Artificial Intelligence Archives - Page 8 of 10

Kavika Roy
August 09, 2021
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10 Steps for Turning Enterprise ML into Success

Artificial intelligence and machine learning (ML) are fast becoming a part of our lives, professional or domestic. Virtual assistants, chatbots, data-driven models for better decision-making, data analytics, and much more result from implementing AI and enterprise ML in the business. AI-powered tools streamline the processes and help enterprises improve quality, increase productivity, enhance customer satisfaction, and get more returns. SMEs can either build an internal team to integrate AI into their business systems or hire AI services offered by the leading consulting companies. Though AI and ML improve the performance of an enterprise, things can quickly go wrong if you don’t have a proper plan to integrate AI into your business. In this blog, we’ll read how to implement ML models in your organization successfully. We know about artificial intelligence and are aware that machine learning is a part of it. But what exactly does a machine learning algorithm do? A machine learning algorithm is software that uses historical and real-time data to help enterprises trace patterns, predict trends, detect fraud, analyze customer behavior, and provide personalized suggestions. The algorithm is written in a way that uses the feedback in the system to improve and deliver better results. Over time, enterprise ML algorithms can perfect themselves and provide accurate predictions. Of course, ML is much more than a simplified explanation. There are different types of ML based on the kind of algorithm used (or how the software approaches learning). Types of Machine Learning Data is a common factor for any machine learning. Reasons to Invest in Machine Learning With AI and ML making great headway in the market, it has become essential for several enterprises to invest in the technology. The following are some reasons you should invest in enterprise machine learning and how it can help achieve your business’s short-term and long-term goals. Inventory Maintenance Inventory management and maintenance is a labor-intensive and time-consuming process. It is even harder for large-scale enterprises with vast production. Machine learning simplifies the process by automating inventory maintenance to minimize the need for human intervention. Reduce Work Pressure When repetitive tasks are automated, employees do not have to spend most of their time doing the same thing repeatedly. Therefore, employees have more time to devote to their projects and less stress completing them on time. Also, with virtual assistants and chatbots enabling self-servicing within the Enterprise, employees can be empowered to become more productive without feeling the pressure. Market Analysis Machine learning algorithms can process data in real-time and detect the latest trends in the market. Suggestions about changing the product’s price, reaching out to a new target audience, managing demand with supply, etc., are possible. Data Sorting and Analytics AI and ML models are an inherent part of data analytics. From collecting data to cleaning and sorting it and data labeling, machine learning can make things easier and complete the task in less time. The data science teams can use enterprise ML models to analyze data faster than before. Decision Making AI and ML software provide accurate insights and predictions that help make the right decisions for the Enterprise. The reports produced are easy to read and can be presented in any format (graphical, tabular, textual, etc.) so that you can understand the insights and know where things stand. Data and System Security Data forms a vital part of every business and has to be protected from external forces. The machine learning algorithm can help enterprises enhance the overall security system in the business. The latest antivirus and spamware software is built using AI and machine learning to identify and prevent cybercrime before affecting the business. Fraud Detection and Prevention When discussing cybercrime, we should also mention fraudulent transactions commonly seen in the eCommerce, insurance, and banking industries. The machine learning algorithm can detect such transactions and alert employees. Many insurance companies and banking institutions have invested in ML-based fraud detection tools. Retailers and eCommerce business owners also integrate AI solutions with their business systems to prevent being duped by fake transactions. How to Ensure Enterprise Machine Learning Success Let us look at the steps for a successful enterprise ML implementation and getting the expected results for your business. 1. Understanding AI and ML and Becoming Familiar with Them The first step to implementing anything would be to know what it is. Unless you and your employees understand how artificial intelligence and machine learning can help you improve, adopting new technology will not help much. Several ML consulting companies assist right from the start and continue to offer support even afterward. They help in training your employees to work on the ML models and increase their work efficiency. Read the use cases shared by the consulting companies and download the whitepapers and understand them. Join online crash courses or training programs to gain a comprehensive idea about AI and ML. 2. Knowing Why You Need ML- Identify the Problems ML Will Solve in the Enterprise Why do you want to invest in Enterprise AI? Which issues do you want to tackle by integrating AI software into the business systems? Saying AI and enterprise ML will simplify the workflow is not enough. You must know where and how to use machine learning algorithms. Start by making a list of problems and gaps in your business system that can be solved using ML tools. Which problems can be solved using NLP, ML, DL, computer vision, etc., and what kind of AI tools will you need to integrate into the business? 3. Prioritizing What You Want from ML and Acknowledging the Talent Gap Investing in ML is a costly affair, whether you own a startup or a large-scale enterprise. You have to prioritize the areas where ML will first be implemented to scale it throughout the organization later. It would help if you also considered your existing talent pool. Can your employees adapt to the changing systems? How many will have to be trained in the first batch? How many new employees should you hire? Even if you

Kavika Roy
August 05, 2021
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How to Use AI to Move IoT to Next Level

Enterprises around the world are widely adopting artificial intelligence. The Internet of Things (IoT) is not far behind either. In fact, with IoT making it easy to collect and share data across a network of devices, it has become a must-have for many industries. However, you need advanced tools and technology to process and analyze the data collected by IoT. That’s why large enterprises have been investing in artificial intelligence to streamline and automate business processes. As a result, Gartner has predicted that around 65% of SMEs and large-scale enterprises will be using IoT by 2024. Combing AI and IoT will take things to the next level by helping businesses use data in a way that’s never been done before. The combination of AI and IoT is a hit because IoT helps digitalize the physical world, while AI solutions make it easy to process and analyze a huge volume of data in less time. What is the Difference Between AI and IoT? Artificial intelligence is a mix of rules and intelligence. Rules are the part where programs are written. Intelligence is the use of machine learning and deep learning to understand data and make sense of it. Internet of Things is a network of several devices that are interconnected and constantly send information to each other. This makes the devices smart and allows you to control them from remote locations. Voice assistants and smart devices at home (smart locks, smart lights, etc.) are examples of IoT in domestic life. The main AI and IoT difference is that the Internet of Things is essentially hardware and network-based. Sensors, data collectors, computers, etc., are connected through a wireless network and send data to a central cloud server. Artificial intelligence usually works with data. Be it data gathering, cleaning, structuring, formatting, analyzing, or visualizing, AI is used to understand data collected by IoT. Internet of Things is less expensive compared to AI but has a higher success rate. The only way IoT can fail would be when the devices and components in the network are damaged or don’t work. However, AI has more chances of failure due to the wrong AI tool, incorrect data processing, or even inaccurate data. As long as you hire reputed AI strategy consulting companies to assist you, you can be assured that your investment will deliver the expected results. Reasons for the Surge in Demand for AI and IoT A survey by SADA Systems in 2018 showed that AI and IoT were the leading technologies that the enterprises adopted. The trend is probable to continue for the foreseeable future. So what resulted in this increase in demand for AI and IoT? AI as a service is helping streamline business processes and adopt the data-driven model to make better decisions in less time. From automating recurring tasks to providing virtual assistants for employees, AI increases productivity, quality and reduces costs. Internet of Things has made it feasible to develop new products and find new ways of distribution. Since devices and cloud computing technology are already available, IoT is very much a reality that’s delivering great results to industries. It is helping solve real-world problems ranging from traffic issues in cities to data sharing in remotely located factories. AI and IoT are providing enterprises with a definite edge over competitors and helping them become industry leaders. Adopting AI has become necessary to survive in this competitive world. Several offshore companies offer customized artificial intelligence solutions to assist SMEs in unlocking the power of data and increase returns. Using AI to Move IoT to the Next Level Let’s see the role of AI in IoT and how artificial intelligence can move the Internet of Things to the next level. Risk is a part of every business. Be it the safety of the workers, maintenance of heavy machinery, unexpected market conditions, or fraudulent transactions, IoT and AI help businesses understand and predict different risky scenarios in the enterprise. This makes them aware of what all could go wrong and how they can prevent it. Data is collected from sensors in the factory, devices worn by workers, transportation vehicles, computers, etc. This data is analyzed using AI to alert the management in time and prevent an accident from happening. Automation has changed the way work is done in enterprises. Instead of putting excess pressure on employees to complete the recurring tasks, the process is automated using artificial intelligence. As a result, employees have more time on their hands to take up complex tasks and be productive without getting stressed out. AI and IoT together assist employees in identifying tasks that can be automated and fine-tuned to increase efficiency. In addition, understanding patterns and processing data in real-time facilitates management to make faster decisions. Artificial intelligence can make IoT scalable in an enterprise of any size. A range of the device can be used as a part of the IoT network. Whether it is a mobile phone or a high-end computer, it serves the purpose when powered by AI. In fact, many organizations use low-end sensors for data collection to manage costs. AI reduces the large volume of data to a manageable level and allows you to add as many devices as necessary to the IoT network. AI and ML solutions use data from IoT to identify patterns and trends in customer behavior. This helps you in developing new products/ services that are useful for customers. It also helps in making changes to the existing products/ services to attract more sales. We can say that using AI, especially NLP (Natural Language Processing), to understand customer behavior will help manufacturers streamline their R&D. Rolls Royce, the British luxury automobile manufacturer, has been using IoT and AI to develop new aero engines. Broken and damaged machinery causes a lot of loss for manufacturers. The only way to prevent unexpected machine breakdowns is by implementing preventive maintenance. But how does the management keep track of so many machines and their maintenance? That’s where IoT has been

Isha
July 26, 2021
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5 Computer Vision Strategies To Implement For Your Business

As humans, we can easily see, process, and act on something that could be considered a visual input. But how can that be replicated in machines? That is precisely what computer vision aims to do. While there may be limitations for a machine to act like humans, they are quite close when it comes to analyzing and acting as programmed to do. To cut it short, Computer Vision can be described as a process when a computer using artificial intelligence is able to identify and process visuals (like photos and videos), extract insights from them to create an appropriate output that makes the process of decision making simpler. According to this market analysis report conducted in 2020, the global computer vision market size was at a value of USD 10.6 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 7.6% from 2020 to 2027. Before going into the computer vision techniques that you can implement for your business, let us understand the basics. Understanding Computer Vision Computer Vision is enabled with the capability to read, identify, classify and verify objects. The recent developments were facilitated by ML or Machine Learning technology, especially the process that requires iterative learning, and significant updates in computing power, data storage, and high-quality yet inexpensive input devices. Before we look at how firms are making use of this technology through in-house computer vision services or by outsourcing it to an expert computer vision consulting firm, we need to understand what goes into making this technology so different. Computer Vision Process 1. Capturing an image When a digital device like a camera or CCTV captures an image, it is basically creating a digital file consisting of zeros and ones in the computer’s language. 2. Processing the image Algorithms are used to determine basic geometric elements to create images out of the acquired binary data. 3. Analyzing and taking action The final component that makes the process of computer vision application successful is the analysis of the data. The system then acts according to the way it is programmed and notifies the administrator or manager. Computer Vision Basics What is computer vision used for? Is computer vision and machine learning the same? A part of computer vision applies machine learning, and they are both spin-offs from AI. However, computer vision involves tasks like image identification and classification, object detection and tracking which is way different from what ML does. Who is making use of Computer Vision? A number of industries have been using Computer Vision to enhance customer experience, reduce costs and increase security. Some of the major players are retail, manufacturing, surveillance, and weather forecast. Top Computer Vision Techniques Used by Businesses As humans, we tend to start using our vision as soon as we are born. The way we process visuals and process them to understand and act is highly difficult to replicate in machines. And while the field of computer vision has been successful in overcoming challenges so far, there is still a lot that needs to be worked on and optimized. Recent developments in neural networks are deep learning initiatives that have greatly advanced the way these visual recognition systems perform. And while it’s easy to learn about the basics of computer vision, implementing it in the right way irrespective of the industry a business belongs to, is difficult. In such a situation computer vision consulting services seem like the best go-to option to implement this technology. And the top features of the technology that these firms make use of in their decision-making strategies are as follows- Image classification The fact that a computer system can identify, analyze, and act almost like a human sounds great. But for that to happen, the visuals that the input device captures must be classified into a particular category to take action. There are a good number of challenges associated with image classification in computer vision like viewpoint variation, scale variation, intra-class variation, image deformation, image occlusions, illumination conditions, and background clutter. To overcome these challenges, computer vision researchers have derived a data-driven approach to solve this. Instead of trying to specify what one type of image category looks like directly in code, they provide the computer with examples of each image class. Learning algorithms are then developed for the computer to learn about the visual appearance of each class so they can go with the classification easily. Object Detection The task to define objects within images involves placing bounding boxes and labels for individual objects. This differs from image recognition and classification in a way that detection would put objects in a particular box after the image classification has been done. Say for example, if there are multiple cars in an image, all cars need to be detected and put in a bounding box. For classification though, there are just two ways to do it- object bounding or non-object bounding. Object detection is important as it helps the system to understand the image or video and prepare for analysis. The major difference between image recognition and object detection is that the latter has the ability to locate objects within an image or other input visual. This can be applied in a number of ways by businesses or retail stores that implement professional computer vision services for crowd managing, self-driven cars, anomaly detection, face detection, and video surveillance. Object Tracking Object tracking involves estimating the state of the target object present in the scene from the information collected. The process involves two levels. First known as Single Object Tracking where the appearance of the target is tracked. And second, MOT or Multiple Object Tracking where a detection step is necessary to identify the targets that can leave or enter the scene. A major challenge in tracking multiple targets originates from the various interactions between objects that can sometimes also have a similar appearance. In recent years, due to the exponential rise in the research of deep learning methods, there has

Kavika Roy
July 16, 2021
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7 Ways Machine Learning Can Help You Handle Business Travel Risks

Traveling to meet clients and stakeholders is a part of the process for several organizations. A good percentage of day-to-day traveling are businessmen and women flying from one city/country to another for meetings and conferences. The industry has changed tremendously over the years. Air travel has become a preferred choice of most enterprises as it saves time compared to other modes of transportation. Allowing people to check-in from their mobile phones instead of waiting for long hours at the airport is one major development. However, the rise of the COVID pandemic in 2020 has disrupted all industries, and the travel sector is no exception. Flights have been canceled; the borders between countries were closed. Even the borders between the states were closed to prevent people from traveling and spreading the virus. While this led to substantial losses, it further pushed the industry to come up with new ways and promote safe travel for people. Technology has become an inherent part of this change. Be it self-serving machines, contactless transactions, or replacing customer care staff with virtual assistants to contain the spread of the virus, artificial intelligence and machine learning or ML can help you beat business travel risks on a large scale. Safety First When Traveling Employee safety has become one of the primary concerns for airports and airlines. The staff is at high risk of getting infected because of the nature of their work. This directly impacts productivity. At the same time, the health of the passengers traveling on the same flights is also a matter of concern. AI solutions are helping to manage these risks and facilitating safe travel while also ensuring that the staff is not exposed to the virus. Facial recognition systems, predictive maintenance, and contactless luggage screening are a few ways in which AI and ML are being used in the aviation industry. Masks, sanitizers, and face shields are an inherent part of the process to stay safe when traveling. But to arrange for safe travel, it is the technology that comes to the rescue. The Need for Technology – What is Machine Learning? Many airlines and airports are investing in ML to get back on track and increase their productivity without risking the lives of the people involved. But what exactly is machine learning? Is there a need to hire the services of a Machine Learning consulting company to adopt new technology? It is a subset of artificial intelligence. It is a method of data analysis that teaches computers to understand data and make a decision with minimum human interaction. This algorithm allows the system to learn from its mistakes and improve its efficiency to produce more accurate results. However, the vital point is that the airlines can be productive even in risky situations like the pandemic by using AI and ML services. Coming to the second question, it is not compulsory to hire offshore AI consulting companies to adopt the technology. However, revamping the business processes is not easy unless there is the right kind of support. This support comes from experienced consulting companies that offer personalized services to various organizations. Ways ML Can Help You Beat Business Travel Risks There are several ways in which machine learning can increase the productivity of airlines. We will first see how ML can reduce business travel risk and then read about how ML can be used in other areas of the travel industry. 1. Customization of the Trips Travel companies and airlines are now offering customized plans based on your travel history. Based on your previous flying experiences and preferences, a personalized travel program will be charted by the machine learning algorithm. There are already apps that use smart calendars to create a travel plan for the sales personnel. X.ai already allows similar functions. The apps have up-to-date information and can plan your entire travel program more effectively as they also consider the risk quotient, the delays in flights, the difference in time zones, and more. Machine learning can reschedule your itineraries based on your travel requirements and factors that affect your program. This is highly advantageous for business travel as it saves time and money for the enterprises who pay the expenses on behalf of the employees. It also helps streamline the trip to make it successful. 2. Self-Service Check-ins for Contactless Transactions Check-ins with as little contact as possible is a great way to prevent the virus from spreading, isn’t it? AI services let you check-in in advance, not just in the airports but also in hotel rooms. Some hotels in countries like Singapore are offering contactless check-ins. You can check-in via the app and also request any extra services you might need. You’ll be provided with a QR code that needs to be scanned at the hotel so that you can check into your room straight away using the kiosk. There is no need to talk to the reception staff or enter your details anywhere. There isn’t any need to sign your entry and risk touching items that could spread the virus. Singapore’s Changi Airport Terminal 4 is using automation, while the CitizenM hotel group is offering self-service check-ins for guests. 3. Humanoid Robots and Robotics to Replace Humans The Bangalore International Airport in India is planning to use humanoid robots in place of human employees. A local startup has been working on building the humanoid to assist passengers during travel. Sirena Technologies has also built the first Indian Humanoid Robot, Nino that enables a novel and modern approach to education. Similarly, airlines are starting to replace robot-driven aircraft tractors to take passengers to their flights. By reducing the involvement of employees, airlines can continue to fly planes and stay productive even in adverse conditions. There is an idea to use automated kiosks to check the temperature of the travelers along with their travel details, all in one place. This will eliminate the need for employees to handle the passports and other papers of the travelers. 4. Screening Luggage Using Artificial Intelligence Luggage

Kavika Roy
July 13, 2021
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How Call Centers Are Being Modified Using Artificial Intelligence?

It has been more than two decades since call centers revolutionized the customer support system for various industries. While some static call scripts and one-size-fits-all strategies may remain unchanged in business outsourcing, technology has played a crucial role in drastically altering the way call centers function. Today, call centers have the unique ability to leverage all available data to drive each customer interaction. These data include various sources like the marketing campaign the customer has viewed multiple times, the type of transaction completed recently, or what a prospective customer looked for in their latest search. Of many technologies that call centers may have leveraged, artificial intelligence is one. Artificial Intelligence in call centers has allowed explosive growth owing to widely available cloud services and machine learning tools. AI in call centers There are two major types of AI in BPOs right now. The first can evaluate vast volumes of data and provide just-in-time insights for the agents to improve their performance on a call. This AI delivers the correct information during a call and ensures the agent or executive is on track with the right information when the caller asks for it. The second type of AI in business outsourcing is conversational AI. It analyses the speech of both the executive and the caller to identify emotions and, ultimately, intent. Conversational AI is mainly used to forecast the impact of a conversation based on vocal ticks, emotional state, and overall engagement level of both the caller and the agent. The sentiment analysis provides valuable real-time feedback on the emotional state of both the customer and agent and intervenes whenever required. Why is AI used in call centers? Ultimately, businesses want to create a personalized, positive experience for customers. And we are all well aware that when it comes to providing a good experience, the credits are always measured in sales, whereas a bad experience can have lasting effects. So how does AI change any of it? Here’s the answer. Automation: AI automatically captures data, routes calls to the suitable agent based on the input and mood from the analyzed data, and creates a profile for future reference that can be used in the call center and other business areas. Analysis: Call center AI provides in-depth analysis of individual calls making it easier for the managers and quality control executives to make decisions. Each call is measured and compared against performance benchmarks to provide a clear picture with an actionable insight of where the agent is performing and lacking. Support: AI is directly integrated with call center service workstations for agents providing immediate insight into the data being captured, the probable outcome of the call, and much more. The result is faster response times, a higher call resolution rate, and happier and motivated call center agents who are now empowered with tools to help them perform better. How is the call center scenario changing with AI? It is no news that call centers strive to provide a seamless and easy experience to customers since they constantly have to face the risk of losing out to a competitor. This report from a survey conducted by American Express found out that prospective or existing customers have bailed from a current purchase because of a poor service experience. To ensure that does not happen, call centers have turned to AI and machine learning solutions to help them take the following best action, turn leads into customers, increase retention rates, propensity product purchase, and much more. Business Process Outsourcing or BPO firms tend to work a lot around data. That is why data-driven call centers look forward to implementing AI solutions to improve customer experience. Here are a few practices for data-driven call centers with AI and big data to enhance selling via a good customer experience. 1. Intelligent call routing One of the significant advantages AI brings to call center finances is saving on human resources costs. Call routing through AI helps get the right customer to the right representative, taking into account the reason for the call and lifetime value and call complexity. A good number of call centers also opt to use skills-based call routing techniques to respond to a promotion. Say, for example, if one team is striving for promotion A and the other for B, AI can quickly identify and analyze the call and route it to the appropriate team. Layering in AI to skills-based call routing also ensures that the customer arrives at the right agent who can guide them in the best possible way. 2. Better analysis for caller feedback Traditional call monitoring cannot pull data from multiple sources in real-time. For example, the performance of call center executives or agents is currently monitored according to human observation. Calls are listened to and analyzed by a small number of ‘human’ managers who may or may not have their own biases that could impact evaluations. The analysis can be inaccurate, directly moving the scoring of agents. If you have ever called a customer support center of an e-commerce firm or any business, you’d be well aware of how they inform you before the issue that the call may get recorded for quality purposes. This indicates that agents receive feedback and support only after the ring, and there is probably no automation or real-time assistance implemented to guide the agent. What gets even more annoying for the customer is that they may have to speak to several agents or keep dialing digits without receiving any fruitful outcome — the result- poor feedback for the agent. AI improves this situation by performing those essential functions like monitoring, analysis, and support in real-time. Much of this function is performed by its branch, machine learning. ML services help in analyzing the mood and response of both the customer and agent on call. When used as feedback, the data enables the agents to respond more effectively, making them both happier, as compared to previous experience. 3. Personality profiling While the main objective of

Kavika Roy
June 01, 2021
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Role of Machine Learning in the Medical Industry

Technology in healthcare helps provide better treatment and save lives. But do you think it is as simple as it sounds? Quality of services, valuing human life, and delivering better outcomes are the primary purposes of using advanced technology like Machine Learning or ML in medicine. Businesses and SMEs are extensively adopting artificial intelligence and machine learning (a subset of AI) in various industries. The medical sector is no different. Many research centers and healthcare organizations have recognized the potential of machine learning and are actively improving their patient care services and administrative processes and systems. Machine learning algorithms become more accurate as they gather and process data over time. As a result, it increases accuracy and efficiency. From screening a patient to prescribing the right medicines, AL can empower the healthcare providers to understand the patients’ condition down to the minute details. Machine Learning and Healthcare ML services help in automating recurring tasks. It saves time for the nurses and allows them to focus on the patient rather than spend their energy on filing records, processing claims. Patient management is one of the most challenging things to handle for healthcare centers. Artificial intelligence and machine learning can help streamline the processes to provide high-quality treatment and services to patients. Chatbots are not the only way to use machine learning. Machine learning is fast becoming a part of the pathology, oncology, and other such departments. ANN (artificial neural networks) helps with image modeling, disease diagnosis, identifying harmful cells (cancerous cells) in the early stages, and so on. Of course, this is only limited to direct patient care. Machine learning algorithms are just as valuable for medical research, where labs run clinical trials, discover and develop new drug combinations, and process a vast amount of historical and real-time data to study and control the spread of an outbreak or an epidemic. It is one of the main reasons why the demand for artificial intelligence in the medical industry has doubled during the last year. The Covid-19 pandemic resulted in various researchers and healthcare centers relying on technology to understand the spread of the virus and look for ways to stop it. Role of ML in Medicine Several organizations hire offshore machine learning consulting companies to implement AI technology in their processes. ML has various roles to play in the medical industry, and here are some of the most important ones. 1. Improving Health Records Data entry might have become more accessible during the last few years, but maintaining the health records up to date is still a labor-intensive job. Nurses and the non-medical staff spend a lot of time updating the records. If this were to be handled by machine learning, wouldn’t it save time, money, and other resources? AI can help build and maintain smart health records for every patient: – Whether it is about storing the records on the cloud and making them easily accessible to the medical staff or – Using ML-based handwriting technology to understand and convert written files into other formats. When the necessary patient details are already available on the file, the doctors will have more data to understand the patient’s medical condition. Hence, it contributes to a higher quality of treatment. 2. Diagnosing Diseases It would seem quite natural that machine learning is very good at diagnosing diseases. This is one of the prominent areas where machine learning is highly effective. Machine learning algorithms can quickly identify diseases like cancer that are hard to diagnose early (especially skin cancer). IBM Watson Genomics by IBM Watson Health in partnership with Quest Diagnostics is a prime example. Using genome-based tumor sequencing with cognitive computing, harmful cells were detected faster and with greater accuracy. Artificial Intelligence consulting services are used by healthcare centers for predictive analytics to diagnose brain diseases like depression. Furthermore, it helps plan a proper treatment chart for the patient before getting too late. 3. Manufacturing Drugs Research and development (R&D) is an inherent part of the medical industry. By using machine learning during the early stages of discovering and developing drugs, researchers can know the possible outcomes and the success of using the medicine to cure a disease. Researchers can identify the potential side-effects of using the drug and find alternate components to reduce the side-effects while increasing efficiency. Next-generation sequencing and precision medicine are two AI technologies used in discovering and manufacturing drugs. Precision medicine helps in identifying multifactorial diseases and finding alternative therapies. Project Hanover (by Microsoft) used ML technology to treat cancer and helps in personalizing the drug combination for patients suffering from Acute Myeloid Leukemia. 4. Personalized Treatment and Medicine Personalization is seen everywhere, and the medical industry is no exception. It helps to provide better and accurate treatment to patients based on their health conditions rather than based solely on the disease troubling them. In a way, this point is an extension of the previous one. Though doctors can only choose from a limited set of data, ML makes giant leaps in this region. IBM Watson Oncology is the first thing that comes to mind when you talk of using the patients’ history to predict their health and make a list of treatment methods that are best suited for their conditions. 5. Diabetes Prediction Diabetes is both common and dangerous. It leads to more health complications by damaging other vital organs in the body such as the heart, kidneys, and even the nervous system. While type-2 diabetes is something many people are familiar with, type-1 diabetes (also called juvenile diabetes) is still not known by many. Do you know that WHO estimated around 1.5 million deaths in 2019 due to diabetes? Using Artificial Intelligence in Healthcare to predict diabetes can help physicians detect the disease in its early stages and help patients find the correct method to control their blood sugar levels. Predicting diabetes can save lives and allow the patients to lead a quality life. 6. Liver Disease Prediction Fatty liver or liver cirrhosis is a liver disease caused by alcohol abuse. The simplest

Kavika Roy
May 25, 2021
3 Comments

How is AI Contributing to the Education Sector?

Artificial intelligence has entered every industry, and the educational sector is no exception. The administrative staff, management, teachers, and students are all using AI in different ways to achieve similar goals. During the last few years, AI has spread its roots much wider and deeper in this sector. Though we haven’t seen humanoids replacing teachers, we sure are seeing AI systems taking over teaching and guiding students during their online courses. Whether it is automating tasks or personalized training courses based on students’ capabilities, AI is changing the way people look at education. Before we say the various ways in which artificial intelligence is contributing to the education sector, let us look at how an educational institution should implement AI in its establishment. Implementation of AI in Education Sector Do you want to implement artificial intelligence solutions in your educational institution? You will first need to define the long-term goals and understand the limitations of the existing system. Without knowing what you want to achieve, investing in AI will not be beneficial. Then, you will either have to hire AI experts to bridge the talent gap or outsource the responsibility to a machine learning consulting company. If you want to hire a third-party company, it is recommended to do so in the beginning. This will ensure that your institutional goals and the company’s services are aligned and in sync with each other. Well-known institutions such as Nuance (Massachusetts), Querium (Texas), Kidsense (California), and many others have successfully adopted AI models in their educational systems. Identify Needs: What do you expect AI to do in your establishment? Which processes do you want to automate? Which training areas do you want to focus on? Determine Objectives: Are you aware of how AI works? Do you know the drawbacks of using AI systems? How do you plan to overcome the challenges in adopting artificial intelligence? Align Processes: The AI solutions you choose should create the right environment to align your establishment’s talent, technology, and work culture. You need to convert your institution into an analytical-based model that focuses on actionable insights to make better decisions. Control the Environment: The new work environment should accommodate and allow humans and AI systems to work in tandem. The processes should be transparent, efficient, effective, and secure. Role of Artificial Intelligence in Education Automating Administrative Tasks Is there an establishment that can function without backend work by the administrative department and the teaching faculty? Whether it is filing paperwork, sending emails and messages, contacting students and their parents, or making periodic reports, the administrative staff constantly works from morning to evening. Similarly, the teachers and professors have to mark the homework, test papers, create a teaching schedule, prepare for the next classes, etc. Do you know that a teacher spends less than half their time actually teaching the students? According to an article posted by The Telegraph, teachers spend only 43% of their time on teaching. The remaining time is allotted to marking test sheets, planning future lessons, and completing the administrative work. The artificial intelligence consultant will help you create an AI system that will allow the staff and the teachers to be more productive at work. Teachers can use smart question generators like PrepAI that help them set better question papers for the tests and save time in the process. Personalizing Learning for Students Personalized learning is pretty much similar to personalized recommendations offered by online marketplaces or Netflix. The courses and subjects are recommended based on the interests and preferences of the students. AI for online learning doesn’t replace teachers. Instead, it helps teachers understand the abilities and limitations of each student. It makes things easier for the students and teachers to create an exclusive course plan for each student. Smart tutoring systems also offer instant personalized feedback to help students understand how they can do better. Teachers can convert their lessons into interactive sessions and flashcards so that students will take more interest in learning the subjects. Creating Smart Content Smart content is a popular topic in the education sector. It is nothing but creating virtual content by digitizing textbooks, notes, creating video lessons and lectures, developing interactive training sessions, and more. AI in education helps create customizable interfaces for the students to learn easily and understand better. Be it the elementary level or the postgraduate level, smart content can be developed for any subject and any class. Microlearning and skills mapping are two concepts that belong to this category. Skills mapping is the process of creating a visual representation of the skills needed by students to perform well in their studies. Microlearning is a skill-based learning module that deals with specific areas to achieve expertise in the field. Breaching the Geographical Barriers Online courses have made it possible for students from any region to join a course from any other country. The physical barriers between countries have been eliminated in terms of online education. Technology made it possible to acquire specialized skills from the best teachers, no matter where they are located. Now, students have even better chances of updating their skills through AI-powered training sessions. Information Technology is one area where numerous courses are using AI. That shouldn’t be surprising since artificial intelligence is an inherent part of the industry. Students and professionals who wish to become AI and ML experts are enrolling for these courses. Education Made Easy for People with Special Needs It has always been a challenging task to create a learning program for people with special needs. Though there are exclusive schools for students with hearing, speech, and visual impairment, the teaching process could have been more effective. Also, students with ADHD, dyslexia, autism, etc., have to be enrolled in special schools to help them deal better. But why can’t these students continue to be a part of regular schools? Is there a way teachers can understand and pay more attention to these kids? Artificial intelligence is making it possible for kids with special needs to study most comfortably. SpeakIt!, Widex’s Evoke, and Empower Me are a few examples of AI in education that

Beatrice Johnson
May 18, 2021
1 Comment

4 New Implementations of Computer Vision in Marketing

For many in the business and marketing world, computer vision is still a new and somewhat obscure concept. However, it is also one that is rapidly becoming more relevant, particularly with regard to the acquisition, service, and retention of customers. Leaders and professionals implement Computer Vision in Marketing, Operations, Sales, Retail, Security, and many other areas. To recap the core concept quickly, we’ll turn to a simple definition from Towards Data Science, which characterizes computer vision as a field of computer science that enables computers to identify and process objects in images and videos the same way that humans do. We would also add that the improvement of augmented reality technology is in some respects extending computer vision into the world — such that computer systems can also recognize real-world objects and images through their own cameras. It’s all extraordinarily impressive technology, and it can be used for a wide range of purposes. In this piece, though, we’re going to look specifically at some of the ways in which computer vision can help businesses in marketing. Visual Search Assistance Monitoring Store Traffic Customer Personalization Searchable Images Visual Search Assistance Nowadays, marketers are assisted by certain automated features that help to make recommendations and narrow down selections for online shoppers. The process can work in different ways, but typically a customer’s search activity produces unseen tags that reflect apparent interest. Those tags can then be used to filter through additional store offerings so that customers can be presented with suggestions they’ll be likely to appreciate. It is a simple, automated means of improving direct customer engagement. Now, however, computer vision in marketing is refining this same general concept. Through this technology, a company’s system can actually recognize — look at, in a sense — what customers are observing. Rather than relying on tags, which can be somewhat vague, a computer can identify customers’ selected items and actually look for similar items or appropriate accompaniments. The potential is there to improve customer engagement with even greater accuracy. Monitoring Store Traffic Some time ago, The Atlantic posted a thorough, interesting article on what stores do to “follow every step you take.” The idea is to track customers within stores in order to gather data that can effectively shape in-store marketing strategies. By tracking customers — through Bluetooth and Wi-Fi signals, the customers’ own smartphones, etc. — companies can gain insight into which products are being favored, how the store layout might be made more effective, and so on. And now, computer vision is essentially simplifying this process for marketers and shows how computer vision in marketing is beneficial. Customer Personalization Customer personalization is something we typically think of as having to do with content marketing and data analytics. In a broader sense, today’s businesses go to great lengths to make sure that their written and shared content is tailored to specific audiences. Ayima Kickstart examines this as an aspect of content SEO — explaining that companies employ “expert writers” to research target audiences and construct content according to that research. Beyond this, on more of a customer-to-customer basis, a lot of modern businesses are also using various analytical methods to track activity and tailor follow-up recommendations as needed. Through those practices, consumers are effectively guided toward conversions: They’re found and spoken to strategically within broader audiences and then tracked and catered to via tracking as they browse or otherwise engage with the business. It’s an effective process, but we’re now beginning to see computer vision in marketing simplifying it. Searchable Images The last benefit of computer vision in marketing that we’ll discuss here — and perhaps the simplest — is its impact on consumer searches. With computer systems better able to recognize and interpret images, consumers now have the option of plugging images into search mechanisms. This means that if a consumer should come across a photo of an intriguing product — or even take that photo personally — it can be used to search for further information. As this practice becomes more common, it will naturally produce benefits for businesses. However, it also gives marketing departments a whole new way to think about image-driven product marketing and social media outreach. Conclusion All of these represent significant changes and advancements. And yet they’re also only the beginning! In our article ‘How is Vision Analytics Retransforming Modern Industries?’ We pointed out that the global computer vision market anticipates a 7.6% CAGR between 2020 and 2027. Due to sharing of visuals among the customers, online marketing using visual datasets has become crucial for marketers. With the help of computer vision, they can gain customer insights, improve the campaigns and ultimately improve their buying behavior. As Computer Vision is maturing with each passing year, it holds many new opportunities for marketers. That amounts to a prediction of significant growth, which means that computer vision in marketing is going to become even more sophisticated — and produce even more beneficial concepts — over time.

Yashica Chopra
March 12, 2021
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10 AI & ML Secrets Your Competitors Use to Win in the Market

Artificial intelligence has been seeing rapid growth during the last few years. More and more organizations from across the world are investing in AI and machine learning technologies. As per a report, the global market value of artificial intelligence is estimated to be $126 billion by 2025. Be it marketing and sales, business intelligence, customer care, logistics, or the banking and financial sector, AI and Machine Learning hacks play a vital role in streamlining the business processes. Artificial intelligence is said to reach $22.6 billion in the fintech market by 2025, and it is said to touch $40.09 billion in the marketing market by the same year. Many large-scale enterprises and smaller businesses alike are looking at AI and ML with anticipation. But not all of them have the necessary talent pool to implement and work with the updated systems. That’s where artificial intelligence consulting firms are stepping into the picture. By providing customized services to these companies, the AI firms help the top management integrate the latest technology into their systems and train employees to work with AI tools. At the same time, some enterprises have failed to become successful by investing in artificial intelligence. And we know that implementing AI and machine learning includes facing challenges related to organizational culture, skill gap, employee psychology, financial limitations, and data management, among other things. Also, the top management has to think of the existing business challenges such as reduced productivity, lengthy product cycles, delayed transportation, unhappy customers, fraudulent transactions, and much more. So how are the leading multinational companies able to overcome so many challenges using AI? What kind of AI Solutions are they using to become successful? Let’s unveil a few Machine Learning secrets your competitors are using to solve their business challenges and succeed in the market. Machine learning algorithms are dynamic in nature and capable of continuous improvement. Across various industries in the market, machine learning is being used predominantly in these ways to overcome business challenges. 10 AI and Machine Learning Hacks Used By Successful Companies 1. Data Analytics – The Importance of Clean Data Data Analytics is the process of collecting, sorting, and analyzing a vast amount of data to derive valuable insights. There is a lot of raw data scattered throughout the enterprise, and, not to mention, the real-time data that’s always available on the internet. Continuously increasing data these days led to a new process called Data Cleaning. The AI solutions company now focuses on clean data along with big data. Data from the past may not always be relevant in today’s world. Using it for analysis and predictions for the future doesn’t make sense, right? For example, businesses that use mobile eCommerce do not need data from the era where mobile phones were not used for shopping. It further takes more time, money, and effort to sort and process unstructured data, arrive at what is essential, and then use it to generate predictive reports. AI can help you identify which data is relevant and which is not so that your team can work only on new and clean data to get better and accurate predictions. 2. Continuous Improvisation of Customer Segmentation Customer segmentation is the technique of classifying customers and target audiences into different sections based on similarities in their purchase behavior, product requirements, etc. Traditional procedures are time-consuming, and the margin for error is also high. Machine learning consulting company uses data mining and ML algorithms to process data and segment customers into different categories. Instead of guessing or going by instinct, use data-driven marketing procedures to understand customers and target audiences. Data is already available in abundance in the form of email newsletters, website visitors, social media posts, and lead capturing information. It will help to identify profitable customer segments and focus on catering to individual customer needs. By doing this, you can increase sales and customer satisfaction at the same time. However, you need to ensure that you have a proper business case before implementing ML for customer segmentation and customer lifetime value (LTV) prediction. 3. An Additional Approach to Demand Forecasting Demand forecasting is a crucial factor in the manufacturing industry. Producing more when the demand is less and producing less when the demand is more will result in losses for the enterprise. Industries have been following traditional approaches to predicting how much they need to manufacture, how much stock has to be stored in the warehouses, and when it has to be moved to wholesalers and distributors, etc. so that the products will be available in the market for customers’ consumption at the right time. But the forecasts have not always been accurate enough, isn’t it? Wouldn’t you want software that gives more than 90% accurate forecasts? An artificial intelligence consultant can create a robust demand forecasting system that analyses more data in less time. It can find the hidden patterns which the age-old methods ignore. And when data prediction is accurate, the decisions made based on the predictions will also be beneficial. Right? 4. Improved Spam Identification Tools for Enhanced Data Security Spam identification may not seem like a big deal when you say it. But when it comes to cybersecurity, this is one of the most important factors. Machine learning came into existence with spam filters in emails. The algorithm would detect emails that seemed dubious, suspicious, and fake. While this is great for personal use, how does it help businesses? Proofpoint said that 88% of the firms from around the world experienced spear phishing in 2019. According to a report by IBM, it took around 207 days to identify a data breach in 2020. AI services include creating a comprehensive security system that prevents cybercriminals from breaching the security walls and compromising confidential data. Some of the leading antivirus software solutions use machine learning algorithms to identify different types of cybercrimes and protect employees from becoming victims. AI firms are also developing data security protocols to help SEMs and institutions add more security

Lakshay Chhabra
June 27, 2020
25 Comments

Facial Recognition For Everyone – A comprehensive guide

Since the 1970s we are trying to make use of the Facial Recognition system to help us in various things, especially in identification. We all have grown up with watching high tech movies showing the use of Facial Recognition technology to identify the friends or enemies, giving access to some data and now even to unlock our mobile phones. We are in the golden age of AI where we want things to work in an advanced way, We are handling issues with much more broaden perspective but sometimes are unable to adapt to these changes at the organisational level. We at DataToBiz are bridging this gap and brings you Facial Recognition for Everyone, Where companies can easily incorporate the power of AI with their current infrastructure. Facial Recognition now a day is widely used in Identification and during this time of an epidemic we can easily avoid touching those fingerprint sensor to mark attendance, We bring out Facial Recognition Solutions where we can mark attendance in your very own device. Our Product AttendanceBro comes with API level integration which enables marking attendance from one’s computer after analysing face and some specific factors. But sometime we might not have an internet connection and want an attendance system that can work on our phone without internet, So here we bring AttendanceBro for Android devices which can work online as well as offline. Here’s our guide on how to build an attendance system which uses Artificial Intelligence to mark attendance of users offline in Android devices : Step 1 : Choosing the right model We at DataToBiz has experienced team which provides AI solution to companies according to the use case. Selecting a model depends on various factors like Number of Users, Nationality, Type of device etc. To know more about selecting a model, feel free to contact us or book an appointment with our AI experts Step 2 : Adding User to Database We will be extensively using google’s ml-vision library to process the model offline in the device. First, we need to make an interface to select the image. Now after making a simple layout of the app, we need to modify the backend of the app.First, we will create a function which will help us getting Image after pressing “Add a Person” button. Then we have to follow few steps to process the image. 1. Feeding our image to a face detector. Here we will first find the face of the person then use those coordinates to crop it out and pass it to the next step. 2. Preprocess the cropped image, perform mean scaling, convert it into a buffer array and extend its dimension so it can be fed to the classifier. 3. Pass processed Image to classifier and hold the result in a variable. Then admin can enter the name of the person and save it in the SQL database which will remain in the android device or can be uploaded to server if needed. Now let’s move to next step of using Face recognition to mark attendance. Step 3 : Marking Group Attendance There might be a situation where we need to mark attendance for 1, 2 or 3 users together. Here we bring Group Attendance option which can mark attendance of N person (If they are clearly visible). Take a look at over all structure of the app. Bonus Step : Liveness Detection If you look at the app structure you’ll notice that along with Add a Person and Group Attendance there’s another option Live Attendance.In facial Recognition the main issue we encounter sometimes is spoofing where an intruder uses a photograph of the user to gain access. So here we bring an anti-spoofing way to mark attendance where a user will have to go through a Liveness Detection process where he will be asked to perform a certain task such as blinking an eye or saying a particular word to mark his attendance. We at DataToBiz are constantly working in utilising the power of Artificial Intelligence in our lives to transform the way we look at problems. We are working deeply in the field of Computer Vision, Data Analysis, Data Warehousing and Data Science. If you have any query, feel free to email us at contact@datatobiz.com or leave a comment.