Artificial Intelligence Archives - Page 6 of 10

Yashica Chopra
June 16, 2022
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Machine Learning for Trading – Can It Predict the Trend?

Machine learning allows machines to trace patterns and identify trends by analyzing huge datasets. Data scientists are working on building ML models to assist traders in day-to-day trades in the stock market. Let’s discuss the role of machine learning in the trading industry. Machine learning is a field of artificial intelligence that deals with how computers process large datasets and learn from them to make decisions and predictions. Machine learning is a vast concept and is classified into supervised and unsupervised-based learning. There are several algorithms designed to train machines on available data. A few of them are Simple & Multi Linear Regression, Logistic Regression, Decision Tree Classification, XgBoost, and many more. The algorithm is trained in different ways depending on the type of datasets fed into the system. Machine learning is almost everywhere in today’s world. From Netflix’s search engine to sales forecasting, supply chain management, manufacturing, healthcare, and more, machine learning is already a part of every industry in multiple verticals. The financial and trading industry also uses machine learning algorithms in different ways. FinTech is changing the way we look at banking and insurance. The crypto market has various AI-based apps that allow users to trade in cryptocurrency with the assistance of advanced algorithms. AI and ML have a role to play in the trading market too. There is no 100% automation yet; ML algorithms assist stockbrokers to offer better services to investors. AI and ML consulting companies offer consulting services to traders and trading establishments to build, customize, and fine-tune the algorithms and automate statistical work. Let’s explore the role of machine learning in trading and see how it can help stockbrokers, traders, and investors. How is Machine Learning Used In Trading? Patterns and trends are an integral part of the trading industry. Machine learning algorithms are good at processing large amounts of data to identify patterns that cannot be easily spotted by humans. The stock market is volatile and prone to be affected by several factors (social, political, economic, demographic, etc.). Being able to correctly predict the trends in advance will help traders minimize the risk of market crashes and increase returns. This makes machine learning a valuable resource in trading. ML algorithms can also be used for: Sentiment Analysis Analyzing the sentiment in the market might help traders determine whether the stock prices for a brand will increase or decrease. Data is collected from multiple sources like social media, websites, forums, news platforms, and so on. Natural Language Processing (NLP) is used to understand the context of the data to determine the market mood. Traders can use this knowledge to adjust their investments and decide if they should buy more stock, sell what they own, or wait for the patterns to establish more clearly. Pattern Detection A majority of the stock market predictions come from hours of manual processing combined with years of experience. Machine learning in stock trading reduces the need for hard labor by automating the analysis. However, human expertise is still vital to deriving insights. The trader should know where to look to identify the patterns. Whether it is designing an automated trading machine or using ML to detect patterns, human experience and intuition are very much necessary for the algorithm to deliver accurate results. ML cuts through the time-consuming tasks of collecting and processing information, but it’s ultimately people who use the insights to make decisions. Real-Time Data Forecasting The machine learning algorithms can be fed more data in real-time. The algorithms are trained to learn and fine-tune the predictions to increase accuracy. For example, aspects like the global weather conditions, political unrest, climate change and its impact on renewable energy, etc., are real-world issues that have a direct impact on the trading industry. The ML algorithms can predict the results of the world’s issues and thus provide a basis for what could happen in the stock market subsequently. Using a combination of algorithms and forecasts will provide better results as multiple factors influence each other. High-Frequency Trading Machine A high-frequency trading machine runs on artificial intelligence. It performs thousands of transactions per day by taking advantage of the subtle changes in the stock market. These changes are almost impossible for humans to track as they happen in a few seconds or minutes (at the most). However, a properly trained trading machine will be fast enough to see the change and use it. That said, similar to pattern detection, this too requires human expertise. The algorithm has to be developed to detect minute changes. Regular updates are necessary to tune the algorithm and minimize errors. The tuning can also be automated to avoid statistical work but still requires human supervision. Chatbots in Trading Another way to use Machine Learning in trading is by developing chatbots for communication. Chatbots in any industry have the same roles and responsibilities. Chatbots communicate with traders and provide the information they ask for (past deals, financial statements, investment records, etc.). The chatbots can also compile a list of trading offers, potential shares to buy, the latest prices, and much more. AI-powered chatbots are faster and more effective than human support teams. A single chatbot can handle multiple conversations at the same time and provide each trader with the required data without feeling tired, getting confused, or making mistakes. The algorithms are designed to take feedback and learn from the previous mistakes to provide better support to the traders. Is Machine Learning Suitable for Day Trading? Day trading is where a trader purchases and sells equities on the same day. Traders buy the stocks of a company in the morning and sell them by the end of the day, preferably to make a profit. Day trading requires a lot of experience and knowledge about the stock market. A minor mistake could lead to losses of thousands and millions. Machine Learning for trading is relatively a new concept, with ML engineers working on developing algorithms that can accurately offer predictions and insights. Different types of algorithms need to be used to develop

Yashica Chopra
June 08, 2022
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Top 10 Benefits of Artificial Intelligence in the Workplace

Artificial intelligence occupies an increasingly important role in the workplace today. From automation to virtual assistance, collaboration, and analytics, AI offers effective solutions to many work-related challenges and creates a positive setup for employees to be more productive. Artificial intelligence is not a new phenomenon. It has been slowly entering different industries and verticals to replace the traditional systems and processes. The need for digital transformation has made artificial intelligence a prime investment option for many enterprises. Forbes has reported that 50% of the businesses intended to invest more in AI and make it a part of the everyday workplace environment. Around 83% of enterprises have increased their budgets for AI and ML since 2019. With each passing year, more organizations are adopting AI tools to automate the processes and stay competitive in the market. The Covid-19 pandemic has further intensified the adoption rate. Remote teams and collaborations have put AI at the front. Digital transformation invariably brings several changes to the workplace. The first factor to consider here is the reaction of employees. What does it mean to the employees when advanced technology is adopted by the business? What changes does AI bring to the work environment? How can the management streamline the business processes while assuring employees about the changes? What is the future of AI in the workplace? Let’s find out. How is Artificial Intelligence Changing the Workplace? Machines (analog and digital) have been a part of the work environment for a long time. AI takes it one step further by making these machines intelligent to reduce the workload on employees and increase productivity. However, it is likely that employees will be wary of artificial intelligence and new technology. The management should first educate and assure employees about the value of using AI in the business. A cohesive workplace where employees and technology go hand in hand will be better equipped to deliver the expected results. Artificial intelligence can be a part of various departments within an enterprise. It can be used by entry-level employees and top management. Data collection, analytics, and reporting are the three major aspects of AI. Streamlining Human Resources Artificial intelligence can help the HR department streamline the recruitment process. Organizations spend millions every year to find the best candidates for different job positions in their business. The entire process often requires a few months only to start again (due to expansion or employee turnover). AI software can automate the initial screening and filter eligible candidates from the large pool of applicants. It can also go through thousands of social media profiles to look for potential candidates. Artificial intelligence can then be used to create and analyze assessments that help the HR team shortlist suitable candidates for the job. Unilever is already using AI to automate the recruitment process. Smart Remote Working Artificial intelligence not only helps in hiring on-site and remote workers but also assists remote workers to do their job effectively. Let’s take maintenance and repairs as an example. Technicians can control semi-automated robots from a distance and use them to make the necessary repairs to the machines. IoT, robotics, and automation make it possible for employees to stay away from the worksite and still get the job done. Robots can be sent to dangerous/ risky workplaces instead of humans. The engineers can stay in safe zones and complete the inspections, repairs, and maintenance works. This makes the workplace safer and allows employees to work at their convenience. Workplace Optimization Using artificial intelligence in the workplace will bring many changes. More sensors and devices will be installed and connected to each other. Data will continuously flow from one end to another. Similar to how smart devices make a smart home, AI devices can create a smart workplace where machines observe, monitor, and analyze humans around them. AI sensors can optimize and personalize the workplace by adjusting the lighting, temperature, etc., of the room based on the employees present at the scene. AI can give management an ideal layout design to optimize space management and create a work environment where employees feel comfortable and productive. Team Collaborations We see two types of collaborations here. One is between humans, and the other is between humans and machines. Accenture reported that the collaboration between humans and machines will substantially increase productivity. Data flow is streamlined within the enterprise so that employees can directly access the necessary information required to complete their job. Data sources can be connected across the enterprise to prevent data loss and keep the knowledge base up to date at all times. AI platforms make it easy for in-house and remote teams to work together, share data, updates, comments, etc., and track the project’s progress with ease. Performance and Productivity Management Artificial intelligence in the corporate environment can help monitor and assess the performance of employees by minimizing human intervention. Instead of team leaders and managers, performance appraisal will be handled by the AI software. This reduces the risk of human bias and personal feelings when measuring the performance of an employee. Similarly, people analytics will give the HR team and the top management with the necessary information about how the workplace is affecting the employees. Are they feeling too stressed? Are the employees not comfortable in the environment? What changes can be made to create a relaxed workplace so that employees become more productive? AI helps provide answers to such questions and more. Self-Servicing, Help Desk, and Chatbots Chatbots are powerful tools that streamline communication internally and externally. For example, chatbots are used within the enterprise to assist employees with day-to-day work-related issues. Employees don’t have to wait for the tech team or HR team to respond to their emails/ messages/ calls and help them with recurring issues. The chatbots and virtual assistants will take care of it. The AI help desk is one of the most popular customer service tools in the market. Many organizations hire AI software development companies to build and customize customer service tools to increase customer satisfaction. Self-servicing empowers employees

Yashica Chopra
May 19, 2022
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Computer Vision in Manufacturing Industry: Explained With Use Cases

Computer vision in manufacturing is streamlining the use of visual data to make faster and better decisions. Factories become safer, and the production quality increases when manufacturers use insights shared by computer vision systems. We’ll discuss the use of CV in the manufacturing industry in detail. The manufacturing industry is adopting the latest technology to modernize processes and increase production. Artificial intelligence, cloud computing, machine learning, the Internet of Things (IoT), Industry 4.0, and computer vision are now a part of the manufacturing industry. Computer vision is used at every stage of manufacturing, right from procuring raw materials to supplying and distributing the finished goods. It has a vital role in bringing flexibility and scalability to the manufacturing unit. It helps increase production while sustaining quality and minimizing the use of resources. Manufacturers hire AI developers and offshore solution providers to digitally transform their infrastructure and integrate it with the latest technology. What is Computer Vision? Computer vision is a field of artificial intelligence and computer science that empowers devices to read, understand, and analyze visual data as humans do. While AI makes machines think, computer vision makes machines see and understand visual information at the pixel level. Images, videos, and all forms of visual inputs can be read and processed through computer vision. The global computer vision market in 2020 was $11.32 billion and is expected to grow at a CAGR (Compound Annual Growth Rate) of 7.3% by 2028. The industrial segment was responsible for around 51% of the market revenue. Computer vision in a range of manufacturing processes contributed more than half the total global share. The Asia Pacific region had a high adoption rate and contributed 40% of the revenue in the same year. But how is computer vision used in the manufacturing industry? With more and more manufacturers planning to adopt computer vision and Industry 4.0, how do they intend to change their systems and processes? Let’s take a look. Use Cases For Computer Vision In Manufacturing Computer vision doesn’t require any physical contact with the machinery. It works simultaneously to process the input data from multiple sources and provide insights to the workers, supervisors, and managers. Product and Component Assembly Computer vision is used to automate the product assembly process. Computer-aided software is used to create 3D designs fed into the system to assemble and manufacture the products. The need for human intervention is minimized by allowing computer vision to assist the machinery in manufacturing the products with greater precision. Computer vision systems work continuously to monitor the assembly line so that defects are identified in the initial stages. This reduces the returns or rejected products and increases customer satisfaction. Every stage of the assembly process is monitored and tracked. The data is stored in the data warehouse or data lake. For example, Telsa manufacturing has automated almost 70% of the manufacturing processes using computer vision. The pharma industry uses computer vision to ensure that the quality of the product is consistent and adheres to industry-wide standards. Predictive Maintenance Predictive maintenance can save millions of dollars for the manufacturing industry. Machinery is the heart of a manufacturing unit. Even a minor breakdown can disrupt the entire process and cause a series of delays that lead to additional expenses. The supply chain will be disturbed when there’s an unexpected halt in production. Computer vision minimizes such disruptions and losses by constantly assessing the health and working condition of the machinery. Heavy machinery runs for long hours in unusual temperatures and environments. They are prone to breakdowns without proper maintenance and care. However, instead of being reactive and repairing the machinery after it breaks down, computer vision uses its algorithm to determine the best time to perform a maintenance check and repair the minor issues. This prevents the trouble from blowing up and causing a full-scale disruption. Furthermore, minor repairs take less time and money than major spare part replacements. 3D Vision to Identify Defects Defects are a bane of the manufacturing industry. The product assembly can’t manufacture with complete accuracy. However, the extent and percentage of defects can be a game-changer for the manufacturer. A computer vision algorithm is trained to identify defects at the micro and macro level. Instead of waiting until the quality testing/ inspection, the products are checked for defects right during the manufacturing process. Even the slightest difference is highlighted so that workers can remove the defective product. Corrective measures can be taken by easily identifying the cause of the defect. Data from the cameras and processed by the computer vision systems and compared with the database to test for quality and defects. This minimizes the loss of money and reputation by preventing defective products from reaching the end-user. Fine-Tune Die-Cutting Molds and dies are an inherent part of the manufacturing industry. Manufacturers spend large amounts of money creating dies to give the products the desired shape and structure. Laser and rotary are two famous methods used to create dies. While high-speed laser light provides precise dies, it cannot cut through tough materials. The rotary technique uses steel blades and physical tools to cut through hard material, but it can be less precise and time-consuming. Computer vision can fine-tune the rotary die-cutting method to make it as precise and fast as the laser technique. In short, computer vision gives the best of rotary and laser methods to increase accuracy and precision in the dies. Since these dies are used to manufacture spare parts and products, the end result will be of better quality. Offshore Artificial Intelligence software development companies can help manufacturers use a computer vision system to guide the die-cutting machine in the right manner. Inspect Packages Packaging is the final stage after manufacturing the product. The package should be strong and capable enough to protect the finished good from damage caused during the shipping and distribution. The package should also ensure that the product inside doesn’t get affected by the changes in the surrounding environment. Industries like pharma, food and beverage, cosmetics, etc., pay extra

Rafiuddin Khan
May 13, 2022
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Importance of Datasets in Machine Learning and AI Research

Most of us nowadays are focused on building machine learning models and solving problems with the existing datasets. But we need to first understand what a dataset is, its importance, and its role in building robust machine learning solutions. Today we have an abundance of open-source datasets to do research on or build an application to solve real-world problems in many fields. However, the lack of quality and quantitative datasets are a cause of concern. Data has grown tremendously and will continue to grow at a higher pace in the future. So, how do we use the huge volumes of data in AI research? Here we will discuss ways to smartly leverage the existing dataset or generate the right datasets for the given requirements. What is a Dataset in Machine Learning? Dataset is a collection of various types of data stored in a digital format. Data is the key component of any Machine Learning project. Datasets primarily consist of images, texts, audio, videos, numerical data points, etc., for solving various Artificial Intelligence challenges such as Why is Dataset Important? We can not have an Artificial Intelligence system with data. Deep Learning models are data-hungry and require a lot of data to create the best model or a system with high fidelity. The quality of data is as important as the quantity even if you have implemented great algorithms for machine learning models. The following quote best explains the working of a machine learning model. Garbage In Garbage Out (GIGO): If we feed low-quality data to ML Model it will deliver a similar result. According to The State of Data Science 2020 report, data preparation and understanding is one of the most important and time-consuming tasks of the Machine Learning project lifecycle. Survey shows that most Data Scientists and AI developers spend nearly 70% of their time analyzing datasets. The remaining time is spent on other processes such as model selection, training, testing, and deployment. Limitation of Datasets Finding a quality dataset is a fundamental requirement to build the foundation of any real-world AI application. However, the real-world datasets are complex, messier, and unstructured. The performance of any Machine Learning or Deep Learning model depends on the quantity, quality, and relevancy of the dataset. It’s not an easy task to find the right balance. We are privileged to have a large corpus of open-source datasets in the last decade which has motivated the AI community and researchers to do state-of-the-art research and work on AI-enabled products. Despite the abundance of datasets, it is always a challenge to solve a new problem statement. The following are the prominent challenges of datasets that limit data scientists from building better AI applications. How to Build Datasets for Your Machine Learning Projects? An Artificial Intelligence application flow is depicted in the diagram below. The first two components are the dataset acquisitions & data annotation section which are crucial to understanding for building a good Machine Learning application. Nowadays, we have ample resources where we can get datasets on the internet either open-source or paid. As you know data collection and preparation is the crux of any Machine Learning project, and most of our precious time is spent on this phase. To solve the problem statements using Machine Learning, we have two choices. Either we use the existing datasets or create a new one. For a highly specific problem statement, you have to create a dataset for a domain, clean it, visualize it, and understand the relevance to get the result. However, if the problem statement is common, you can use the following dataset platforms for research and gather data that best suits your requirements. Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. Best Dataset Search Engine Platforms for a Machine Learning Challenge Below is the list of a few dataset platforms, that allow us to search and download data for Machine Learning projects and experiments. Most of the datasets are already cleaned and segregated for ML and AI project pipeline. However, we have to filter and utilize them according to our specifications. Custom Dataset can be created by collecting multiple datasets. For example, if we want to build an app to detect kitchen equipment, we need to collect and label images of relevant kitchen equipment. For labeling the images, we can run a campaign to collect data by encouraging users to submit or label images on a platform. They can be paid or rewarded for the task. Here are a few options that can be used to get data quickly for your requirements. A synthetic dataset is created using computer algorithms that mimic real-world datasets. This type of dataset has shown promising results in the experiments conducted to build Deep Learning models to create more generalized AI systems. Different techniques can be leveraged to generate a dataset. Nowadays, researchers and developers utilize game technology to render realistic scenarios. Game framework unity is used to create datasets of particular interest and then used in the production of real-world data. Unity report shows that the synthesized dataset can be used to improve models’ performance. For instance, computer vision models use synthetic images to iterate fast experiments and enhance accuracy. Generative Adversarial Networks (GANs) are also used to create synthetic datasets. These are neural network-based model architectures used for generating realistic datasets. Most use case requires data privacy and confidentiality. Hence, these networks are utilized to generate a sensitive dataset that is hard to acquire or collect from public sources. Data Augmentation is widely used by altering the existing dataset with minor changes to its pixels or orientations. It’s helpful when we are out of data to feed our Neural Network. However, we cannot apply the augmentation technique to every use case as it may alter the real result output. For instance, in the medical domain dataset, we cannot augment more data from the

Drishti Patel
May 09, 2022
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Role Of Artificial Intelligence In Web Development

Artificial Intelligence (AI) has evolved into a promising field in recent years, and now it’s an indispensable part of the web development world. Web development is an industry where user experience is everything. Any challenges regarding customized user experience can be easily solved using AI. The end question still remains, is hiring an AI/ML development company worth your web development needs? Around 38% of people will stop engaging with a website if the content or layout is unattractive. It means that you only get 0.5 seconds to make an impression on your visitor. Also, 90% of users leave a website because it is poorly designed. These are just a few stats that emphasizes the importance of having a website that has got everything from layout & design to loading speed right. AI/ML can make it much easier for web developers to deliver high-quality results faster. By using AI, you can improve the user experience of your website with chatbots, web design, marketing strategy, etc. How Artificial Intelligence Is Used In Web Development 1. For Faster Coding Coding is considered a problematic or challenging task, which it is. With AI, coding is way quicker and manageable. Artificial Intelligence in web development automates multiple tasks that help developers to create code more efficiently. Automated coding has massively cut down processing times and has allowed developers to build more intuitive apps in a short timeframe. 2. For More Personalized User Experience (UX) The whole fuss about getting everything right is about this one thing, providing a personalized and best user experience to all the visitors. AI-powered websites offer a personalized experience to all visitors, making the experience better for them and encouraging participation. 3. In Chatbots For 24*7 Support & Better Reply Predictions Not all businesses can afford to have support staff that work round the clock. Instead, you can hire a machine learning engineer to develop a chatbot for your website. Chatbots provide faster customer service and help manage customer requests better. On average, chatbots have increased sales by 67% for the companies who use them on their website. This is because chatbots make the customer experience better for every customer. 4. For Better Voice Search Optimization 58% of consumers are using voice search features to find information on local businesses. For a business website, voice search adaptability is an essential accessory if you want to gather more traffic and provide an easier way for your customers to find you. Virtual assistants such as Google Home, Siri, Cortana, and Alexa use AI to recognize and react to voice commands. Many people prefer to use voice search instead of typing their searches as this is a more convenient way. The Future of Artificial Intelligence In Web Development Even with all these benefits of AI in web development, in the end, it’s a machine. And a machine works best with human intervention every now and then. With AI, web developers can create better websites in terms of design and performance in a shorter period of time. But on the other hand, AI can provide better results with human intervention wherever possible. Creativity is something that only a human mind can have. This is the main reason why human intervention is necessary with AI to generate the best results, especially in a creative field like web development. Final Thoughts Artificial Intelligence and machine learning have evolved a lot since their initial discovery, but there’s still huge potential yet to be discovered. With the increase in the demand for AI in several aspects of human society, we will further see even more major developments and progress in this field. It still needs human intervention on a large scale to produce the best results, but it may be possible in the near future that AI alone will be enough. This technology will provide benefits to both businesses and their customers due to its fast learning abilities and speedy results. Would you use AI to help your web development team deliver better results?

Sunaina Agarwal
May 02, 2022
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How Can AI Transform Wealth Management?

A Gartner report suggests that by the end of 2024, nearly three-quarters of companies will shift from pilot to operational AI. Artificial Intelligence has been changing the dynamics of different industries. And the finance industry is no exception here. AI is also fast becoming an integral part of wealth and asset management companies. Financial advisors or wealth managers have been providing effective investment solutions to the wealthiest individuals and businesses for ages now. Yet, according to eMarketer: 37% of clients worldwide prefer their fund managers to choose hybrid advisory engagement for leveraging automation in wealth management. Thus, managers are adopting artificial intelligence in wealth management for advanced projects. It also helps them provide enhanced customer experience, investment decisions, and risk management. How Does AI Help in Asset Management? Several financial institutions and wealth managers are discussing AI and its soaring benefits. Yet, a lot are reluctant to invest in AI for asset management. As asset means large holdings, it also means significant responsibility. This is where AI asset management can support managers in many facets. Automating Task Artificial intelligence is becoming a valuable addition to wealth management. It can help identify trends in market performance and suggest portfolio composition. Furthermore, AI can help automate tasks such as investment research and portfolio management. This analysis helps to reduce costs for clients. It also increases the accuracy and timeliness of the advice provided. AI asset management can result in tremendous innovations which would not happen otherwise. It can also encourage tech-savvy strategies for managers to deliver value. Thus, wealth managers can make a client’s financial journey more successful. Improved Decision Making Wealth management is a complex and challenging task. It requires the manager’s ability to make sound judgments about financial investments. This is where AI comes to their rescue. It offers immense potential to automate and streamline processes for advisors. It also supports them with insights that may not be able to detect. For example, an AI-powered platform can scan large data sets and identify trends. These custom-made platforms are particularly designed by AI companies to provide greater efficiency and accuracy in investment decisions. Thus, improving decision-making. Identifying Risk The use of artificial intelligence is gaining much popularity. This is because AI in investment management can identify risk in a portfolio. Also, it helps in offering suggestions for improvement. With this, advisors can ensure that a client’s money is being managed responsibly. And also avoid putting them at risk for financial instability or loss. It also provides guidance on best practices for saving and effective investment strategy. Data Insights AI can support managers identify integral data points. These include account balances and transactions. Further, advisors can manage their client’s data, identify patterns, and predict future behavior. This allows advisors to provide better service and recommendations. Also, make better investment decisions and stay ahead of the competition. It also helps advisors find new customer segments and identify fraudsters. Can AI Replace Fund Managers? There is no doubt that the incorporation of AI is revolutionary and it accelerates the overall functioning of asset management. It also makes data processing and investment strategizing proficient. However, a complete replacement of manual labor with machine learning might not be the future. The reason is, that artificial intelligence needs to undergo certain scrutiny and security assessment to make these solutions reliable. We also need businesses to develop a sturdy infrastructure that welcomes innovations for optimum usage. Regardless of AI’s advantages like higher efficiency, and reduced speed, complete replacement wouldn’t take place. Where and How to Adopt AI in Wealth Management? If you want to get started with artificial intelligence in wealth management, there are a few things that every manager must analyze before starting. Hence, by automating tasks and providing personalized recommendations, AI can support managers in providing efficient portfolio management with wiser recommendations. Advisors who embrace AI will be in a competitive position and offer quality services.

Pawan Chabra
April 29, 2022
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How Computer Vision is Transforming the Global Transportation Industry?

Technology has played a critical role in transforming the way individuals interact with each other over the past few decades. The changes may have started with communication, the technological advancements have quickly moved into other areas like manufacturing, after-sales service, and logistics, among others. For instance, mobility is one of the most important pillars that contribute to economic activity in any economy. Modern technological solutions combined with the power of artificial intelligence and machine learning are transforming the global transportation industry. One of the critical components of this tech machine is computer vision. Experts believe that computer vision in transportation is a necessity that has multi-pronged benefits and applications across many use cases. The level of success of computer vision is increasingly playing a big role in changing the status quo in the transportation industry. What is Computer Vision? Computer vision is a part of artificial intelligence solutions that helps computers to derive actionable meaningful information from various input sources like images, videos, etc. In other words, if AI systems allow computers to think like humans, computer vision enables computers to see, observe and understand visuals like humans. The benefits of computer vision capabilities are not only limited to the transportation industry. Computer vision systems are already playing a big role in transforming many other functions like healthcare, manufacturing, agriculture, retail, etc. Benefits of Computer Vision Computer vision can help in a variety of ways. Some important benefits of computer vision are as follows: Computer Vision in Transportation Industry Computer vision or vision transport tracking is critical to the future of the transportation industry mainly because of its multi-use-cases across areas like self-driving cars, traffic management, parking management, road condition monitoring, and more. For instance, according to a CDC report, around 1.35 million people across the world are killed on roadways each year. In fact, crash injuries are the 8th leading cause of death globally. Computer vision is at the center of driving intelligent transportation systems across markets. Computer vision is playing a critical role in the following areas in the transportation industry: Self-Driving Cars We are living in an era where self-driving driving or autonomous vehicles are a reality. As companies are working on improving the capability, reliability, and safety of self-driving cars, computer vision is driving this change from the front. As vision transportation tracking helps in identifying and classifying static and moving objects, computer vision has played an integral role in making self-driving cars a reality. Traffic Management The advancement in the field of computer vision has paved the way for efficient traffic management and flow analysis. As computer vision helps in providing accurate information like traffic density, freeway traffic count, etc., it results in better traffic management and improved road safety. Computer vision is also expected to play an integral role in futuristic public transportation going forward. Parking Management Computer vision is widely used as the solution for parking management in place of costly sensor technology that requires regular maintenance. It is expected that the parking management solution would soon be used along with the license plate recognition solution to identify which vehicle is occupying which parking spot. Road Condition Monitoring Computer vision has been found useful in monitoring road conditions. This helps in decreasing safety risks for vehicles and pedestrians and improving road maintenance efficiency. Many countries are using computer vision to trace, track and improve road conditions for improved mobility. Governments usually hire computer vision as a service for road condition monitoring. To sum up Computer vision has provided several groundbreaking innovations to the transportation industry. It is expected to play an increasingly important role in coming up with solutions that are expected to decide the roadmap for how transportation will change in the future.

Rajat Mehrotra
April 27, 2022
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Artificial Intelligence in HR: Human Resources Reinvented

A company’s success depends upon how effectively and efficiently it manages its 3Ts, time, talent, and technology. Given the plethora of jobs today, it is humanely impossible to find the right candidate at the right time. That’s where the third ‘T,’ technology comes to the rescue. Artificial intelligence is one such advanced technology that constantly changes the HR domain. From hunting to onboarding, artificial intelligence is now playing a significant role. But before we understand how AI is transforming human resource management, let’s first understand why and what is Artificial intelligence in hr or the human resources department. AI refers to the ability of machines to interpret and process information like humans. In HR, AI can not only manage the recruitment process but can also help onboard candidates smoothly. Why AI in HR? With the changing dynamics of the business environment, there is a need to manage the organizational workforce. We, as humans, have a limited capacity to do everything, and doing routine tasks might become tedious. This eventually affects the overall productivity and efficiency. That’s where the magic of Artificial Intelligence works the best. Here’s how AI and human resources go hand-in-hand: 24*7 Access With the 24*7 availability of AI Chatbots, any potential talent can easily understand the procedure and other relevant details about the company or particular topic. The use of conversational AI in human resources promotes connection and relationship building. Enhances Efficiency The repetitive task can be shifted from manual mode to automation. This shift gives the workforce more time to concentrate on additional tasks, eventually boosting the overall productivity and efficiency. Personalized Communications AI can ensure personalized communication, especially with the newly recruited talent. With AI tools like Talmundo, where the talent can break the ice through a chatbot. Analytics AI can use real-time data to identify emerging problems when employees raise similar concerns and address them before escalating. Role of Artificial Intelligence in Transforming Human Resource Management The role of artificial intelligence in HR functions is endless, as it is constantly changing the HR working dynamics. The following explains how is AI being used in HR and recruitment: Screening Out Potential Candidates As soon as any company calls for an application, a large number of applicants start forwarding their application to the respective HR. Here, scanning the resumes manually may overlook the best potential candidates, given the number of applicants. However, artificial intelligence HR software like HireLakeAI can be leveraged to manage the hiring process. From shortlisting the resume of candidates to the final round of interviews, this AI-based solution helps HR in making ideal recruiting decisions. Creating a Diverse Work Environment While scanning and shortlisting profiles, HR may unknowingly be biased based on age, gender, etc., making the recruitment process unfair. But AI is trained and programmed to filter out any kind of bias during the screening process, making the procedure fair, unbiased and equal. This emerging role of artificial intelligence in HR functions like screening and finalizing potential candidates helps build a diverse team, leading to a better and more productive work environment. Building an Interactive Candidate Experience Usually, it is pretty difficult for HR to communicate with all the candidates. This leads to a bad impression of the company. But AI chatbots are now used to build an interactive candidate experience. For example, chatbots can guide the candidate for further process, schedule interviews, etc., ensuring a smooth and interactive recruitment procedure. Smooth Onboarding Traditionally, the onboarding process was time-consuming once a candidate was recruited, as the candidate had to manage documentation. However, AI has made this process easier by digitizing and automating documents. They also help properly organize the documents and store them in zip files. Moreover, AI can also track when the hired candidate has gone through these documents and remind them to provide a digital signature. Application of AI in HR Hunting Ideal Candidates Artificial intelligence in HR can help in attracting potential candidates by analyzing the user’s search patterns and advertising companies that are open to recruiting such specific job roles. For example, LinkedIn uses simple AI to target the potential candidate. It attracts them using some recommendation algorithms, that is “related jobs for you” feature. Effective Communication Not having proper communication might lead to a stressful work environment. Therefore, a better communication process is important to have clarity on the job responsibilities. Using AI solutions, the HR department can streamline proper and productive communication in the company, efficiently comprehending texts and voice messages and detecting their tone. Moreover, this AI solution in HR can highlight the present tone so that the person communicates professionally, without any bias, and in a non-offensive tone. Data Collection There is more to a candidate than what his resume displays. Therefore, it is equally important to dive more into the candidate’s profile by digging the social media, past company reviews, etc., to look out for any scope of concern. Artificial Intelligence tools provide efficient data aggregation techniques to collect information. For example, eBay and IBM use AI-powered data aggregation to extract data from a variety of sources, store them and analyze them to conclude the work experience and market value of the potential candidate. Talent Management Hiring a candidate through resume shortlisting and interviews is just one of the many ways a company acquires a candidate. However, the candidate is more likely to join a company if he sees the potential for growth and advancement in his career by working in the company. Artificial intelligence for HR can benefit here by streamlining the process of connecting him/her to someone in the company who already has proficiency in that skill. Why Should You Opt For AI-Based HR Solutions? Enhances Productivity Managing the human workforce of the company can be a nerve-wracking task. But using AI-based solutions in HR, the HR team can easily plan out a proper workflow from start to end rather than shifting a task to a particular day. This will eventually lead to a faster process without compromising on the

Rajat Mehrotra
April 06, 2022
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AI in Gaming: Smarter and Intelligent Gaming Experience

With an estimate to hit the CAGR of 12% by 2025, the gaming industry is growing significantly. According to the reports, Artificial Intelligence (AI) and Virtual Reality (VR) are an integral part of the global gaming industry. Moreover, it is assumed that almost $4.5 billion were spent by immersive gaming (a game with a real-like world) players in 2020 alone. Though gaming is one such industry where AI set its footsteps a long time ago, let’s understand what is the role of AI in gaming. What is AI in Gaming? Since AI inculcates human intelligence into machines, making them function like humans and carry out actions like humans have changed the gaming industry drastically. With the application of artificial intelligence in gaming, now players encounter a realistic gaming experience on a virtual platform and showcase their skills to their full potential against an AI-based worthy opponent. For example, Mortal Kombat is a duo fighting game where the AI-based opponent learns your fighting skills and defends itself by foreseeing them beforehand, giving you a much more challenging environment to play in, underlining why AI is used in games. However, AI in the gaming industry had existed even before the gaming industry gained its recognition. It was in the 1950s when the MiniMax algorithm was used to develop the chess game; it was the first example of how artificial intelligence is used in gaming. This algorithm worked on choosing the best possible move based on the contemporary board setting. ‘Donkey Kong’ was another example that used AI to enhance players’ difficulty levels. Nevertheless, the use of AI in the gaming industry was still underdeveloped back then since the opponents repeated the same mistakes and were incapable of self-learning the gamer’s tactics. But things changed when AI gaming examples like GTA, Need for speed, etc., gave a new dimension to the gaming world. Today, with more advanced features and attributes, artificial intelligence in gaming has completely revolutionized the gaming experience. Advantages of AI in Gaming Here are a few advantages of AI in gaming: Stimulates interest Gaming is a pervasive recreational activity and a getaway from stressful and hectic life. After all, playing games helps in creating a sense of satisfaction and achievement. However, now the AI-based opponents are trained with a variety of styles and techniques, giving you a tough fight. As a result, you start getting involved in strategy building and brainstorming new tactics to defeat the opponent, who is way too powerful for predictive and straightforward gameplay styles. Moreover, it gives a realistic approach to the game. Hence, artificial intelligence in the gaming industry is giving an open and challenging platform for enthusiastic gamers to play at their full potential, therefore, stimulating emotions. Developing smarter games A new range of smarter games across a variety of genres is giving rise with the help of Artificial Intelligence. AI-based Voice Intelligence is one of the significant uses of artificial intelligence in game development. Also, AI can assist in planning and building a strong framework of gameplay. An excellent example of this would be, using the reinforcement learning technique of AI to make the game characters self-learn and function as per the designed surroundings. For instance, in ‘Assassin’s Creed’, the enemies self-learn the gamers attacking and defending strategies and attack back strategically when you as a player would be vulnerable or prone to damage. More realistic games The advancement of artificial intelligence and gaming has led to gameplays having interactive 3D visualizations and merging games with Augmented and Virtual Realities. It gives a realistic feeling, especially when games follow the laws of physics. Moreover, such gameplay is not only visually appealing, but they also give the gamers a feeling of living in a parallel world where they have the freedom to do what they want. For example, ‘Grand Theft Auto’ is a widely popular game where the user experiences living in a whole new virtual city where you can roam, drive luxurious cars, go shopping, etc. Ways to Implement AI in Games Decision Trees Decision Trees are the primary AI methods for designing games. These supervised learning models are used to predict and classify outcomes. They predict the outcome of the desired action done by the user by following simple decision-making rules based on the data that it gets, unlike the Narrative-based games that use DTs to proceed with one among their various alternative narratives depending on the actions of the gamer. With AI, it is easy to interpret results from Decision Trees (DTs) and display them with interactive visualizations. In AI-based gaming, for example, the game ‘Star Wars Jedi: Fallen Order’ uses DTs to display hints of the past and future outcomes of the protagonist, given the specific circumstances you are in. So, you can now make a reasonable choice among the available options and direct the gameplay’s narrative in the way you want. Neural Networks Neural Networks can model a reality-based world and create complex gaming scenarios. Moreover, they cover up the limitations of basic AI techniques, self-learn, and adapt to the constantly changing gaming environments. The Neural Network algorithm interprets data in two ways. They are either manually trained through vast data and then launched in the game or learned in real-time during the gameplay through the live generated data. The latter helps in improving the gaming agents even while the game is played. They also help in controlling Non-Playable Characters (NPCs) and gaming environments. Reinforcement Learning This Machine Learning method functions based on trial and error. The model is trained by letting itself act on certain scenarios and self-learning based on good or bad outcomes from those actions. Then, the algorithm remembers the bad results and finds a way to avoid them in further steps. This technique is quite beneficial in designing Non-Playable Characters (NPCs) and enhancing their decision-making skills when they are put in new environments. Games are the primary targets for testing reinforcement training because this technique has been in practice for a long time. Many computer

Kavika Roy
March 23, 2022
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The Ultimate Guide to AIOps: The Future of IT Operations

The modern world is gearing up for the Fourth Industrial Revolution, with smart devices in its hands and rocket-fast data processing capabilities empowering business operations. Amidst the trends, automation and digitization emerge as the two major agendas that exist on the majority of business portfolios. More than 80% of business organizations are going to increase their spending on automation. And recent market studies predict that the digital process automation market will hit the 16.12 bn USD mark by 2026, demonstrating a CAGR of 12.98%. As the automation needs in modern workplaces are reaching new heights and IT operations are looking for smarter, more intuitive, and reliable ways to automate and digitize routine tasks, people are turning to AI. And promising to fulfill such a diverse set of process automation requirements in IT is AIOps. Coined by Gartner in 2016, AIOps stands for Artificial Intelligence for IT Operations and combines ML and big data for process automation in IT operations, such as causality determination, anomaly detection, event correlation, etc. In upcoming sections, you can learn about AIOps in detail, why and how you should consider them for your business, and major ways AIOps will change the face of IT operations in 2022. What is AIOps? AIOps is an industry category that uses AI and ML analytics for automating, streamlining, and enhancing IT operations analytics. AIOps provides a real-time understanding of any type of underlying issues in the IT organizations and real-time insights into various processes. It also offers predictive business intelligence in various forms and across a wide range of business processes because of its smart analytics capabilities. Increasing complexities of IT systems, organizational data growing at an extreme pace, and frequent industry disruptions, such as remote work, new trends in technology, rapidly changing markets, etc., have fuelled the demand for AIOps. Why AIOps: Exploring the Need for Futuristic IT Ops Tech AIOps software not only empowers the organizations to manage the heaps of data they generate, but it will also help in harnessing the true power of this data via analytics. You can identify any hidden patterns for growth, cost efficiency, redundancy, productivity, etc., and gather actionable insights from them. Apart from the data insights derived from historical organizational data, you can also use predictive analytics to have futuristic insights into various segments that are reliable and data-driven. But then again, mere insights cannot solve the massive automation and digital transformation needs; right? Hence, AIOps also caters to the team collaboration needs and provides inferences drawn from the data insights, such as: Finally, AIOps aids in automating the response and remediation in various instances, to facilitate precise and quick process automation across your business model. The following image shows some of the core functionalities of AIOps, as discussed above: Now that we have had an overview, let us move on to discuss some key use cases of AIOps. AIOps: Key Use Cases While there can be n number of business use cases for AIOps, we are discussing the top 5 core use cases that target the key problem areas when it comes to operations, processes and automation. 1. Big Data Management While big data is a powerful technology to manage, process and make sense of the tons of organizational data that is generated, managing the volume, variety, velocity and efficiency of big data usage is also a challenge. As more and more firms are using big data analytics, getting the best value out of these efforts is also a must. Hence, proper management of big data is one of the most crucial use cases of AIOps. 2. Performance Analysis Organizational performance analysis is an extremely daunting task as it requires micro-monitoring apart from micro-management. Now, you might think that using time tracking and employee tracking software can do the job to a greater extent. However, when it comes to getting granular insights about the results, driven by individual employee performance, this software crumbles down. Having impeccably smart capabilities stemming from AI, ML, NLP, etc., AIOps is your eye to the nitty-gritty details related to performance analysis in any department and for any number of employees. It can rapidly gather and process the vast amount of event logs and data to drill down to the root cause of factors that are obstructing the overall productivity of your organization. 3. IT Service Management IT Service Management or ITSM refers to designing, building, supporting, delivering, and managing IT services in an organization. All the processes, policies, and procedures that facilitate the delivery of IT services to the end-users in an organization also come under ITSM. AIOps uses artificial intelligence to discover any underlying issues in ITSM and helps fix them quickly, thereby improving the efficiency across all the departments. AIOPs also find usage in data monitoring and infrastructure process management. It can make more accurate predictions for planning capacity, allowing you to maximize your resource and inventory space utilization. You can also identify, predict, analyze and prevent any underlying IT service issues. 4. Event Correlation and Analysis When it comes to event storms, most of the traditional analytics software fails because they lack the ability to provide insights into these storms. For example, suppose your event correlation report suggests a delay in project delivery for 2 of your crucial clients, as a designer has given a notice period and you still have not hired a new designer. Now, even if you “know” what is going to happen, there is no suggestion or insight to rely upon. AIOps, on the other hand, can help you make the best decision for remedying the situation by grouping similar events. So, you can either get an insight about a solution that came to your rescue in such a situation sometime earlier, say a few months, or you can get to know which one is the best option – outsourcing, a quick hire, or assigning the work to other designers in the company. So, AIOps reduces the burden of the IT teams when it comes to managing organizational events and offers