Introduction to AI-Driven Digital Transformation

Overview

This Lecture is recorded in 8 parts and gives an introduction and motivation for the class. This and other lectures in class are divided into “bite-sized lessons” from 5 to 30 minutes in length; that’s why it has 8 parts.

Lecture explains what students might gain from the class even if they end up with different types of jobs from data engineering, software engineering, data science or a business (application) expert. It stresses that we are well into a transformation that impacts industry research and the way life is lived. This transformation is centered on using the digital way with clouds, edge computing and deep learning giving the implementation. This “AI-Driven Digital Transformation” is as transformational as the Industrial Revolution in the past. We note that deep learning dominates most innovative AI replacing several traditional machine learning methods.

The slides for this course can be found at E534-Fall2020-Introduction

A: Getting Started: BDAA Course Introduction Part A: Big Data Applications and Analytics

This lesson describes briefly the trends driving and consequent of the AI-Driven Digital Transformation. It discusses the organizational aspects of the class and notes the two driving trends are clouds and AI. Clouds are mature and a dominant presence. AI is still rapidly changing and we can expect further major changes. The edge (devices and associated local fog computing) has always been important but now more is being done there.

B: Technology Futures from Gartner’s Analysis: BDAA Course Introduction Part B: Big Data Applications and Analytics

This lesson goes through the technologies (AI Edge Cloud) from 2008-2020 that are driving the AI-Driven Digital Transformation. we use Hype Cycles and Priority Matrices from Gartner tracking importance concepts from the Innovation Trigger, Peak of Inflated Expectations through the Plateau of Productivity. We contrast clouds and AI.

C: Big Data Trends: BDAA Course Introduction Part C: Big Data Applications and Analytics

• This gives illustrations of sources of big data.
• It gives key graphs of data sizes, images uploaded; computing, data, bandwidth trends;
• Cloud-Edge architecture.
• Intelligent machines and comparison of data from aircraft engine monitors compared to Twitter

D: Computing Trends: BDAA Course Introduction Part D: Big Data Applications and Analytics

• Multicore revolution
• Overall Global AI and Modeling Supercomputer GAIMSC
• Moores Law compared to Deep Learning computing needs
• Intel and NVIDIA status

E: Big Data and Science: BDAA Course Introduction Part E: Big Data Applications and Analytics

• Applications and Analytics
• Cyberinfrastructure, e-moreorlessanything.
• LHC, Higgs Boson and accelerators.
• Astronomy, SKA, multi-wavelength.
• Polar Grid.
• Genome Sequencing.
• Examples, Long Tail of Science.
• Wired’s End of Science; the 4 paradigms.
• More data versus Better algorithms.

F: Big Data Systems: BDAA Course Introduction Part F: Big Data Applications and Analytics

• Clouds, Service-oriented architectures, HPC High Performance Computing, Apace Software
• DIKW process illustrated by Google maps
• Raw data to Information/Knowledge/Wisdom/Decision Deluge from the EdgeInformation/Knowledge/Wisdom/Decision Deluge
• Parallel Computing
• Map Reduce

G: Industry Transformation: BDAA Course Introduction Part G: Big Data Applications and Analytics

AI grows in importance and industries transform with

• Core Technologies related to
• New “Industries” over the last 25 years
• Traditional “Industries” Transformed; malls and other old industries transform
• Good to be master of Cloud Computing and Deep Learning
• AI-First Industries,

H: Jobs and Conclusions: BDAA Course Introduction Part H: Big Data Applications and Analytics

• Job trends
• Become digitally savvy so you can take advantage of the AI/Cloud/Edge revolution with different jobs
• The qualitative idea of Big Data has turned into a quantitative realization as Cloud, Edge and Deep Learning
• Clouds are here to stay and one should plan on exploiting them
• Data Intensive studies in business and research continue to grow in importance