Sharon Malzberg Shared a Presentation about Artificial Intelligence: Tutorial For Beginners

The 10 Principles of Applied Artificial Intelligence 1
The 10 Principles of
Applied Artificial Intelligence
How to Implement AI in Your Software Solution

Artificial intelligence (AI) is rapidly
moving from the laboratory and into
business and consumer applications.

The result is a fundamental shift
in how software is built, and
what it’s capable of doing.

The 10 Principles of Applied Artificial Intelligence 4The 10 Principles of Applied Artificial Intelligence 4
The sort of AI
we mean isn’t the artificial
general intelligence
of science fiction.

Instead, we’re talking
about the specialized
uses of artificial
narrow intelligence
that are already being
used to power some
of today’s most
successful technology
businesses.

In this SlideShare, we’re going
to take a look at the principles
governing how that AI should
be applied to your business.

It’s important to point out that
applied artificial intelligence
(applied AI) has a lot in common
with applied analytics.

That’s because in some
ways applied analytics is
a subset of applied AI.

But our applied AI
principles extend the
applied analytics
model with…

ü An increased focus on
the full automation of
business processes.

ü The use of richer, rapidly
evolving machine learning
techniques.

ü Better articulated
performance objectives
and model accuracy.

ü Appropriate use of
human judgement and
automated predictions.

ü Continuous learning
and adaptation through
feedback loops.

ü Approaches to limit
the impact of model
error and bias.

But the biggest
difference of all may
be in its potential for
business impact.

While applied analytics helps optimize
processes, applied AI has the long-term
goal of total process automation.

With that context in
mind, let’s take a look
at the principles
of applied AI.

Understand all
processes.
1

As with analytics, it’s
important to start with
the processes.

That’s because applied AI has the
potential to affect every process
through augmentation or automation.

So consider customer, partner
and competitor processes, as
well as those you control today.

And even entirely
new processes that
weren’t possible before.

Document the decisions
and actions for each.

And understand which parts of the
process are driven by humans, and
which could be optimized, augmented,
automated or re-invented.

2Prioritize the most
valuable processes.

Identify the processes
where better outcomes
or greater efficiency would
have the highest impact.

Think about how you
might extend those
processes further.

And go beyond
analytics.

Shift your thinking from from
injecting insights into
existing processes…

…to automating entire
business processes.

Start with the desired outcomes that
would have the highest impact within
your customers’ processes.

Then identify where processes
are short on instrumentation
(data gaps) and integrations
(connectivity gaps).

Design frictionless
integrations.
3

Try to design solutions that
are as automated, natural
and complete as possible.

To do so, take an
outside-in approach.

Think about how your AI-enabled
process or solution will be
integrated (and used).

Then identify interdependencies
with other processes such as...

How other software
processes (or humans) will
access its capabilities.

The more integrated your
applied AI solution is,
the greater the value.

Integrate human
judgement as
required.
4

Understand the roles humans
play in your solution.

Humans may be sources of data,
trainers, reviewers or users.

Having humans in the loop
may be a user, market or
regulatory requirement.

So even if full automation is
possible, human oversight
may be necessary.

Understand
performance
objectives.
5

Know the model performance levels
required to add business value.

Model performance includes
error rates, consistency,
bias and explainability.

Determine the cost of
accuracy vs. the business
impact of false predictions.

Avoid models where the
impact of false predictions
is greater than the business
can tolerate.

Consider whether the
explainability of the model
is a requirement or not.

If it is, complex methods
like deep learning may
be unsuitable.

Match modeling
techniques to
requirements.
6

Machine learning techniques
are changing fast.

Ensure your team
stays current.

But don’t get carried away
with the latest and greatest.

Aim for the simplest model
that can do the largest part
of the job.

Ensure you test model
performance (against
requirements) and
compare approaches.

And have a strategic plan
for evaluating new and
more sophisticated models.

Capture relevant
data to support
your models.
7

Collect comprehensive, aggregated
and diverse data from your target
business processes.

You will use this
experience data
to train your models.

Include input, context, action and
outcome data from end-to-end
processes in real time.

And derive general and
edge case experience data,
using simulation techniques
if necessary.

Manage quality
for error and bias.
8

Machine learning is only fair
and objective if it’s free of bias.

Implement processes to monitor
for bias and other errors such as
inaccurate predictions.

Bias may come from
existing business
processes and their data.

And remember that any
human input into AI
training or model
evaluation predictions
could be biased itself!

Build fault tolerance
as part of model
integration.
9

Like all software, machine
learning models will have errors,
behave unexpectedly and
sometimes even fail.

And some machine
learning models will
change over time with
new data.

Automation will also
amplify the impact
of errors.

Traditional approaches
to error detection and
elimination will be
inadequate.

So increase system
dependency through
fault tolerance.

• Rule-based circuit breakers
• Human and AI-based monitoring and audits
• Check-and-balance redundancy
• Mandatory human review
• Kill switches
• Least-harm actions
Consider techniques like:

Defend your legal
and ethical stance.
10

Understand and articulate the
overall benefits of artificial
intelligence to your domain.

Those might include:
• Efficiency gains
• Eliminating repetitive tasks
• Reducing risk of error
• Helping humans solve the unsolvable

Frame ethical and legal
discussions in the context
of the overall benefits.

And support your
arguments with facts.

Consider external factors
such as legislation as well
as customer sentiment.

In fact, regulation may restrict
automation levels to reduce risk
and consequence.

Don’t gloss over the
replacement of human
effort with AI automation.

And be open and
transparent.

Applied AI is already
fundamentally changing the
nature of work and life.

Understand all processes.
Prioritize the most valuable
processes.
Design frictionless integrations.
Integrate human judgement
as required.
Understand performance
objectives.
4
7
8
9
1
2
3
Match modeling techniques to
requirements.
Capture relevant data to support
your models.
Manage quality for error and bias.
Build fault tolerance as part of
model integration.
Defend your legal and ethical
stance.
Big, long-term strategic changes lie
ahead. To deal with them, remember
these principles:
6
5
10

Want to learn more about
artificial intelligence?
Visit georgianpartners.com

Sharon Malzberg Shared a Presentation about Artificial Intelligence: Tutorial For Beginners

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Sharon Malzberg Shared a Presentation about Artificial Intelligence: Tutorial For Beginners