AI: It’s Implications and Threat
Evolution of the workforce, for example, can pose a risk. AI can replace most
of the workforce, which means loss of employment for most of the labour. The
uncertainty of how exactly AI would affect the economy can also be challenging
for some. Since the world is getting smaller, AI would need to work by rules
that stand globally, rules that allow for effective interaction all over the
world. Imposition of such rules isn't at all an easy task. Regulation of AI is
tricky too; with the introduction of new technologies, the older regulatory
rules can easily be obsolete. The development of AI also allows for
malpractices, such as hacking or AI trafficking. Built-In-Bias allows for the
programmer of the AI to introduce, either intentionally or unintentionally, a
Bias. An Artificial Intelligence working with a bias or learning from biased
data would also produce biased results. This can give an arbitrary group, in
some cases, an unfair advantage over the others, although the outcome of a
biased AI being ‘unpredictable’ isn’t any less of a nuisance. “It’s really
easy to give AI the wrong problem to solve.” Which, she says, can be quite
destructive.
Bankers say artificial intelligence will separate winners and losers
Banks recognise the importance of investing in technology to improve customer
services, with AI’s potential to personalise customer experience seen as an
attractive prospect. Some 77% of respondents said AI will separate the winners
and the losers. Digital advisers and voice-assisted engagement channels will
be the destination for a large part of AI investments, said the
report. Beyond AI, there has been an increased acceptance that new
technology will drive banking over the next five years, with 66% of banking
executives agreeing, compared with 42% in the same survey in 2019. Almost
half (45%) of the 300 senior executives questioned globally are planning to
transform into digital ecosystems to improve customer experience and introduce
new revenue streams. This will see a shift in the way banks develop
software with an increase in use of DevOps. Most respondents (84%) agreed
that DevOps will drive transformation in core banking. The report also
said that the Covid-19 pandemic has accelerated digital transformation at
banks.
Measuring AI Performance On Mobile Devices And Why It Matters
The key here is to better understand what a specific benchmark metric is
actually testing. Does the test represent as close to real-world workloads as
possible? An ideal benchmark uses actual applications that a consumer would
use, but short of that it could employ the same core software components of
popular apps instead, to represent realistic performance expectations. And in
this case, that means we need to understand what NNs these benchmark tools are
testing against, and what mathematical precision and AI algorithms are being
used to process workloads on them. What makes for a good AI benchmark for
mobile devices is a relatively deep, nuanced subject, but the long and short
of it is virtually all mobile NPUs (Neural Processing Units, or dedicated AI
engines) employ either INT8 or quantized mathematical precision, or FP16
floating point precision, to make use of popular NNs like ResNet-34 or
Google’s DeepLab-v3 for image classification and segmentation in apps, for
example. Is that a cat or a dog? What sort of color balance should be applied
in this camera shot? These are the kinds of questions the AI is trying to
infer answers for from the phone’s environment, in an imaging workload example
at least, though there are many others.
Blazor RenderTree Explained
Blazor is a new single-page application (SPA) framework from Microsoft. Unlike
other SPA frameworks such as Angular or React, Blazor relies on the .NET
framework in favor of JavaScript. Blazor supports many of the same features
found in these frameworks including a robust component development model. The
departure from JavaScript, especially when exiting a jQuery world, is a shift in
thinking around how components are updated in the browser. Blazor’s component
model was built for efficiency and relies on a powerful abstraction layer to
maximize performance and ease-of-use. Abstracting the Document Object Model
(DOM) sound intimidating and complex, however with modern web application it has
become the normal. The primary reason is that updating what has rendered in the
browser is a computationally intensive task and DOM abstractions are used to
intermediate between the application and browser to reduce how much of the
screen is re-rendered.
Email is biggest security risk
With organisations spending big on cloud, and not so much on keeping older
on-premises kit up to date, there has been an increase in obsolete and
unpatched network devices that contain software vulnerabilities, which NTT
said introduces risk and exposes organisations to information security
threats. The remarks were made in a report from the global giant that was
based on more than 1,000 clients, covering over 800,000 network devices in
five regions, across multiple industry sectors. In the report, NTT found 46.3%
of organisations' network assets were ageing or obsolete. It said obsolete
devices had, on average, twice as many vulnerabilities per device when
compared with ageing and current ones, at 42.2 security advisories per device.
It said such risk is intensified when a business does not patch a device or
revisit the operating system version for the duration of its lifetime, which
NTT said many do not do. "In this 'new normal' many businesses will need, if
not be forced, to review their network and security architecture strategies,
operating, and support models to better manage operational risk," NTT
executive vice president of intelligent infrastructure Rob Lopez said, in
light of more people working remotely due to the COVID-19 pandemic.
CSO's Guide to 'Employee-First' Security Operations During COVID-19 & Beyond
Schedule regular (if not daily) meetings to ensure issues are being addressed
and strategies are being changing as needed in real-time. This team should have
full business representation, including executive staff, regional leaders, and
security operations representatives. Although many businesses may currently have
these teams in place, it's important that proactive planning remains a top
priority even as offices begin to reopen. This team, and the lessons they
provide, will be crucial for any future pandemics or crises that pose a threat
to business continuity, allowing employees to act faster and make informed
decisions. Due to the rise of remote work and expanded attack services, phishing
attacks have also seen a significant acceleration with employees being enticed
by fake password management, executive updates, and GoFundMe messages. To
decrease the impact of these attacks, it's important to keep employees informed
of the latest threats and how they can protect themselves or seek support if
they have become a victim. Employee education is essential, including training
on how to lock down home routers with complex passwords and leverage data loss
prevention (DLP) technologies.
Managing the Security of Cloud-Native Architectures
It’s a DevOps world—everyone’s trying to move faster. Productivity increases,
but so does the security risk. Yesterday, the best practice was to
re-architect the code before it went into production on the standard
operations platform chosen by IT. Today in the interest of speed,
organizations are deploying applications developed on containers straight into
production, managing them with Kubernetes and running them somewhere in the
cloud (potentially still on-premises, but frequently on a public cloud
service). In this model, both the developers and the operations team need to
become more security-aware, and security must be fully integrated into the
software life cycle. Many of our customers are experimenting with technologies
from different vendors, running on multiple cloud providers, and even
deploying applications across multiple platforms at once. This keeps your
options open for either cost optimization or to utilize the stack that best
fits a given need, and avoids vendor lock-in, but can be difficult on
developers, particularly at the serverless level where standards are still
emerging.
AI has a big data problem. Here's how to fix it
With data from a pre-COVID environment not matching the real world anymore,
supervised algorithms are running out of examples to base their predictions on.
And to make matters worse, AI systems don't flag their uncertainties to their
human operator. "The AI won't tell you when it actually isn't confident
about the accuracy of its prediction and needs a human to come in," said Barber.
"There are many uncertainties in these systems. So it is important that the AI
can alert the human when it is not confident about its decision." This is what
Barber described as an "AI co-worker situation", where humans and machines would
interact to make sure that gaps aren't left unfilled. In fact, it is a method
within artificial intelligence that is slowly emerging as a particularly
efficient one. Dubbed "active learning", it consists of establishing a
teacher-learner relationship between AI systems and human operators. Instead of
feeding the algorithm a huge labeled dataset, and letting it draw conclusions –
often in a less-than-transparent way – active learning lets the AI system do the
bulk of data labeling on its own, and crucially, ask questions when
it has a doubt.
Work from Home: Changing Enterprise Risk and Careers
Interestingly, about three-fourths of those organizations surveyed said they
have more than 76% of their employees working from up — that’s up from 25% in
2019. Still, a third of those surveyed said their organization is ill-prepared
or not prepared to support remote working. Yet, 75% of businesses transitioned
to remote working within 15 days. “Surprisingly, less than a third expressed
cost or budget problems, demonstrating the urgency to support their business.
Additionally, more than half (54%) expressed that COVID-19 has accelerated
migration of users’ workflows and applications to the cloud,” stated the
report. How are survey respondents securing their staff who work from home?
The survey found the most common to be endpoint security, firewalls, virtual
private networks and multi-factor authentication. The 2020 Remote
Work-From-Home Cybersecurity Report is based on a survey of 413 security
decision makers, conducted in May of this year, within multiple industries,
including financial services, healthcare, manufacturing, high-tech, government
and education.
Q&A on the Book Learning to Scale
If we go back to its origins, "lean" refers to the study of Toyota management
practices outside of Toyota. It was started by a MIT research project in 1985,
which compared the Japanese and occidental approaches to automotive
manufacturing. At that time Toyota was already showing exceptional performance,
and it ended up becoming the world's largest manufacturer 20 years later. What
Toyota understood early on is that the western approach to industrialization,
with a strong focus on processes and management by objectives, leads to employee
disengagement and poor performance. An industrial operation is a very complex
system, involving thousands of people for a single car, and subject to tens of
thousands of daily problems. You need a skilled and creative workforce to be
able to adapt to the resulting complexity. Toyota managers realized that
most of these problems were the result of people's misconceptions about their
work. They developed the Toyota Production System, which we now call the
Thinking People System, as a comprehensive approach to developing team members
by helping them study these problems in depth ...
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