Tips for using chatbots to improve your marketing

September 25, 2019 | 88 views

Chatbots are both the wave of the present and the future. Research estimates that by next year, a whopping 80% of businesses will utilize this kind of AI technology, primarily as a real-time chat tool with consumers via instant messaging. How can you best refine your chatbot technology so it pleases your customers and provides them with the info and convenience they need to buy your products over and over again? Try these tips. Penn State scientists have done some extensive and fascinating research on how consumers react to chatbots. Surprisingly, study authors Eun Go and S. Shyam Sundar found that the more personable a chatbot is, the less customers like it.

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Kavaliro

Founded in 2010, Kavaliro has grown to become a leader in professional services and workforce solutions. We provide clients, contractors, and employees opportunities to achieve success. Through top technologies, agility, and fluid communication. Using best practices and optimal strategies, Kavaliro provides employers with solutions by delivering the most tailored solutions to ensure the ongoing success of all types of businesses.

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FUTURE TECH

AI's Impact on Improving Customer Experience

Article | July 14, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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SOFTWARE

The Evolution of Quantum Computing and What its Future Beholds

Article | July 14, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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FUTURE TECH

Language Models: Emerging Types and Why They Matter

Article | July 26, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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SOFTWARE

Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

Kavaliro

Founded in 2010, Kavaliro has grown to become a leader in professional services and workforce solutions. We provide clients, contractors, and employees opportunities to achieve success. Through top technologies, agility, and fluid communication. Using best practices and optimal strategies, Kavaliro provides employers with solutions by delivering the most tailored solutions to ensure the ongoing success of all types of businesses.

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Tableau Expands Data Capabilities With AI-Driven Insights

Demand Gen Report | September 26, 2019

Tableau Software has released new data capabilities with the addition of Explain Data, which aims to enhance statistical analysis and help users gain AI-driven insights from their data. The company said that the new capability uses statistical methods to determine potential explanations for what could be influencing a data point, while providing further understanding of that explanation with interactive visualizations. With Explain Data, we're bringing the power of AI-driven analysis to everyone and making sophisticated statistical analysis more accessible so that, regardless of expertise, anyone can quickly and confidently uncover the 'Why?' behind their data," said Francois Ajenstat, Chief Product Officer at Tableau. "Explain Data will empower people to focus on the insights that matter and accelerate the time to action and business impact."

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AT&T turns up AI for drones, load balancing, 5G build out

Fierce Telecom | September 26, 2019

As part of its close embrace network artificial intelligence (AI), AT&T is testing drones in New Jersey to inspect cell sites, and researching the use AI to define network policies. AT&T's Mazin Gilbert outlined his company's blueprint for artificial intelligence and machine learning on Tuesday during a keynote address at the TM Forum Action Week conference. While AT&T has successfully embraced virtualization of its network using software-defined network (SDN) and network function virtualization (NFV)—it's on track to reach its goal of 75% virtualization of the core network by next year—Gilbert said that's not enough in a world of 5G and IoT and increasing consumption of bandwidth. The network can't be just software," said Gilbert, vice president, advanced technology systems, AT&T Labs. "The network needs to be autonomous and pretty much zero touch. It needs intelligence to know when it repairs itself, when it secures itself. The network needs to be contextual, personalized."

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Mednet Releases New Expanded API for All-in-One eClinical Platform

PR Newswire | September 26, 2019

Mednet, a healthcare technology company, announced it has released an enhanced API for its eClinical software solution, iMednet. The new API delivers increased functionality of the iMednet platform and simplifies the process for data extraction, increasing the speed in which clinical research teams can share and export study data into other software or platforms. The enhanced API was recently released in beta and is available to all customers. Enhanced data integration capabilities represent a key component in Mednet's near-term development plan to fully optimize its comprehensive, all-in-one eClinical solution to support the future of clinical trials. As the clinical trial industry evolves, study designs are becoming more complex. There is also a growing number of new data sources, presenting both new opportunities and requirements and demanding clinical research to adopt new processes for collection, storage, analysis and reporting of data. Mednet is in the midst of a multi-release plan to address the evolving needs and opportunities for clinical trials and expanding its data extraction capabilities represents a critical step.

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Tableau Expands Data Capabilities With AI-Driven Insights

Demand Gen Report | September 26, 2019

Tableau Software has released new data capabilities with the addition of Explain Data, which aims to enhance statistical analysis and help users gain AI-driven insights from their data. The company said that the new capability uses statistical methods to determine potential explanations for what could be influencing a data point, while providing further understanding of that explanation with interactive visualizations. With Explain Data, we're bringing the power of AI-driven analysis to everyone and making sophisticated statistical analysis more accessible so that, regardless of expertise, anyone can quickly and confidently uncover the 'Why?' behind their data," said Francois Ajenstat, Chief Product Officer at Tableau. "Explain Data will empower people to focus on the insights that matter and accelerate the time to action and business impact."

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AT&T turns up AI for drones, load balancing, 5G build out

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As part of its close embrace network artificial intelligence (AI), AT&T is testing drones in New Jersey to inspect cell sites, and researching the use AI to define network policies. AT&T's Mazin Gilbert outlined his company's blueprint for artificial intelligence and machine learning on Tuesday during a keynote address at the TM Forum Action Week conference. While AT&T has successfully embraced virtualization of its network using software-defined network (SDN) and network function virtualization (NFV)—it's on track to reach its goal of 75% virtualization of the core network by next year—Gilbert said that's not enough in a world of 5G and IoT and increasing consumption of bandwidth. The network can't be just software," said Gilbert, vice president, advanced technology systems, AT&T Labs. "The network needs to be autonomous and pretty much zero touch. It needs intelligence to know when it repairs itself, when it secures itself. The network needs to be contextual, personalized."

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Mednet Releases New Expanded API for All-in-One eClinical Platform

PR Newswire | September 26, 2019

Mednet, a healthcare technology company, announced it has released an enhanced API for its eClinical software solution, iMednet. The new API delivers increased functionality of the iMednet platform and simplifies the process for data extraction, increasing the speed in which clinical research teams can share and export study data into other software or platforms. The enhanced API was recently released in beta and is available to all customers. Enhanced data integration capabilities represent a key component in Mednet's near-term development plan to fully optimize its comprehensive, all-in-one eClinical solution to support the future of clinical trials. As the clinical trial industry evolves, study designs are becoming more complex. There is also a growing number of new data sources, presenting both new opportunities and requirements and demanding clinical research to adopt new processes for collection, storage, analysis and reporting of data. Mednet is in the midst of a multi-release plan to address the evolving needs and opportunities for clinical trials and expanding its data extraction capabilities represents a critical step.

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