Human beings are constantly making sense of unstructured data. Now neural networks can be trained to not only learn from humans but surpass the human ability to understand critical data and leverage it to derive actionable insights.
Look at the above image. What do you see? It’s simply a newspaper cartoon. You and I are effortlessly good at recognizing what we see.
We can also spot
the fag end of a long, grey tail and run out of the room screaming rat! Or
we can see a leg and know whether it’s a person or a table.
Computers, on the
other hand, have long struggled with acquiring this basic, almost trivial human
ability. They need image labels to declare
what a dog looks like and then learn to identify it by going through thousands
of confusing dog pictures where unhelpful owners dress them in flashy outfits.
This is because
data is conventionally processed in a structured form:Rows and
columns of neat numbers in an Excel Sheet which are easy to make sense of. One
can quickly search in a database represented by numbers and process it through
traditional models. But when it comes to unstructured data, it’s not remotely
What’s The Big Deal?
Almost 80% of the
data that businesses produce — from audio files, images, videos — is
unstructured and hence, it remains underutilized. Instead, we tinker with
structured data lakes — like accounting information — by using cloud computing.
data like customer feedback or a product photo associated with an order can
deliver big insights. What if it could be easily processed not just through
traditional models or cloud computing but on the edge (read: locally) by using
the device’s computational power?
machine learning and computing power can actually make it happen. Artificial
Intelligence’s ability — aided by neural networks — to handle unstructured data
is a long-awaited disruption.
Insights In Satellite Data
data, for instance. It can track displaced refugees to deliver aid; it can
determine economic activity by counting cars; it can monitor air quality. But
say, we only manage to acquire a limited number of images or a video that
carries this huge volume of information. The AI and machine learning rule book
says we need multiple such files to train the neural network but the available
data is sparse. Thankfully, GANs can mimic any distribution of data. They can
be taught to replicate videos or images and their output is freakishly similar
to the original.
The next problem
is identifying what’s in the images. Current satellite
photographs are manually labelled. Experts painstakingly identify regions, tag
them and label them. But deep learning approaches such as Region-based
Convolutional Neural Network (R-CNN) can automate the whole process.
They divide the
image into various regions and identify the objects in them. This sounds
familiar because most of us have seen how Google divides street images, for
instance, and asks us to identify traffic lights to prove that we’re humans.
The neural network is simply learning through our selection.
algorithm is similarly trained to identify objects — anything from a fisher’s
ship to a lighthouse or a tree — in a satellite image. A leap from the slow
process of manual identification and tagging, isn’t it?
technology is advancing rapidly. An advancement called YOLO, You Only Look
Once, ensures that instead of dividing an image into regions and trying to
identify objects in each region, it can simultaneously look, tag and classify —
all at the same time.
Clearly, it’s not
just humans who can make sense of unstructured data anymore.
Speech is another
example of unstructured data that has seen spectacular progress. Human speech
is inherently ambiguous and yet, we’ve managed to achieve end-to-end speech
recognition and brought Siris and Alexas into our lives.
speech recognition and transcription is easier when it is used for widely
spoken languages such as English. When it comes to languages such as Pashto or
say, Kongo, where the volume of data available for training is limited,
recognition and transcription becomes a challenge. But tools like KALDI and Esp-net
are coming in handy and helping us comprehend the limited speech data,
healthcare: We previously discussed how chronic
inflammation can be a measure for wellness but in order
to measure chronic inflammation, we need a lot of data. Even if doctors had
access to it through bio-impedance; wearables that measured heart rate
variability; or images of tongues, eyes, or nails, they would still need to
call a battery of tests before correlating it to chronic inflammation.
intelligence can bridge that yawning gap. Deep neural network can be trained to
process patient data from multiple units: point data, time series data, and
images, to draw life-changing insights that would have been previously
impossible. Going forward, these insights can not only be data-driven but also
work as brainstorming triggers to help us solve complex problems creatively.
The folks at
Myelin Foundry are employing multiple tactics to make sense of unstructured
data. “We love complexity”, Gopichand Katragadda, the Founder and CEO, says.
“We thrive in our ability to gain insights from unstructured data. While
labeling reduces complexity, useful information is also lost in the process.
Our differentiation is to provide content-aware insights at the edge,
protecting data privacy and data security. “
On the Edge:
Enabling Security and Privacy
that neural networks need data to acquire the human abilities of easy
identification and distinction. But acquiring large datasets often thwarts
What if the
unstructured data could be computed locally to draw inferences? On your
existing hardware, and not by syncing it to the cloud. That’s where edge comes
computers today have tremendous computational power that remains
under-utilized. If a user wants to keep their data private and prevent it from
being shared with the cloud, an edge device can process it locally (or on the
device) and make it happen.
It assures users
the privacy that would be impossible if the data were shared with the cloud.
Along with privacy, imagine how easily crucial healthcare data could be
processed locally to provide actionable insights in far-flung towns and rural
areas with limited access and connectivity.
computational power of the smartphone and TV is used, it could also change the
world of entertainment. We could use super-resolution instead of a
tiresome constant back-and-forth with the cloud, and drastically reduce
buffering and improve users’ quality of
The benefits are
numerous. From healthcare, national security, to speech recognition,
unstructured data on the edge is at the heart of many developments.
And it’s quickly becoming obvious that the evolving AI
technology and neural networks can help unlock its vast, untapped potential to
learn from and aid, not replace, human work.
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