Spin-based silicon quantum electronic circuits offer a scalable platform for quantum computation. They combine the manufacturability of semiconductor devices with the long coherence times afforded by spins in silicon. Advancing from current few-qubit devices to silicon quantum processors with upward of a million qubits, as required for fault-tolerant operation, presents several unique challenges. One of the most demanding is the ability to deliver microwave signals for large-scale qubit control. ... Completely reimagine the silicon chip structure is the solution to the problem. Scientists started by removing the wire next to the qubits. They then applied a novel way to deliver microwave-frequency magnetic control fields across the entire system. This approach could provide control fields to up to four million qubits. Scientists added their newly developed component called a crystal prism called a dielectric resonator. When microwaves are directed into the resonator, it focuses the wavelength of the microwaves down to a much smaller size.
One of the primary challenges is that leadership can often be a barrier when an organization is seeking to become more agile. According to last year’s Business Agility Report from Scrum Alliance and the Business Agility Institute, this is the most prevalent challenge that agile coaches report. Some reasons for this include a lack of buy-in and support, resistance to change, having a mindset that’s not conducive to agility, a lack of alignment between agile teams and leadership, lack of understanding, and a deeply rooted organizational legacy regarding management styles. Overcoming legacy structures, cultures, and mindsets can be difficult. Some coaches have reported that leaders view agile as being “for their staff” and not for them. Additionally, leaders may have competing priorities – such as retaining control – which can hinder organization-wide adoption of agile methodologies. Any leader considering an agile transformation must understand that in order to succeed, full executive buy-in is needed and that they too will need to change their way of working and thinking.
API providers use rate limit design patterns to enforce API usage limits on their clients. It allows API providers to offer reliable service to the clients. This also allows a client to control its API consumption. Rate limiting, being a cross-cutting concern, is often implemented at the API Gateway fronting the microservices. There are a number of API Gateway solutions that offer rate-limiting features. In many cases, the custom requirements expected of the API Gateway necessitate developers to build their own API Gateway. The Spring Cloud Gateway project provides a library for developers to build an API Gateway to meet any specific needs. In this article, we will demonstrate how to build an API Gateway using the Spring Cloud Gateway library and develop custom rate limiting solutions. A SaaS provider offers APIs to verify the credentials of a person through different factors. Any organization that utilizes the services may invoke APIs to verify credentials obtained from national ID cards, face images, thumbprints, etc. The service provider may have a number of enterprise customers that have been offered a rate limit - requests per minute, and a quota - requests per day, depending on their contracts.
Conversational agents are a dialogue system through NLP to respond to a given query in human language. It leverages advanced deep learning measures and natural language understanding to reach a point where conversational agents can transcend simple chatbot responses and make them more contextual. Conversational AI encompasses three main areas of artificial intelligence research — automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS or speech synthesis). These dialogue systems are utilised to read from the input channel and then reply with the relevant response in graphics, speech, or haptic-assisted physical gestures via the output channel. Modern conversational models often struggle when confronted with temporal relationships or disfluencies.The capability of temporal reasoning in dialogs in massive pre-trained language models like T5 and GPT-3 is still largely under-explored. The progress on improving their performance has been slow, in part, because of the lack of datasets that involve this conversational and speech phenomena.
Having a career in cybersecurity typically requires logic, discipline, curiosity and the ability to solve problems and find patterns. This is an industry that offers a wide spectrum of positions and career paths for people who are neurodivergent, particularly for roles in threat analysis, threat intelligence and threat hunting. Neurodiverse minds are usually great at finding the needle in the haystack, the small red flags and minute details that are critical for hunting down and analyzing potential threats. Other strengths include pattern recognition, thinking outside the box, attention to detail, a keen sense of focus, methodical thinking and integrity. The more diverse your teams are, the more productive, creative and successful they will be. And not only can neurodiverse talent help strengthen cybersecurity, employing different minds and perspectives can also solve communication problems and create a positive impact for both your team and your company. According to the Bureau of Labor Statistics, the demand for Information Security Analysts — one of the common career paths for cybersecurity professionals — is expected to grow 31% by 2029, much higher than the average growth rate of 4% for other occupations.
Propagating algorithms across an IIoT/IoT network to the device level is essential for an entire network to achieve and keep in real-time synchronization. However, updating IIoT/IoT devices with algorithms is problematic, especially for legacy devices and the networks supporting them. It’s essential to overcome this challenge in any IIoT/IoT network because algorithms are core to AI edge succeeding as a strategy. Across manufacturing floors globally today, there are millions of programmable logic controllers (PLCs) in use, supporting control algorithms and ladder logic. Statistical process control (SPC) logic embedded in IIoT devices provides real-time process and product data integral to quality management succeeding. IIoT is actively being adopted for machine maintenance and monitoring, given how accurate sensors are at detecting sounds, variations, and any variation in process performance of a given machine. Ultimately, the goal is to predict machine downtimes better and prolong the life of an asset.
RPA can allow businesses to reallocate their employees, removing them from repetitive tasks and engaging them in projects that support true growth, both for the company and individual. Work were human strengths such as emotional intelligence, reasoning and judgment are required typically bring greater value to the company, and, they’re also often more personally rewarding. This can raise job satisfaction and help retain employees. Further, the ability to reallocate employees can enable a business to apply their useful company knowledge to other value-adding areas, supplement talent gaps and more. Of course, there’s the attraction of being able to do one’s job more efficiently, without manual processes that can make time drag. For instance, let’s say you’re at that same investment firm and there a rapidly growing hedge fund, requiring human resources (HR) to onboard a lot of people fast. Between provisioning accounts, providing access to the right tools, sending out emails and more, there’s a lot of work involved. With a RPA bot, 20 new people could be processed at once, with the HR person monitoring progress through a window on the corner of their screen, which also notifies them if anything needs their attention.
Ultimately, organizations may not have much choice but to adopt XAI. Regulators have taken notice. The European Union's General Data Protection Regulation (GDPR) demands that decisions based on AI be explainable. Last year, the U.S. Federal Trade Commission issued stringent guidelines around how such technology should be used. Companies found to have bias embedded in their decision-making algorithms risk violating multiple federal statutes, including the Fair Credit Reporting Act, the Equal Credit Opportunity Act, and antitrust laws. "It is critical for businesses to ensure that the AI algorithms they rely on are explainable to regulators, particularly in the antitrust and consumer protection space," says Dee Bansal, a partner at Cooley LLP, which specializes in antitrust litigation. "If a company can't explain how its algorithms work [and] the contours of the data on which they rely … it risks being unable to adequately defend against claims regulators may assert that [its] algorithms are unfair, deceptive, or harm competition." It's also just a good idea, notes James Hodson, CEO of the nonprofit organization AI for Good.
The value of a machine learning algorithm is inherently related to the quality of the data used to develop it, and faulty inputs can produce thoroughly problematic outcomes. This broad concept is captured in the familiar phrase: "Garbage in, garbage out." The data used to develop a machine-learning algorithm might be skewed because individual data points reflect problematic human biases or because the overall dataset is not adequately representative. Often skewed training data reflect historical and enduring patterns of prejudice or inequality, and when they do, these faulty inputs can create biased algorithms that exacerbate injustice, Slaughter notes. She cites some high-profile examples of faulty inputs, such as Amazon's failed attempt to develop a hiring algorithm driven by machine learning, and the International Baccalaureate's and UK's A-Level exams. In all of those cases, the algorithms introduced to automate decisions kept identifying patterns of bias in the data used to train them and attempted to reproduce them. ... "
The modern business world is increasingly driven by technology. As we move to a more interconnected and complex environment, the demand for suitable technologies is increasing – this is so much so that an average enterprise pays for approximately 1,516 applications. With a shift to remote working, we’re also seeing an overwhelming imperative to migrate to the cloud, and today, application costs are estimated to make up 80 per cent of the entire IT budget. Industry analyst Gartner has even forecasted that worldwide IT spending will reach $4 trillion in 2021. The modern chief information officer (CIO) is responsible for understanding these technology costs and bringing them under control – and a key enabler of this is enterprise architecture (EA). By providing a strategic view of change, EA ensures alignment of the business and IT operations, facilitating agility, speed and the ability to make real-time decisions based on reliable and consistent data. So, what are the common challenges of spiralling technology costs and how can EA help to reduce this pressure for CIOs?
Quote for the day:
“Patience is the calm acceptance that things can happen in a different order than the one you have in mind.” -- David G. Allen