EDUCATION,LITERACY AND JUSTICE Public Pedagogy Piece Jonathan Rohit Bhavanasi PRIMER You’re unlikely to meet a business executive who would deny the potential impact of AI on an industry. It wouldn’t be ”cool” to do so. And more importantly, it would be shockingly short - sighted. Every major survey points the same way. 66% of sales teams call out the transformative ability of AI/expert systems when used for customer engagement. 62% of the top performing salespeople expect an acceleration in guided sales. 80% of B2B marketing executives expect AI to revolutionize the industry. One can go on and on ad infinitum. There is a mistake with the above scenario, one with dire consequences. All the data and perspective we have on the adoption of AI is exclusively from a business application perspective. That is to say, as a society we are woefully unprepared for the transformative effect AI will have on our education systems. The following deck is intended to serve as a primer on the evolution of AI, specifically what it entails and represents for status quo employment. This document is intended to be a companion piece to the CW final paper and is intended to provide context with respect to the same. THE GLOBAL AI MARKET BY NUMBERS $13 - 16 Trillion The potential impact of AI on global GDP by 2030 30% By 2021 - 22, a third of B2B companies will employ “AI” in sales Of sales teams that use AI claim an increase task efficacy 85% High performers in B2B sales roles are almost 5 times more likely to use AI than lower performing reps 4.9x ... increase sales of new products and services by more than 10% 3 in 4 organizations that implement AI driven systems... 76% of companies have grown their sales teams after implementing AI in sales Source: MXV Consulting The use of AI in B2B sales ARTIFICAL INTELLIGENICE IS EVOLVING RAPIDLY Artificial Intelligence Analysis of structured data Supervised learning Unsupervised learning Analysis of unstructured data Easier for systems to analyze and interpret Displayed as rows and columns Examples are Excel sheets and SQL data More complex to analyze and interpret Has no predefined format Examples are images, audio, video, emails & texts Used for product recommendations Applications in data visualization Learning is done without prior knowledge Learning is done with prior knowledge Used for predictions – win rates/deals at risk Used for market forecasting DATA ANALSIS IS MOVING FROM STRUCUTRED TO UNSTRUCTURED Structured data Unstructured Data Structured data consists of any quantitative data that follows a defined model, i.e a tabular format that has a relationship between rows and columns Data is easy to export, store and organize, and is currently optimized for processing through data analytics software. It usually consists of objective facts and numbers This includes social media, emails, audio and video clips, blog posts, images etc. This type of data is not easy to organize and analyze Unstructured data is qualitative data that does not follow a defined model. This type of data makes up more than 80% of all data generated today In the past, data analysis could only be run using structured data, improvements in technology have enabled companies to use unstructured data to gather insights MACHINE LEARNING IS MOVING FROM SUPERVISED TO UNSUPERVISED Supervised learning Supervised learning is a method of machine learning where the machine learns based on a correct labelling of items For example, to teach a machine program how to autonomously identify fruits in a bowl, supervised learning requires a training data set After training, the machine can classify a new object as an apple or a banana without intervention The machine has learnt under supervision, with a dataset on which to base inference/decisions Examples of labelled items: Banana: YELLOW,LONG,BLACK SPOTS Apple: RED,ROUND,DEPRESS ION AT THE TOP Unsupervised learning Unsupervised learning is a system where the machine learns and then classifies new objects autonomously i.e with no correctly labelled datasets on which to base assumptions With the above example, the machine has no prior knowledge (training dataset) from which to identify the fruits in the bowl The machine learns on its own. Classifying each object according to its characteristics. It does not know what an apple is but knows its physical properties As a result, when the machine is given another apple, it recognizes the similarities between the two and puts it in the same group/classification AI USE CASES CAN TURBO CHARGE OPERATIONAL TEAMS Enhanced Lead Generation using NLP systems Dynamic Lead Qualification Intent based classification Higher Engagement Employment of bots and reinforcement learning Smarter Prioritization of Leads Prospect behavior driven Better Sales Pitches Identifying patterns of client behavior Individualized Nudges Incentivize sales reps Conversational Intelligence Guide and coach workflow teams Easier And More Accurate Forecasting Guide and coach workflow teams Automated And Improved Decision Making Improves over time Enhanced Engagement For Existing Accounts Chatbot enabled Dynamic Pricing Mechanisms Real time market data Accurate Cross Selling Segmenting and clustering client data CASE STUDY WHEN ALGORITHIMS BECOME MANAGERS At Uber, an algorithm can perform many tasks that a manager would usually perform Track Statistics : Cancellation rates, trips completed, ratings etc. Provide Motivational Nudges : “Great work, you’re now in the top 10% of drivers!” Guide Drivers : Point them towards areas with high demand/fares Who is responsible for faulty information flows? Source: The New York Times Can employees effectively voice grievances when every decision is data driven ? Can management get away with errors or wrongdoings by blaming faulty software/glitches? Questions that remain unanswered: HOWEVER,AUTOMATION ALSO OPENS UP NEW OPPORTUNITIES Certain tasks can be automated - Lead qualification - Forecasting quarterly revenue - Determining effective pricing - Management Information System Reports ... and certain others can be augmented - Client intelligence - Coaching - Motivating Building and growing relationships with new and existing clients Creating strong sales programs Conceptualizing and implementing creative sales and marketing Identifying emerging markets and opportunities Allowing individuals to focus on... END OF DOCUMENT