Fundamentals
Traditional vs Modern AI
Search algorithms to navigate paths.
Machine learning for dynamic path finding in robotics.
Symbolic representation to encode knowledge.
Knowledge graphs and embeddings for semantic understanding.
Rule-based systems with IF-then logic.
Decision trees and random forests for predictive analytics
Predefined optimization rules.
Machine learning algorithms for data optimization and pattern recognition.
Expert systems with predefined rules for medical diagnosis.
Machine learning models for clinical decision support, predicting outcomes based on patient data.
Formal logic to draw conclusions.
Deep learning in NLP to understand and generate human language.
Traditional AI relies heavily on fixed guidelines, limiting its adaptability to the variability seen in real-world situations. Modern AI techniques such as machine learning (ML) differ from traditional AI by training algorithms to recognize patterns and make predictions instead of relying on explicit rules alone. This data-driven methodology enables ML models to adjust and improve over time, making them more adaptable than traditional AI. Deep learning (DL) is a branch of machine learning that improves AI by using neural networks to learn from large datasets. DL models can perform complicated tasks like image and speech recognition, which are not possible for traditional AI. Though traditional AI is logically transparent and easily traceable in decision-making, its rigidity and limited scalability are serious disadvantages when matched against modern approaches like deep learning.
A major limitation of traditional AI is its lack of understanding. Traditional AI systems work with pre-defined rules and do not comprehend the context, generalizing from limited information. Such rigidity means that they cannot handle new or unexpected events. For example, a conventional AI-based chatbot may be unable to deal with ambiguous or complex queries outside its programmed responses, leading to poor user experience. This restriction emphasizes the need for more flexible AI systems that can learn and grow from new data.
Machine Learning vs Deep Learning
Two main subsets of modern AI is Machine Learning (ML) and Deep Learning (DL)
Machine Learning
Consists of algorithm using statistical techniques to improve specific tasks as they gain more experience over time.
Simply speaking, ML use training data to train a model then allows that model to try to make predictions for new data that was not trained on
Example of ML: email spam filters in which algorithms try to identify and filter out spam emails based on learned patterns from previously categorized emails
Deep Learning
Use neural networks with many layers (referred to deep neural networks) to learn from vast amounts of data.
Example of DL is image and sound recognition system
Machine Learning and Deep Learning Techniques and Methodology
Machine Learning encompasses many techniques and methodology, some of which are:
Supervised
Unsupervised
Reinforcement
Deep Learning
Supervised Learning
Common approach in which models are trained on labeled data. This mean each input has a corresponding output.
Includes method like regression and classification
Example: retiredment preparedness
Machine Learning and Deep Learning Applications and Use Cases
Predictive Analytics
Image Recognition
Recommendation Systems
Speech Recognition
Anomaly Detection
Natural Language Processing
Credit Scoring
Autonomous Driving
Predictive Maintenance
Medical Image Analysis
Customer Segmentation
Algorithm Trading
Reference
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