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 IntelligenceHow 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.


2 Prioritize 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.

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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:

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

https://georgianpartners.com/thesis/applied-artificial-intelligence/

https://georgianpartners.com/thesis/applied-artificial-intelligence/

sharon malzberg shared a presentation about artificial intelligence: tutorial for beginners

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