The Merging Of Human And Machine. Two Frontiers Of Emerging Technologies
The field of human and biological applications has many applications for
medical science. This includes precision medicine, genome sequencing and gene
editing (CRISPR), cellular implants, and wearables that can be implanted in
the human body The medical community is experimenting with delivering
nano-scale drugs (including anti-biotic “smart bombs” to target specific
strains of bacteria. Soon they will be able to implant devices such as bionic
eyes and bionic kidneys, or artificially grown and regenerated human organs.
Succinctly, we are on the cusp of significantly upgrading the human ecosystem.
It is indeed revolutionary. This revolution will expand exponentially in the
next few years. We will see the merging of artificial circuitries with
signatures of our biological intelligence, retrieved in the form of electric,
magnetic, and mechanical transductions. Retrieving these signatures will be
like taking pieces of cells (including our tissue-resident stem cells) in the
form of “code” for their healthy, diseased or healing states, or a code for
their ability to differentiate into all the mature cells of our body. This
process will represent an unprecedented form of taking a glimpse of human
identity.
Five ‘New Normal’ Imperatives for Retail Banking After COVID-19
The current financial crisis highlights an already trending need for
responsible, community-minded banking. How financial institutions respond to
the COVID-19 crisis — and the actions they take as the economy begins to right
itself — will influence their reputations in the long-term. Personalized
service and community-mindedness have never been more important. The approach
to providing them, however, will often be different from the past.
Data-powered audience segmentation can help banks and credit unions
proactively anticipate the needs of their customers, then offer services and
solutions to solve them. Voice-of-consumer and social listening tools can help
financial institutions understand and monitor their brand perception. It’s
important to develop a process and allocate resources to engage with consumers
in the digital space. For example, when complaints or concerns are raised on
social media or other channels, they should be triaged quickly and
effectively. If this capability is something you previously have put off
developing, it’s time to re-prioritize. According to EY’s Future Consumer
Index, only 17% of consumers surveyed said they trusted their financial
institutions in a time of crisis.
The 7 Benefits of DataOps You’ve Never Heard
The convergence of point-solution products into end-to-end platforms has
made modern DataOps possible. Agile software that manages, governs, curates,
and provisions data across the entire supply chain enables efficiencies,
detailed lineage, collaboration, and data virtualization, to name a few
benefits. While many point-solutions will continue, success today comes from
having a layer of abstraction that connects and optimizes every stage of the
data lifecycle, across vendors and clouds, in order to streamline and
protect the full ecosystem. As machine-learning and AI applications expand,
the successful outcome of these initiatives depends on expert data curation,
which involves the preparation of the data, automated controls to reduce the
risks inherent in data analysis, and collaborative access to as much
information as possible. Data collaboration, like other types of
collaboration, fosters better insights, new ideas, and overcomes analytic
hurdles. While often considered a downstream discipline, providing
collaboration features across data discovery, augmented data management, and
provisioning results in better AI/ML outcomes. In our COVID-19 age,
collaboration has become even more important, and the best of today’s
DataOps platforms offer benefits that break down the barriers of remote
work, departmental divisions, and competing business goals.
Generation Data: the future of cloud era leaders
What’s more, with most organisations adopting multiple cloud environments,
data is more fragmented than ever before. As such, businesses are looking to
data governance specialists (not just data scientists, but data engineers
too) to ensure that there is a catalogue of where the data resides, across
the different landscapes to ensure it’s well secured and well governed. It’s
important to have people who can spot risks associated with where data is –
or in some cases – isn’t stored, whilst deploying artificial intelligence
(AI) to adopt new roles to secure it within the cloud environments. Cloud
specialists can take on several different job titles within the business and
at some organisations, a single data leader like the CDO must seamlessly
shift between multiple roles in order to achieve success. Meanwhile at
others, a team of data leaders each having a specialised role under a
unified data strategy is a model for success. What’s clear is that as data
becomes part of everyone’s working lives, to ensure we’re not short on
talent, businesses need to engage with a wide range of individuals such as
citizen integrators and citizen analysts to upskill within existing roles
and to truly democratise data. This means equipping existing and future
employees with the skills needed to garner insights from existing data sets.
Standing Out with Invisible Payments: The Banking-as-a-Service Paradox
Industry players, such as regulators, FinTech partners, and businesses in the banking, financial services and insurance industries, are starting to realise that it is not ideal to be a ‘jack-of-all-trades’. In fact, the core of BaaS is built upon strategic collaboration. As such, there should be more acceptance of strengths and weaknesses from financial players so they can better identify what they are good at and what they need help with. Essentially, financial players need to ‘piggyback’ on either big banks or other financial service partners with strong regulatory license network. Furthermore, if they can identify a market that is underpenetrated, this is a good opportunity to work with existing players to fill the gap. For instance, the recent partnership between InstaReM and SBM Bank India allow users to remit money to more markets and send funds overseas in real-time. As its licensed banking partner, InstaReM will facilitate international money transfers from India to an expanded list of markets, including new destinations such as Malaysia and Hong Kong. In another example, Nium’s partnership with Geoswift, an innovative payment technology company, will enable overseas customers to remit money into China.How state and local governments can better combat cyberattacks
Hit by ransomware and other attacks, state and local governments are
obviously aware of the need for strong cybersecurity. And they have taken
certain measures to beef up security. Many local governments have hired top
cybersecurity people and created more effective teams. The recent
Congressional Solarium Commission on Cybersecurity stressed the need for
better security coordination among local governments, the federal
government, and the private sector. The State and Local Government
Cybersecurity Act of 2019 legislation passed last year is designed to foster
a greater collaboration among the different parties. But government agencies
are not all alike, especially on a local vs. state level. Differences exist
in funding and preparedness. Security policies can vary from one agency to
another. Plus, the effort to digitize systems and services at such a rapid
pace means that security sometimes gets left behind. Looking at open-source
data on 108 cyberattacks on state and local municipalities from 2017 to late
2019, BlueVoyant found that the number rose by almost 50%. Over the same
time, ransomware demands surged from a low of $30,000 a month to as high as
almost $500,000 in July 2019, according to the report.
How AI can enhance your approach to DevOps
Companies can resort to AI data mapping techniques to accelerate data
transformation processes. At the same time, machine learning (ML) used in
data mapping will also automate data integrations, allowing businesses to
extract business intelligence and drive important business decisions
quickly. Taking it a step further, organizations can push for
AI/ML-powered DevOps for self-healing and self-managing processes,
preventing abrupt disruptions and script breaks. Besides that, organizations
can opt for AI to recommend solutions to write more efficient and resilient
code, based on the analysis of past application builds and performance. The
ability of AI and ML to scan through troves of data with higher precision
will play an essential role in delivering better security. Through a
centralized logging architecture, employees can detect and highlight any
suspicious activities on the network. With the help of AI, organizations can
track and learn of the hacker’s motive in trying to breach a system. This
capability will help DevOps teams to navigate through existing threats and
mitigate the impact. Communication is also a vital component in DevOps
strategy, but it’s often one of the biggest challenges when organizations
move to the methodology when so much information is flowing through the
system.
Shifting the mindset from cloud-first to cloud-best using hybrid IT
Those who are approaching the cloud for the first time face the classic
question around which type of service to choose, public or private. Both
have different use-cases and can be critical for businesses in achieving
their objectives. For instance, the public cloud is agile, scalable and
simple to use, great for teams looking to get up and running quickly.
However, the private cloud offers its own benefits, chiefly a greater degree
of control over data and performance. As organisations hosting their data in
a private cloud are in full control of that data, there’s typically a more
consistent security posture and a greater degree of flexibility and control
over how that data is used and managed. Moreover, the private cloud can
typically deliver faster and higher through-put environments for those
mission critical applications that cannot run in the public cloud without
business impacting performance issues. However, companies risk getting
caught in the cloud divide, feeling as though public cloud is not
appropriate for their enterprise applications, or that on-prem enterprise
infrastructure isn’t as user-friendly, simple or scalable as the public
cloud. Ultimately organisations should be able to make infrastructure
choices based on what’s best for their business, not constrained by what the
technology can do or where it lives.
5 Critical IT Roles for Rapid Digital Transformation
Information security leaders are the individuals who protect the information
and activity of an organization. These professionals lead the charge in
establishing the appropriate security standards and implementing the best
policies and procedures needed to prevent security breaches and confiscated
data. As more information and activity happens within the cloud
infrastructure during a transformation, security has to be a top priority.
Given the current situation, the rise of online activity has led to an
increase in cyber-attacks. With digital transformation, businesses need
assurance that their technologies are adequately protected. An InfoSec
leader will help quarterback the security game plan as well as monitor for
abnormal activity and handle the recovery should any issues arise. Since
data analysts are there to retrieve, gather and analyze data, they hold an
important role in the digital transformation journey. Technology opens the
doors to a world of data that must be uncovered and understood to deliver
any real value. The insights data analysts can provide allow organizations
to take a data-driven approach to the decision-making process. Since there
is a lot of uncertainty in the current business climate, data analysts are a
huge asset.
Facing gender bias in facial recognition technology
Our team performed two separate tests – first using Amazon Rekognition and the
second using Dlib. Unfortunately, with Amazon Rekognition we were unable to
unpack just how their ML modeling and algorithm works due to transparency
issues (although we assume it’s similar to Dlib). Dlib is a different story,
and uses local resources to identify faces provided to it. It comes pretrained
to identify the location of a face, and with face location finder HOG, a
slower CPU-based algorithm, and CNN, a faster algorithm making use of
specialized processors found in a graphics cards. Both services provide match
results with additional information. Besides the match found, a similarity
score is given that shows how close a face must match to the known face. If
the face on file doesn’t exist, a similarity score set to low may incorrectly
match a face. However, a face can have a low similarity score and still match
when the image doesn’t show the face clearly. For the data set, we used a
database of faces called Labeled Faces in the Wild, and we only investigated
faces that matched another face in the database. This allowed us to test
matching faces and similarity scores at the same time. Amazon Rekognition
correctly identified all pictures we provided.
Quote for the day:
"Hold yourself responsible for a higher standard than anybody expects of you. Never excuse yourself." -- Henry Ward Beecher
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