Scientists removed major obstacles in making quantum computers a reality
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.
Agile strategy: 3 hard truths
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.
Custom Rate Limiting for Microservices
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.
Google Introduces Two New Datasets For Improved Conversational NLP
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.
Addressing the cybersecurity skills gap through neurodiversity
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.
Realizing IoT’s potential with AI and machine learning
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.
Understanding and applying robotic process automation
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.
It's time for AI to explain itself
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.
AI ethics in the real world: FTC commissioner shows a path toward economic justice
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. ... "
How to navigate technology costs and complexity with enterprise architecture
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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
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