Notes on Psychology in Digital Business and AI

Psychology in Digital Business Notes

Artificial Intelligence in Business

  • The term Artificial Intelligence (AI) refers to systems capable of performing tasks that typically require human intelligence.

  • Key Milestone: In 2016, AI defeated the human world champions in Go, marking a significant leap in AI capabilities.

  • AI systems are evolving rapidly, showcasing advancements in technology and algorithms.

Understanding AI Development

  • Hype Cycle: The development of AI technologies follows a hype cycle, where initial excitement can lead to inflated expectations before more realistic assessments emerge.

  • AI has gained traction significantly, often reaching 100 million users within months of launch due to enhanced software, big data availability, and improved hardware.

Characteristics of Human Intelligence Applied to AI.

  • Sensorimotor Intelligence: Utilization of human senses to interact with the environment.

  • Cognitive Intelligence: Learning, understanding, and applying acquired knowledge.

  • Emotional Intelligence: Recognizing emotional states in others (e.g., sadness, joy).

  • Social Intelligence: Understanding and perceiving group behavior and dynamics.

Core Components of AI

  1. Perceiving: Ability to capture and detect inputs (e.g., visual, auditory).

  2. Understanding: Processing and interpreting data.

  3. Acting: Executing actions based on interpreted information.

  4. Learning: Adapting over time based on experiences and new information.

Machine Learning & Deep Learning

  • Machine Learning (ML): Refers to algorithms that learn from data to make predictions or decisions. Examples include:

    • Algorithms in online retail learning customer behavior without explicit programming.

    • Automated labeling of images based on human-guide d training.

  • Deep Learning: A subset of ML involving neural networks that analyze data with multiple layers of abstraction.

The Internet of Things (IoT)

  • Definition: Networking of everyday objects (tools, devices, sensors) through unique identifiers, enabling data exchange at scale.

  • This integration contributes to the vast amounts of data that AI systems leverage for improvements in machine learning algorithms.

Algorithms

  • Definition: Mathematical-statistical models that analyze data to derive insights or promote decision-making.

  • Economic significance: Algorithms drive innovation and value creation in businesses.

Classes of AI Applications

  1. Intelligent Automation: Integrate AI into existing processes for enhancement (e.g., AI in video surveillance to identify suspicious behavior).

  2. Novel Applications: New technology-driven solutions that have emerged from AI integration (e.g., digital home assistants).

Prerequisites for Implementing AI

  1. Data Quantity: Ample data for training effective models.

  2. Data Quality: Consistent and accurate data for reliable outputs.

  3. Data Legality: Compliance with regulations around data protection and user privacy.

Key Challenges and Misinterpretations in AI

  • Errors and Misclassification: Examples like the "Anne Hathaway effect" illustrate how algorithms can misinterpret data signals, leading to erroneous outcomes in financial markets.

Future Trends in AI

  • Quantum Computing: Potential to revolutionize computing power and significantly advance AI capabilities; currently in research stages.

  • Anticipated new applications continue to emerge as technology evolves, showcasing the transformative power of AI in business and daily life.