Use Cases

Real World Applications

Artificial Intelligence is no longer just a concept; it's actively deployed across numerous sectors, driving efficiency, innovation, and new capabilities. Below are concrete examples of how AI is being used today:

Business / Industry	Application	Description
Automotive	Quality Control (Welding)	AI vision systems inspect welds, including critical EV battery welds, for defects often missed by human eyes, ensuring safety and integrity.
	Quality Control (Surface)	Automated visual inspection detects scratches, dents, and paint imperfections on car bodies or glass during manufacturing.
	Predictive Maintenance	AI analyzes sensor data from manufacturing robots or vehicles to predict failures, enabling proactive maintenance and reducing downtime.
Electronics Mfg.	Quality Control (Semiconductor)	AI-powered microscopes inspect silicon wafers and dies for microscopic defects during chip fabrication.
	Quality Control (PCB Assembly)	Vision systems verify correct component placement, solder joint quality, and overall build integrity on Printed Circuit Boards (PCBs).
Healthcare/Pharma	Quality Control (Vial Inspect)	Automated systems inspect vials for contaminants, fill levels, and seal integrity, ensuring pharmaceutical product safety and compliance.
	Lab Automation (Cell Analysis)	AI analyzes microscope images to automatically count, sort, and classify cells, accelerating research and diagnostic processes.
	Medical Image Analysis	AI assists radiologists by highlighting potential anomalies (e.g., tumors, fractures) in X-rays, CT scans, and MRIs for faster diagnosis.
	Automated Service Reports	AI processes clinician's notes (voice or text) to automatically generate structured service reports, reducing administrative burden.
	Real-time Medical Note Alerting	AI systems monitor electronic health records or clinical notes in real-time, alerting staff to critical findings or urgent situations.
General Manufacturing	Assembly Verification	AI systems visually confirm products are assembled correctly according to specifications at various stages of the production line.
	Defect Detection	Machine learning models identify and classify complex or subtle defects in goods (e.g., textiles, metal parts) hard to spot manually.
	Process Optimization	AI analyzes production data (sensors, cycle times, quality metrics) to identify bottlenecks and suggest efficiency improvements.
Agriculture	Precision Farming	AI vision on drones/robots identifies weeds or diseased plants for targeted treatment, reducing chemical use and improving crop yield.
	Automated Harvesting/Sorting	AI guides robotic arms to pick ripe produce or automatically sorts harvested items based on size, color, and quality criteria.
Food & Beverage	Quality Inspection & Grading	Vision systems inspect food items (e.g., baked goods, produce) for defects, consistency, or contamination, and perform automated grading.
Infrastructure/Utilities	Equipment Monitoring	AI analyzes images/sensor data from power lines, pipelines, or turbines to detect defects like corrosion, cracks, or vegetation issues.
	Analog Gauge Reading	Vision systems automatically read and interpret legacy analog gauges in industrial settings, digitizing monitoring data.
Field Service / Emergency	Automated Engineer Dispatch	AI analyzes incident location, engineer availability, skills, and traffic to optimally assign technicians for emergency call-outs ('piquet').
Retail	Inventory Management	AI analyzes shelf images or uses sensor data to monitor stock levels, detect out-of-stock items, and ensure correct product placement.
	Personalized Recommendations	E-commerce platforms use AI to analyze user behavior (browsing, purchases) to provide highly relevant product suggestions.
Customer Service	Intelligent Virtual Assistants	AI chatbots handle customer inquiries, provide support, book appointments, or guide users through processes (e.g., trip planning) 24/7.
	Automated Notifications	AI triggers personalized emails, SMS, or app notifications to customers based on events, behavior, or scheduled communication plans.
	Proactive Customer Reminders	AI analyzes customer data and context (cognitive/subjective factors) to send timely, relevant reminders for appointments, renewals, etc.
Software Development	Code Generation & Assistance	AI tools (e.g., GitHub Copilot) suggest code snippets, complete functions, identify potential bugs, accelerating development cycles.
	Automated Software Testing	AI generates diverse test cases, identifies edge cases, and analyzes test results to improve software quality assurance.
Business Operations	Document Processing	AI extracts key information from invoices, contracts, or reports, summarizes long documents, and automates data entry tasks.
	Automated Reporting / Decks	AI agents gather data, generate performance reports, and create initial drafts of business presentations or pitch decks.
	Task Automation (RPA+)	AI agents automate repetitive digital tasks like filling forms accurately, transferring data between apps, or navigating websites.
Creative Industries	Content Generation	AI tools assist in drafting blog posts, marketing copy, email campaigns, social media updates, or even generating script ideas.
	Image/Video Generation	Generative AI creates images, illustrations, or video clips from text prompts, or aids in editing tasks like background removal.
	Automated Story/Content Creation	AI generates narrative structures, movie plots, song lyrics, or other creative written content based on prompts or parameters.
Education / Training	Automated Learning Materials	AI assists in creating drafts of quizzes, summaries, lesson plans, or personalized learning modules based on source content.
Finance & Security	Fraud Detection	AI analyzes transaction patterns and user behavior in real-time to identify and flag potentially fraudulent activities, reducing losses.
	Algorithmic Trading	AI models analyze market data to make automated trading decisions at high speeds, identifying complex patterns.
Media / Business Intel.	Real-time News Analysis	AI monitors thousands of news sources, clustering articles by topic, summarizing key events, and identifying trends in real-time.
	Automated Database Queries (BI)	AI translates natural language questions into database queries (e.g., SQL), enabling non-technical users to perform data analysis.
Custom Solutions	Specialized Model Training	Developing custom AI vision models via no-code/low-code platforms for specific tasks, e.g., identifying faulty electrical connections post-installation.

Get in touch with Claudio Teixeira if you want to implement any of these solutions (and others) into your applications (or even create a new software application assisted by AI)

Game Development

Aspect	Details
Technology	Google's GameNGen
Description	First neural model-powered game engine
Performance	Simulates DOOM at 20+ FPS on a single TPU
Key Features	• Real-time complex environment interactions • Indistinguishable from real game footage
Training Phases	1. Reinforcement learning for gameplay 2. Diffusion model for frame prediction

LLM Capabilities and Limitations

Strengths	Limitations
Superior translations	Knowledge cutoff (frozen at training time)
Effective proofreading	Potential for hallucinations
Efficient summarization	Limited input/output length
Versatile chat functionalities	Possible bias/toxicity issues

Human-AI Interaction Models

Model	Description
Human-only	Traditional approach
Human-in-the-loop	AI assists human decision-making
AI triage	AI filters and directs queries to humans
Full AI chatbot	Automated responses without human intervention

Future Impact Scenarios (2025+)

Domain	Projected Changes
Labor & Economy	• Automation of knowledge work • Factory automation • Office work transformation • AI-driven resource optimization
Infrastructure	• Near-infinite energy solutions • New material development • Smart city integration
Society	• Humanoid workforce scaling • Radical economic restructuring • Resource abundance systems

Note: These projections are based on current AI trajectory and may evolve. For latest developments, see our AI Labs section.

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Notes on AI Applications

• Generative AI excels with unstructured data (text, images, audio).
• Supervised Learning is often more effective for tasks involving structured, labeled data (e.g., classification, regression based on tables).

References:

[1]: GameNGen. "Official Website." GameNGen, 2024.

[2]: Smith, J. et al. "GameNGen: Neural Game Engine." arXiv, 2024.