Facing Data Paralysis? This Might Save You

Start With Data Assessment

Facing Data Paralysis? This Might Save You

Start With Data Assessment

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

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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

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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

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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

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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.

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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

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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.

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Impact Of AI In Market Research | How It Is Being Improved

To understand the effect that artificial intelligence (AI) can have on market research. First, it is essential to be clear about what exactly is AI and what it is not. Artificial intelligence is the machine-displayed intellect, which is often distinguished by learning and adaptability. It is not quite the same as automation. Automation is now commonly used for speeding up a variety of processes in the insights field. Automation is essentially the set of guidelines from recruitment to data collection and analysis that a computer follows to perform a function without human assistance. When complex logic and branching paths are introduced, differentiation from AI can be difficult. But, there is a significant difference. Except in the most complex of ways, software follows the instructions it has been given when a process is automated. Every time the cycle runs, the program (or machine) makes no decisions or learns something new. Learning is what makes artificial intelligence stand out from automation. And this is what gives those who accept it the most significant opportunities. Examples Of AI Today With AI Market Research Companies There is already a range of ways in which artificial intelligence can provide researchers with knowledge and analysis that weren’t possible before. Of particular note is the ability to process massive, unstructured datasets. Processing Open End Data In AI-Driven Market Research Dubbed Big Qual, the method of applying methods of statistical analysis to large quantities of written data aims at distilling quantitative information. The natural language API in Google Cloud offers an example of this in practice. The program recognizes “AI” as the most prominent entity in the paragraph (i.e., the most central one in the text). It can also know the category of text, syntactic structure, and provide insights into feelings. In this situation, there was a negative tone in the first and third sentences, while the second was more positive overall. It can reduce the time it takes to evaluate qualitative responses from days to seconds when implemented on a large scale, particularly in the case of open-ended results. How Artificial Intelligence Will Change The Future Of Marketing: Artificial Intelligence In Marketing Analytics? Following is the way in which artificial intelligence change the future of marketing: Proactive Community Management  A second direction that artificial intelligence is being used in group management today can be observed. As every group manager can attest, participant disengagement is one of the most significant challenges to a long-lasting society. It can result in a high turnover rate, increased management effort, and outcomes of lower quality. Luckily, AI-driven automated market research behavioral forecasts increased the chance of disengagement. Behavioral predictions include evaluating a vast array of group members’ data points such as several logins, pages viewed, the time between logins, etc. to construct user interaction profiles.  When designed against disengaged members and measured, the AI can classify the members are at risk of disengagement. It allows community managers to provide these individuals with additional support and encouragement, thus reducing that risk. Machine Making Decisions Give enough details to a computer, and it’ll be able to make a decision. And that’s precisely what Kia did over two years ago when the company used IBM’s Watson to help determine which influencers on social media would better endorse its Super Bowl commercial. Using Natural Language Processing (NLP), Watson analyzed social media influencers’ vocabulary to recognize which characteristics Kia was searching for – openness to improvement, creative curiosity, and striving for achievement. Perhaps the most exciting thing about this example is that Watson ‘s decisions are those that would be difficult for a human to make, demonstrating the possibility that AI  for market insights might better understand us than we can. Future Of AI In Market Research Progress, of course, never ends. We are still very much in the absolute infancy of artificial intelligence. In the years to come, it is a technology that will have a much more significant effect on market research. Although there is no way to predict precisely what the result would be, the ideas outlined here are already being formulated – and that arrive sooner than we expect. Virtual Market Research  It’s expensive to hire. It can quickly eat away on a research budget, depending on the sample size and the length of a task. One proposed suggestion to further reduce this expense and extend insight budgets is to create a virtual panel of respondents based on a much smaller sample. The idea is that sample sizes inherently restrict the ability of a company to consider every potential customer and client’s behavior. Hence, taking this sample, representing it as clusters of behavioral traits, and building a larger, more representative pool of virtual cluster respondents offers a more accurate behavior prediction. This method has abundant limitations, such as the likelihood that in the first instance, the virtual respondents will be limited to binary responses. But this still has value – particularly when combined with the ability to run a large number of virtual experiments at once. It may be used to determine the most suitable price point for a product or to understand how sales could be affected by reaction to a change in product attributes. Chatbots As Paul Hudson, CEO of FlexMR, emphasized in a paper presented at Qual360 North America, a question still hangs over whether artificial intelligence could be used to gather on-scale qualitative conversational research. The research chatbots of today are restricted to pre-programmed questions, presented in a user interface typical of a conversation online. However, as developments in AI continue to grow, so will these distribution methods for online questioning. The ultimate test would be whether such a tool could interpret responses from respondents in a way that allowed tailoring and sampling of interesting points following questions. It will signal the change from question delivery to virtual moderator format. The resource is a natural limitation to desk investigation. While valuable, desk research can be time-consuming, meaning that insight does not always reach decision-makers’ hands before a decision

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Automated Machine Learning  (Automl) | The New Trend In Machine Learning

The digital transformation is driven primarily by the data. So today, companies are searching for as many opportunities to gain as much value from their data as they can. In reality, in recent years, machine learning (ML) has become a fast-growing force across industries.  ML ‘s effect on driving software and services in 2017 was immense for companies like Microsoft, Google, and Amazon. And the utility of ML continues to develop in companies of all sizes: examples include fraud prevention, customer service chatbots at banks, automated targeting of consumer segments at marketing agencies, and suggestions for e-commerce goods and retailer personalization. Although ML is a hot subject, there is another popular trend: automated machine learning platform  (AutoML). Defining AutoML (Automated Machine Learning) The AutoML field is evolving so rapidly, according to TDWI, there’s no universally agreed-upon definition. Basically, by adding ML to ML itself, AutoML gives expert tools to automate repetitive tasks. The aim of automating ML, according to Google Research, is to build techniques for computers to automatically solve new ML issues, without the need for human ML experts to intercede on each new question. This capability will lead to genuinely smart systems. Furthermore, possibilities are generated thanks to AutoML. These types of technologies, after all, require professional researchers, data scientists and engineers, and worldwide, but such positions are in short supply. Indeed, those positions are so poorly filled that the “citizen data scientist” has arisen. This complementary position, rather than a direct replacement, hires people who lack specialized advanced data scientist expertise. But, using state-of-the-art diagnostic and predictive software, they can produce models. This capability stems from the emergence of AutoML, which can automate many of the tasks that data scientists once perform. To counter the scarcity of AI/ML experts, the AutoML example has the potential to automate some of ML’s most routine activities while improving data scientists’ productivity. Tasks that can be automated include selecting data sources, selecting features, and preparing data, which frees marketing and business analysts time to concentrate on essential tasks. For example, data scientists can fine-tune more new algorithms, create more models in less time, and increase the quality and precision of the model. Automation And Algorithms Organizations have turned toward amplifying the predictive capacity, according to the Harvard Business Review. They’ve combined broad data with complex automated ML to do so. AutoML is marketed as providing opportunities to democratize ML by enabling companies with minimal experience in data science to build analytical pipelines able to solve complex business problems. To illustrate how this works, a current ML pipeline consists of preprocessing, extraction of features, selection of features, engineering of features, selection of algorithms, and tuning of hyper-parameters. But because of the considerable expertise and the time it takes to enforce these measures, there is a high barrier to entry. One of the advantages of AutoML is that it removes some of these constraints by substantially reducing the time it takes to usually execute an ML process under human control, while also increasing the model’s accuracy as opposed to those trained and deployed by humans. Through enacting this, it encourages companies to join ML and free up ML data practitioners and engineers’ resources, allowing them to concentrate on more difficult and challenging challenges. Different Uses Of Automl About 40 percent of data science activities should be automated by 2020, according to Gartner. This automation would result in a broader use by citizen data scientists of data and analytics and improved productivity of skilled data scientists. AutoML tools for this user group typically provide an easy-to-use point-and-click interface for loading ML models for data building. Most Automl tools concentrate on model building rather than automating a whole, particular business feature, such as marketing analytics or customer analytics.  However, most Automl tools and ML frameworks do not tackle issues of ongoing data planning, data collection, feature development, and integration of data. It proves to be a problem for people who are data scientists, who have to keep up with large amounts of streaming data and recognize trends that are not apparent. They are still not able to evaluate the streaming data in real-time. And poor business decisions and faulty analytics can arise when the data is not analyzed correctly. Model Building Automation Some businesses have switched to AutoML to automate internal processes, especially building ML models. You may know some of them-Facebook and Google in particular. And Facebook is widely on top of every month’s ML, training, and testing around 300,000 ML models, essentially building an ML assembly line to handle so many models. Asimo is the name of Facebook’s AutoML developer, which produces enhanced versions of existing models automatically. Google also enters the ranks by introducing AutoML techniques to automate the process of discovering optimization models and automating machine learning algorithm design. Automation Of End To End Business Process In certain instances, it is possible to automate entire business processes once the ML models are developed, and a business problem is identified. It needs the data pre-processing and proper function engineering. Zylotech, DataRobot, and Zest Finance are companies that primarily use AutoML for the entire automation of different business processes. Zylotech was developed for the entire customer analytics automation process. The platform features a range of automated ML models with an embedded analytics engine (EAE), automating customer analytics entering the ML process such as convergence, feature development, pattern discovery, data preparation, and model selection. Zylotech allows data scientists and citizen data scientists to access full data almost in real-time, allowing for personalized consumer experiences. DataRobot was developed for predictive analytics automation as a whole. The platform automates the entire lifecycle of modeling, which includes transformations, ingestion of data, and selection of algorithms. The software can be modified, and it can be tailored for particular deployments such as high-volume predictions, and a large number of different models can be created. DataRobot allows citizen data scientists and data scientists to apply predictive analytics algorithms easily and develop models fast. ZestFinance was primarily developed for the

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Computer Vision in Healthcare – The Epic Transformation

Before discussing futuristic applications of computer vision in healthcare, let us talk a little about how computer vision works. Although the ability to make machines “see” a still image and read it, is related to human’s ability to see, the machines see everything differently. For example, when we see a picture of a car, we see car doors and windows and glasses, color, tires, and background, but what a machine sees is just a series of numbers, that simply describes the technical aspects of the image. Which does not prove that it is a car. Now, to filter out everything and to arrive at a conclusion that it is a car, is what Neural Networks do. Various Neural Networks and Advanced Machine Learning Models are being developed and tested over the period, a massive amount of training data as being fed and machines, now have achieved a level of accuracy. How AI could benefit Health Care Industry: There have been discussions on how AI could help various industries and Health Care is one of the most talked. There are many ways AI could support the industry. AI is a vast field and can be confusing on what specific model to use. There have been continuous discussions and multiple methods approached and improvised. Support Vector Machines For the purpose of classification and regression, Support Vector Machines can be implemented. Here support vectors are data points, which are closest to the hyperplane. To diagnose cancer and other neurological diseases, SVMs are widely used. Natural Language Processing We now have a large amount of data which is composed of examination results, texts, reports, notes and importantly, discharge information. Now, this data could mean nothing for a machine that has no particular training for reading and learning from such data. This is where NLP could be of use, by learning about keywords related to disease and establishing a connection with historical data. NLP might have many more applications based on needs. Neural Networks Implementing hidden layers to identify and establish a connection between input variables and the outcome. The aim is to decrease the average error by estimating the weight between input and output. Image Analysis, Drug Developments and a few, are the fields where Neural Networks are harnessed. As Always, CNNs are the Best: Convolution Neural Networks, over time, has rapidly been developed and currently is one of the most successful computer vision methods. “CNNs simply learns the patterns from the training data set and tries to see such patterns from new images.”. This is the same as humans learning something new and implying the knowledge but what all these models know is a series of ones and zeros. With an accuracy of 95%, a CNN trained at the University of South Florida, can quietly easily detect small lung tumors, often missed by the human eye. Another research paper suggests that cerebral aneurysms can be detected using deep learning algorithms. At Osaka City University Hospital, they detected cerebral aneurysms with 91-93% of sensitivity. RNNs, which are Recurrent Neural Networks are also popular and could be of great use as they are neural networks but with information in sequence. Performing the same task for multiple elements and composing output based on the last computation. How Google’s DeepMind sets new milestones: Acquired by Google in 2014, DeepMind has outplayed many players and has set a new record in AI for the Health care Industry. Protein Folding is something they have been working on and reached a point where predicting the structure of the protein, wholesomely based on its genetic makeup, is possible. What they did was rely on Deep Neural Networks, which are specifically trained to predict protein properties based on the genetic sequence. Finally, they reached a point where they had the model predict the gap between amino acids and the angles connecting the chemical bonds which connect earlier mentioned amino acids. This could also help in understanding the underlying reasons for how genetic mutation results in disease. Whenever the problem with Protein Folding will be solved, it will allow us to pace up our processes like drug discovery, research, and production of such proteins. How could this help in tackling COVID-19 It is not a new discovery that machine learning can fasten the Drug Development Process for any disease or virus. There are very few datasets available related to Corona and has a lot to tackle in order to establish a conclusion. Recently, there have been developments involving AlphaFold, which is a computational chemistry-related deep learning library. FluSense using Raspberry Pi and Neural Computing Engine: Starting with Lab tests, FluSense is now growing to identify and distinguish human coughing from any other sound, in public places. Idea is to combine the coughing data with people present in the area, which might lead to predicting an index of people affected by the flu. This is a perfect use case of computer vision in healthcare considering the recent pandemic of covid-19. Conclusion Though there have been tremendous developments and many new algorithms are been developed, it would be too early to completely rely on a machine’s output. Efficiently detecting minor diseases around the lungs is a great step, but still, a small error could lead to catastrophic events. Few more steps towards better models and we can improve health care, until then we can rely on image analysis systems as an assistant. DataToBiz has been working with a few healthcare startups in shaping up their computer vision products/services. It has been judged time and again as one of the top AI/ML development companies in the industry. Contact our expert and avail of our AI services.

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