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Bringing AI to Business Intelligence Integrated Knowledge Solu0ons
h3ps://iksinc.wordpress.com/home/ [email protected]
Agenda
• Current BI environment • What is AI? • AI Technologies • How is AI being used by businesses? • Roadmap for bringing AI to BI • Summary
Current BI Environment
Why so much clamor for AI? What is missing in BI? Lets look at BI value chain
What’s Missing?
• “What is happening?” • “Why is it
happening?” Not much problem there.
Current BI tools are good at answering:
However, there are inherent dangers in manually searching for rela0ons/pa3erns/trends in charts/dashboards.
Analysts' biases are unavoidable; A study at Bayer about 10 years ago found that 70% of analysis could not be replicated by changing the analyst.
Gartner defines prescrip0ve analy0cs as: “…the applica0on of logic and mathema0cs to data to specify a preferred course of ac0on. While all types of analy0cs ul0mately support be3er decision making, prescrip0ve analy0cs outputs a decision rather than a report, sta0s0c, probability or es0mate of future outcomes.”
AI
Predic?ve vs. Prescrip?ve Analy?cs
Predic0ve analy0cs is simply focused on the outcome – good for a sports be3or Prescrip0ve analy0cs is what the coaching staff needs
What is AI?
What Characterizes Intelligence • Ability to interact with real world • To perceive, understand, and act
• Searching the best solu0on • Reasoning and planning • Modeling the environment • Solving new problems, planning, and making decisions • Ability to deal with uncertain0es
• Learning and adapta0on
What is AI? • The term ar0ficial intelligence was coined by John McCarthy circa 1956. He defined it as “the science and engineering of making intelligent machines”
Ar0ficial intelligence is technology that appears to emulate human performance typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialogs with people, enhancing human cogni0ve performance (also known as cogni0ve compu0ng) or replacing people on execu0on of non-‐rou0ne tasks.
Gartner Defini0on [email protected]
Weak AI
• Also known as Narrow AI • a descrip0ve term used for AI that can demonstrate human like intelligence, but only for a specific task or tasks. Majority of today's AI systems fall in this category.
Ar?ficial General Intelligence (AGI)
• Also known as Strong AI • a term used to describe a certain mind-‐set of ar0ficial intelligence development. Strong AI’s goal is to develop ar0ficial intelligence to the point where the machine’s intellectual capability is func0onally equal to a human’s.
Ar?ficial Super Intelligence (ASI) • A term used for AI of the future. It will be be superior to any level of human intelligence and will (poten0ally), if allowed, be in complete control of its own decision making.
“It seems probable that once the machine thinking method had started, it would not take long to
outstrip our feeble powers... They would be able to converse with each other to sharpen their wits. At some stage therefore, we should have to expect the
machines to take control.”
Alan Turing, the 'godfather of AI'
from Nick Bostrom’s latest book: ‘Superintelligence: Paths, Dangers, Strategies
AI Technologies
Learning in AI • Most domina0ng subfield of AI today. Machine learning is concerned with making computers learn to make predic0ons/decisions without explicitly programming them. Rather a large number of examples of the underlying task are shown to op0mize a performance criterion to achieve learning. • Two major styles of machine learning: Supervised and unsupervised
Supervised Learning • Training data comes with answers, called labels • The goal is to produce labels/answers for new data
Supervised Learning Models
• Classifica0on models • Predict whether a customer is likely to be lost to compe0tor • Tag objects in a given image • Determine whether an incoming email is spam or not
Supervised Learning Models
• Regression models • Predict credit card balance of customers • Predict the number of 'likes' for a pos0ng • Predict peak load for a u0lity given weather informa0on
Unsupervised Learning
• Training data comes without labels • The goal is to group data into different categories based on similari0es
Grouped Data
Unsupervised Learning Models • Segment/ cluster customers into different groups • Organize a collec0on of documents based on their content • Make Recommenda0ons for products
Deep Learning • It’s a subfield of machine learning that has shown remarkable success in dealing with applica0ons requiring processing of pictures, videos, speech, and text. • Deep learning is characterized by: • Extremely large amount of data for training • Neural networks with exceedingly large number of layers • Training 0me running into weeks in many instances • End to end learning (No human designed rules/features are used)
Natural Language Processing & Speech Recogni?on • NLP & speech recogni0on are those subfields of AI that make it possible for machines to communicate with humans by understanding wri3en or spoken text • The text could be structured or unstructured. • These two subfields of AI are finding many applica0ons in the industry to build new UIs that are proving more effec0ve. • Alexa, Cortana, Siri are all examples of these AI technologies. • IBM Watson is another example of using NLP to assist in evidence based medicine
Named-‐En?ty Recogni?on • It’s a subfield of NLP; Named En0ty Recogni0on (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names. • Helpful for automa0c informa0on extrac0on to build rela0onships between different en00es. Think of Jeopardy.
Op?miza?on and Planning • Lots of overlap with opera0ons research • AI centric op0miza0on methods: • Gene0c algorithms • Based on natural selec0on in a popula0on
• Simulated annealing • Based on crystal forma0on in solids through cooling
Op?miza?on and Planning • Ant colony op0miza0on
• Based on how ants leave markers for other ants
• Par0cle swarm op0miza0on • Based on behavior of the flock of birds, pool of fishes etc.
How is AI being used by businesses?
ORION, an acronym that stands for On-‐Road Integrated Op0miza0on and Naviga0on, is perhaps the largest commercial analy0cs project ever undertaken. It’s required well over a decade to build and roll out, and more than $250 million of investment by UPS.
Savings in driver produc0vity and fuel economy: $300 -‐ $400 million a year, 100 million fewer miles driven and a resul0ng cut in carbon emissions of 100,000 metric tons a year.
• Vetride system is an another example of prescrip0ve analy0cs developed by a local company • The system is being used at 64 VA sites all over USA and is installed approximately in 1200 vehicles • Provides veterans transporta0on at demand op0mizing a number of parameters with many constraints
How to Get Started with AI?
Some Poten?al Sugges?ons
• Candidate task characteris0cs for ini0al AI projects: • Complexity level : low to medium • High volume and repe00ve • No legal or ethical risks • Fair level of user interac0on, internal or external
Whit Andrews, Gartner VP
Michael Azoff, Ovum Principal Analyst
Three Phase Process
• Prepara0on phase • Structure preparedness • Organiza0onal preparedness • Knowledge preparedness
• Buy-‐in and value crea0on • Create awareness and value proposi0on • Brainstorm project ideas • Demo value
• Organiza0on wide adop0on [email protected]
Summary
Summary
• AI is set to play a big role in businesses across a wide spectrum • Tune out the hype and focus on how you can ini0ate a low risk, low complexity project to get started. • Be op0mis0c, that is an AI trait
As John McCarthy so perfectly stated back in 1956 -‐ “As soon as it works, no one calls it AI
anymore.”
