SMART ROBOTS: THE POTENTIAL BENEFITS OF COMBINING AI WITH ROBOTICS

| February 27, 2020

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What would you call a machine that looks like a human? Obviously a Robot! Robots are machines or mechanical human beings that are designed to assist humans with laborious and complex tasks. However, such robots are no more just mechanical design rather they have become smarter with time and advancement of technologies. AI developments have induced evolution and better capacity in robots. Even robotics and AI together can revolutionize almost any industry for the greater good. As the industry is realizing the combined potential of both the technologies, will we see the combination anytime soon?

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How to Mitigate Robotic Process Automation Implementation with Low-code Development

Article | April 22, 2020

Ease in doing business.” That is what every C-level execs strive to achieve in their business process and it is no secret that they’ve increasingly turned to Robotic Process Automation (RPA) to streamline enterprise operations. The first digital computers were invented mostly to calculate tasks but as the technology progressed, we learnt to program hand-code automation through bespoke applications. What brought the RPA into existence was the slow and laborious hand-code automation. But, as we no longer need to keep our fingers glued to systems to enter data fields and value, there exists some brittleness to the robotic process automation. Table of Contents: - What is Robotic Process Automation? - What ails Robotic Process Automation? - What is Low-Code Development? - Why to program in a Low-Code Development environment? - How does Low-code development help in mitigating RPA implementation? - Concluding Thoughts What is Robotic Process Automation? Deloitte defines RPA as software that “automates repetitive, rules-based processes usually performed by people sitting in front of computers.” Picture your mouse automatically scanning your email for 70 new unread invoices, adding the data to a spreadsheet, and inputting information into your CRM, while sending two outliers to an employee for manual review – all within a fraction of the time it would take a person to do the same tasks and with far fewer errors. RPA workflows are established on logic-based inputs and tasks across applications for the bot to efficiently carry out manual, repetitive tasks with greater accuracy. Additionally, by separating the uniquely human skills like critical thinking, empathy, and decision making from the manual, repetitive tasks, corporations can provide a more fulfilling and rewarding career for their employees. Sounds great, right? Of course, it does. That’s why it’s the fastest-growing market in enterprise software, with 48% of companies saying they are planning to invest in RPA and is projected to be worth nearly USD 4B by 2025. Corporations across industries are buying in to streamline a wide variety of operational tasks, connect legacy systems, and drastically remove errors introduced by humans. Operations that can benefit from RPA technology include: Generic office tasks – gathering quarterly cross-department data into an excel sheet, automating CRM inputs, and inventory management. Back office processes – instead of five people checking for new orders and applying discounts, the tasks are reorganized so the employee is providing a human-level of validation to the order. Manufacturing – order fulfillment, purchase order processing, and transportation and inventory management. Retail – product categorization, automated checkout, and delivery tracking. Customer service – credit checks, account number assignment, and activation tasks can be allocated to bots and employees can speak to a customer and apply empathy and discernment to the situation at hand. What ails Robotic Process Automation? The raised fostering of RPA highlights the advantages of the modern technology, however the trip of automation is not without some bumps in advance. Presently, a bulk of RPA options deal with a typical weak point– a small adjustment to information layouts, service procedures, or application user interface can lead the whole software program to damage down. By style, RPA is durable software program however that likewise shows its frailty in adjustments. If anything changes, that can break the automation. - Jason Bloomberg, Leading IT market expert For instance, Bloomberg discussed, if an interface component like a switch relocates or transforms dimension, the automation may damage. Or probably the information style adjustments due to the fact that a person included a brand new area. “In other cases, the business requirement for the process logic changes, requiring a rework of the bot.” RPA functions best with older and also tradition applications powered by regular procedures that go through little adjustment and also secure information layouts. For companies looking to take advantage of the modern technology, the brittleness of RPA might lead to tightening alternatives and also applications in companies. Financial establishments, as an example, are typically wed to tradition systems and also applications, for which RPA is well matched to aid take care of. However, in a vibrant electronic age– which calls for service dexterity– RPA’s absence of versatility when incorporated can be restricting. What is Low-Code Development? Low-code software development could be compared to a car manufacturing assembly line. Both processes automate difficult and time-consuming tasks, in order to increase delivery speed and free up people to focus on high-level tasks. In technical terms, low-code is a set of tools that developers can use to build applications inside a drag-and-drop visual interface – including complete UI, integrations, data management, and logic. READ MORE: DISPELLING FIVE MYTHS OF LOW-CODE APP DEVELOPMENT Why to program in a Low-Code Development environment? In a quote to address the brittleness of RPA, the arising idea of low-code reveals appealing possibility. Its ability to faster way and also separate software program parts streamlines the design procedure. For RPA software program that calls for an upgrade, low-code offers the all set-to- code design to convenience the restructuring of systems. Low-code simplifies the work of developers, whether they be building applications or constructing bots. But even more importantly, low-code empowers developers and business stakeholders to work together more effectively to manage change in the behavior of the software. - Jason Bloomberg, Leading IT market expert In significance, low-code opens brand-new opportunities for designers to focus on establishing special software program systems that are matched for particular companies. READ MORE: BENEFITS OF LOW CODE DEVELOPMENT WITH REUSABLE COMPONENTS How does Low-code development help in mitigating RPA implementation? Here’s where low-code development can save the day. Low-code platforms enable cross-functional teams of professional developers, citizen developers, and functional staff to easily collaborate and connect multiple applications for end-to-end solutions. Because the platforms are built on open standards and are cloud-native, they can easily connect internal legacy and third-party applications in a bot-friendly interface and quickly establish bot workflows that model the real business processes. Enterprise RPA initiatives can get off the ground in a fraction of the time without bringing on additional staff and infrastructure. What does low-code and RPA implementation success look like in real life? Just ask Avertra and 2 Sisters Food Group. Avertra provides technology and consulting solutions for telecom and utility companies, including a modular digital customer experience framework built with the Mendix ecosystem and integrated via API with enterprise solutions like ERP systems, work management applications, and external data sources. Alongside their clients, Avertra establishes which processes to automate, builds user stories, and deploys bots which then follow workflows, transfer data between systems, select appropriate resolution paths, and follow through with documentation and compliance – all within a fraction of the time it takes an individual agent. Meanwhile, UK poultry supplier 2 Sisters used low-code to implement RPA across 11 accounting transactional processes, moving from 100% manual work to 97% automated within six weeks. They used Mendix to build a data-structuring application that extracts, parses and cleans the data. 2 Sisters was able to reduce their customer invoice verification process from 65% of invoices needing manual data verification to only 8%. Manual data entry was nearly eliminated, save for a few outliers identified by the bots, and employees have more time to analyze the data and costs. Low-code enables both technical and non-technical users to play an active role in implementing and maintaining RPA initiatives, taking the burden off of the IT team, operating securely within their infrastructure and parameters, and reducing the need for additional developers. Avertra empowered their client’s citizen developers to make workflow iterations in the Mendix platform based on data results and their internal business knowledge. With the assistance of Mendix partner AuraQ, 2 Sisters built 300 unique customer remittance templates in 3 months and over 3,000 have been created to date (and they’re still going). The beauty of low-code platforms is that applications can be easily adjusted as the business evolves, RPA technology improves, and new automation opportunities are identified, enabling companies to be more agile and competitive. Avertra’s clients have used data insights to produce new and revised resolution paths addressing outlying issues not caught by the RPA framework and 2 Sisters is now analyzing their data to identify their next digital transformation target. Their investments in RPA implementation and low-code development have quickly paid off and will continue to return dividends in the months and years to come. Concluding Thoughts: Low-Code Development is the simpler way to adjust and improve RPA as per the business demands. With the entry of IoT powered by high-speed 5G, low-code programing is touted to be the tool to speedy up RPA innovations. AI has become the most important trend in the low-code RPA market thus making implementation of RPA with low-code quick and agile. READ MORE: THREE SMART WAYS TO USE LOW-CODE DEVELOPMENT PLATFORMS

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Basics of Artificial Intelligence and Machine Learning

Article | April 22, 2020

Lately, we all often come across two very hot buzzwords — Artificial Intelligence (AI) and Machine Learning (ML). Perhaps the impact of artificial intelligence and machine learning on today’s business world is more than our daily lives. According to a Bloomberg report, around $300 million were invested in 2014 to promote AI-powered startups. It was 300% more than the previous year’s investment in venture capital. It’s hard to deny the fact that artificial intelligence and machine learning are all around us. Whether it is about protecting confidential information at work or just playing your favourite games on PS5, AI and ML are there. Researchers, scientists, computer engineers, and analysts are working hard together to pass on human-like intelligence in machines so that they can think and act according to real-life scenarios. Businesses have changed their approach to AI keeping enterprise adoption in mind rather than treating it as just a research topic. Tech giants such as Google, Facebook, Microsoft have already invested billions in Artificial Intelligence and Machine Learning and already have started to reshape the customer experience. But the AI and ML incorporation we see today is just the tip of an iceberg. In the coming years, you will see them take over products and services one after another. What Is Artificial Intelligence and Machine Learning? It is nowadays common to see several companies marketing themselves as AI-powered startups even though their operations don’t really revolve around AI. To understand this type of gimmicky marketing, it is essential to first understand what Artificial Intelligence and Machine Learning are. Let’s be clear in the beginning about one fact — AI and ML are not the same things. If you think they are, kill this perception before it makes things very confusing. Both these terms crop up especially when the discussion is about the use of Artificial Intelligence in marketing, the use of Machine Learning in marketing, analytics, Big Data, and the modern-day tech that is transforming the world. To ease down the learning, here’s the best answer: Artificial Intelligence is a science used to develop systems that can mimic decision-making and behaviour like humans. In simple words, the main application of Artificial Intelligence is to make intelligent machines. Machine Learning is the subset of artificial intelligence that uses data to perform tasks. It involves designing and applying the data models or algorithms that can learn from their past experiences. There’s a subset of Machine Learning, too — Deep Learning. It counts on multilayered neural networks to perform tasks. Early Days of Artificial Intelligence The early mentions of AI trace back to Greek mythologies that have stories of a mechanical man that could mimic our own behaviour. Plus, the early computers were termed as “logical machines'' in Europe. These machines could solve arithmetic operations and even store memory. Scientists, fundamentally, were inspired by them to create mechanical brains. Over time, technology got more and more modern. And, our understanding of how the human mind works improved. Both these factors lead to the current AI revolution. Today, the use of AI is more focused on mimicking the decision-making process of humans rather than performing complex calculations. The prime motive of this is to allow machines to think and act more like humans. AI-powered machines that are designed to act intelligently come into two basic groups — General AI and Applied AI. General AIs are relatively less common and can theoretically handle any task. The most exciting improvements in the field of AI are happening in this specific area. In fact, it’s generalized AI that led to the rise of Machine Learning. On the other hand, applied AIs are designed to perform relatively smaller tasks like smartly trading shares and stocks, or guiding an autonomous vehicle to its destination, etc. The Rise of Machine Learning As mentioned earlier, Machine Learning is a subset of AI and can also be treated as the current state-of-the-art. It came into reality primarily because of the two major breakthroughs — the rise of the internet and human realization. In 1959, an American pioneer in the field of computer gaming and AI, Arthur Samual, realized that it can be possible to teach machines how to learn to perform tasks themselves rather than us telling them how to. As long as the emergence of the internet is concerned, that helped scientists with tons of digital information that could be analysed for the betterment of AI and eventually, ML. After these innovations, it was more efficient for scientists and engineers to program machines in a way that they learn to think like humans and then connect them to the internet so that they have all the needed information. Vertical AI And Horizontal AI No matter what kind of AI research it is, knowledge engineering is its essential part. Machines need plenty of information to think and act like humans. Therefore, AI needs access to objects, categories, properties, and relations between them to apply knowledge engineering. AI is responsible for generating analytical reasoning power, problem-solving abilities, and common sense in machines. And, it is not an easy task! The way AI serves us can be divided into two parts — Vertical AI and Horizontal AI. Vertical AI is used to perform single jobs such as automating repetitive tasks, scheduling meetings, etc. Vertical AI bots are so accurate in performing a single job that people often mistake them for human beings. Horizontal AI, on the other hand, can handle more than one task at the same time. The best examples of horizontal AI are Alexa, Siri, and Cortana. Different Types of Machine Learning ML can be best used to fix complex tasks such as enabling self-driving cars, face recognition, credit card fraud detection, etc. It uses huge, complex algorithms that keep on iterating frequently over big data sets. The following are the 3 major Machine Learning areas: ● Reinforcement Learning ● Unsupervised Learning ● Supervised Learning Reinforcement Learning In reinforcement machine learning, algorithms allow machines and software agents to automate ideal behaviour within a particular context to improve the performance of an overall system. It is characterised by learning problems rather than learning methods. If any method can solve a problem, it can be a reinforcement learning method. This Machine Learning technique assumes that the dynamic environment is connected to a software agent such as a computer program, bot, or robot. Ultimately, it chooses a specific action in order to rapidly deliver the most efficient result. Unsupervised Learning Due to the involvement of unclustered data, unsupervised machine learning is more complex than others. With it, the machine has to learn independently without any supervision. No fixed or correct solution is provided for any problem in this technique. The algorithm has to identify the data patterns and find the solution. The recommendation engines we see on several eCommerce websites and Facebook friend requests suggestions are the best examples of this sort of Machine Learning. Supervised Learning Training datasets are used in supervised learning. The algorithms are created in such a way that they can analyse the data patterns and develop an inferred function. The produced correct solution is then used to map new examples. The best example of supervised machine learning is credit card fraud detection. Final Words Artificial Intelligence and Machine Learning never fall short to surprise us with their exciting innovations. Their impact has reached all the industries including eCommerce, customer service, finance, education, healthcare, pharma, infrastructure security, and whatnot. Needless to say, all these industries are very keen on reaping all the benefits of Artificial Intelligence and Machine Learning. The human-like AI was an inevitable thing as most technologists thought. Today, we are indeed closer to this goal than ever. This exciting journey in the past couple of years is the result of how we predict AL and ML works. FAQs Why is AI Marketing important? With AI marketing, businesses and marketers can analyse and consolidate a large amount of data from emails, social media, and other platforms faster. The achieved insights can be used to improve campaign performance and eventually boost the returns on investment in a relatively lesser time. AI marketing is the best and the most efficient way to eliminate the risks of human errors while optimizing and streamlining the campaigns more effectively. The following benefits of AI marketing justify the attention it has received all over the world. ● A better understanding of your consumers ● Optimization of digital advertising campaigns ● Offer comprehensive customer profiles ● Allow real-time interactions with consumers ● Refined content delivery ● Reduced marketing costs ● Improved ROI Is artificial intelligence and machine learning the same? The straight answer to this question is NO. They are not the same thing. AI allows machines to learn human behaviour while ML is the subset of AI that teaches machines to learn on their own with the help of past data. Does AI need machine learning? Fundamentally, ML is not required for AI as AI systems do not need to be pre-programmed. Instead of such software agents, they get help from algorithms that can use their own intelligence to solve queries. These can be Machine Learning algorithms such as Deep Learning neural networks and Reinforcement Learning algorithms. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is AI Marketing important?", "acceptedAnswer": { "@type": "Answer", "text": "With AI marketing , businesses and marketers can analyse and consolidate a large amount of data from emails, social media, and other platforms faster. The achieved insights can be used to improve campaign performance and eventually boost the returns on investment in a relatively lesser time. AI marketing is the best and the most efficient way to eliminate the risks of human errors while optimizing and streamlining the campaigns more effectively. The following benefits of AI marketing justify the attention it has received all over the world. A better understanding of your consumers Optimization of digital advertising campaigns Offer comprehensive customer profiles Allow real-time interactions with consumers Refined content delivery Reduced marketing costs Improved ROI" } },{ "@type": "Question", "name": "Is artificial intelligence and machine learning the same?", "acceptedAnswer": { "@type": "Answer", "text": "The straight answer to this question is NO. They are not the same thing. AI allows machines to learn human behaviour while ML is the subset of AI that teaches machines to learn on their own with the help of past data." } },{ "@type": "Question", "name": "Does AI need machine learning?", "acceptedAnswer": { "@type": "Answer", "text": "Fundamentally, ML is not required for AI as AI systems do not need to be pre-programmed. 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Global Health Crisis Likely to Prompt Growth in AI Sector

Article | April 22, 2020

In the wake of the coronavirus crisis, lockdowns and social distancing are accelerating the adoption of e-commerce, online learning, remote work, and online entertainment. And since online platforms and marketplaces generate so much data and rely so heavily on artificial intelligence (AI), their growth will only expand the AI gold rush. Already, global e-commerce sales have grown more than 20% per year each of the past three years. The number of students taking at least one course online has grown by almost two percentage points per year. And the percentage of Americans who sometimes telework has grown by one percentage point per year.

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URL tracking using UTM parameters: a simple explanation

Article | April 22, 2020

With Google Analytics, you can determine where the clicks to a certain website or webpage come from. However, this analysis isn’t the most precise method. For example, you can only find out whether traffic came from a specific source such as Twitter, but not whether the tweets from your own company were responsible for this linkage. In other words, you won’t be able to tell exactly which version of your call-to-action generated more clicks if both versions linked to the same URL. But there’s a solution: using UTM parameters.

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Spotlight

GlobalLogic

GlobalLogic is a full-lifecycle product development services leader that combines deep domain expertise and cross-industry experience to connect makers with markets worldwide. Using insight gained from working on innovative products and disruptive technologies, we collaborate with customers to show them how strategic research and development can become a tool for managing their future.

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