AI in Business Unit 1

Study Notes

AI is a branch of computer science aimed at developing machines that can perform tasks requiring human-like intelligence, such as learning and reasoning.

Operational Efficiency: Automates repetitive tasks, reducing operational costs and allowing staff to focus on strategic roles.
Enhanced Decision-Making: Analyzes large datasets quickly to recognize patterns, aiding in strategic decisions and supply chain optimization.
Customer Experience: Personalization engines and chatbots improve customer satisfaction and lessen support workload.
Marketing and Sales: Targets marketing efforts, identifies high-value leads, and refines sales strategies.
Risk Management: Real-time fraud detection enhances compliance and mitigates legal risks.

AI excels in processing large data volumes rapidly, facilitating significant research advancements.
Example: An AI robot named 'Eve' discovered a toothpaste ingredient with potential to cure malaria.

Protects against cyber threats as data transitions to IT networks and the cloud.
Cognitive AI: Detects and analyzes threats, improving decision-making capabilities.
AI tools like IBM Resilient automate cybersecurity processes, while Recurrent Neural Networks (RNNs) efficiently scan transactions for fraud.

Improves accuracy and efficiency of data analysis through iterative learning.
Tools like Google Analytics’ Analytics Intelligence and Helixa.ai provide insights and predict customer behavior, benefiting decision-making.
Risk Assessment: AI models review historical data patterns to evaluate risks for lenders.
Loan Processing: Automates evaluations, speeding up approvals and enhancing customer experience.

Integration of AI into finance has transformed traditional methodologies, enhancing fraud detection, algorithmic trading, and risk management.

Advancement of autonomous vehicles and traffic optimization presents significant developments in the transportation sector.

Definition: Self-driving vehicles utilize AI for navigation and decision-making without human input.
Technologies Involved:
- Sensor Fusion: Combines various sensors to understand the vehicle's environment in real-time.
- Machine Learning: Algorithms improve decision-making capabilities through data lessons.
- Control Systems: AI manages vehicle functions based on real-time traffic conditions.

A subset of AI that enables machines to mimic human intelligence through problem-solving.
Supervised Learning: Learns from labeled datasets to relate inputs with outputs.
- Examples include predicting house prices based on features.
Unsupervised Learning: Identifies patterns and structures within unlabeled datasets.
- Applications include clustering and anomaly detection.

Virtual Nursing Assistants: Provide continuous support, answering queries and streamlining workflow for clinical staff.
Dosage Error Reduction: AI identifies potential medication errors, improving patient safety.
Less Invasive Surgeries: Robotic assistance in surgeries leads to reduced risks and better patient recovery.
Fraud Prevention: Analyzes claims data to detect fraudulent patterns, reducing costs for consumers.

Early Detection: AI analyzes comprehensive patient data to identify early disease signs effectively.
Example: Higher accuracy in detecting cancers through mammogram analysis compared to human radiologists.
Clinical Decision Support: Integrates patient data with extensive medical knowledge to enhance clinician decision-making.