AI Strategy – Workshop 2 (AI Foundations)

The Appleton Greene Corporate Training Program (CTP) for AI Strategy is provided by Mr. Stambaugh Certified Learning Provider (CLP). Program Specifications: Monthly cost USD$2,500.00; Monthly Workshops 6 hours; Monthly Support 4 hours; Program Duration 12 months; Program orders subject to ongoing availability.

If you would like to view the Client Information Hub (CIH) for this program, please Click Here

Learning Provider Profile

Mr. Stambaugh has decades of experience designing, planning, and implementing complex technology transformations in public and private organizations. He has led enterprise-level programs focused on Information Security (InfoSec), industrial SCADA deployments, telecommunications modernization as well as advanced analytics / artificial intelligence (AI) / machine learning deployment – and managed complex national technology and operational teams at the VP and director level. He has deep experience in the energy, utilities, geospatial, and telecommunications sectors, operating in Canada and the United States. This experience is supported by a master’s-level technical degree and nearly ten years as a science and technology columnist with the Canadian Broadcasting Corporation (CBC) on radio and national television.

He has leveraged this broad background in technology transformation into a successful Artificial Intelligence (AI) implementation practice, assisting organizations with the complex but critical task of creating an AI strategy and then developing and executing their implementation strategy. He is excited to leverage this experience to support other organizations on their AI journey through this program.

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MOST Analysis

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Mission Statement

Overall this workshop is designed to provide a foundation and important context for participants by defining key terms, highlighting the history and background of Artificial Intelligence / Machine Learning (with a specific focus on where it has been successfully deployed into organizations in the past), and by providing an overview of key AI models in use today.

Understanding where AI/ML technologies have come from, as well as some of the recent key innovations in the space will better enable participants to understand the opportunities and challenges will present for their organization. Getting a high-level overview of core terms as well as common AI models will provide a common language for decision makers to use with their AI technical teams (internal or external) as they develop and implement their AI strategy.

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Objectives

01. Terms, Concepts & Definitions: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
02. A Brief History: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
03. AI Models: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
04. Regression: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
05. Deep Learning: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
06. Generative AI: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
07. CNNs: departmental SWOT analysis; strategy research & development. 1 Month
08. AI for Conversation: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
09. AI for Audio: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
10. Current AI Applications: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
11. Future AI Applications: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month
12. Summary & Review: departmental SWOT analysis; strategy research & development. Time Allocated: 1 Month

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Strategies

01. Terms, Concepts & Definitions: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
02. A Brief History: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
03. AI Models: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
04. Regression: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
05. Deep Learning: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
06. Generative AI: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
07. CNNs: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
08. AI for Conversation: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
09. AI for Audio: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
10. Current AI Applications: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
11. Future AI Applications: Each individual department head to undertake departmental SWOT analysis; strategy research & development.
12. Summary & Review: Each individual department head to undertake departmental SWOT analysis; strategy research & development.

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Tasks

01. Create a task on your calendar, to be completed within the next month, to analyze Terms, Concepts & Definitions.
02. Create a task on your calendar, to be completed within the next month, to analyze A Brief History.
03. Create a task on your calendar, to be completed within the next month, to analyze AI Models.
04. Create a task on your calendar, to be completed within the next month, to analyze Regression.
05. Create a task on your calendar, to be completed within the next month, to analyze Deep Learning.
06. Create a task on your calendar, to be completed within the next month, to analyze Generative AI.
07. Create a task on your calendar, to be completed within the next month, to analyze CNNs.
08. Create a task on your calendar, to be completed within the next month, to analyze AI for Conversation.
09. Create a task on your calendar, to be completed within the next month, to analyze AI for Audio.
10. Create a task on your calendar, to be completed within the next month, to analyze Current AI Applications.
11. Create a task on your calendar, to be completed within the next month, to analyze Future AI Applications.
12. Create a task on your calendar, to be completed within the next month, to analyze Summary & Review.

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Introduction

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From theoretical ideas, artificial intelligence (AI) and machine learning (ML) have developed into transforming technologies changing sectors and fostering innovation in many others. Knowing the path of artificial intelligence and machine learning from its birth to its present uses helps one to better understand how these technologies might be successfully included into contemporary companies.

Historical Context

As a discipline of study, artificial intelligence started in the middle of the 20th century thanks to the pioneering efforts of people like John McCarthy and Alan Turing. Due to restricted practical applications resulting from computing power and data availability, the first decades were distinguished by notable theoretical developments but little use. Reduced financing and attention defined the AI winter seasons; then, resurgence times marked by technology innovations reignited excitement.

The introduction of ML, especially in the 1980s and 1990s, signaled a dramatic turn toward data-driven methods. Task like pattern recognition, natural language processing, and predictive analytics started to show promise for ML algorithms—capable of learning from and making predictions based on data. The recent AI/ML rebirth has been driven by the exponential increase in data generation, improvements in processing capacity, and the evolution of complex algorithms together with changes in computational power.

Successful Deployments

AI/ML technologies have been successfully deployed in various organizations, demonstrating their potential to enhance efficiency, decision-making, and innovation.

Customer Service
Customer service is among the most obvious uses for artificial intelligence/machine learning. Businesses such as Amazon and Bank of America have included artificial intelligence-driven chatbots to answer consumer questions, offer assistance, and simplify contacts. Offering 24/7 help and much shortened response times, these chatbots use natural language processing (NLP) to interpret and answer consumer questions.

Predictive Maintenance
Predictive maintenance applications of artificial intelligence/machine learning find utility in manufacturing and industrial industries. ML techniques are used by companies like General Electric (GE) to examine machinery and equipment data in order to forecast failures before they materialize. By means of this proactive strategy, equipment lifetime is extended, maintenance expenses are lowered, and downtime minimized.

Personalized Marketing
Retailers using AI/ML for tailored marketing include Netflix and Spotify. These businesses offer customized recommendations that improve user involvement and pleasure by means of an analysis of user behavior and preferences. Along with increasing customer retention, this stimulates sales and income generation.

Healthcare Diagnostics
Artificial intelligence/machine learning has been applied in healthcare to raise patient outcomes and diagnosis accuracy. To help clinicians diagnose ailments and suggest therapies, systems such as IBM Watson Health examine medical records, academic papers, and clinical trial data. This offers individualized care strategies and aids in early condition identification.

Recent Innovations

Recent major developments in artificial intelligence and machine learning include Google’s AlphaFold, which remarkably accurately predicts protein folding, and OpenAI’s GPT-3, which can create human-like prose. In fields including language translation, drug discovery, and autonomous systems, these discoveries are creating fresh paths for artificial intelligence/machine learning uses.

Opportunities and Challenges

Knowing the historical background and effective implementations of artificial intelligence and machine learning helps companies to find chances where these tools might be used to address certain problems. Deploying AI/ML does, however, also present difficulties including ethical questions, data privacy issues, and the demand for trained expertise. Companies have to evaluate these elements closely and create plans to properly and successfully include artificial intelligence and machine learning.

Examining past deployments and new developments helps participants to fully grasp the possible uses and difficulties of artificial intelligence and machine learning, so better positioning their companies to exploit these strong technologies for operational excellence and competitive advantage.

Addressing Organizational Pain Points with AI/ML Context and Background

Knowing the background and context of artificial intelligence and machine learning will assist companies greatly overcome many obstacles. Here we explore particular pain areas and how a strong knowledge of artificial intelligence and machine learning might help to reduce them.