Top Humanoid Robots Innovations So Far
Sophia is a social humanoid robot. She was activated in February 2016 and made
her first public appearance at South by Southwest Festival in mid-March 2016 in
Austin, TX. Since its launch, Sophia has garnered a lot of media coverage,
featuring numerous high-profile interviews, events, and panel discussions across
the world. ... Toyota T-HR3 is a third-generation humanoid robot, which was
designed from the get-go to be remote-controlled by a human. It is 1.5-meter
tall, weighs 75 kilograms, and has torque-controlled freedom of 32 degrees with
a pair of 10 fingered hands. The robot is designed to be a platform with
capabilities that can safely assist people in a different variety of settings
like home, medical facilities, disaster-stricken areas, construction sites, and
outer space. ... E2-DR is a disaster response robot from Honda that is able to
navigate through dangerous, complex environments. The robot looks like a
humanoid, and heavier and tougher than the company’s Asimo, first presented in
2000. Honda E2-DR is designed to perform as a rescuer in a broad range of
situations dangerous for human rescuers
OpenAI, ChatGPT and the intensifying competition for data management within the supercloud
What many industry analysts are seeing, much to the chagrin of large data/search
players like Google, is that OpenAI has leaped to the forefront of providing the
capabilities to handle the data requirements of the supercloud. A lot of this is
due to the concentrated capabilities within ChatGPT born from tedious underlying
work, such as the training of machine learning models, according to Xu. As a
result, companies need to be proactive enough to see the AI technologies as
critical to a supercloud future instead of just being in the count while leaving
AIOps on the back burner. For most of the Fortune 500 companies, your job is to
survive the big revolution,” Xu said. “So you at least need to do your
walmart.com sooner than later and not be like GE with a lot of the hand-waving.”
Microsoft, for its part, has shown some of that foresight, as it’s recently
invested around $10B into OpenAI and worked with the company across several
areas, including its OpenAPI services.
Overcoming Challenges in Privacy Engineering
The bigger the company, the greater the likelihood that there’ll be considerable
amounts of legacy code lurking in the depths of the organization’s systems. Very
few developers properly understand legacy code, so it’s usually highly opaque.
Some employees might know the connections for some of the lines of code, and
some sections might have been replaced more recently, but in general there’s
very poor visibility into which services are related to which database, which
services are sharing data with which other services, and other aspects of legacy
code. On top of all this, data mapping projects are caught in a tech version of
Zeno’s paradox. Most of the projects that are being mapped are live projects,
which means that more data, more tables, and more connections are being added on
a continual basis. But most data mapping is currently carried out manually. The
map is out of date as soon as it’s completed, because of the speed at which live
projects expand. There’s no way that human employees can keep up with the pace
at which new data and relationships are added to the project.
Cisco Report Shows Cybersecurity Resilience as Top of Mind
The report delves into the factors that could provide the biggest gains in
enterprise security resilience, whether based on culture, IT environment, or
security technology. Cisco took these factors and devised a security resilience
scoring system based on seven areas. Those most closely adhering to these core
principles are in the top 10% of resilient businesses. Those missing most of
these elements are in the bottom 10%. Culture is especially vital. Those with
poor security support from the C-suite score 39% lower than those with strong
executive support. Similarly, those with a thriving security culture score 46%
higher than those lacking it. But it isn’t all about culture. Staffing, too,
played a definite role, whether based on experienced staff, certification and
training, or the sheer number of internal resources. The report shows those
companies maintaining extra internal staffing and resources to respond to
incidents gain a 15% boost in resilient outcomes. In other words, headcount can
mean the difference between faring well and poorly during an event.
How distributed architecture can improve the resilience of your organization
Distributed architecture is not exactly a new thing to the average IT
department, but organizations aren’t always aware of all the benefits that it
provides – things like improved scalability, performance, cost savings and
resiliency. ... Cost savings is a common driver for establishing a distributed
architecture. By setting up multiple nodes, you can route traffic through the
nearest node instead of relying on more simple call trafficking rules - like
all participants connect to the node closest to the first person to join the
call. Bandwidth consumption on WAN networks can be very expensive, with
transatlantic costs especially high. With Pexip, nodes can be placed within
your internal network to reduce the cost of the traffic on WAN networks. An
added cost-saving feature from Pexip comes from our media transcoding. Media
streams coming back from Pexip are reduced in size as they travel between
nodes. Since Pexip handles the compute, you’re left with a more efficient
media traffic flow that costs less. Distributed architecture means that your
entire deployment is more resilient.
Hardening The Last Line Of Defence For Financial Organizatins
IT infrastructure and security operations teams live in two worlds that are
often separated by design. Whilst the SecOps teams want to regulate all access
as strictly as possible, the IT infrastructure teams need to be allowed to
access all important systems for backup. Many of these teams are not
collaborating as effectively as possible to address growing cyber threats, as
a recent survey found out. Those respondents who believe collaboration is weak
between IT and security, nearly half of respondents believe their organisation
is more exposed to cyber threats as a result. For true cyber resilience these
teams must work closely together, as the high number of successful attacks
proves that attack vectors are changing and it’s not just about defence, but
backup and recovery. ... If financial organisations want to achieve
real cyber resilience and successfully recover critical data even during an
attack, they will have to modernise their backup and disaster recovery
infrastructure and migrate to modern approaches such as a next-gen data
management platform.
Effective business continuity requires evolution and a plan
IT and cybersecurity teams can work with other business decision-makers to
assess risk levels for each system. This involves comparing the organization's
business model against the IT infrastructure to determine which systems are
mission-critical to operations. During the risk analysis, key considerations
-- such as whether the organization can survive without email for a week, what
systems are regularly backed up and what systems are cloud-based vs. on
premises -- should be weighed and addressed. Organizations may want to assign
tiers to each system to define which ones must be restored the fastest. It's
often the safest course to colocate critical systems or keep certain backup
systems offline. Ensure the colocation isn't connected to the corporate
network via Active Directory and that it's segmented from other systems, as
compromises can occur if the colocation is the primary environment for data
storage and has a connection to the corporate network. Colocation lets
organizations bring the most essential systems back online and continue
operations, even if core systems have been breached or otherwise disrupted.
Four Steps To Self-Service Data Governance Success
Data governance can help teams oversee and control access to confidential
information. You could unlock automation for data security faster with a
no-code/low-code approach. A no-code approach could make self-service data
governance easier by handling all of the complicated things behind a simple
interface. Your data teams won't have to write hundreds of lines of code to
handle complex, repetitive procedures like applying granular access policies
to many users simultaneously. To simplify your transition to no-code, start
with a pilot. Look for no-code/low-code technology that lets you move quickly
into implementation. Prioritize options that let you sign on to the service in
minutes without requiring long-term contracts. Then, connect your cloud
database and control access with classification-based policies that don't
require your team to write code to allow only approved users to view the data.
When the situation calls for more customization, like trying to see who has
access to your cloud database, test the low-code capability. ... A
no-code/low-code capability could make the job of managing data governance
infinitely easier.
Top 5 Considerations for Better Security in Your CI/CD Pipeline
Securing running microservices is just as crucial to an effective CI/CD
security solution as is preventing application breaches by moving security to
the pipeline’s earlier stages. The context necessary to comprehend Kubernetes
structures — such as namespace, pods and labels — is not provided by
conventional next-generation firewalls (NGFW). Once the perimeter has been
compromised, the risk of implicit trust and flat networks on thwarting
external attacks provides attackers a great deal of surface. As a result, it’s
important to leverage a platform that enables continuous security and
centralized policy and visibility for efficient and effective continuous
runtime security. The majority of application teams automate their build
process using build tools like Jenkins. Security solutions must be included in
popular build frameworks to bring security to a build pipeline. Such
integration enables teams to pick up new skills quickly and pass or fail
builds depending on the requirements of their organization.
4 High-Impact Data Quality Issues That Are Easily Avoidable
In the modern data stack, data quality issues can range from semantic and
subjective – which are hard to define – to operational and objective, which
are easy to define. For instance, objective and easier-to-define issues would
be data showing up with empty fields, duplicate transactions being recorded,
or even missing transactions. More concrete, operational issues could be data
uploads not happening on time for critical reporting, or a data schema change
that drops an important field. Whether a data quality issue is highly
subjective or unambiguously objective depends on the layer of the data stack
it originates from. A modern data stack and the teams supporting it are
commonly structured into two broad layers: 1) the data platform or
infrastructure layer; and, 2) the analytical and reporting layer. The platform
team, made up of data engineers, maintains the data infrastructure and acts as
the producer of data. This team serves the consumers at the analytical layer
ranging from analytics engineers, data analysts, and business stakeholders.
Quote for the day:
"Don't be buffaloed by experts and
elites. Experts often possess more data than judgement." --
Colin Powell
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