The Reality Behind The AI Illusion
So far, AI has shown some impressive results in narrow application areas only,
like chess-playing computers beating world chess champions and supercomputers
beating human Jeopardy champions. However, these are computers programmed to
solve one specific problem and cannot interpret more complex and multilayered
challenges beyond the given task. This is exactly what Moravec's paradox states;
though it may be easy to get computers to beat human chess champions, it may be
difficult to give them the skills of a toddler when it comes to perception and
mobility. While AI has not reached human performance, it brings valuable
solutions to many real-world problems quickly and effectively. From enhanced
healthcare, innovations in banking and improved environmental protection to
self-driving vehicles, automated transportation, smart homes and chatbots, AI
can offer simpler and more intelligent ways of accomplishing many of our daily
tasks. But how far can AI go? Will it ever be able to function autonomously and
mimic cognitive human actions? We cannot envision how AI will end up evolving in
the far-off future, but at this point, humans remain smarter than any type of
AI.
Mastering the Data Monetization Roadmap
The Data Monetization Roadmap provides both a benchmark and a guide to help
organizations with their data monetization journey. To successfully navigate the
roadmap, organizations must be prepared to traverse two critical inflection
points: Inflection Point #1 is where organizations transition from data as a
cost to be minimized, to data as an economic asset to be monetized; the “Prove
and Expand Value” inflection point; Inflection Point #2 is where
organizations master the economics of data and analytics by creating composable,
reusable, and continuously refining digital assets that can scale the
organization’s data monetization capabilities; the “Scale Value” inflection
point. Carefully navigate these two inflection points enables organizations to
fully exploit the game-changing economic characteristics of data and analytics
assets – assets that never deplete, never wear out, can be used across an
unlimited number of use cases at zero marginal cost, and can continuously-learn,
adapt, and refine, resulting in assets that actually appreciate in value the
more that they are used.
Will AI Make Interpreters and Sign Language Obsolete?
One of Google’s newest ASR NLPs is seeking to change the way we interact with
others around us, broadening the scope of where — and with whom — we can
communicate. The Google Interpreter Mode uses ASR to identify what you are
saying, and spits out an exact translation into another language, effectively
creating a conversation between foreign individuals and knocking down language
barriers. Similar instant-translate tech has also been used by SayHi, which
allows users to control how quickly or slowly the translation is spoken. There
are still a few issues in the ASR system. Often called the AI accent gap,
machines sometimes have difficulty understanding individuals with strong accents
or dialects. Right now, this is being tackled on a case-by-case basis:
scientists tend to use a “single accent” model, in which different algorithms
are designed for different dialects or accents. For example, some companies have
been experimenting with using separate ASR systems for recognizing Mexican
dialects of Spanish versus Spanish dialects of Spanish. Ultimately, many of
these ASR systems reflect a degree of implicit bias. In the United States,
African-American Vernacular English ...
Bad cybersecurity behaviors plaguing the remote workforce
Over one quarter of employees admit they made cybersecurity mistakes — some of
which compromised company security — while working from home that they say no
one will ever know about. 27% say they failed to report cybersecurity mistakes
because they feared facing disciplinary action or further required security
training. In addition, just half of employees say they always report to IT when
they receive or click on a phishing email. ... As lockdown restrictions are
lifted, six in 10 IT leaders think the return to business travel will pose
greater cybersecurity challenges and risks for their company. These risks could
include a rise in phishing attacks whereby threat actors impersonate airlines,
booking operators, hotels or even senior executives supposedly on a business
trip. There is also the risk that employees accidentally leave devices on public
transport or expose company data in public places. ... As cybersecurity will be
mission-critical in the new work environment, it’s encouraging that 67% of
surveyed IT decision makers report that they have a seat at the table when it
comes to office reopening plans in their organizations.
Microsoft's new security tool will discover firmware vulnerabilities
Today, ReFirm needs you to provide the firmware files, but Microsoft plans to
create a database of device information, Weston says. "You plug in CyberX and it
discovers the devices, it monitors them and it asks ReFirm 'do you know anything
about IoT device X or Y'. Hopefully we've pre-scanned most of those devices and
we can propagate the information -- and for anything we don't have, there's the
drag-and-drop interface to do a custom analysis." Having that visibility of
what's on your network and whether it's safe to have on your network is a good
first step. The Azure Device Updates service can already push IoT firmware
updates out through Windows Update. Microsoft's bigger vision is to create a
service based on Windows Update that can handle a much wider range of
third-party devices, says Weston. "We're going to take Windows Update, which
people already at least know and trust on Patch Tuesdays, and we want to push
the IoT and edge devices into that model. Microsoft's update system is a pretty
known commodity -- just about every government regulator out there looked at it
in one form or another -- and so we feel good about being able to move customers
towards it."
Deep Learning, XGBoost, Or Both: What Works Best For Tabular Data?
Today, XGBoost has grown into a production-quality software that can process
huge swathes of data in a cluster. In the last few years, XGBoost has added
multiple major features, such as support for NVIDIA GPUs as a hardware
accelerator and distributed computing platforms including Apache Spark and Dask.
However, there have been several claims recently that deep learning models
outperformed XGBoost. To verify this claim, a team at Intel published a survey
on how well deep learning works for tabular data and if XGBoost superiority is
justified. The authors explored whether DL models should be a recommended option
for tabular data by rigorously comparing the recent works on deep learning
models to XGBoost on a variety of datasets. The study showed XGBoost
outperformed DL models across a wide range of datasets and the former required
less tuning. However, the paper also suggested that an ensemble of the deep
models and XGBoost performs better on these datasets than XGBoost alone. For the
experiments, the authors examined DL models such as TabNet, NODE, DNF-Net,
1D-CNN along with an ensemble that includes five different classifiers: TabNet,
NODE, DNF-Net, 1D-CNN, and XGBoost.
Insider Versus Outsider: Navigating Top Data Loss Threats
While breaches from outside cybercriminals are becoming more complex and require
more resources to combat, companies mustn’t lose sight of a data-loss cause
closer to home – their employees. In their day-to-day positions, employees are
entrusted with highly sensitive information, from financial and personally
identifiable information (PII) to medical records or intellectual property.
While employee error is a major source of security breaches, a well-trained
employee who knows how to take the proper precautions is a key defense from
attacks and breaches. Over the course of their daily responsibilities, employees
can mistakenly share that information outside of the secure network. Often, this
data loss occurs through email, such as mentioning restricted information in
outside correspondence or attaching documents that may violate customer or
patient privacy. For example, let’s say that an employee is working on a
presentation that contains confidential data. They hit a roadblock while trying
to fix a formatting issue and in their race to meet the looming deadline, they
decide to reach out to a friend for help and send the presentation via email
with the confidential data included.
Lawmakers Urge Private Sector to Do More on Cybersecurity
Treating cybersecurity as a core business risk and devoting the appropriate
resources to it is now essential, said Tom Kellermann, head of cybersecurity
strategy at software firm VMware Inc., who also sits on the Secret Service’s
Cyber Investigation Advisory Board. “Cybersecurity should no longer be viewed as
an expense, but a function of conducting business,” he said. Christopher
Roberti, senior vice president for cyber, intelligence and supply chain security
policy at the U.S. Chamber of Commerce, which says it is the world’s largest
business association, said companies don’t stand a chance against determined
nation-state attacks regardless of cybersecurity investments. Partnerships
between the government and the private sector are essential, he said.
“Businesses must take necessary steps to ensure their cyber defenses are robust
and up to date, and the U.S. government must act decisively against cyber
criminals to deter future attacks. Each has a role to play and both need to work
closely to do more,” Mr. Roberti said.
AI Centers Of Excellence Accelerate AI Industry Adoption
It is important to note that there are several functional and operational models
that enterprises are adapting in regard to CoE. The change management model
focuses on emphasizing the prospective innovation that artificial intelligence
can provide for business stakeholders in the organization. Central to this model
is education and training of executives and business units. In addition to
change management, the Sandbox approach is another central model, in which the
CoE acts as the company’s hub of innovation and R&D. This model emphasizes
proofs of concepts and different emerging technologies. The key is alignment of
business units around POCs and being accountable for the initial launch and
development of per-subject use cases. Lastly, the Launchpad model for the CoE
leverages and builds upon the capabilities of existing data scientists,
engineers, and developers. The CoE deploys top subject-matter experts to across
departments to conduct hands-on training and education and scope out early stage
business solutions.
Kubernetes: 5 tips we wish we knew sooner
“One thing that’s better to learn earlier than later with Kubernetes is that
automation and audits have an interesting relationship: automation reduces human
errors, while audits allow humans to address errors made by automation,” Andrade
notes. You don’t want to automate a flawed process. It’s often wise to take a
layered approach to container security, including automation. Examples include
automating security policies governing the use of container images stored in
your private registry, as well as performing automated security testing as part
of your build or continuous integration process. Check out a more detailed
explanation of this approach in 10 layers of container security. Kubernetes
operators are another tool for automating security needs. “The really cool thing
is that you can use Kubernetes operators to manage Kubernetes itself – making it
easier to deliver and automate secured deployments,” as Red Hat security
strategist Kirsten Newcomer explained to us. “For example, operators can manage
drift, using the declarative nature of Kubernetes to reset and unsupported
configuration changes.”
Quote for the day:
"Well, I think that - I think
leadership's always been about two main things: imagination and courage." --
Paul Keating
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