Shared Presentation

AI vs Traditional Computing

The Strategic Shift in Computing

Table of Contents

01

The Core Difference: Rules vs. Examples

02

The "Silicon Brain": Neuromorphic Engineering

03

Programming vs. Training: The Duolingo Analogy

04

How It Works: Probability and Prediction

05

Business Implication 1:

06

Business Implication 2: The Need for Prompt Engineering

07

The Risks: Hallucinations and Bias

08

Strategic Hybrid: Reasoning and Record-Keeping

09

Conclusion:

The Core Difference: Rules vs. Examples

1

Explicit Instructions

Traditional computers follow explicit instructions defined by programmed software.

2

Learning from Examples

AI systems learn from examples and data, identifying patterns without explicit programming.

3

User Quote

Traditional Computers follow instructions; AI learns from examples.

4

Handling Complex Problems

This shift allows AI to handle complex, unstructured problems that are difficult to solve with traditional methods.

02

The "Silicon Brain": Neuromorphic Engineering

1

Different Architecture

AI is not merely a software update but a fundamentally different architecture.

2

Mimics Biology

Neuromorphic engineering mimics the structure and function of biological neural networks.

3

User Quote

Artificial intelligence infrastructure is designed to mimic the human brain.

4

Parallel Processing

This allows for parallel and distributed processing, similar to the human brain, enabling faster and more efficient computation for certain tasks.

03

Programming vs. Training: The Duolingo Analogy

1

Code vs. Data

Traditional programming involves writing explicit code, while AI training involves feeding data and adjusting parameters.

2

Iterative Feedback

The Duolingo analogy: AI learns through iterative feedback, similar to how language learners improve through practice and error correction.

3

User Quote

I am a level 26 French learner and I am currently in 10th place in the diamond league.

4

Generalization & Adaptation

This training process enables AI to generalize and adapt to new situations, unlike traditional programs with fixed rules.

04

How It Works: Probability and Prediction

1

Probability & Statistics

AI models use probability and statistical patterns to predict the next best option based on training data.

2

Learned Distributions

The AI doesn't simply match exact patterns but uses learned probability distributions.

3

Token Prediction

Each token (word or subword) prediction is based on complex statistical relationships.

4

Generative Process

The model generates responses token by token, influenced by the prompt and previously generated tokens.

05

Business Implication 1: Insight vs. Certainty

1

Predictive Insights

AI offers predictive insights, enabling businesses to anticipate trends and make proactive decisions.

2

Traditional Certainty

Traditional software provides certainty through precise calculations and rule-based operations.

3

Scenario Strengths

AI excels in scenarios with uncertainty and incomplete information, while traditional software is ideal for tasks requiring accuracy and repeatability.

4

Practical Application

This means AI can assist in forecasting sales, while traditional systems manage accounting.

06

Business Implication 2: The Need for Prompt Engineering

1

Quality of Predictions

The quality of AI predictions depends on the clarity and precision of the prompts.

2

Better Questions

Better questions lead to better predictions; prompt engineering is crucial for maximizing AI's value.

3

User Quote

The better the prompt, the better the response. It is critical to write a clear and concise prompt if you want the most accurate answer.

4

Investment in Training

Organizations need to invest in training and resources for effective prompt engineering.

07

The Risks: Hallucinations and Bias

1

Inaccurate Outputs

AI systems can generate inaccurate or nonsensical outputs (hallucinations) due to limitations in training data or model design.

2

Data Bias

Bias in training data can lead to unfair or discriminatory outcomes.

3

Human Review

Human review is critical for identifying and mitigating these risks.

4

Robust Procedures

Implementing robust testing and validation procedures helps ensure responsible AI deployment.

08

Strategic Hybrid: Reasoning and Record-Keeping

1

Hybrid Approach

A strategic hybrid approach leverages the strengths of both AI and traditional computing.

2

AI for Reasoning

Use AI for reasoning, prediction, and complex problem-solving.

3

Traditional for Records

Use traditional systems for reliable record-keeping, data storage, and deterministic tasks.

4

User Quote & Efficiency

Using AI for reasoning and Traditional systems for record-keeping. This combination maximizes efficiency and minimizes risks.

09

10

✤ AI represents a fundamental shift in computing, offering new opportunities for businesses.

Conclusion: Partnering with the "Brain" to Increase Sales Productivity

✤ By partnering with AI, organizations can enhance sales productivity, improve decision-making, and gain a competitive edge.

✤ Embracing a strategic hybrid approach is key to unlocking the full potential of AI while mitigating its risks.