COMM190 - Bus Tech Final Exam

Post Midterm: Dashboards

Dashboards - what and why?

• WHAT is a dashboard?

  • a dashboard is a tool used to visually display data to gain deeper insight into the overall well-being of the organization, a department, or even a specific process

• WHY are dashboards important?

  • connecting dashboards to specific metrics or key performance indicators (KPIs), you gain vital business intelligence and the ability to dive deep into specific pieces of information to continually monitor success

• WHAT can you do with a dashboard?

  • provide up-to-date information and context to help inform business decisions and empower employees

    • performance measurements

    • data transparency and accessibility

    • detect changes

    • forecasting

• WHAT are the benefits of a dashboard?

4 types of data analytics

what is the data telling you?

from least to most valuable and complex

descriptive: whats happening in my business

• comprehensive, accurate and live data

• effective visualization

diagnostic: why is it happening?

• ability to drill down to the root-cause

• ability to isolate all confounding information

predictive: what’s likely to happen

• business strategies have remained fairly consistent over time

• historical patterns being used to predict specific outcomes using algorithms

• decisions are automated using algorithms and technology

prescriptive: what do I need to do?

• recommend actions and strategies based on champion/challenger testing strategy outcomes

• applying advanced analytical techniques to make specific recommendations

Data Cube

Dashboard Visual

Dashboards with AI

• manus AI example

• can also create dashboards with other AI tools

• Python is a great programming language for creating visualizations

Building a Dashboard in Excel: Pivot Table Components

Artificial Intelligence

“because of AI, 100% of jobs will be different”

• IBM CEO Ginni Rometty (2019)

“AI is the new electricity. It will transform every industry and create huge economic value”

• Dr. Andrew Ng

“The development of full AI could spell the end of the human race”

• Stephen Hawking

“AI is highly likely to destroy humans”

• Elon Musk

“Elon Musk’s doomsday AI predictions are ‘pretty irresponsible'"

• Mark Zuckerberg

reactive maintenance vs predictive maintenance

(not on slides)

• Reactive Maintenance: This approach follows a "run-to-failure" logic, where repairs only happen after a machine breaks down, leading to high costs from unplanned downtime and emergency fixes.

• Predictive Maintenance (AI): Using IoT sensors and machine learning, this method analyzes real-time data like vibration and heat to identify failure patterns and perform maintenance just before a breakdown occurs.

• Strategic Impact: While predictive maintenance requires more upfront investment in technology, it significantly extends asset life and reduces long-term operational expenses by transforming "surprises" into scheduled, minor tasks.

Robotic Process Automation

• robotic process automation is a form of business process automation that allows anyone to define a set of instructions for a robot or ‘bot’ to perform

• these bots are capable of mimicking most human-computer interactions to carry out a ton of error-free tasks, at high volume and speed

• the “robot” in robotic process automation is software robots running on a physical or virtual machine

In the beginning - Dartmouth College (1956)

The 1956 Dartmouth Summer Research Project on Artificial Intelligence established AI as a formal academic field. Organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, it provided the discipline with its name and foundational goals.

Highlights

• The Goal: The organizers believed that every aspect of learning or intelligence could be precisely described and simulated by a machine.

• The Name: John McCarthy coined "Artificial Intelligence" to distinguish the field from cybernetics and computer science.

• The Breakthrough: Allen Newell and Herbert Simon introduced the "Logic Theorist," the first program designed to mimic human problem-solving skills.

• The Impact: Though the workshop did not achieve immediate human-level AI, it shifted computing from simple number crunching toward symbolic logic and language.

Quick Stats

• Location: Dartmouth College, Hanover, New Hampshire.

• Funding: A $7,500 grant from the Rockefeller Foundation.

• Duration: Eight weeks during the summer of 1956.

From slide:

• this conference is “to proceed on the basis of the conjecture that every aspect of learning of any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”

  • Professor John McCarthy, 1956 (convenor of the AI meeting)

What is Artificial Intelligence?

• there is much debate surrounding what is artificial intelligence

  • some feel that it is acting and thinking like a human, while others feel it is to act and think rationally

• think humanly, act humanly

• think rationally, act rationally

What is AI: Act Humanly

What does it mean to act like a human?

Need to be able to…

• natural language processing

  • communicate successfully in a human language

• knowledge representation

  • store what it knows or hears

• automated reasoning

  • answer questions and to draw new conclusions

• machine learning

  • adapt to new circumstances and to detect and extrapolate patterns

• computer vision and speech recognition

  • vision and speech recognition to perceive the world

• robotics

  • manipulate objects and move about

What is AI: Think Humanly

What does it mean to think like a human?

• to say that a program thinks like a human, we must know how humans think

  • we can learn about human thought in 3 ways:

1. introspection

• trying to catch our own thoughts

2. psychological experiments

• observing a person in action

3. brain imaging

• observing the brain in action

once we have enough information, we have a working model

• if the program’s input-output matches corresponding human behaviour, we have evidence of human thinking

What is AI: Act Rationally

What does it mean to act rationally?

• This is the field of logical thinking that many philosophers and mathematicians have been working on

• In artificial intelligence, it is called Logicism, which hopes to build intelligent systems

• Though the systems are intelligent, it does not generate intelligent behaviour

What is AI: Think Rationally

What does it mean to think rationally?

• It is called the Rational Agent Approach

• Computer programs are expected to do something, but agents are expected to do more;

  • operate autonomously

  • perceive the environment

  • persist over a prolonged time

  • adapt to change

  • create and pursue goals

Artificial Intelligence: Broad

• AI, in the broadest term, applies to any technique that enables computers to mimic human intelligence, using logic, if-then rules, decision trees and machine learning

  • Time Magazine, 2017

Machine Learning

• Machine learning is a method of data analysis that automates analytical model building

• It is a branch of AI based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention

  • SAS (2017)

Neural Networks

• software constructions modelled after the way adaptable networks of neurons in the brain are understood to work, rather than through rigid instructions predetermined by humans

  

A simple neuron

• a perception takes several binary inputs (x1, x2, x3), and generates an output

• weights (w1, w2, w3), representing the importance of each input is identified

• the neuron’s output is 0 or 1

• the neuron’s output is determined by whether the weighted sum (w1×1 + w2×2 + w3×3) is greater than some threshold value

Types of machine learning

supervised learning

• a model uses known input and outputs to generalize future outputs

unsupervised learning

• the model doesn’t know input or outputs, so it finds patterns in the data without help

reinforcement learning

• where the model interacts with its environment and learns to take actions that will maximize rewards

Supervised learning

• Learn by identifying patterns in data that is already labelled

• Classification:

  • fraud detection

  • image recognition

  • customer retention

  • medical diagnostics

  • personalized advertising

• Regression:

  • product sales prediction

  • weather forecasting

  • market forecasting

  • population growth prediction

Unsupervised learning

• The machine must uncover and create the labels itself

• Clustering:

  • product recommendations

  • customer segmentation

  • targeted marketing

  • medical diagnostics

• Dimensionality reduction:

  • visualization

  • natural language processing

  • data structure discovery

  • gene sequencing

    

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machine learning examples

a tale of two games

• chess

• GO

Chess

• IBM Deep Blue

• vs

• Garry Kasparov, May 11, 1997

GO

• DeepMind AlphaGo

• vs

• Fan Hui (Go), 2015

IBM’s Deep Blue was programmed with decision trees or equations on how to evaluate board positions of with if-then rules

AlphaGo learned how to play Go essentially from self-play and from observing big professional games

WHY NOW? - machine learning

• increased availability: in volume, velocity, and variety of data

• as of 2025, the world generates approximately 463 exabytes of data daily, which is equivalent to 463 million terabytes

• to put this in perspective, this data generation is comparable to the storage capacity of about 212 million DVDs

• this exponential growth in data creation is driven by factors such as increased internet usage, the proliferation of Internet of Things (IoT) devices, and the expansion of cloud-based services

• significant improvements in computing power and storage (at affordable cost)

computing power improvements

• 1970s-1980s

  • early personal computers, like the Apple II (1977), operated at speeds around 1 MHz (million instructions per second)

  • limited multitasking capabilities; computations took hours or days

• 1990s-2000s

  • pentium processors (1993) reached speeds around 100 MHz - 1 GHz

  • performance improvements enabled graphical interfaces, gaming, and productivity software

• 2000s-2010s

  • multi-core processors emerged, allowing parallel processing

  • typical CPU speeds increased significantly (2-3 GHz per core became common)

  • GPUs (Graphics Processing Units) became popular for complex computations (AI, rendering, gaming)

• 2010s-today

  • high-performance GPUs and specialized AI hardware (ex// NVIDIA GPUs and TPUs) now deliver petaflops of computing power

  • modern GPUs (like NVIDIA A100 or H100) handle thousands of parallel processes, vastly accelerating AI tasks