Data Science and AI for Business

Flashcards created based on the following DataCamp courses: app.datacamp.com/learn/courses/implementing-ai-solutions-in-business ; https://campus.datacamp.com/courses/generative-ai-for-business/introduction-to-generative-ai-1

artificial intelligence (AI)

• development of computer systems that can perform tasks that normally require human intelligence (e.g. perception, reasoning, learning etc.)

stages of AI evolution

1. Symbolic AI (1960s/70s)

   • representing knowledge with symbols and rules

2. Connectionist AI (1980s/90s)

   • models inspired by the structure and function of the human brain

   • learn from data to make decisions based on patterns and associations

3. Machine learning (2000s)

   • statistical algorithms to learn from data

4. Deep learning (2010s)

   • artificial neural networks to process complex data (e.g. images, speech, language)

applications for AI

• wide ranging applications

• image processing

• speech recognition

• Natural Language Processing (NLP)

• autonomous vehicles

• robotics

key elements of an AI strategy

1. Develop vision

2. Assess the risks

3. Develop action plan (change management)

4. Buy-in

phases of AI implementation

A successful AI solution implementation will generally follow five broad steps from ideation to production:

1. Education → understand what AI is and use cases it is great for

2. Identification → determine which use case in your business is best for AI

3. Proof of Concept (POC) → build out a small-scale AI solution to test and learn

4. Scale → implement a full AI solution

5. Align culture and skills → change processes and upskill the workforce to leverage the AI solution

generative AI vs adaptive AI

• generative AI refers to a class of artificial intelligence that is capable of generating new content, such as text, images, or music, based on input data

• adaptive AI describes systems that learn and evolve over time through interaction with their environment and user inputs

• Unlike generative AI, adaptive AI modifies its behavior based on experiences, allowing it to improve its performance and tailor responses in real-time

responsible AI

• approach to developing, assessing, and deploying AI systems in a safe, trustworthy, and ethical way

• AI ethics board → dedicated to strengthening the responsible AI principles and governance framework

Key elements:

• accountability → who is responsible?

• inclusivity

• fairness

• transparency and explainability

• security

• reliability