Real-World Use Cases of AI

• AI has real-world use cases in media and entertainment, retail, healthcare, life sciences, financial services, and manufacturing.

Media and Entertainment

• AI content generation can create scripts, dialogues, or complete stories for films, TV shows, and games.
• AI enables immersive and interactive virtual reality environments for games or simulations.
• AI can generate articles or summaries based on raw data or events.

Retail

• AI generates product review summaries so consumers can find pertinent information quickly.
• AI models pricing scenarios to determine optimal pricing strategies that maximize profits.
• AI creates virtual models of customers for virtual try-ons, improving the online shopping experience.
• AI generates the most efficient store layouts to improve the customer shopping experience and boost sales.

Healthcare

• AWS HealthScribe allows healthcare software vendors to build clinical applications that automatically generate clinical notes from patient-clinician conversations.
• AI generates personalized treatment plans based on a patient's genetic makeup, lifestyle, and disease progression.
• AI enhances, reconstructs or generates medical images, like X-rays, MRIs, or CT scans, aiding in better diagnosis.

Life Sciences

• AI generates new potential molecular structures for drugs, accelerating drug discovery and reducing costs.
• AI predicts the 3D structures of proteins based on their amino acid sequence, crucial for understanding diseases and developing new therapies.
• AI generates designs for synthetic biological systems, such as engineered organisms or biological circuits.

Financial Services

• AI helps create synthetic datasets to improve AI and ML systems by simulating various money-laundering patterns for fraud detection.
• AI simulates market scenarios and helps in the creation and management of robust investment portfolios.
• AI generates effective communication and negotiation strategies for debt collection to increase successful collection rates.

Manufacturing

• AI analyzes historical production data to predict maintenance schedules that provide efficient machine outputs and reduce downtimes.
• AI generates efficient production processes by modeling different scenarios and optimizing for variables such as cost, time, and resource usage.
• AI is used to create new product designs based on set parameters and constraints, optimizing for factors like cost, materials, and performance.
• AI helps generate new material compositions with desired properties.

AI Applications: Computer Vision

• Computer vision allows computers to interpret and understand digital images and videos
• Deep learning has revolutionized computer vision through techniques for image classification, object detection, and image segmentation.
• Examples include autonomous driving, use in healthcare and medical imaging, and public safety/home security applications.

AI Applications: Natural Language Processing

• NLP deals with the interaction between computers and human languages and performs tasks such as text classification, sentiment analysis, machine translation, and language generation.
• Examples of NLP applications can be found in insurance, telecommunications, and education.

AI Applications: Intelligent Document Processing

• IDP extracts and classifies information from unstructured data, generates summaries, and provides actionable insights.
• IDP use cases are in financial services, lending, legal, and healthcare

AI Applications: Fraud Detection

• Fraud detection identifies and prevents fraudulent activities or unauthorized behavior with a system, process, or transaction.
• Examples of fraud detection can be found in financial services, retail, and telecommunications

When AI and ML are appropriate solutions

• When coding specific rules is challenging due to the complexity and number of variables.
• ML solutions are appropriate when the project scope is challenging such as scaling to millions of data points.

Machine Learning Techniques: Supervised Learning

• Supervised learning uses labeled training data to determine the patterns and relationships between inputs and outputs.
• Uses cases include fraud detection, image classification and diagnostics

Supervised Learning: Classification

• Classification assigns labels or categories to new, unseen data instances based on a trained model.
• Use cases include fraud detection, image classification, and diagnostics

Supervised Learning: Regression

• Regression predicts continuous or numerical values based on one or more input variables.
• Uses cases include advertising popularity prediction, weather forecasting, estimating life expectancy, and population growth prediction.

Machine Learning Techniques: Unsupervised learning

• Unsupervised learning trains the model on unlabeled data.
• The algorithm discovers hidden patterns or structures within the data without prior information or guidance.

Unsupervised Learning: Clustering

• Clustering groups data into different clusters based on similar features or distances between data points to better understand the attributes of a specific cluster.
• Use cases include customer segmentation, targeted marketing, and recommended systems.

Unsupervised Learning: Dimensionality Reduction

• Dimensionality reduction reduces the number of features or dimensions in a dataset while preserving the most important information or patterns.
• Use cases include big data visualization, meaningful compression, structure discovery, and feature elicitation.

Machine Learning Techniques: Reinforcement Learning

• Reinforcement learning involves an agent that continuously learns through trial and error as it interacts in an environment.
• Reinforcement learning is useful when the reward of a desired outcome is known, but the path to achieving it requires trial and error to discover.

Capabilities of Generative AI

• Adaptability: adapts to various tasks and domains by learning from data and generating content tailored to specific contexts or requirements
• Responsiveness: GenAI models generate content in real-time, which results in rapid response times and dynamic interactions
• Simplicity: GenAI can simplify complex tasks by automating content creation processes.
• Creativity and exploration: GenAI models can generate novel ideas, designs, or solutions by combining and recombining elements in unique ways
• Data efficiency: GenAI can learn from relatively small amounts of data and generate new samples consistent with the training data.
• Personalization: GenAI can create personalized content tailored to individual preferences or characteristics, which enhances user experiences and engagement.
• Scalability: GenAI models can generate large amounts of content quickly.

Challenges of Generative AI

• Regulatory violations of sensitive data might inadvertently generate an output that violates regulations, such as exposing personally identifiable information (PII). To mitigate, implement strict data anonymization and privacy-preserving techniques during model training and conduct thorough audits
• Social risks are a possibility of unwanted content that might reflect negatively on your organization. To mitigate, test and evaluate all models before deploying them in production.
• Data security and privacy concerns: information shared with your model can include personal information and can potentially violate privacy laws. To mitigate, implement cybersecurity measures, such as encryption and firewalls.
• Toxicity is that GenAI models can generate content that is inflammatory, offensive, or inappropriate. To mitigate curate the training data by identifying these phrases in advance and removing them from the training data, and using guardrail models to detect and filter out unwanted content.
• Hallucinations happen when the model generates inaccurate responses that are not consistent with the training data. To mitigate, teach users that everything must be checked while further mitigating by checking that content is verified with independent sources and marking the content as unverified.
• Interpretability can be a risk when users might misinterpret the model’s output, which could lead to incorrect conclusions or decisions. To mitigate, use specific domain knowledge for model development and performance.
• Nondeterminism occurs when the model might generate different outputs for the same input that can cause problems in applications where reliability is key. To mitigate, perform tests on the model to identify any sources of nondeterminism. Run the model multiple times and compare the output to ensure consistency.

Factors to consider when selecting generative AI models

• Model types
• Performance requirement
• Capabilities
• Constraints
• Compliance

AI Models

• AI 21 labs (Jurassic-2 models): Text generation, Summarization, Paraphrasing, Chat, and Information extraction
• Amazon (Amazon titan): Text generation, Summarization, Paraphrasing, Chat, and Information extraction
• Anthropic (Claude): Text generation, Summarization, Paraphrasing, Chat, and Information extraction
• Stability AI: Text generation, Summarization, Paraphrasing, Chat, and Information extraction
• Cohere (command): Text generation, Summarization, Paraphrasing, Chat, and Information extraction
• Meta (llama): Text generation, Summarization, Paraphrasing, Chat, and Information extraction

Performance Requirements

• Accuracy and reliability of output models should be tested against different datasets and monitored over time to ensure consistency

Constraints

• Computational resources such as GPU power, CPU power, or memory
• Data availability such as size and quality of training data
• Deployment requirement such as on premises or cloud

Capabilities

• Generative AI encompasses a wide range of capabilities for different tasks with varying degrees of output quality and levels of control or customization such as text generation or images

Compliance

• Generative AI models can pose moral concerns, including biases, privacy issues, and potential misuse that should adhere to relevant regulation guidelines, fairness, transparency or traceability, accountability, hallucination, and toxicity.

Cost

• Consider the trade-off between the size and the speed of the model. Larger models are usually more precise, but they are expensive and offer few deployment options, while smaller models are cheaper and faster, and they offer more deployment alternatives.

Business Metrics for Generative AI

• User satisfaction gathers user feedback to assess their satisfaction with the AI-generated content or recommendations.
• Average revenue per user (ARPU) calculates the average revenue generated per user or customer attributed to the generative AI application
• Cross-domain performance measures the generative AI model's ability to perform effectively across different domains or industries.
• Conversion rate monitors the conversion rate to generate content or recommend desired outcomes, such as purchases, sign-ups, or engagement metrics.
• The efficiency metric evaluates the generative AI model's efficiency in resource utilization, computation time, and scalability