Artificial Intelligence – Page 8

7 Benefits of Artificial Intelligence in Advertising

In the past, advertisements were created with traditional methods such as art and copywriting. However, with the prevalence of advertising on social networking sites, it is now crucial for companies to get their message across in a way that is concise and compelling. To gain a competitive edge through distinguishing ads, they need to understand how their customers are engaged with the current ads. Brands need to be smart to not only reach their audiences but also capture their attention. Yet, the growing competition can be challenging for businesses trying to build client relationships and more importantly, loyalty. So, how are brands coping with this increasingly demanding scenario? In the current digital wave, brands are improving advertisement targeting by using machine learning techniques. It has led to a new wave of advertisements that are more realistic and engaging than ever before. According to the Times of India, “AI is predicted to take up to 80 percent of global advertising spend in the world.” Additionally, using artificial intelligence enables targeted marketing campaigns by automatically recognizing consumer preferences and behaviors. By understanding customer preferences and behavior, advertisers can create more personalized ads that are more likely to be effective. What Is Artificial Intelligence (AI) and How Does AI Work in Advertising? Artificial intelligence (AI) is a branch of computer science that leads to the designing of computer programs that can simulate human intelligence. With programs that can reason, learn, and act autonomously, they can make decisions, solve problems, and understand complex concepts like humans. Furthermore, AI is gaining popularity due to its application in different industries such as HR, education, and retail to name a few, that are helping humans resolve complex issues and develop credible solutions. With artificial intelligence (AI) evolving every day, the advertising industry is also growing. With advances in AI, companies can target ads more accurately and create more engaging content. It has led to a rise in new marketing techniques such as chatbots and natural language processing. The use of artificial intelligence for advertising also allows companies to measure the effectiveness of their advertising campaigns in real time. AI can help create targeted ads, personalize customer experiences, and optimize campaigns. But how does AI work in advertising? Benefits of Using AI in Advertising Artificial intelligence is still a relatively new technology, but its potential applications are endless. Furthermore, how ai is used in advertising is proving to be particularly useful for the advertising industry. Some benefits of using AI in advertising include: 1. Target Your Ads More Effectively Through Data Advertisers are always looking for new and effective ways to target their ads. Recently, there has been a lot of talk about using big data to help improve ad targeting. Big data is a term used to describe a large amount of data that is now available due to the growth of digital technologies. This data can be used to predict consumer behavior and preferences for creating targeted ads. According to Forbes, “The most recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks”. 2. Helps Optimize Your Campaigns for Better Results Artificial intelligence is being used increasingly in digital marketing, as it can help optimize campaigns for better results. AI can identify what is working and what isn’t in a campaign, and make changes accordingly. Optimized campaigns designed as per the audience’s needs help improve customer engagement and retention. It also provides companies with increased ROI. 3. Helps Save Money and Time With Increased Sales AI can help you save time and money because it can swiftly categorize the huge amounts of data that are available in this day and age. It also spots trends and patterns which are useful in making better advertising decisions. Companies like Amazon are using AI for driving pricing strategies that help to reduce prices for more sales. They have also used AI to understand and leverage pricing strategies, like when the demand for a product increase, they increase the product price too. Thus, helping companies augment more sales and revenue. 4. Reduce the Risk of Losses in Ads By using AI to help you with the marketing process, you can reduce the amount of money that you would otherwise lose if there were an error in your campaign. One of the benefits of using AI for ad campaigns is that it can help identify which ads are most likely to result in a sale. This allows businesses to focus their efforts on the ads that are most likely to be successful, reducing wasted money and time on ineffective campaigns. Another benefit of AI is that it can help identify potential threats early on in the campaign lifecycle. By identifying threats early, businesses can take steps to mitigate the risks. 5. Improving Customer Experience With AI, you can have better customer communication and a more personalized style of service. It can help companies learn about their customers, understand their needs and desires, and then predict their future behavior. It allows companies to provide customers with personalized and the best possible service and products. For example when Nike leveraged AI to provide its customers with the capability to design their sneakers in-store. It helped them to collect more data and design future products accordingly. Such AI-backed advertising techniques are immensely crucial for customers who are more likely to purchase when they feel that their preference is being well taken care of. 6. Reach More Customers With Your Ads In the age of social media, finding new and innovative ways to reach your target audience is more critical than ever. AI can also help you target your ads more effectively by learning what works and what does not. Like Coca-Cola, the largest beverage company analyzed 120,000 pieces of content on social media to understand the demographics and customer behaviors. They also analyzed customers who actively talked about their product and brand. Thus,

How Can Businesses Adopt AI Technology to Achieve Their Goals

Artificial intelligence is a dynamic force that keeps the industry moving forward to conquer more technologies. From manufacturing to hospitality to retail and aerospace, AI is being adopted by several organizations across all industries. However, businesses are still in varying stages of adopting AI in their enterprises. While the top companies have added AI technology as an integral part of their systems, SMEs still use AI to develop pilot projects for certain departments like sales, marketing, etc. Let’s learn about the adoption of AI in certain industries and how they leverage it to achieve their goals. Why Artificial Intelligence Is Important The social applications of AI, such as home assistants, smartphone applications, etc., are different from enterprise-level applications. The latter is on a much complex and wider scale, encompassing various systems and processes in the business. Artificial Intelligence Types Unlike, in theory, AI is not a one-size-fits-all solution. It is vital to understand what the enterprise needs before investing in AI solutions. While pre-designed software is useful, it doesn’t deliver results like customized AI systems. But to understand this difference, we need to know more about artificial intelligence, the types, and what AI aims to achieve as an end goal. AI is classified as follows- Narrow AI It is used for goal-oriented purposes such as facial recognition, speech recognition, etc., where machine learning algorithms complete the task based on a set of narrow constraints. General/ Strong AI It mimics human intelligence and behavior so that it can solve any problem and not be limited to specified tasks. We still haven’t successfully created AI machines with cognitive abilities. Artificial Superintelligence This is currently in the theoretical stages. ASI machines are supposed to not just mimic humans but become one of them by being self-aware. Artificial Intelligence vs. Machine Learning In simple terms, AI is an umbrella concept where machine learning and deep learning, understand data and make decisions based on it. ML algorithms are used to quickly process vast amounts of data and deliver valuable insights. What is the End Goal of AI? AI sounds fascinating, and we can see that large-scale enterprises and global organizations are already getting results from AI. But questions are still aplenty for new businesses that want to hire AI services to revamp their systems. The ultimate aim of using AI is to get measurable results to prove that the enterprise is better than its competitors. AI works the best with a centralized system where data is collected from several sources and is stored in a data warehouse or a data lake for further cleaning and processing. Artificial intelligence drives ROI for businesses and delivers long-term results when the technology used aligns with the systems in the enterprise. AI is not limited to scaling but also to measure the results and get the most out of data. Why Should Companies Adopt AI? What makes AI so important that companies adopt and integrate it as a part of their business processes? How does AI help organizations overcome challenges and become industry leaders? The following are some uses of artificial intelligence that empower businesses to perform better in the competitive market and grab the opportunities that come their way. 1. Artificial intelligence enables better customer service and support. AI is used to create and deploy chatbots, provide smart suggestions in emails, develop human-like interactive voice response systems, automate ticket generation and routing complaints, and make it easy for customer service agents to solve an issue in less time. The live chat option on many websites has customized chatbots to respond to users’ queries. 2. AI helps with target marketing and generating more leads. Artificial intelligence is used to create content for promotional emails, develop ads for each customer segment, provide product recommendations based on customers’ purchase and search history, speech recognition, etc. Amazon’s website is the best example of target marketing using AI. 3. Use AI in the HR department to hire eligible candidates and create customized training programs for employees. HireVue is a well-known AI-powered platform that helps more than 700 leading brands to hire some of the best talents in the market. Vodafone, Unilever, and Oracle are some companies that use HireVue for recruiting purposes. Similarly, artificial intelligence helps create different training modules based on employees’ skills. 4. AI streamlines the supply chain and shortens the delivery cycle. Amazon has the best warehouse management system powered by AI. Artificial intelligence helps in managing inventory, increasing safety for warehouse employees, planning the travel route for deliveries, finding alternate delivery methods, and so on. 5. AI makes the business more secure by enhancing cybersecurity. Many of the latest antivirus software and malware are developed using artificial intelligence. The software is trained to identify phishing attacks and malicious links before employees fall prey to cyberattacks. The FaceID access feature of iPhones is a small yet everyday example of how AI is used for cybersecurity. 6. AI technology is used to detect fraudulent transactions and minimize losses. AI processes vast amounts of data in a few milliseconds and identifies patterns in transactions. Many banking and financial institutions have started to use AI and ML-based fraud detection software to identify unscrupulous customers and cybercriminals with stolen identities. Teradata provides AI-powered fraud detection software to banks. 7. Data is put to good use through AI applications. Data collected from social media and other sources is processed in real-time to extract meaningful and actionable insights. AI tools are used for predictive analytics and data visualization. Tableau is an example of AI-based data visualization software. 8. AI makes the workplace safer for employees, especially in industries like oils and mining. Artificial intelligence is used to monitor the work environment using drones, surveillance cameras, etc., and determine that the workers are not at risk. AI is used with IoT (Internet of Things) to collect data and process it to identify any signs of accidents or risk. AI helps the management create a safer working environment in factories. 9. AI optimizes the use of resources and helps the enterprise and employees

AI in Stock Market: Predicting the Ups and Downs

If you think AI still has a long way to go before it enters the stock market, you’re mistaken. Because hundreds, probably more companies have already started hitting the plans to use AI for stock market trading. For instance, Goldman Sachs, a renowned bank in the USA, led a $72.5 million funding round in an AI and ML specialist (H20.ai) to adopt AI models within the equities trading business. And this was in 2019. You can only wonder how many more companies in the finance sector have adopted AI to benefit from the stock market. But the question is, does AI in stock market work? This blog talks about everything from what AI is and how it is impacting the stock market to AI that can predict the stock market and companies that use AI in different ways to profit from the stock market. You’ll also find some popular tools that you can use for stock market analysis and so much more. So, read in full. Before we move to our main topic, let’s briefly understand what AI is. What is AI? AI or Artificial Intelligence is a technology that lays stress on developing intelligent machines that can work like humans. Common examples of AI in real life are speech recognition, facial ID, weather prediction, etc. Machine learning, an application (or subpart) of Artificial Intelligence, enables machines to learn with experience just like humans. With machine learning, machines are fed with different sources of data which they use to learn, evolve and become better on their own. And this technology has a major impact on the stock market. How? Find out below. How is AI Impacting the Stock Market? AI Reduces Research Time for Finding Stocks When it comes to finding the right stock, a lot of research goes into it. For instance, you have to analyze financial statements, revenue, P/E ratio, dividends paid, profit, loss, and whatnot. And this requires an immense amount of time. However, AI in the stock market solves this problem. By using AI analytics (that uses machine learning), you can crunch all types of data from different sources within seconds. And this way, finding the right stock to invest in becomes a piece of cake. How is this impacting the stock market? AI has reduced both the time and effort required to invest in a stock. People, especially in India, are crazily opening dematerialized (demat) accounts as they can easily choose which one to invest in. For instance, according to Business Standard (published on 15th April 2022), the number of demat accounts in India jumped 63% to a whopping 89.7 million in the financial year 2021-2022. AI Is Completely Free from Emotional Influence Emotional investing is one of the biggest enemies of retail investors. It’s basically a phenomenon wherein individuals base their investment decisions on emotions such as greed or fear. And such financial decisions based on emotions are usually less intelligent. For instance, if you fear that a stock may go down and sell all your holdings without solid research, what you’re doing is an emotional investment. And it’s highly likely that you’ll lose money. Fortunately, AI in the stock market can help. Artificial intelligence knows no emotion. All it knows is algorithms, logic, and the data it’s fed. This way, you can refrain from making a hasty decision based on emotions and thus save money. How is this impacting the stock market? As AI is pushing emotional investing out of the picture. It’s making investors smart. People are now able to make decisions that are based on data and logic. This way, they’re becoming smarter and losing less money. AI Helps with Prediction Prediction is an integral part of the stock market in general and stock trading in specific. An investor performs technical analysis by understanding the historical data, noticing patterns and then analyzing the fundamentals to determine what will happen to the stock. And you can call this phenomenon prediction, based on which, buying/selling decisions are made. While some people are remarkably good at predicting the stock market, the risk of losing always prevails. Because missing out on even the tiniest bit of information can lead to horribly wrong outcomes. However, AI can help. AI in the stock market can help you perform technical, fundamental analysis and recognize patterns better than humans. What’s more, AI can process the latest news, blogs, and interviews to understand and predict how the stock will perform in a more accurate manner. And this improves the overall chances of you making the right prediction. How is this impacting the stock market? While you can call prediction a calculated guess, several people rely on AI for the same. And with AI in the picture, predictions are becoming more reliable, which is motivating more people to invest in the stock market. Also, it’s fueling startups operating in this domain to work harder and produce better systems. AI Helps Automate the Sale and Purchase of Stocks Don’t have time to stay in front of your TV/laptop/phone screen for stock updates? Well, AI platforms make this possible. With the help of AI tools such as eToro Copy Trading, you can automate trades and set your entire or some of your portfolios on autopilot. The platform will charge you a fee and will use AI to make buying or selling decisions. Sounds lucrative, right? Please Note. No matter how lucrative it may sound, you should always have control over the decisions you make. Completely relying on AI for anything, let alone the stock market isn’t recommended. How is this impacting the stock market? With the ability to automate trades, AI is making the stock market more accessible. More people, regardless of their profession, are entering the industry relying on AI-based automated trading tools. AI Helps Reduce the Overall Cost of Trading Hiring a stockbroker is quite costly. You need to pay a commission for every trade the stockbroker makes on your behalf. However, with AI in the stock market, you can reduce these costs to

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

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

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

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

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?

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.

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.