5 benefits to replacing analog components with DSP software

January 7, 2019 | 70 views

The Internet of Things is unleashing an unimaginable growth in the number of connected devices. In fact, Arm predicts that 1 trillion devices will be connected by 2035, many of which are sensor systems that are designed to sense and interact with the world around them. The world around us is analog, and analog sensors are often used to provide the sensing mechanism to the microcontroller. Analog circuits are often big, bulky and can add significant costs to a device. Using modern microcontrollers with digital signal processing (DSP) software means it is possible to convert many analog circuits into equivalent software.

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AI TECH

AI's Impact on Improving Customer Experience

Article | July 20, 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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FUTURE TECH

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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SOFTWARE

Language Models: Emerging Types and Why They Matter

Article | August 8, 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

Tellabs

Tellabs is leading the future of networking with access solutions for today, poised to deliver services for the future. Tellabs has delivered carrier-class access solutions to our service provider customers for more than two decades. We are now expanding that leadership, defining the future of networking across enterprise, government and telecom to keep people connected. Tellabs Technology is there, from the critical last mile to the flexibility of the cloud.

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SOFTWARE

Gnani.ai Launches armour365™ Voice Biometrics Software Based on Patented Tech

Gnani.ai | September 22, 2021

Gnani.ai, a frontrunner in Conversational AI and voice security domain, today announced the launch of its home-grown Voice Biometrics software. Christened armour365™, the biometric solution boasts of path-breaking features to cater to new and emerging risks in fraud prevention and information security. armour365™ Voice Biometrics works on 300 plus proprietary audio features and comes with out-of-the-box integrations to multiple contact center software providers and messaging apps. The solution is equipped with top-of-the-line features like “anti-spoof layer,” “replay attack detection” and “one enrollment” to offer unparalleled security and CX for industries such as Contact Centers, Banks, Defence, Healthcare, etc. for applications ranging from omnichannel customer authentication to secured access to sensitive devices. Voice Biometrics can be a blessing in disguise for Contact Center and Infosec leaders battling fraud and data theft through various communication channels. With a response time of less than 500 milliseconds, armour365™ has been engineered to help IT and security teams to replace error-prone and legacy authentication methods like PINs and passwords to realize the potential of Voice Biometrics being truly contactless. Ganesh Gopalan, Co-founder and CEO, Gnani.ai armour365™ can be implemented and integrated into any CRM for seamless authentication without the need for coding or a device interface. We are thrilled to launch armour365™ with industry-leading accuracy and thankful to our Engineering and Product teams to have pulled this off indigenously. We’re confident our customers will benefit immensely with this ‘low-cost and no-code solution’ built to offer reliable voice security. Ananth Nagaraj, Co-founder and CTO, Gnani.ai Gnani.ai plans to host the offering on major cloud marketplace platforms for customers and developers to take advantage of competitive pricing and ease of accessing the solutions through APIs. About Gnani.ai Gnani.ai, a Samsung Ventures funded company, is a leader in the voice-led conversational AI automation space. Gnani.ai offers no-code multi-modal and multi-channel bot automation platforms. With partners like Avaya, Nvidia, and Intel, Gnani.ai is leading the Conversational AI revolution.

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Google, Amazon and Microsoft give input to new health AI standard

pharma news | February 27, 2020

The US-based Consumer Technology Association (CTA) has developed the first ever accredited standard for use of artificial intelligence in health care, with input from tech giants such as Amazon, Microsoft, and Google. More than 50 organisations, from tech giants to startups and healthcare industry leaders, have developed the American National Standards Institute (ANSI) accredited quality mark. The standard is part of the CTA’s new initiative on AI and is the first in a series that aims to set a foundation for implementing medical and health care solutions built on the technology. One issue that the standard aims to resolve is the way that AI-related terms are used in different ways, leading to confusion, particularly in the healthcare industry.

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Cavium makes its ARM for data centers push with new servers

networkworld | June 21, 2017

The initial efforts to bring ARM-based processors in the data center were not terribly successful. Calxeda crashed and burned spectacularly after it bet on a 32-bit processor when the rest of the world had moved on to 64-bits. And HPE initially wanted to base its Project Moonshot servers on ARM but now uses Intel Xeon and AMD Opteron. That’s because the initial uses for ARM processors were low-performance applications, like basic LAMP stacks, file and print, and storage. Instead, one company has been quietly building momentum for high performance ARM processors, and it’s not Qualcomm.

Read More

SOFTWARE

Gnani.ai Launches armour365™ Voice Biometrics Software Based on Patented Tech

Gnani.ai | September 22, 2021

Gnani.ai, a frontrunner in Conversational AI and voice security domain, today announced the launch of its home-grown Voice Biometrics software. Christened armour365™, the biometric solution boasts of path-breaking features to cater to new and emerging risks in fraud prevention and information security. armour365™ Voice Biometrics works on 300 plus proprietary audio features and comes with out-of-the-box integrations to multiple contact center software providers and messaging apps. The solution is equipped with top-of-the-line features like “anti-spoof layer,” “replay attack detection” and “one enrollment” to offer unparalleled security and CX for industries such as Contact Centers, Banks, Defence, Healthcare, etc. for applications ranging from omnichannel customer authentication to secured access to sensitive devices. Voice Biometrics can be a blessing in disguise for Contact Center and Infosec leaders battling fraud and data theft through various communication channels. With a response time of less than 500 milliseconds, armour365™ has been engineered to help IT and security teams to replace error-prone and legacy authentication methods like PINs and passwords to realize the potential of Voice Biometrics being truly contactless. Ganesh Gopalan, Co-founder and CEO, Gnani.ai armour365™ can be implemented and integrated into any CRM for seamless authentication without the need for coding or a device interface. We are thrilled to launch armour365™ with industry-leading accuracy and thankful to our Engineering and Product teams to have pulled this off indigenously. We’re confident our customers will benefit immensely with this ‘low-cost and no-code solution’ built to offer reliable voice security. Ananth Nagaraj, Co-founder and CTO, Gnani.ai Gnani.ai plans to host the offering on major cloud marketplace platforms for customers and developers to take advantage of competitive pricing and ease of accessing the solutions through APIs. About Gnani.ai Gnani.ai, a Samsung Ventures funded company, is a leader in the voice-led conversational AI automation space. Gnani.ai offers no-code multi-modal and multi-channel bot automation platforms. With partners like Avaya, Nvidia, and Intel, Gnani.ai is leading the Conversational AI revolution.

Read More

Google, Amazon and Microsoft give input to new health AI standard

pharma news | February 27, 2020

The US-based Consumer Technology Association (CTA) has developed the first ever accredited standard for use of artificial intelligence in health care, with input from tech giants such as Amazon, Microsoft, and Google. More than 50 organisations, from tech giants to startups and healthcare industry leaders, have developed the American National Standards Institute (ANSI) accredited quality mark. The standard is part of the CTA’s new initiative on AI and is the first in a series that aims to set a foundation for implementing medical and health care solutions built on the technology. One issue that the standard aims to resolve is the way that AI-related terms are used in different ways, leading to confusion, particularly in the healthcare industry.

Read More

Cavium makes its ARM for data centers push with new servers

networkworld | June 21, 2017

The initial efforts to bring ARM-based processors in the data center were not terribly successful. Calxeda crashed and burned spectacularly after it bet on a 32-bit processor when the rest of the world had moved on to 64-bits. And HPE initially wanted to base its Project Moonshot servers on ARM but now uses Intel Xeon and AMD Opteron. That’s because the initial uses for ARM processors were low-performance applications, like basic LAMP stacks, file and print, and storage. Instead, one company has been quietly building momentum for high performance ARM processors, and it’s not Qualcomm.

Read More

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