Virtualization Basics: Windows on Macs

January 2, 2019 | 63 views

A common reason for running the Windows operating system (OS) on a Mac computer is to bypass compatibility issues. Virtualization is the only way to efficiently install OS-specific software on any machine, so let’s go over some of the ways this solution creates synergy between the two platforms.

Spotlight

MOHARA

MOHARA is a global product, technology and venture development specialist who partner with startups and corporates. New and existing businesses make the same innovation mistakes every day - technology is the end game, not the starting point. Without laying the right foundations to truly innovate, businesses expose themselves to problems like poor product-market fit, little return on investment and negative perceptions of innovation.

OTHER ARTICLES
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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AI TECH

Language Models: Emerging Types and Why They Matter

Article | July 20, 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

MOHARA

MOHARA is a global product, technology and venture development specialist who partner with startups and corporates. New and existing businesses make the same innovation mistakes every day - technology is the end game, not the starting point. Without laying the right foundations to truly innovate, businesses expose themselves to problems like poor product-market fit, little return on investment and negative perceptions of innovation.

Related News

ISB, Microsoft to set up artificial intelligence digital Lab

The Financial Express | August 28, 2019

Artificial Intelligence (AI) spans every major industry and is likely to dramatically transform industries. With better algorithms and large amounts of data, AI has the potential to perhaps outperform human decision-making. Performance differentials between firms on account of their proficiency with AI will also likely intensify over the coming years and will shake up most industries. “AI is a gamechanger to drive new business models and transform today’s businesses and workplaces,” remarks Anant Maheshwari, president, Microsoft India. Taking a cue, the Indian School of Business (ISB) and Microsoft India have inked a new partnership to take forward their shared vision for an AI-empowered India. Through the creation of the AI Digital Lab, the two organisations will collaborate in research which will use AI and Machine Learning (ML) to study issues that are relevant for business and public policy. In addition, the partnership will also introduce a new executive programme titled “Leading Business Transformation in the Age of AI” in October 2019 which will equip business leaders with tools and strategies to transform their respective organisations to AI-driven organisations.

Read More

NVIDIA Partners with VMware, Brings AI-Boosting GPUs

Market Realist | August 27, 2019

Chipmaker NVIDIA (NVDA) is teaming up with VMware (VMW) and Amazon (AMZN) to speed up artificial intelligence (or AI) tasks. On Monday, NVIDIA announced that it would release accelerated GPU technology for VMware Cloud on Amazon’s cloud division, AWS (Amazon Web Services). The new virtual GPU technology would help the joint customers of NVIDIA and VMware streamline their workflows. However, the companies have not disclosed the release date of the software. NVIDIA to release new GPU technology. The company’s new software would be called vComputeServer, which would support the AI needs of large businesses using AWS. This technology would allow customers to move VMware vSphere-based applications to the cloud easily. Customers would also access high-performance computing, machine learning, data analytics, and video processing applications.

Read More

Google Search Console API Removes App Indexing Features

SE Roundtable | August 27, 2019

This should come as no surprise, but Google has removed the app indexing features within the Search Console API. Google announced this on its blog the other day saying it "no longer support these Android app search appearance types." Google removed app indexing from the user interface back in February - so again, removing it from the API makes sense. Also, all those complaints about the indexed counts not showing up in the API. Google made sure to mention that as well, saying "Additionally, for the Sitemaps API, we're no longer populating data on indexing status of submitted sitemap files in the "Indexed" field." Does this mean it is gone forever? Specifically with the indexed count, Google may bring that back. John Mueller from Google said on Twitter "I'm still hopeful we can get the sitemaps indexed counts back."

Read More

ISB, Microsoft to set up artificial intelligence digital Lab

The Financial Express | August 28, 2019

Artificial Intelligence (AI) spans every major industry and is likely to dramatically transform industries. With better algorithms and large amounts of data, AI has the potential to perhaps outperform human decision-making. Performance differentials between firms on account of their proficiency with AI will also likely intensify over the coming years and will shake up most industries. “AI is a gamechanger to drive new business models and transform today’s businesses and workplaces,” remarks Anant Maheshwari, president, Microsoft India. Taking a cue, the Indian School of Business (ISB) and Microsoft India have inked a new partnership to take forward their shared vision for an AI-empowered India. Through the creation of the AI Digital Lab, the two organisations will collaborate in research which will use AI and Machine Learning (ML) to study issues that are relevant for business and public policy. In addition, the partnership will also introduce a new executive programme titled “Leading Business Transformation in the Age of AI” in October 2019 which will equip business leaders with tools and strategies to transform their respective organisations to AI-driven organisations.

Read More

NVIDIA Partners with VMware, Brings AI-Boosting GPUs

Market Realist | August 27, 2019

Chipmaker NVIDIA (NVDA) is teaming up with VMware (VMW) and Amazon (AMZN) to speed up artificial intelligence (or AI) tasks. On Monday, NVIDIA announced that it would release accelerated GPU technology for VMware Cloud on Amazon’s cloud division, AWS (Amazon Web Services). The new virtual GPU technology would help the joint customers of NVIDIA and VMware streamline their workflows. However, the companies have not disclosed the release date of the software. NVIDIA to release new GPU technology. The company’s new software would be called vComputeServer, which would support the AI needs of large businesses using AWS. This technology would allow customers to move VMware vSphere-based applications to the cloud easily. Customers would also access high-performance computing, machine learning, data analytics, and video processing applications.

Read More

Google Search Console API Removes App Indexing Features

SE Roundtable | August 27, 2019

This should come as no surprise, but Google has removed the app indexing features within the Search Console API. Google announced this on its blog the other day saying it "no longer support these Android app search appearance types." Google removed app indexing from the user interface back in February - so again, removing it from the API makes sense. Also, all those complaints about the indexed counts not showing up in the API. Google made sure to mention that as well, saying "Additionally, for the Sitemaps API, we're no longer populating data on indexing status of submitted sitemap files in the "Indexed" field." Does this mean it is gone forever? Specifically with the indexed count, Google may bring that back. John Mueller from Google said on Twitter "I'm still hopeful we can get the sitemaps indexed counts back."

Read More

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