Nuclear quantum computing: It’s coming
You can’t just upload a neural network to a quantum computer and expect to act
like it’s been supercharged. The algorithms we’re currently able to run on
cutting-edge quantum systems are more like super-challenging math problems that
can still be verified using classical means. Unfortunately, the long and short
of it is usually: the more qubits you have the more errors you get. The new
research hopes to alleviate that by creating a new way to handle qubit
operations, thus allowing gate-based quantum computer systems to scale. ... It’s
likely just as safe as using lasers to create qubits out of light, maybe even
safer. But the researchers are hoping it’s the foundation for a paradigm that
will be much easier to scale than other systems. At the end of the day this is
all exciting news. It’s rare to see a peer-reviewed quantum computing
breakthrough because the field is incredibly challenging. Getting three in the
same day is a eureka moment in its own right. Of course, it could take a while
for these early experiments to pan out and turn into full-fledged quantum
computers.
Upholding digital ethics with identity and access management
One area closely aligned with ensuring digital ethics and putting in place the
right protocols to cope with our new digital processes is human resources (HR).
This part of the business has had to make notable changes over the last couple
of years, as it has started to rely more heavily on technology. During the
pandemic, HR processes such as hiring, conflict resolution, onboarding and
offboarding, and other HR-related activities could no longer follow the same
face-to-face processes they had historically; workarounds were needed. HR
managers had to interview via Zoom; they were required to handle conflict
resolutions remotely and virtually, and so much more. Coupled with this HR teams
had a new challenge: to re-invent their processes to fit the new virtual world –
while ensuring that this environment has the right digital ethics for the
organisation. This is where an identity and access management solution (IAM) can
help less technical individuals. In applying digital ethics, security of
personnel data is paramount for organisations, and IAM solutions can help make
some important security requirements of remote working easier to overcome Let’s
look at how an IAM solution can ensure the security, ethics and privacy of data.
Data Fabrics: Six Top Use Cases
Data fabrics are central data management frameworks that allow organizations to
access their data from any endpoint within a hybrid cloud environment. “They use
technologies and services to enrich the data and make it more useful for users,”
explains David Proctor, senior database manager at Everconnect, which remote
database administration and support. Data fabrics are becoming increasingly
popular as organizations turn to digital storage methods. As a company grows,
storage can become more complex as data is stored in different locations that
are inaccessible to other parts of the organization, Proctor observes. “Data
fabrics standardize … and make data accessible for everyone regardless of their
location/position in the company.” In a nutshell, data fabric technology is the
glue that binds all an organization’s data systems together into a cohesive and
uniform layer, says Sean Knapp, founder and CEO of Ascend.io, which offers an
autonomous dataflow service. It allows data engineers to build, scale, and
operate continuously optimized, Apache Spark-based pipelines with less
code.
UK Issues Fresh Proposals to Tackle Cyberthreats
The government has sought to widen the scope of the law to include Managed
Service Providers, which provide specialized online and digital services such as
security services, workplace services and IT outsourcing. "These firms are
crucial to boosting the growth of the country's 150.6-billion-pound digital
sector and have privileged access to their clients' networks and systems," the
report says. "While the regulations apply to some digital services such as
online marketplaces, online search engines and cloud computing, there has been
an increase in the use and dependence on digital services for providing
corporate needs such as information storage, data processing and running
software." Expanding NIS regulations to include MSPs will allow smaller
businesses to attain a higher level of cyber resilience, says Tim Mackey,
principal security strategist at the Synopsys Cybersecurity Research Center. The
recent Log4Shell vulnerability has illustrated that cyber resilience is a
function of how well software supply chains are understood, he says.
Quantum computing is coming. Now is the right time to start getting ready
Evidence suggests that message is already getting through: three-quarters
(74%) of senior executives believe organisations that fail to adopt quantum
computing soon will fall behind quickly, according to a recent survey by
quantum company Zapata Computing and Wakefield Research. Di Meglio believes
the secret to successfully understanding where your business might potentially
create a quantum advantage is to focus on developments that are already being
made around new instruments, tools, and methods of collaboration. He says
early preparatory work will help CIOs and their businesses to identify the
right skills, technologies and partners for quantum success in the longer
term. As part of this process, CIOs and their executive partners must look to
build collaborative teams, where all the necessary skills for quantum are
brought together and then exploited in the most useful way. "Quantum computing
is a very multidisciplinary area. Organisations, institutions and universities
really need to work to break the silos in-between these areas," he says.
The importance of securing machine-to-machine and human-to-machine interaction
The challenge associated with interconnecting and providing the right level of
access to disparate workloads introduces a host of new security and compliance
challenges. For instance, the sheer number of secrets used by
machine-to-machine and human-to-machine interactions has proliferated
dramatically due to automation, containerization, DevOps initiatives, and so
on. In this hybrid multicloud environment I explained above, there is a risk
of having separate islands of secrets. It is difficult for security teams to
see how many secrets are in use overall, who uses them, and where. And if they
can’t see them, how can they ensure they are safe? Another challenge
associated with the automation/DevOps trends is how secrets are used. It is
too often that we see secrets hardcoded in source code or configuration files,
in plain text, which are then uploaded to public repositories such as GitHub.
These secrets, and especially the ones used by privileged users such as
network or security admins, and DevOps engineers, have traditionally been
managed by Privileged Access Management (PAM) solutions.
Open source creates value, but how do you measure it?
Beyond updating our understanding of innovation outputs with open source,
there are many more innovation questions: How does open source software
contribute to innovation as an input, and can targeted research funding for
open source increase this contribution? Further research should build on
initial measurement efforts[7] to understand how and to what extent open source software accelerates
scientific research; As open source business models have evolved over
time, how have firm contributions to open source changed? Amid these business
innovations, particularly the rise of cloud-based software as a service, what
is the relative contribution to open source from these big cloud
companies?; How do we value the contributions of innovations in developer
tools to open source, including maintainers’ productivity and workload?
...; What is the economic impact—at both an organizational and
economy-wide level—of new institutional approaches to open source, including
the Open Source Program Office, pioneered in industry that is now percolating
into the public and social sectors?
Why Artificial Intelligence (AI) pilot projects fail: 4 reasons
Not every person working on an AI-based project is an AI genius. However,
successfully deploying an AI solution requires a general understanding by
every employee and end-user. Everyone within an organization should understand
the possibilities and limitations. With a lack of knowledge by all involved
comes a lack of deployment. ... Everyone from executives to employees needs
open feedback loops to allow for discussions on AI and getting people
acquainted with the solution. Those more familiar with AI then have the
opportunity to clearly communicate the level of interaction it requires to
ensure everyone has the correct information needed for maximum efficiency.
Leading the change management to implement AI for digital transformation
success is not limited to the role of a CIO or IT team. Instead, businesses as
a whole need to work together to ensure every department has the proper tools
and technologies in place to their respective standards.
Closing the agile achievement gap
The primary role of the lean portfolio management (LPM) function in
agile-minded organizations is to align agile development with business
strategy. In most cases, this function is made up of staff from the
organization’s finance, IT, and business units, and also draws on expertise
and input from human resources and IT teams. Most important, the LPM function
aligns the annual planning and funding processes with the agile methodology.
It also establishes objectives and key results and key performance indicators
(KPIs) to measure the effectiveness of the work being done and to keep
deliverables on track. These tasks are often time-consuming and involve large
change management efforts, which is why the LPM function must be implemented
early in the process. A wholesale retail company needed to define and
implement an LPM function at the outset of its agile transformation. The
company needed to modernize its workforce and IT operating model and employ a
product-centric mindset on projects.
HR and data: what gets measured gets improved
Used wisely, data has colossal power. This was recognised by the management
theorist, Peter Drucker, who reportedly said, “What gets measured gets
improved”. The trick is to understand the value of data, measure the right
things and then make sense of it all to inform decisions. And huge swathes of
the economy are now doing so – often using AI – to drive innovation and
accelerate growth. Sadly, HR is lagging. When searching the top HR degrees in
the UK, few of them focus on data as a major part of the job. Out of 39
modules, over three years, one degree course lists “managing data” just once.
And if you ask most people why they got into HR, it’s about relationships —
making people’s working lives better, supporting others and helping employees
thrive. These are all vital, but it often means data is ignored, despite it
having a huge role to play in meeting these goals. This is a fact recognised
by the CIPD. It says too few organisations use HR data and analytics to help
inform strategic decisions about how they invest in, manage and develop their
workforce to deliver on their business strategy.
Quote for the day:
"If you don't find a leader, perhaps
it is because you were meant to lead." -- Glenn Beck
No comments:
Post a Comment