Data annotation is the process of labeling data to make it easy for machines to access it.

Why did humans start making machines? The immediate answer would be to make a mechanical and computerised model that works like humans. Yes, humans wanted machines to imitate whatever they do. The purpose of artificial intelligence is no different. If we look at the things that artificial intelligence-powered machines are doing for us today, most of them try to minimize our work by taking over the routine, time-consuming jobs. In order to make machine learning models advanced, they should be trained with datasets. That is where data annotation makes its debut.

Artificial intelligence and machine learning have changed the way we live. Starting from product recommendations and search engine results to self-driving cars and autonomous drones, everything is powered by artificial intelligence. However, this would be impossible without data annotation. Today, we are building a future where automation and autonomous-powered working is everything. To create such automated applications and machines, the datasets need to be trained properly. However, since the datasets are very huge and the human mode of training won’t help, artificial intelligence companies use data annotation to label the content and use it for machine learning models’ training. By implying data annotation, machine learning models get to be fed with well trained and labelled datasets. In this article, we take you through the basics of data annotation, explain its types, and list the use cases.

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What is data annotation?

In simple terms, data annotation is the process of labelling data to make it easy for machines to access it. Data annotation is specifically important for supervised machine learning as the models rely on labelled datasets to process, understand, and learn from input patterns to arrive at desired outputs.

Data comes in various forms like text, image, video, documents, etc. But such diverse types can’t be fed into a machine learning model without segregating and sorting it according to their varieties. Therefore, data annotation acts as an intermediary tool to mitigate training issues. By using data annotation, companies can train their machine learning models with the right tools and techniques. In a machine learning model, data annotation takes place before the information gets fed to a system. The process is similar to how we teach kids. For example, in order to teach them about a ball, we either show the picture or a real ball. Similarly, data annotation labels the object as ‘ball’ in the dataset and feeds it to the machine learning model. Some of the uses of data annotation are listed as follows,

• While using annotated data to train a machine learning model, the accuracy of its mechanism will be higher.
• Machine learning models trained with annotated data leverages a seamless experience for end-users.
• Even virtual assistants or chatbots use the trained dataset to answer users’ queries.
• In search engine recommendation, a machine learning model trained with annotated data provides comprehensive results.
• Besides helping on large scale, data annotation can help with localized labelling based on geolocations. It locally labels information, images, and other content.

What is human-annotated data?

Despite the sophistication technology is enjoying, they will be nothing without humans help. It is no different while training a machine learning model. Human help big time in making machines learn about the way the world functions. Therefore, data annotation loops humans in the training process to improve performance.

But why is human-annotated data important in machine learning? Humans have a special talent called judgement and hunch, which machines don’t possess. The recent developments in the technology industry are pointing to developing machines that can think like humans. That is where human-annotated data comes into the picture. Human-annotated data introduces subjectivity, intent, and clarification, making machines determine whether a search result is relevant.

Types of data annotation

Text annotation: Today, most companies are moving to automatic models, especially, text-based to power their working system. Owing to the increasing adoption, text annotation has become the centre of attention recently. Text annotation includes a wide variety of annotations like sentiment, intent, and query.

Video annotation: When it comes to video annotation, humans are seen as a good source to train the datasets. For example, companies use human assistance in search engine results. They collect the input from many people in terms of their preferences and promote similar content to others.

Image annotation: Image annotation is very important in training a dataset. Many technologies including computer vision, robotic vision, facial recognition, etc. rely on image annotation to label and interpret image forms. To train the models with image data, metadata must be assigned to the images in form of identifiers, captions, or keywords.

Audio annotation: Audio annotation is quite different from the other types of annotation. Unlike others, audio annotation takes an in-depth step to transcribe and time-stamp the speech data, including transcription of specific pronunciation and intonation.

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A closer look at how it offers the ability to go beyond typical predictive maintenance strategies.

Industry 4.0 is transforming the manufacturing world as companies take advantage of new smart connectivity technologies to optimize their factories. In the next four years alone, these new technologies will help manufacturers and suppliers add nearly $4 trillion in value, one recent reportpredicted.

Machine learning in particular has vast potential, allowing industrial engineers to go beyond traditional analytical tools and extract game-changing insights from the voluminous amounts of data they’re often already collecting on the factory floor. But amid all the hype, it’s crucial to understand exactly what machine learning can do, and what it can’t.

If you’ve already seen machine learning applied in a maintenance context, chances are that it was being used for anomaly detection—in other words, highlighting rare events, with the idea being predicting breakages before they happen. Predictive maintenance is a common use case for machine learning, because it seems so intuitive. If you know in advance when something will break down, you should be able to save a lot of money and time that you’d otherwise spend doing costlier reactive maintenance.

But there’s one problem with this technique: it doesn’t work reliably.

Anomaly detection works by dumping all your historical and real-time data for a given machine into the software, so the machine learning models can learn the machine’s “normal” behavior. When the machine exceeds the specified range—for example, spiking to a temperature of 250 degrees, in contrast to the usually observed 200—the software sends you a notification that something isn’t right.

However, factories are complex. Machine temperature fluctuates based on hundreds of factors, from production volume to the season. This has major implications for machine learning models. They often make mistakes when learning “normal” behavior, resulting in numerous false positives.

The biggest cost of a false positive (in addition to wasting your time) is that it erodes trust in the technology. In extreme cases, this can result in a cry wolf scenario like theBP leak, which happened because there were so many false positives, the engineers shut down the alarms.

Seeing the Big Picture

While predicting events might sound tempting, you’ll get sounder results by usingexplainable machine learning to forecast trends. Imagine if the weather app told you: Today, the pressure is much higher than the seasonal average, so today you can expect an abnormal weather event.

Compare that with the typical weather forecast: Next Monday, we’re likely to see light rain, based on various factors such as temperature and pressure.

Which is more useful?

Explainable machine learning works the same way. By analyzing trends over time, these models can predict when certain machines will need maintenance, and even reveal how to prolong their lifetime.

After detecting erratic sensor data from a heat exchanger, for instance, it might suggest that the device will need to be serviced in six weeks’ time. If you have a scheduled maintenance shutdown in two weeks, you know that the machine can be safely ignored until the next shutdown, letting you focus on other high-priority tasks.

You can also use machine learning to figure out how to extend a device’s lifetime, delaying the need for costly repairs. In a typical factory, many factors contribute to degradation. By using explainable machine learning to see what makes specific equipment age faster, you can take action to prolong its usefulness.

And while anomaly detection methods typically rely on a mysterious black box algorithm, explainable machine learning allows you to take advantage of domain experts on the factory floor, who can tell the software which trends to forecast based on their experience. The software can then analyze these trends over time and make predictions that relate directly to engineers’ needs and priorities.

Machine learning has vast potential to transform daily maintenance operations. It will help you save time, money, and effort and understand the interconnected nature of your factory operations, which will be increasingly critical as we move towards a smartly connected future.

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Artificial intelligence (AI) has grown exponentially in recent years. It’s able to match and, in some cases, exceed human accuracy at such tasks as reading comprehension, image recognition, and text translation. However, an area that is seeing massive opportunities that weren’t possible previously is GIS (geographic information systems). GIS is a computer system that displays and analyzes geographically referenced data.

In broad terms, AI is the capacity for computers to perform tasks that usually require some degree of human intelligence. Machine learning is an approach that can perform this method. It utilizes algorithms to acquire information from the data to provide the necessary answers. For instance, machine learning can help with automated territory map generation.

Although machine learning has been a crucial part of GIS software in Clustering, Classification, and Geographically Weighted Regression, spatial analysis can now go further by using deep learning tools. Let’s look at some cases of deep learning’s application in geographically referenced information.

Deep Learning’s Application in Geographically Referenced Information

• Image Classification

Deep Learning can be used to determine whether a photo is type A or B so as to categorize geotagged photos.

• Instance Segmentation

Instance segmentation is a more exact Object Detection method from which the precise shape of an object in an image can be derived. Using this method, GIS can be combined with LiDAR data to recreate buildings in 3D.

• Semantic Segmentation

This process classifies each image pixel, so it belongs to a specific class. In GIS, this approach can be used for Land Cover Classification.

• Object Detection

Object detection is a computational approach that finds objects within an image by coding and locating them. In GIS, combining this process with aerial photography, satellite imaging, or drone photography makes it possible to map objects of interest.

Machine Learning and Location Intelligence for Real-World Applications of GIS 

Using location intelligence, GIS technology, and Machine Learning automation, industries are becoming more innovative and gaining real-time insight. By combining these methods, businesses are gaining the ability to map, analyze, and share data in the context of location. For instance, they can spot trends and make predictions to support market assessments, site selection, asset tracking, risk management, and various other central business needs. Simply put, machine learning manages complex data, and location intelligence provides the data with crucial location context. 

Let’s look at some real-world examples of how these tools are being applied in various industries.

• Retail Industry

In the retail industry, machine learning and location intelligence have many applications. Retailers can use these tools for site selection, optimizing their supply chain, and location-based advertising. These tools can also help with customer support, providing personalized customer experiences, and setting prices.

• Government Agencies

Government agencies apply machine learning algorithms on georeferenced satellite and drone imagery. This allows them to automate model growth scenarios and fieldwork, predict crop yields and assess the health of crops in real-time.

• Logistics

Drivers, route planners, and operations managers can use AI to track assets in real-time, anticipate future supply needs, accurately predict arrival times, and fill in the gaps in road network databases.

• Finance

Machine learning helps banks and financial analysts detect fraud, perform predictive risk assessments, and plan either one branch location or a network of multiple locations.about:blank

• Manufacturing

When it comes to the manufacturing industry, manufacturers can use AI systems to automate inspections and quality control, optimize supply chain logistics, plan predictive maintenance, and flag any unusual activities that can slow production.

Conclusion

Artificial Intelligence is changing the GIS landscape by using deep machine learning to improve the analysis of geographically referenced information. AI, specifically machine learning and location intelligence, is also being used to help various industries analyze and improve their processes.

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The Unwavering Importance of Big Data Automation in Businesses.

Inventions and discoveries of new trends in the market are getting more refined constantly. Big data automation is undoubtedly one of the most complex and troublesome technologies that is altering the dominion of technology, on a whole, significantly. However, irrespective of the complex nature of big data automation, it remains to be a crucial aspect in organizations and its multifarious benefits cannot be overlooked. The nerve of big data automation lies in finding out patterns that consist of projecting values.

The Benefits of Big Data Automation

Industries and organizations receive a deluge of data on a daily basis. Data is then analyzed to harness valuable insights from it. Reportedly, the automation of big data has induced massive benefits in the companies, improving operational competence, improved self-service modules, and increased scalability of big data technologies.

The Promising Impact of Automation on Data Science

In an international conference on data science and analytics, conducted by the Institute of Electrical and Electronics Engineers (IEEE), the model of big data automation was focused on. The objective of the conference was to observe and deduce the multiple ways in which big data automation can have significant impacts on data science. It was observed that the role that automation plays in data science depends on few important factors.

The Study of Time-varying Big Data

This particular factor depends on a pragmatic approach in which the categorization of analytics is made into diverse segments. The study was conducted to find any definite volume of data over a considerable period of time.

The Function in Data Preparation

In the case of predictive analysis, the time required is actually reduced by automation. Predictive analyses are often complex and thus, it demands a robust language that makes identification of prediction problems easy and lucid. Big data automation provides a tailored framework that can work with diverse specifications automatically.

Extricating Prediction Features and Indicating Them

The objective of implementing data automation is to present it in a measurable format. Additionally, automation is deemed as an astute assistant of data analysts as it helps in finding out the main prediction problems in a uniform format.

In the Praise of Big Data Automation

Big data automation plays an impeccable role in determining the improvement trajectory of data science. Automation in data science has actually opened avenues for businessmen in leveraging its numerous factors and eliminating the complexities. The fact that the model is a self-service one also makes it cost-effective. Besides, it also helps data scientists and analysts to be attentive towards value-added activities and deep competencies.

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If you analyze closely, artificial intelligence is everywhere around you. You carry it in your phone in the form of almost every social media app, Alexa, Siri, Google is AI-powered digital assistants, and high-end cars come with AI-enables self-parking systems. These are some of the many examples of how this futuristic technology is impacting our daily lives. 

Not just for entertainment, AI has uses in almost every industry to speed up common actions. In the on-going pandemic, AI has gone far as to even detect potential COVID-19 cases within a particular radius using bots. Let’s take a look at how artificial intelligence has affected every aspect of life, from leisure to work. 

Business Front 

At work, artificial intelligence has improved manpower morale and business efficiency. If a company is looking to hire employees, artificial intelligence programs help in identifying ideal profiles to create an interview pool. Business giant JP Morgan and fast-food company McDonald’s uses Pymetrics which is an AI-powered software that collects data to process potential candidates for company hiring. Pymetrics assess profiles on the basis of the person’s resume and objective behavioral data that is checked according to the requirements of the company. 

Social Networking 

Facebook is notoriously famous for its controversies. Artificial intelligence helped Facebook’s algorithm detect all types of content that violated its social hate policy. As a result of that AI-program, 97% of the hate-fueling content was removed without being reported by the users. Not just this, the ads you actively see on any social media platform are backed by AI and machine learning. 

Academics

Coronavirus drastically changed everyone’s daily routine. One of the changes was online school/college/university classes. This distance learning has put significant pressure on educational institutes to retain student attention during virtual tutoring. Affective Spotlight, a Microsoft tool, uses artificial intelligence to test the attention levels during the class. It analyses body movements, facial expressions, body language which is given numerical values for the participants. 

Healthcare 

Not swaying away from the topic of the on-going global pandemic, the healthcare sector had to make too many dynamic changes to serve better patient care. Many hospitals and clinics adopted telemedicine practices where they put AI-powered bots for first-level patient interaction (making appointments), providing initial diagnosis, reminding patients to take medicines on time, educating them about common tests and procedures, etc. AI is also helping in the vaccine drive by identifying asymptomatic COVID-19 patients and digitizing patient files for a better digital tracking system. 

Gambling 

Gambling involves taking big risks. Rdentify uses artificial intelligence to monitor live chat and identify problem gamblers. Its AI technology can monitor live chats and online gambling habits, providing players with a scoreboard of risk percentage for every game. The machine learning system then uses the risk percentage to intimate the player with a good time to leave the game and also highlights if a person is addicted to gambling. Rdentify has been adopted by many AAMS safe online casinos. This application’s scoring system is in sync with the operator’s CRM which helps in calculating the risks in real-time. Thanks to that, grave gambling risks will be reported immediately to customer support. To control the growing gambling issues in many parts of the world, Rdentify has partnered with AgeChecked, a verification and monitoring software. 

Artificial Intelligence makes our lives better in many ways, be it leisure or jobs. Artificial intelligence has a bright future in human life as scientists continue to work on this ever-developing technology. We are already seeing chatbots, virtual assistants, home robots making simple life decisions for us, be it telling Siri to make a grocery list to using Rdentify to stop gamblers from burning a hole in their wallets.

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Artificial Intelligence is rolling out across the aviation industry to a greater and greater extent. It could even hold the key to a speedier post-pandemic recovery. Let’s take a look at how its branch of machine learning is already impacting everyday aspects of travel, including how tickets are priced, point-to-point routes, fuel consumption optimization, and biometric boarding.

“AI is coming and it will have no mercy for any obstacles on its way. Companies can choose to resist and maintain status quo to extend their survival period, or embrace AI and be part of the ongoing revolution,”  – IATA, AI in Aviation White Paper, 2018.

What is machine learning?

Machine earning, or ML, is a type of cumulative Artificial Intelligence, or AI. It is a method of data analysis that allows computer systems to learn through experience. Algorithms and statistical models analyze patterns, which they then use to improve themselves. This (hopefully) leads to better and better decisions with minimal human intervention.

The larger the volumes of data, the more accurate the algorithm’s evolution through ML will be. And what generates an amazing amount of data (apart from Netflix-views and internet searches) every single day? Airlines and their passengers. And the latter are beginning to put it to very good use.

Dynamic ticket pricing

The airline industry is considered one of the most advanced in using complex pricing strategies. Most travelers seek to obtain their airline tickets for the lowest price possible. Airlines, on the other hand, want to maximize their revenue. Machine learning algorithms can help both parties in their quest for the best deal.

To determine optimum ticket prices, airlines need to forecast demand on a specific time of year and a particular day. Furthermore, they need to account for factors such as holidays, events, and festivals.They also need to keep on top of what the competition is doing, at what time people are more inclined to purchase what kind of tickets, and predicted fuel prices.

To stay in the proximity of the continuously moving sweet spot, today’s data systems are making billions of predictions per day to this effect. When ML is implemented to its current potential, a single model can make about two million evaluations – per second.

Of course, these algorithms can also work in the customer’s favor by figuring out at what time of day and which day of the month ticket prices will be lower. As airline application of ML increases, we are bound to see even more optimal airfare finder services pop up.

Route planning

When determining route and frequency demand for specific city-pairs, especially with the rise in point-to-point travel, carriers must consider hundreds of factors. Demographics, industry connections, time of the week and day, season, holidays, events, fuel price, etc., all decide whether or not a route will be profitable and when.

To determine optimal routes and schedules, ML can handle much more data than traditional analytical tools. It can analyze search engine data, booking agent data, social media posts and comments, along with recruitment and professional sites, to determine both leisure and business travel demand.

The ability to analyze large amounts of data and continually draw new conclusions from them will be invaluable to airlines and agile revenue management as the industry emerges into a post-pandemic landscape much different than the one of 2019.

Onboard sales and food supply

What a person eats and drinks on board an aircraft varies greatly, not only from individual to individual and types of travel but also depending on destination and time of day. 20% of all food produced by in-flight catering is wasted every single year.

To minimize both food waste and financial losses, carriers need to analyze previous data of onboard sales and adapt their offerings. The more customizable the in-flight experience becomes, the more sophisticated algorithms airlines need to perfect the supply vs. demand snack situation.

In June last year, easyJet, which said in its 2020 annual report that it aims to become the world’s most data-driven airline, hired British AI-firm Black Swan Data to help it analyze customer food consumption.

“For someone like easyJet, it’s likely that 40pc of their fresh food will be wasted,” Steve King, Black Swan Data’s CEO, told the Telegraph at the time. “The aviation world is insane because there’s no data, it’s like a website where you delete the data every day. It wouldn’t work.”

Fuel consumption

Along with labor, fuel is an airline’s biggest operating cost, accounting for close to a quarter of expenses. Not only that, but aviation is responsible for about 2.4% of global fossil fuel CO2 emissions. To become more efficient, better calculations for exactly how much fuel is needed for a specific flight are required. Enter machine learning.

According to AI Trends, before Southwest Airlines started a pilot project for predictive models in 2016, the airline was producing 1,200 fuel demand forecasts every month – working with spreadsheets. It would take analysts three days each month to compose the forecasts, which in many cases turned out to be less accurate than ideal.

The computer system generated 9,600 forecasts for each of the close to 100 airports Southwest serves for each month, in 60% less time. While the exact cost savings were never publicized, Doug Gray, Director of Southwest’s analytical data services, confirmed that they were “substantial.”

Biometric boarding

Machine learning techniques are also applied to biometrics. For facial recognition to work it needs to be trained using ML algorithms. This is done by processing thousands upon thousands of images searching for patterns of features.

The technology has been around for some time. However, the access to massive amounts of facial data (all those selifes and tagged photos on the internet) and cheaper computing power has seen the multi-layered deep learning Artificial Neural Networks involved in the process make giant leaps in the past few years. Delta Air Lines recently launched the first US domestic digital identity test at Detroit Metropolitan Wayne County Airport.

The convergence of AI and biometrics could not have come at a better time as airports and airlines all over the world are implementing contactless procedures to keep travel as safe as possible for both employees and and passengers.

Artificial intelligence is not only here to stay, but it can help the aviation industry recover much faster from what continues to be the most severe crisis in its history. As IATA says, you either resisting and hang on just a little bit longer, or you embrace it and become part of the revolution.

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Artificial Intelligence has been on the tech agenda for more than two decades now, although its impact on our day-to-day lives is perhaps yet to be fully realized. Perceptions of AI’s innovation are broad and have altered over time.

For many, AI is an exciting new tool that will improve the quality of our lives, at home and work, by being able to deliver functions that will save us time and enhance our experiences in the world of leisure and entertainment. But for others, AI is perceived as a threat to our livelihoods. Old-fashioned perceptions push the narrative that technology must be managed and developed slowly – or not developed at all – to protect our ways of life.

This has contributed to AI receiving only sporadic funding and being dismissed by some as a pipe dream. However, we will discuss the positive impact that AI is having on the world of digital entertainment, and in particular in the world of bingo and online casino gaming.

That’s entertainment

Although many may not realize AI is already readily utilized as a part of several mainstream digital entertainment experiences. For example, Netflix viewers will receive recommendations of what to watch next based on the technology. AI is used to interpret data and produce an algorithm that displays film and TV suggestions that will likely appeal to the user based on their previous habits. In this instance, AI is helping to deliver a much more bespoke, personalized experience to paying subscribers.

In gaming, AI can be used to set a difficultly level based on the player’s abilities and can make configuration recommendations to enhance the player’s experience. Where human guidance is not possible, AI helps to keep new players on track.

Other gaming sectors, such as online casino gaming, have contrasting relationships with AI. Some platforms utilize the technology in a similar way to traditional console titles to automatically change personalization. It can also be used to speed up manual processes, such as repeating a previous bet or warning the player against ‘twisting’ in a game of blackjack when they have a good hand. Others instead use more tried and tested innovations such as random number generators to ensure games are fair.

Some businesses in the casino sector have adopted the technology to try and enhance the experience for players.  Sue Dawson from Best New Bingo Sites explains how “In real money online gaming, AI can be used for targeting marketing and advertising so that players receive promotional offers that are tailored to their preferences and behavior. For instance, you might receive an offer of free spins for the slot game you play most often at the time of day you’re most likely to play. The games themselves are strictly regulated, though, and must use verified RNG to ensure that all players have the same chance of winning.”

The future

As is the case in any area of technology, it’s fascinating to speculate what the future may hold for AI. Already developers are experimenting with its use in spheres like art and even literature. Art AI Gallery offers images for sale that have been generated by artificial intelligence, while developers have experimented with using AI to write plays, make music, and script films.

It’s clear from the evidence that AI is already a major part of many lives, and as the technology behind it advances, it’s likely that will see further leaps forward taken in the years and decades to come.

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