Source

Machine learning and deep learning, by Christian Janiesch &Patrick Zschech & Kai Heinrich

Main Themes

• The definitions and relationships between artificial intelligence (AI), machine learning (ML), shallow machine learning, deep learning (DL), and artificial neural networks (ANNs).
• How shallow ML and DL build analytical models.
• Challenges in applying ML and DL to build intelligent systems.

Most Important Ideas/Facts

• AI aims to enable computers to perform tasks that usually require human intelligence, while ML, a subset of AI, allows computers to learn from data to automate analytical model building.
• Shallow ML relies on handcrafted features and explicit programming for model building, while DL, using deep neural networks, can automatically learn complex patterns from raw data.
• Three main types of ML are supervised learning, unsupervised learning, and reinforcement learning.
• Building an effective analytical model requires careful consideration of the algorithm/architecture, hyperparameters, and training data, often involving trade-offs.
• Biases in data, such as human prejudices, can be adopted and even amplified by ML/DL models.
• Concept drift, where relationships between input data and the target variable change over time, requires strategies to maintain the model's effectiveness.
• The black-box nature of some ML/DL models necessitates explainable AI (XAI) techniques to provide understandable insights into their decision-making process.
• Transfer learning enables the adaptation of pre-trained models to specific tasks using smaller datasets, but care must be taken to avoid introducing biases or vulnerabilities.

Key Results

• DL models, particularly effective with large, high-dimensional datasets, often outperform shallow ML models in tasks like image and text processing.
• The choice between shallow ML and DL depends on factors like data size, dimensionality, desired interpretability, and computational resources.
• Successful real-world applications of ML/DL require addressing challenges like managing the model's complexity, mitigating bias and drift in data, ensuring explainability, and leveraging transfer learning effectively.

Supporting Quotes

• "Instead of codifying knowledge into computers, machine learning (ML) seeks to automatically learn meaningful relationships and patterns from examples and observations."
• "Deep neural networks overcome this limitation of handcrafted feature engineering. Their advanced architecture gives them the capability of automated feature learning to extract discriminative feature representations with minimal human effort."
• "For any real-world application, intelligent systems do not only face the task of model building, system specification, and implementation. They are prone to several issues rooted in how ML and DL operate, which constitute challenges relevant to the Information Systems community."

Future Directions

• AI as a service (AIaaS), offering pre-trained models and AI resources, is expected to shape the future of electronic markets and intelligent systems.
• Further research is needed to provide guidance on building and deploying responsible and effective AI systems, addressing challenges like bias mitigation, explainability, and transfer learning in real-world scenarios.

Link

https://www.researchgate.net/publication/350834453_Machine_learning_and_deep_learning