Six Trends in IoT and Edge Computing to Track in 2019

DINESH CHANDRASEKHAR | January 14, 2019 | 72 views

It is that time of the year – to call out predictions and trends for the year. The two hot areas that are enabling digital transformation across all industry verticals are IoT and edge computing. Let us look at what to expect in 2019 and beyond for IoT. In this post, I am going to focus only on the B2B IoT space and not on the consumer IoT side.

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Orbus Software is an independent software vendor and a leading global provider of software solutions for Enterprise Architecture, Business Process Analysis, Application Portfolio Management, and IT Governance, and is the sole developer and distributor of the iServer Business and IT Transformation Suite.

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SOFTWARE

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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AI TECH

The Evolution of Quantum Computing and What its Future Beholds

Article | July 20, 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 14, 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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Orbus Software

Orbus Software is an independent software vendor and a leading global provider of software solutions for Enterprise Architecture, Business Process Analysis, Application Portfolio Management, and IT Governance, and is the sole developer and distributor of the iServer Business and IT Transformation Suite.

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Cloudera & Hortonworks Close Merger, Aim to Build Cloud Analytics Muscle

Light Reading | January 03, 2019

Enterprise data analytics provider Cloudera has completed its merger with competitor Hortonworks, the company said Thursday. The new Cloudera will focus on delivering analytics from any cloud, from the edge to core, using artificial intelligence and 100% open source. The two companies announced their merger in October. Once fierce rivals, their bigger problem now is competition from industry giants such as Amazon Web Services Inc. and Microsoft Corp. (Nasdaq: MSFT), as well as specialized startups such as Databricks and Snowflake. Cloudera and Hortonworks have complementary strengths -- Cloudera with machine learning and AI, and Hortonworks with edge and IoT. Indeed, the short statement from Cloudera Inc. announcing the merger Wednesday twice uses the phrase "from the edge to AI" (which doesn't actually make sense but yeah, whatever, two buzzwords in five words so I guess that's a marketing win). Though Cloudera is billing the deal as a merger of equals; Cloudera stockholders get about 60% of the new company, with Hortonworks stockholders having the remainder. Tom Reilly, CEO of the old Cloudera, will continue as boss of the combined company. And the new Cloudera has about $720 million in revenue with more than 2,500 customers. Cloudera touts its unique support for all four of the major cloud providers -- Microsoft, Amazon, Google and IBM. And it also supports on-premises data analytics, providing hybrid cloud support that its bigger public cloud competitors lack, executives say.

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Cloudera & Hortonworks Close Merger, Aim to Build Cloud Analytics Muscle

Light Reading | January 03, 2019

Enterprise data analytics provider Cloudera has completed its merger with competitor Hortonworks, the company said Thursday. The new Cloudera will focus on delivering analytics from any cloud, from the edge to core, using artificial intelligence and 100% open source. The two companies announced their merger in October. Once fierce rivals, their bigger problem now is competition from industry giants such as Amazon Web Services Inc. and Microsoft Corp. (Nasdaq: MSFT), as well as specialized startups such as Databricks and Snowflake. Cloudera and Hortonworks have complementary strengths -- Cloudera with machine learning and AI, and Hortonworks with edge and IoT. Indeed, the short statement from Cloudera Inc. announcing the merger Wednesday twice uses the phrase "from the edge to AI" (which doesn't actually make sense but yeah, whatever, two buzzwords in five words so I guess that's a marketing win). Though Cloudera is billing the deal as a merger of equals; Cloudera stockholders get about 60% of the new company, with Hortonworks stockholders having the remainder. Tom Reilly, CEO of the old Cloudera, will continue as boss of the combined company. And the new Cloudera has about $720 million in revenue with more than 2,500 customers. Cloudera touts its unique support for all four of the major cloud providers -- Microsoft, Amazon, Google and IBM. And it also supports on-premises data analytics, providing hybrid cloud support that its bigger public cloud competitors lack, executives say.

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