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The symbiosis of RPA and machine learning

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Software bots can successfully automate routine and repetitive tasks to increase business productivity, but on their own, are unable to provide depth or insight into what tasks are actually being performed. Using the latest in machine learning, robotic process automation is breathing new life into bot capabilities and opening up new doors for enhanced business productivity. Across the enterprise, robotic process automation (RPA) is increasingly handling routine and time-consuming tasks and challenging how businesses operate. This form of digital transformation is already showing a significant return on investment for early adopters, and the switch from legacy business systems to the integration of RPA technology allows enterprises to become more competitive, efficient and flexible. In the past, RPA tools were successful at executing specifically defined tasks, but limited in the sense that they could not adjust to changing conditions or learn from experience.


How to Implement a Ticket Triaging System with AI

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Customer queries are the bane of most customer support teams, not because they don't like dealing with them, but because they don't have a proper process in place that lets them handle excessive ticket volumes easily and effectively. When a support ticket drops into a queue, or an agent receives an email with a customer issue, the ticket or email might pass through three different agents before finally landing in the correct hands to deal with the issue – leading to bottlenecks and bad customer experiences. Bugs, forgotten passwords, system errors, integration queries… There are so many different issues that agents have to deal with, so that the customer remains happy and the company retains them. And while customer support endeavors to respond to queries as quickly as possible, it's difficult when faced with huge volumes of tickets. On top of that, more and more customers expect immediate responses – 64% of consumers and 80% of business buyers said they expect companies to respond to and interact with them in real time.


Why Chatbots Should Be Part Of Your Crisis Communications Plan

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Back in 1966, the first chatbot, Eliza, was created by Joseph Weizenbaum at MIT. Eliza operated very simply and ran scripts imitating a therapist. Users would input words to Eliza's system and Eliza would use those words to choose a prescripted response. While Eliza certainly wasn't up to handling the complex tasks of today's chatbots, the idea that people could interact meaningfully with chatbot technology kicked off nearly 60 years ago and remains a viable communication process today. In fact, on Facebook alone, there are reportedly 300,000 chatbots currently active and more than eight billion messages are exchanged via chatbots every month. I get it, you get, the business world gets it.


Adversarial Reprogramming: Exploring A New Paradigm of Neural Network Vulnerabilities

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Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake. An adversarial attacker could target autonomous vehicles by using stickers or paint to create an adversarial stop sign that the vehicle would interpret as a'yield' or other sign. A confused car on a busy day is a potential catastrophe packed in a 2000 pound metal box. So far, the majority of adversarial attacks, the attacker designed few perturbations to produce an output specific to a given input. The attacks consisted of untargeted attacks that aim to degrade the performance of a model.


Responsible AI Design Using Human-Centred Design - Express Computer

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You know for sure AI has formally gone mainstream when it is a hot topic among government circles. To give a few instances -- the Central Board of Secondary Education (CBSE) has partnered with Microsoft to enable tech skills development, while the Indian army is seeking intuitive AI assistance in its weapon systems and surveillance programmes. And more recently, the Housing Ministry has ordered AI algorithms to cut down bookkeeping corruption incidences. But the most prominent development has been the proposal of Rs.7,500-crore plan by Niti Aayog to create an institutional framework for AI in the country. And while the public sector is poised to play a crucial role in the growth of India's future, the increasing adoption of AI in the corporate world is calling for experts to create responsible AI technologies.


The Future of Call Centers Relies on AI

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Working in call centers is not exactly a job that's spotlighted as enjoyable. For years, the industry was plagued by high turnover rates, agent burnout and other disjointed problems that were highlighted each time irate customers turned to online media to detail their experiences. In recent years, companies have put more focus on improving the work conditions in call centers so that agents feel more at ease and enjoy their jobs. When agents are happy, the project that happiness on to customers. After all, answering call after call and striving to achieve excellent service every time can be a daunting task.


AI's Mark on UC, and How it's Shaping the Future of Engagement

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Over the last few years, digital transformation has had a dramatic effect on the way businesses communicate with consumers. Now that smartphones are the main way that consumers engage with an organization, the focus is shifting toward creating a better customer experience overall. In fact, according to Forrester, 72% of today's businesses state that improving customer experience is a top priority. As the percentage of consumers interacting with businesses via digital channels increases, organizations are adjusting to meet consumers where they are -- and trying to anticipate where their preferences are headed in the next three to five years. Although the outlook of retaining loyal customers may appear grim for certain organizations, the same advancements in technology that ignited this focus on digitization are now offering businesses the opportunity to elevate their customer experience to retain and satisfy customers.


Three Things to Know About Reinforcement Learning - KDnuggets

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If you are following technology news, you have likely already read about how AI programs trained with reinforcement learning beat human players in board games like Go and chess, as well as video games. As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it's the right approach for a given problem. If we simplify the concept, at its foundation, reinforcement learning is a type of machine learning that has the potential to solve toughdecision-making problems. Reinforcement learning is a type of machine learning in whicha computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment.


TUGU Insurance launches AI-enabled app for agents and policyholders

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Indonesian insurance provider TUGU Insurance has launched the "tdrive" mobile app to deliver a seamless brand experience to its agents and customers. Developed by Singapore-based insurtech company Zensung, the app leverages artificial intelligence and Internet of Things to prevent and reduce fraudulent claims. The app is currently available for TUGU Insurance's auto insurance products, but plans to expand it to other verticals such as travel, medical and home insurance are in the pipeline. While enabling agents and policyholders to buy insurance and submit their claims digitally, the app also aims to help them understand potential risks. According to a press release, tdrive includes functions to promote safe driving and drivers safety as well as carbon footprint awareness for drivers and organisations.


Augmenting the Modern Workforce with Computer Vision

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Computer vision is the ability to extract meaning and intent out of visual elements, such as faces, objects, scenes and activities. Our company, Deepomatic, a computer vision company founded in Paris, recently launched in the North American market. Our proprietary technologies, Deepomatic Studio and Deepomatic Run, provide companies with the tools – both in the form of software and managed services – to build, operate and deploy their own enterprise-level artificial intelligence applications. In the European market, we work with global organizations, including Airbus, Belron, and the Compass Group, on a number of use cases, from automated checkout to smart CCTV. In the North American market, we are focused on enabling the augmented worker to achieve a more seamless workflow through computer vision technology in industries including insurance, telecommunications and quick serve restaurants (QSR).