-consuming and expensive. On an Internet-wide scale, resorting to manual categorization is impossible.

By monitoring attitudes and opinions about products, services, or even customer support effectiveness continuously, brands are able to detect subtle shifts in opinions and adapt readily to meet the changing needs of their audience.

For online data, the insight lies in how people online are talking about your brand. For proprietary data, such as customer satisfaction or employee satisfaction reviews, the key business insight is in properly gauging the satisfaction level of respondents.

Most popular applications of sentiment analysis in real life

Social media monitoring

Social media posts often present some of the most truthful points of view about products, services, and businesses because users offer their opinions unsolicited. They are simply compelled to tell the world how they feel. Whichever industry you work in  retail, finance, tech, health, government you probably receive a lot of feedback on social media. And, you’re looking at hours, maybe even days, to process all that data manually. 

But, with the help of machine learning software, you can wade through all that data in minutes, to analyze individual emotions and overall public sentiment on every social platform.

Product Analytics

Using sentiment analysis to look at product analytics can help your company keep an eye on what’s working and what’s not.

By segmenting certain features of your product through analysis, you can create marketing campaigns to target certain groups who may have shown interest in that specific feature. Or better yet, use their sentiment to change a feature that you thought was great but the customer actually hates.

The best part about tracking product analytics is that when customers give feedback, they really want to give it. They may mention certain additions to a product that you hadn’t thought of that they would love to see.

Improving Your Customer Support

Customer support management presents many challenges due to the sheer number of requests, varied topics, and diverse branches within a company not to mention the urgency of any given request. 

Sentiment analysis with natural language understanding (NLU) reads regular human language for meaning, emotion, tone, and more, to understand customer requests, just as a person would. You can automatically process customer support tickets, online chats, phone calls, and emails by sentiment, which might also indicate urgency, and route to the appropriate team.

Sentiment analysis can automatically mark thousands of customer support messages instantly by understanding words and phrases that indicate negativity.

Market and competitor research

Another use case of sentiment analysis is market and competitor research. Find out who’s trending among your competitors and how your marketing efforts compare. Get a comprehensive view from the ground, from every aspect of your and your competition’s customer base.

Analyze your competitor’s content to find out what works with the public that you may not have considered. You’ll understand your strengths and weaknesses and how they relate to that of your competitors. 

Final Words

Sentiment Analysis is one of those technologies, the usefulness of which wholly depends on the understanding of its capabilities. It can be extremely useful if you know how to use it and it can be completely useless if you apply it on something it is not supposed to do. 

Tagx provides sentiment analysis services to a wide range of industries, using our expert workforce’s insights to make each interpretation meaningful. We are experts in interpreting the feelings of a different group of people against various individuals, from social media to other useful online platforms.

The insurance industry leads the way in its AI implementation. For each and every insurance actor, artificial intelligence and image recognition present opportunities to offer an enhanced user experience, to optimise costs, or even to free up staff from time-consuming and low added value tasks 

Computer Vision for Insurance

Computer vision offers the ability to automate, scale, and enhance risk evaluation while seeing gains in operational efficiency and cost reduction. Insurers now have access to an unprecedented quantity of image and video data. The carriers are beginning to invest in machine vision technology to process this data, programmatically analysing risk factors and making sense of these vast image stores. Machine vision represents the leading edge of AI. Since insurance has always been data- intensive, it is perfectly poised to be significantly impacted by AI.

Computer vision helps insurers automate, scale, and enhance risk evaluation while seeing gains in operational efficiency and cost reduction. It will enable insurers to redefine how they should work, how they should create innovative products and services, and how they should deliver customer experiences. Machine vision will allow insurers to redefine existing processes, create innovative products, and transform customer experiences. Machine vision is going to unlock trapped value in new and existing datasets, leveraging the data by creating ways across the entire value chain.

Application of Computer Vision for Insurance Industry

Vehicle Damage Assessment

Inspection is usually the first step in a damage insurance claims process, whether it’s an automobile, mobile phone or property. Assessing the damages to calculate an estimate of repair costs can be a challenging task for insurance providers. Deep Learning models can be used to detect the different types, area, and severity of damage with greater accuracy and automate the claims process.The machine learning model will be trained on thousands of images of damaged cars labeled according to severity of the damage and paired with the repair costs to fix it. 

It reduces the time it takes for customers to receive their payouts and avoids claims leakage, saving insurers money. TagX can label  images and video of damaged cars , phones and other claimed properties of customers for such automated models

Insurance Claims Processing 

Computer vision brings to insurers in terms of reduced insurance claims processing and settlement cycle time. It also lowers cost per claim, increases appraisal accuracy and reduces adjuster travel time and costs. It all results in fewer fraudulent claims, enhanced customer satisfaction and easy adoption of insurance smart devices.

Insurers are widely using NLP to improve their claims processing and customer servicing operations. NLP is being used to scan existing policies and structure the framework of new policies to make the insurance process more efficient. NLP is also used for scanning ambiguities in claim reports for quick fraud detection.

Analysis for Natural disaster damage

Computer vision helps Manage risk and reduce costs  to aid in processing damage assessment. Using aerial imagery and geospatial applications , it helps to assess property damage throughout the evacuated areas. Identify homes that have been completely destroyed or even partially damaged to calculate insurance claims. This prevents fraudulent claims of damaged property from weather-related events. During the catastrophic Hurricane Harvey, insurance agencies used drones to inspect roads, railway tracks, oil refineries, and power lines in Houston. This made the process accurate with no scope of human error. 

Conclusion

There are a lot of new applications of computer vision algorithms in the insurance industry.But only those insurance companies that are on top of their data and ensuring it is ready for AI will have the real advantage over their competitors.TagX can help in the analysis and categorization of images in an effective and scalable manner.

When it comes to processing and analyzing insurance applications, insurance claims, reviewing medical records for identifying risk, or even gauging customer sentiment, having high-quality annotated data will help drive success across many areas where AI is being employed.

Traditionally, the process of looking for, buying, renting, or selling properties has been a manual task. People used to spend a lot of time to shortlist and finalize their dream property. But in the era of digitization, the entire process has become highly streamlined, automated and accurate. The real estate business is much more than just a ‘perfect house’ in an exotic location. It deals with huge volumes of data in terms of buyer/seller preferences, financial and risk-taking capabilities, etc. 

The real estate sector is changing to accommodate a data-driven approach to solutions. AI and machine learning technologies assist real estate professionals streamline their efforts through automation and predictability. This is beneficial to both property purchasers and tenants.

Benefits of computer vision for Real estate 

Below you can read about the most popular benefits:

• users can search the properties with a voice-enabled command
• prospects can search property listings that meet their requirements via mobile devices;
• technology increases the number of pictures real estate platforms can support
• Using Predictive property analytics to offer better recommendations

Applications of Computer Vision in Real Estate

Image Tagging

Image tagging takes advantage of the current technologies used in the real estate sector. There are millions of images available on the real estate portal. The metadata of these images can be used to provide a better experience to the user. The image search engine results can be improved drastically by using metadata. With advanced computer vision technologies, firms can avoid writing long descriptions about products on their websites. 

The metadata can be used to tag specific items in the image so users can find further information about the properties they are interested in. With the addition of thousands of images to the platform every day, the technology can be vastly improved upon to provide a minimalistic, non-obtrusive user experience. Buyers can quickly identify properties and decorative materials that match their needs and preferences, and sellers can also provide better information to their clients.

Value Prediction

Determining the property value in real estate has always been a challenging task. Present appraisal techniques are mainly based on earlier selling price but fail to take into consideration other factors that contribute towards property value such as environmental changes, infrastructure improvements, transportation costs, etc. Machine learning-based tools help in resolving this issue by calculating property value keeping in mind all the above-mentioned factors. This gives accurate price estimates to brokers who can then make a steady initial offer to prospective buyers adhering to market standards.

Property Comparison

With the use of computer vision, users can compare properties room-for-room. Users can select properties by comparing various options and analyzing their space, amenities, natural lighting, and other features. With the thousands of images available online, computer vision can help users compare properties inch for inch. Multiple images available for a single room helps the user for a better comparison and provide a better judgment regarding the property. 

With further development and research, the use of computer vision in real estate can transform the property buying and selling experience along with financial benefits for all the parties involved. Combined with AR and VR, it has the potential to disrupt the real estate sector completely. Computer vision will provide users with a more photo-centric property buying experience.

Enhanced data management

Estate brokers have to deal with huge chunks of data, including property appraisal reports, legal papers, sales details, zoning regulations, etc. As the broker is on the verge of finalizing a contract, the documentation increases and so does the data. Now, lease, partnership, and non-disclosure agreements are also added to the existing list of mandatory documents. Though in today’s scenario everything is digital, still a human presence is required to deal with massive data. 

Machine learning ensures that the data collected is accurate and authentic through continuous analysis. If any inconsistency is observed, such as invalid characters, missing signatures, vacant fields, etc, the machine learning-powered tool notifies it. This helps to securely store data without any replication and streamlines data management in the real estate business.

The Future

Advancements in machine learning have been made at a rapid pace, and the opportunities for real estate will continue to grow over the coming years  and will likely be further accelerated by the Covid pandemic.

Since AI systems are proficient in synthesizing data and carrying out tasks, they can provide both buyers and sellers with quick and easy transactions. Best of all, these innovations can reduce operational costs and improve the quality of customer services. The age of enjoyable and frictionless real estate transactions is dawning.

There is an option that many businesses and researchers must make when developing AI or machine learning algorithms.Since self-learning models need a large amount of annotated data to train before going live, the question arises,whether the model can be trained on manually labeled data or automated labeled data or both.

Data Labeling

First of all let’s understand the importance of data labeling for artificial intelligence.Data labeling is the process of making the objects recognizable to machines through Computer Vision. is the process of labelling images, video frames, audio, and text data that is mainly used in supervised machine learning to train the datasets 

Labeled Data

Labeled data, which is a collection of data samples that have been tagged with one or more labels, play an important role in many software organizations in today’s market. It can help in solving automation problems, training and validating machine learning models, or analysing data. Many organizations therefore set up their own labeled data gathering system which supplies them with the data they require. Labeling data can either be done by humans or be done via some automated process

Manual Labeling

The first and most well-known approach to labeling visual data is manual: people are tasked with manually identifying objects of interest in the image, adding metadata to each image corresponding to the nature and/or position of these objects. Manual data labeling generally means individual annotators identifying objects in images or video frames. This is a labor intensive and time consuming process.

While each labelling instance can only take a few seconds, the cumulative effect of thousands of images may cause a backlog and impede a project. As a result, a growing number of AI developers are turning to skilled data annotation services like TagX .There are organizations today which focus solely on providing dataset labeling services. 

When it comes to precision and accuracy in training datasets, well-trained human annotators remain the gold standard. Manual marking captures the edge cases that automated systems overlook, and experienced human managers can ensure data consistency across massive quantities of data.

Automatic Labeling

Automatic labeling refers to any data point labeling that is not conducted by humans. This could mean labeling by machine learning models, by heuristic approaches, or a combination of the two. A heuristic approach refers to passing single data points through a predefined set of rules that determine the label. These rules are often set up by human experts that can recognize the underlying factors that determine the label of the data point. Heuristic approaches have the advantage of being cost efficient since for each type of data point the rules can be set up by only a single or a few human experts. The labeling itself is also relatively efficient since each data point will be passed through a limited number of rules. 

However, if the structure of the data of interest is changing over time, these rules may become irrelevant or even faulty which will decrease the accuracy of the labels or even render the algorithm unusable until the changes are accounted for. Furthermore, the data may be of such a nature that it is difficult to express these rules or the experts do not know the individual algorithmic steps they themselves take to evaluate a data point. For example, humans have an easy time recognizing the difference between a dog and a cat in an image, but do not know exactly what steps the brain takes to make this distinction.

Semi-Automatic Labeling

Curating training data is typically thought of as a strictly manual process. However, predictions subverts this notion by adding a machine into this labor intensive development process. Given that training data takes the same form as predictions, a model’s output can be used to make an initial annotation of raw data in real time. This data can then be fed through the training data pipeline where it can be improved upon by a labeling review team. The improved annotations would then be fed back into the model to increase its prediction accuracy. This tight feedback loop is referred to as semi-automatic labeling.

As opposed to the traditional process of labeling being a purely human process, predictions inserts mechanical automation into the training data loop. And, as opposed to models in production being a purely machine driven process, assuring quality predictions with low confidence scores are ways to improve and update increasingly performant models.

Conclusion

In manual data labeling, these two steps are very crucial – first, data labeling, and second, checking and verifying to ensure the quality of annotations. In automated data labeling, the data labeling and verification process takes lesser time compared to the manual annotation.Though the automated data labeling process provides multiple times faster speed labeling and the advancement of technology brings more efficiency and quality, Human-in-the-loop is important to ensure quality and accuracy while labeling the data for Machine Learning.

TagX provides you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Our text, image, audio, and video annotations will give you the courage to scale your AI and ML models. Regardless of your data annotation criteria, our  managed service team is ready to support you in both  deploying and maintaining your AI and ML projects. 

Employees are without a doubt, One of an organization’s most important intangible assets. They contribute significantly in terms of benefit and sales. As a result, every organisation aims to recruit the best talent available.

The good news is that the Human Resources Department is up to the task of digital transformation. Yeah, the recruiting process is becoming more digitised. With the advent of technology, we have come across a range of creative recruiting software that provides a variety of advantages.

The growing use of AI in recruiting is viewed as a challenge by the recruiter community, who are concerned that AI will eat their jobs and render them obsolete. But, so far, this is a myth. AI isn’t intended to take the place of recruiters; rather, it’s meant to help them speed up the hiring process.

So how will AI  change recruiting?

First of all, it’s an opportunity to automate low-level tasks. Providing decision-makers with more detailed information, it immediately reduces operational costs. Modern recruitment software has many benefits besides automating administrative workload. There is also more specialized software which allows employers to see how a candidate demonstrates his or her skills in practice. 

Benefits of using AI for Recruitment:

Enhancing quality of hiring

HR personnel must choose the best candidates from a large pool of applicants. The entire process can be separated into many stages automatically thanks to Artificial Intelligence. Recruiters will gather more data on each applicant and thereby more accurately assess them. Many AI-based solutions exist that use special algorithms to determine candidates’ skills and experience.

Unbiased Recruitment

When it comes to finding the perfect candidate for the job, the last thing we want is for our judgment to be clouded by bias. Fortunately, there are some interesting applications of AI in recruitment that can help reduce bias. AI-powered preselection software uses predictive analytics to calculate a candidate’s likelihood to succeed in a role. This allows recruiters and hiring managers to make data-driven hiring decisions rather than decisions based on their gut feeling.

Automation saving Time

The majority of recruiters are extremely busy. The more time-consuming and boring activities that can be automated, the better. For example, using an AI-driven chatbot can eliminate a slew of these tedious tasks. Consider the following scenarios: answering (simple) candidate questions, arranging interviews, and screening applicants. Things that are unquestionably necessary and must be completed, but that can also be automated.

Targeted Hiring made easy

Artificial intelligence is by now revolutionizing the hiring process because recruiters and hiring managers are able to aim more fit candidates than ever before. AI now lets them aim searches by job title, industry, site, household income, earnings, schooling, age, expenditure habits and more. The disadvantage of this is that they can do this lacking ever conversation to the candidate.

Upgrade candidate Experience

Let’s consider the AI-powered chatbot. It never takes a day off, so it’s still available to answer questions from candidates. Even at 12 a.m. On a Sunday afternoon, for example. As a result, it will more effectively direct candidates through the recruitment process, providing immediate responses when they need them. And this is only one example of how incorporating AI into the recruiting process will help.

On a final note

As the use of AI in recruitment becomes more commonplace, people are inventing more and more ways to make recruitment even faster, easier and fairer. 

The basic idea of making better hiring decisions remains the same, only now those hiring decisions are made with the greater good – meaning the performance of the team as a whole and by extension, the entire organization – in mind. Slowly but surely, AI is finding its way into every part of the recruitment process, from sourcing through pre-selection and interviewing to reference and background checks and determining fair compensation. 

TagX Annotation Services

We provide you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Our text, image, audio, and video annotations will give you the courage to scale your AI and ML models. Regardless of your data annotation criteria, our  managed service team is ready to support you in both  deploying and maintaining your AI and ML projects. 

We live in an experience economy, where customers want a personalised experience. Instead of the one-size-fits-all strategy used in the past, they would prefer to visit an eCommerce store and have their needs catered to them individually. Businesses all over the world are using AI to provide consumers with unique items that they are most likely to be interested in.Quality data annotation, on the other hand, will be crucial in the development of cutting-edge AI technology.

In this article, we’ll look at how eCommerce companies are integrating AI into their service offerings, as well as the types of data annotation that are needed to do so.

AI for Ecommerce

 Artificial intelligence (AI) has drastically changed the world of online shopping. It provides services to customers in many ways from ensuring security to providing assistance and making things in a more proper and easy manner. It helps the Retail/  e-commerce space to provide services to their customers on the next level and create satisfactory online shopping experiences.

Due to intelligent solutions that are helping to transform the e-commerce market, AI is one of the fastest technical achievements. With the support of data annotation and labelling services offered by Data Labeling companies like TagX, which make complicated tasks simpler, AI and Machine Learning are assisting in delivering the best and most stable shopping experience.

Both retailers and consumers benefit from AI, data labelling, and data annotation in online shopping. Many e-commerce companies are already using AI to improve user experience, and many more are in the process of doing so. Improving the quality of the search engine using machine learning is one of the most critical and beneficial tasks in the retail/ecommerce space.

E-commerce Use-Cases

Product Recommendation

A user’s search for goods and services on an e-commerce platform can be made simpler with AI. Customers search for convenience as a differentiating factor in an online store much of the time. By training algorithms to connect products with keywords, AI will recommend products to us and assist us in doing so.It turns the buying process efficient for the customer and drives sales for the business.

Visual Search

E-commerce platforms that use Computer Vision to incorporate a visual search feature allow customers to take a picture or upload an image of an object of interest. AI analyses the item’s characteristics and may suggest related items in their online and offline stores. Options to narrow down search results by personal preference can be added to this recommendation engine.AI ensures customers find exactly what they are looking for each time they visit the platform, greatly increasing sales revenue and opportunities to provide further product recommendations.

Product Review Moderation

Customers may use product review pages on ecommerce sites to interact with the website and provide feedback. The website has no influence about how consumers react to good or poor goods, but it does have control over which reviews appear on the site. It is important to ensure that the product review page does not contain any offensive language or content. This can be ensured and screened by the Natural Language Processing capabilities of AI.

Image-Product Tagging

The most effective way to draw a customer’s attention is through visual representation. Particularly in e-commerce, where physical stores have the benefit of touch and feel. To prevent consumer misunderstanding, the images and product description should be identical, and the image quality should meet the website’s expectations.You can train the system through ML to tag specific descriptions and keywords with images and to verify the quality of those images simultaneously.

Data Annotation Services for Ecommerce 

Content Moderation

Our team helps Ecommerce-oriented clients keep content secure and reliable on marketplaces and aggregated data pages. Not-safe-for-work tagging and platform moderation are examples of work.

Categorization

To boost search relevance and customer experience for online customers, teams of content experts quickly and reliably categorise e-commerce content by multiple attributes.

Deduplication

By enhancing product discovery, you can reduce consumer dissatisfaction and speed up the checkout process. Deduplication and the removal of obsolete listings are crucial.

Data Matching

Our Tagging work, which is applicable to product data matching, keeps e-commerce listings up to date in real time and gives retailers a competitive edge in terms of product pricing.

Conclusion

If you’re an ecommerce company that hasn’t embraced machine learning, you’ll be left behind. After all, the advantages of technology to your industry are numerous. Machine learning can help you improve your efficiency in a variety of areas, including customer service and inventory management.

It’s also simpler than you would think to take advantage of solutions in the field. By learning more about the fundamentals of ecommerce machine learning, you’ve already taken the first step. All that’s left now is to figure out what you want the technology to do for you and get to work.