applied artificial intelligence - trends and principles

AI Trends

Applied Artificial Intelligence

georgianpartners.com Applied Artificial Intelligence

AI is software that:Perceives its environment;

Adapts to new information;

Learns from every interaction;

Takes action to achieve its goals;

Optimizes to specific outcomes.

What is Artificial Intelligence?

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Artificial intelligence will animate the world the internet connected and electricity illuminated.

What Will Change?


How Soon Is It Coming?

“Take X and Add AI” (2014)

“Take AI and Add X” (2016)

Kevin Kelly, Wired

“The business plans of the next 10,000 startups are easy to forecast”


AI techniques are experiencing a surge in algorithm innovation in step with the rapid advancement of sensors, data and computation.Live, interactive, automatically generated, and often self-correcting real-time data are fueling real-time decisions and real-time responses. Cloud computing, massively parallel processing and new types of processing units are powering AI techniques that simply weren’t practical before.

Artificial Intelligence is rapidly moving from the laboratory towards business and consumer application.

Cheaper Computin

g

Faster Data

Better Algorithm

s

Why Now?

Commodity

Commodity

Unique & Valuable


Software is undergoing a fundamental shift from doing only what we tell it to do; to learning to sense its environment, make decisions, and take action.

Why Should You Care?

• Business will race to cognitize processes.

• Early movers will gain sustained advantage.

• Disrupt or be disrupted.


• Once AI works, the next transaction is almost free.

• AI will provide service levels that will disrupt competition.

• Faster delivery, higher availability, more convenience, deeper engagement and reduced cost will disrupt industries.

Why Not Wait?


Industrial Age 19th Century

Machines performthe dirty work.

Industrial equipment from looms to the cotton gin.

Relieves humans of onerous manual labor.

Information Age20th Century

Machines perform the dull and routine work.

Automated interfaces from airline kiosks to call

centers.Relieves humans of routine

transactions and clerical chores.

2nd Machine Age21ST Century

Machines perform the decision-making.Intelligent systems from airfare pricing to health

diagnostics.Make better choices than humans reliably and fast.

Thomas H. Davenport and Julia Kirby, Beyond Automation

A New Age?


Machine Learning is the foundation of AI. It provides algorithms and methodologies to learn from inputs, experience and desired outcomes. It returns a model that can make decisions and take actions.

What is Machine Learning?

ComputerData

AlgorithmsOutput

ComputerData

OutputAlgorithms

TraditionalProgramming

MachineLearning


Learners predict outcomes…

...and optimize to minimize the difference between their predictions and actual outcomes.

What Does “Learn” Actually Mean?


What Are The Approaches and Types of Machine Learning?

Supervised Learning: Learn from data labeled by desired outcome

Unsupervised Learning: Learn to cluster & summarize similar inputs

Deep: Learn through a hierarchy of simple to complex concepts

Reinforced : Learn by continually interacting with an environment

Active: Learn by asking questions to increase confidence

Evolutionary: Learn to optimize using introduced randomness


How Have Artificial Neural Networks Evolved?

ArtificialNeural

Networks

Back Propagatio

n

MachineLearning

StatisticalAlgorithms

ArtificialNeural

Networks

Perceptron

ArtificialNeural

Networks

Deep Learning

1970 1980 1990 2000 2010

ArtificialNeural

Networks

DeepReinforcemen

tLearning

2020


What New Theories are Emerging?

Computers can process information 5 million times faster than a brain, but have difficulty mirroring basic human skills such as catching a ball. Researchers are exploring prediction & memory to close the gap• The brain constantly predicts, whether it be a few

milliseconds or years into the future, to make decisions and take action

• We rely extensively on our memory of performing similar tasks in the past and improve with practice

• Thinking is a form of pattern-matching; associating new decision / action information with stored information


What Distinguishes Artificial Intelligence?

Actively“Learns”

Continuously learns from every interaction.

Adapts“To Applications”Changes with data to evolve to new uses.

Anticipates“Outcomes”Uses machine

prediction to make decisions.

Aware“Of Context”

Perceives its environment through

data / sensors.

Acts“Independently”

Acts directly or recommends actions.

Achieves“Goals”

Optimizes towards better and better

outcomes.

Augments / Assists

“Humans”Enhance humans with Artificial Intelligence.

Auto / Autonomous“Processes”

Replace humans with Artificial Intelligence.

“The 10 A’s of AI or AI10”


An Example?

https://youtu.be/-96BEoXJMs0

https://youtu.be/-96BEoXJMs0


ClassifiersSemantic Abstractio

nPath

Planner Controller

ActuatorCommands

Hand-designed by experts Learned from experience data

Camera

FeatureExtractor

CameraEnd-to-End Process

ActuatorCommands

An Example?

• Trained on the complete end-to-end process • No intermediate steps• Maps vision directly to steering


What’s the Big Breakthrough?

Anticipate the range of scenarios a vehicle may encounter and program

decision algorithms accordingly.

Predict how humans would react to each second of

incoming data about their environment.


Prediction Is The Basic Unit Of Intelligence!Applied Analytics“Start with the

insights”

Inject insights to improvebusiness processes

Don’t Stop !Insights feed experiences

AppliedArtificial Intelligence

“Start with the experiences”

Augment / Automate (human)business processes

Start Now !Experiences feed insights

Pred

icti

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Machine LearningPowers

PredictionPrediction

PowersArtificial Intelligence

Artificial IntelligencePowers

Automation

Principles



Applied artificial intelligence provides a framework to identify and prioritize opportunities to accelerate business using AI, along with the principles to guide AI implementation and operation. Applied AI focuses on amplifying a company’s experience and expertise to improve and automate business process.


Principles of Applied Artificial Intelligence

Amplify Experience

Transfer Expertise

Earn Trust

1. Build on your strengths.2. Capture relevant data and context.3. Predict what experts do.4. Innovate around prediction.5. Focus on end-to-end process.6. Build experts into the loop.7. Diversify learning.8. Practice to perfect.9. Align accuracy with AI interaction.10. Optimize on a few levers to win. 11. Increase data velocity.12. Guard against error and bias

Automate Process

13. Build trust to drive adoption.14. Defend your ethical stance.


Build on your strengths.

Automate where you excel from your customers’ perspective. Identify skills, expertise and credentials that differentiate your business, considering both subject matter expertise and functional expertise. Prioritize experience with the potential to drive expansion and new business.

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Amplify Experience


Capture relevant data and context. Record expert processes to capture experience data. Identify and secure your top operators with the most experience and record their inputs, decisions, actions and outcomes. Extract insights that assist with their decision-making and gather context that influences their decisions.

2

Amplify Experience


Predict what experts do.

Predict the decisions experts will make as a proxy for human judgment. Substitute machine prediction as the criteria for decision-making to enable automation. Iteratively replace higher levels of expert judgement with prediction to automate more process.

3

Amplify Experience


Innovate around prediction.

Machine prediction is quickly becoming better, faster and cheaper than human prediction. Reframing judgment into prediction will scale decision-making and thereby create complimentary business opportunities. Organizations should plan and be ready to exploit the adjacent opportunities machine prediction will create .

4

Amplify Experience


Focus on end-to-end process.

Teach AI to augment or automate complete processes. Resist the traditional scientific method and the engineering approach of decomposing complex processes into simpler sub-processes. Complexity is not a barrier to AI automation. AI can learn, iterate and improve much faster than code can be programmed.

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Transfer Expertise


Build experts into the loop.

Teach AI to predict what experts will do both by example and through trial. Incorporate approaches that enable an AI to self-evaluate and seek input from experts where it needs it most. Keep experts in the loop to accelerate AI learning and increase confidence over time.

6

Transfer Expertise


Diversify learning.

Incrementally expose AI to an ever widening variety of users to enable it to learn to adapt to new situations over time. Push AI boundaries by continually exposing it to new experience. Where necessary, simulate rare use cases to further broaden AI experience.

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Transfer Expertise


Practice to perfect.

Repetition and ongoing coaching reinforces human skill and leads to better more consistent outcomes. Similarly, AI learns from its mistakes, successes and expert reinforcement to optimize outcomes. Extensive and continuous repetition strengthens AI confidence and decision pathways.

8

Transfer Expertise


Align accuracy with AI interaction.

Perfect accuracy is seldom achievable nor is it a real customer requirement. Carefully determine the levels of accuracy an AI has to achieve to add business value. Achieve the necessary accuracy to augment then assist humans, before introducing automated process with increasing autonomy.

Automate Process

9


Optimize on a few levers to win.

AI only needs to improve a few success metrics to significantly improve your process. Faster delivery, higher availability, more convenience, deeper engagement and reduced cost are key levers to consider. Identify the lever that can create the most initial value, optimize for it, then tackle the next most impactful.

Automate Process

10


Increase data and process velocity.

The AI race is a race to learn from experience data. More frequent observation will improve AI performance. As importantly, faster data will permit faster process and thereby accelerate the rate of AI learning. Implement architectures that support learning from continuous, real time data.

Automate Process

11


Guard against error and bias.

Real world experience data can introduce both error and bias. Unlike static codebases, AI continues to learn and adapt through interaction. Use a mix of automation and human oversight to continually monitor and improve quality. Carefully reduce the level of human oversight over time, but never eliminate it.

Automate Process

12


Build trust to drive adoption.

Deploy AI-solutions gradually to build both traction and trust. Causality is both difficult to determine and explain with AI. Focus instead on communicating improved outcomes to offset user need for rationale. Empower users to gradually share more context in step with the value they derive.

Earn Trust

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Defend your ethical stance.

AI will raise ethical, social and legal issues. Carefully consider both the potential benefits and consequences of error. Develop an argument, supported with facts, to highlight benefits. Be open and transparent about the cost of error. Acknowledge that increased automation will shift workloads and impact jobs.

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Earn Trust

Maturity Levels



Applied AI maturity levels provide a tool to evaluate the current state of a company’s readiness for applied AI, and its subsequent operational maturity. It a useful tool to both evaluate current state and evolve to future states of maturity.

Level 1: Evaluates key strategic assets of a company that indicate whether it is better prepared to implement applied AI than its competitors.

Level 2: Looks at the tactical planning of a company to evaluate whether they have business process advantage to applying and operating AI.

Level 3: A company is already operational with applied AI and their effectiveness and efficiency is evaluated against principles.

Maturity Levels


Principle Level 1 Level 2 Level 3

1. Build on your strengths.Your unique domain experience and expertise differentiates your business.

Your roadmap continually reinforces your domain experience and expertise.

You amplify your core experience and expertise with automation.

2. Capture relevant data and context.

You capture the inputs, decisions, and outputs of your experts.

You systematically seek more context that influences your experts decisions.

You capture complete experience data in real time mirroring the real world.

3. Predict what experts do.You codify your expertise to inform decision making.

You use machine prediction to model your experts responses.

You predict the decisions and actions of experts as a base for automation.

4. Innovate around prediction.You employ machine prediction / predictive analytics in your business.

You anticipate opportunities that arise from better, faster and cheaper prediction.

You achieve early-mover advantage by innovating around prediction.

Amplify Experience - Maturity Levels



5. Focus on end-to-end process.

Your organization focuses on end-to-end process improvement.

You constantly strive to optimize process by eliminating intermediate steps.

You automate complete end-to-end processes.

6. Build expert into the loop.Your domain experts are integral to your product lifecycle.

Your domain experts intercede to influence better outcomes.

Your domain experts transfer learning to automated processes.

7. Diversify learning.Your user base is comprised of more diverse customers than your competitors.

Your leverage your user’s diversity to expand the use cases you can support.

You constantly learn new use cases and incorporate them into your processes.

8. Practice to perfect.Your user base is larger and/or more engaged than your competitors.

You leverage your volume / frequency advantage o reinforce best practices.

Your confidence continually improves over time with practice.

Transfer Expertise - Maturity Levels



9. Align accuracy with AI interaction.

Your market advantage includes the accuracy and consistency of your outcomes.

You gauge accuracy from your users view before introducing any new automation.

You systematically revise and track accuracy for different types of user interaction.

10. Optimize on a few levers to win.

You have a service level advantage along one or more key performance indicators.

You constantly strive to increase your service level advantage over your competitors.

Your key performance indicators are disrupting your industry.

11. Increase data velocity.You capture and process interaction data in near real time.

You are moving successfully to a data streaming architecture.

Your core processes are automated in real time.

12. Guard against error & bias.You publish statistics on your overall level of error and bias.

Your predictions and recommendations include specific error and bias metrics.

You monitor, adjust and correct error and bias in real time.

Automate Process - Maturity Levels



13. Build trust to drive adoption.Your users value better outcomes over better explanations.

Your users readily adopt updates that drive better outcomes from you.

Your users trust you to improve outcomes without knowing the underlying logic.

14. Defend your ethical stance.You have an ethical stance on the application of technology.

Your ethical stance has been tested and successfully defended.

You are viewed as an industry leader in the ethical application of technology.

Earn Trust - Maturity Levels

Questions?

applied artificial intelligence - trends and principles

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