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What is Augmented Intelligence?
Augmented Intelligence mines data to
extract insights about the dynamics of your
business – automatically - to assist humans
in learning and decision making. It's about
productive collaboration of human
intelligence and artificial intelligence.
Why is it Useful for the Enterprise?
1. Many enterprises have vast amounts of
data in the cloud and on-prem, yet their
decisions are made with incomplete,
sometimes anecdotal, information -
partially because not all the relevant
information is readily available, and partly
because the terabytes of information that's
available is opaque, making it difficult to
mine the insights and the clues that lie
buried deep, hidden from executives. BI
tools require manual effort, can miss out
key insights.
www.EazyML.com
EazyML’s Augmented Intelligence makes
insights quicker, and more importantly,
comprehensive - this makes enterprises
discover insights about their business
dynamics that otherwise may have gone
unnoticed by analysts – potentially,
worth millions of $s to P&L That’s why
Gartner projects Augmented Intelligence
to generate a whopping $2.9T of value-
add for businesses by EOY:
Gartner: Gartner Says AI Augmentation
Will Create $2.9 Trillion of Business Value
And why’s EazyML’s Augmented
Intelligence unique? Because it generates
a confidence score for each insight to
make data-driven decision-making
actionable. Please do review my recent
article on Forbes about it - hot off the
press: https://www.linkedin.com/posts/
deepak-dube-phd-7217a0131_council-
post-transparent-machine-learning-
activity-6753436239724081152-SrVj
If that was known, then the edits to the
training data bias could be specific and
targeted, fixing the issue. This is precisely
what EazyML’s explainable AI does,
explaining the reasons for each prediction;
very importantly, it accompanies the
reasons with a confidence score so as to
not mislead.
offending records to
the training data to
influence the new
model; it fixes the
error, but now another
error surfaces that was
earlier correct – the
struggle for data
scientists goes on.
What was precisely the
reason why the model
predicted incorrectly?
2. As we mentioned above, enterprises have
tons of data. Data, unfortunately, has biases
– referred to as the training data bias. The
model suffers from biases, making incorrect
predictions. If only an ML platform could
help find out these biases in advance!
EazyML’s Augmented Intelligence does
exactly that. It mines the data to extract the
insights (the rules of how predictors
combine to determine the value of an
outcome). Subject matter experts can study
the insights, convince themselves that it
follows the principles of science, the best
area practices, and if it doesn’t, work on the
training data to remove the bias, before
using it.
There are additional ways in which EazyML
battles data bias comprehensively –
importance of predictors and comparing test
to training data.
www.EazyML.com
3. Let’s say the model still makes predictionsthat are incorrect. Data scientists add the
EAZYML MAKES YOUR DATA FROM PUZZLING TO INSIGHTFUL IN MINUTES
Why EazyML for Augmented Intelligence?
1. Which predictors are important, how
important, how do they interplay with
each-other (sequence and threshold) –
comprehensive intelligence from data:
Typically, most ML systems use Shapleys
to accurately display which predictors are
important, and how important are they,
for an outcome; EazyML goes a step
beyond to assign thresholds to each
predictor so that the analysts understands
the precise range in which the results
hold, vitally important for data-driven
decision-making.
2. Lucid explanation with Rules and
Thresholds – easy to understand in a form
humans typically relate to: According to
Jim Guszcza, Chief Data Scientist at
Deloitte, humans comprehend
explanations best as rules-n-thresholds;
EazyML displays it in that format via GUI
and API.
www.EazyML.com
3. Correctness of explanation evaluated by
Confidence Score – makes it actionable: A
prediction has to be accompanied by a
confidence score so that it doesn’t mislead.
ML platforms don’t always do that. When
they do, they report on the accuracy of the
model – RMSE or kappa, for instance – as a
measure of how well does the model
predicts, in general. What’s the equivalent
measure of how accurate is the explanation
for a prediction? Or for the insight? No
such measure exists. EazyML has had to
work hard to invent a measure, its
confidence score, and put it through a large
number of experiments to validate its
efficacy. Without the all-important score,
the explanations derived by Augmented
Intelligence is academic, not actionable.
4. Fits well in the enterprise workflow by
integrating with your existing BI tools using
standard APIs – easy to trial, easy to
deploy: To democratize Augmented
Intelligence, you need an ML platform
that’s easy to use, intuitive to work.