Technology Trends to Learn in 2021

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Top 5 Technology Trends to Learn in 2021 Beyond
INSIGHTS
Emerging tech has changed the world in
unprecedented ways in the last decade, making the
world almost movie-like in its advancements.
Space travel has come to the private sector, and
someone somewhere conceived of driverless cars
possibly as a reaction to seeing several
vehicular accidents. Who knows.
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Were just past the midpoint of 2019, and it is a
good time to take stock of the trends that we can
expect to transform our future in 2020. And
perhaps gain an edge by upskilling early in those
areas? Augmented Analysis The next level up
from natural language processing (NLP) and
machine learning is the combination of the two
augmented analysis. It was coined by Gartner as
the future of data analytics. Lets take a
closer look at what exactly this new term
actually means. As with everything else in data
science, one starts with data. It is gathered
from a multitude of sources, both public and
private, then automatically processed to yield
insights that are useful to business operations
and readable by human beings. The key difference
between regular analytics and augmented analytics
is that a data scientist isnt required to
interpret patterns in the data. It is
understandable by the non-tech members of the
team. This isnt to say that organizations will
be able to do away with data scientists
altogether far from it. Augmented analytics
will help with the drudgery associated with
analysis, like the collection of data and
preparation, thus assisting data scientists in
other, more intelligent aspects of their
work. Quantum Computing The next technology on
our list strays into the domain of physics, at
least to grasp the underlying concept
superposition. In classical computers yes, in
this context, normal computers are considered
classical the fundamental base unit of all
operations is a bit. A bit can take one of two
states 1 or 0. Never both at the same time, and
thus it is considered to be binary in
nature. Quantum computing uses something called
a qubit which, contrary to an ordinary bit, can
hold both 1 and 0 at the same time. This
phenomenon is known as superposition. Superpositi
on isnt a magical state, but more the ability to
predict the outcome of a state before it
actually occurs. If this is done for lots of
qubits, their proposed outcomes can be
mathematically related to each other, another
concept known as entanglement. Thus, the outcomes
are calculated before they ever occur.
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The reason that quantum computing is so next-gen
is that it is super fast. However, there are
still kinks in the system, leading to unreliable
outcomes. So their widespread use is still
debatable at this stage. Deep Reasoning In
spite of deep learning is as amazing as it is, it
still has limitations. It excels at relating
inputs to outputs when both are clearly defined.
It relies on existing patterns derived from
processing large datasets to make decisions.
There is no inherent reasoning, like a human
being can perform. For instance, deep learning
can learn to detect a disease rapidly based on
the analysis of symptoms, checked against a
library of saved symptom patterns, using decision
trees to arrive at that conclusion. However, it
cannot understand what that disease is. Deep
reasoning aims to mimic human intelligence, by
using common sense. Small Data The concept of
Small Data isnt a new one. Like its counterpart
Big Data, it has been around for a long time,
but it is experiencing a resurgence in popular
consciousness. We all know that Big Data refers
to large volumes of data that are analyzed to
extract patterns through computations, which
then, in turn, yield actionable business
insights. The quantity of data makes it
impossible for an individual or even a team of
individuals to parse manually, and thats where
small data picks up the slack. But first, a
definition by Allen Bonde, research director at
Forrester Small data connects people with
timely, meaningful insights (derived from big
data and/or local sources), organized and
packaged often visually to be accessible,
understandable, and actionable for everyday
tasks. In short, small data can be used to draw
intuitive conclusions from data that can be
actioned faster. By no means is small data
meant to replace big data it is more to provide
actionable insights faster, while big data is
being processed. Computer Vision Unsurprisingly,
as the name suggests, computer vision is about
teaching computers how to see. Simple enough,
conceptually, it is incredibly hard to bring into
being.
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There is often confusion between digital image
processing and computer vision. However, the
former is quite simply a computer being able to
identify components of images, whereas the latter
is a computer being able to identify objects in
images, understand what those objects are, and
most importantly what to do about that
information. Interested in learning more?
GreyAtom has the program for you Deep Learning
with Computer Vision. Learn more about it and
how it can help boost your Data Science career
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