AI-Supported Software System

An AI-Supported Software System is a software-based system that can solve AI-supported tasks (which require AI capabilities).

AKA: AI-Enhanced System, AI-Driven System, Intelligent Software System.
Context:
- Input(s): AI-supported system domain data, AI-supported system task specifications, AI-supported system user requirements
  - Optional: AI-supported system configuration parameters, AI-supported system domain constraints
- Output(s): AI-supported system task results, AI-supported system AI-generated content, AI-supported system responses
- Performance Measure(s): AI-supported system task accuracy, AI-supported system response quality, AI-supported system reliability
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- It can (typically) be supported by AI Technologies, such as AI models, ML technologies, NLP technologies, Computer Vision technologies, GenAI technologies, etc.
- It can (typically) implement AI-supported system processing layer with AI-supported system model execution, AI-supported system model registry, and AI-supported system feature store.
- It can (typically) maintain AI-supported system data management layer with AI-supported system data ingestion, AI-supported system data storage, and AI-supported system data retrieval.
- It can (typically) provide AI-supported system integration layer with AI-supported system API gateway, AI-supported system message queue, and AI-supported system service mesh.
- It can (typically) utilize AI-supported system technical frameworks such as AI-supported system deep learning frameworks, AI-supported system distributed computing frameworks, and AI-supported system inference optimization frameworks.
- It can (typically) employ AI-supported system MLOps practices including AI-supported system continuous integration, AI-supported system model monitoring, and AI-supported system deployment automation.
- It can (typically) implement AI-supported system responsible AI practices through AI-supported system fairness assessment, AI-supported system transparency mechanisms, and AI-supported system ethical guideline adherence.
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- It can (often) be based on AI Platforms or AI Frameworks.
- It can (often) implement AI-supported system data pipeline with AI-supported system data collection and AI-supported system preprocessing.
- It can (often) manage AI-supported system integration with AI-supported system component assembly and AI-supported system interface implementation.
- It can (often) be represented by an AI-Supported System Architecture.
- It can (often) be based on AI-Supporting Cloud Services such as AI-supported system managed AI services, AI-supported system serverless computing, and AI-supported system containerized deployment.
- It can (often) implement AI-supported system explainability mechanisms for AI-supported system transparent decision-making and AI-supported system audit trail.
- It can (often) be implemented as an AI-Supported Software Service that delivers AI-supported system capability through AI-supported system service models, AI-supported system API endpoints, and AI-supported system consumption-based pricing.
- It can (often) continuously improve through AI-supported system learning mechanisms including AI-supported system feedback loops, AI-supported system performance monitoring, and AI-supported system model retraining.
- It can (often) adapt to AI-supported system changing requirements via AI-supported system dynamic configuration, AI-supported system capability extension, and AI-supported system contextual adjustment.
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- It can range from being an Human-Directed AI-Supported System to being an Autonomous AI-Supported System, depending on its AI-supported system operational independence.
- It can range from being an AI-Assisted Software System to being an AI-Powered Software System, depending on its AI-supported system AI integration level.
- It can range from being a Domain-Specific AI-Supported System to being an Open-Domain AI-Supported System, depending on its AI-supported system application scope.
- It can range from being a Simple AI-Supported System to being a Complex AI-Supported System, depending on its AI-supported system architectural complexity.
- It can range from being a Single-Task AI-Supported System to being a Multi-Task AI-Supported System, depending on its AI-supported system task diversity.
- It can range from being a Static AI-Supported System to being an Evolving AI-Supported System, depending on its AI-supported system learning adaptability.
- It can range from being an AI-Supported Software System with Basic Capabilities to being an AI-Supported Software System with Advanced Capabilities, depending on its AI-supported system functional sophistication.
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- It can require AI-supported system resources such as AI-supported system GPU infrastructure, AI-supported system high-bandwidth networks, and AI-supported system distributed storage.
- It can involve AI-supported system risk management including AI-supported system data privacy, AI-supported system model bias, and AI-supported system decision impact.
- It can need capabilities like AI-supported system AI expertise, AI-supported system MLOps knowledge, and AI-supported system integration skills.
- It can address AI-supported system regulatory requirements such as AI-supported system AI governance and AI-supported system data protection.
- It can consider AI-supported system cost factors including AI-supported system development costs and AI-supported system operational expenses.
- It can include AI-supported system application layer with AI-supported system user interface, AI-supported system business logic, and AI-supported system monitoring.
- It can maintain AI-supported system governance frameworks with AI-supported system policy enforcement, AI-supported system impact evaluation, and AI-supported system accountability tracking.
- It can address AI-supported system regulatory compliance through AI-supported system documentation practices, AI-supported system audit procedures, and AI-supported system certification processes.
- It can often require AI-supported system infrastructure layer with AI-supported system compute resources, AI-supported system network fabric, and AI-supported system storage systems.
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Example(s):
- AI-Based Interactive Services (provides AI-supported system capability through AI-supported system interactive interfaces), such as:
  - ...
- Enterprise AI-Supported Systems, implementing AI-supported system processing layers, such as:
  - Business Intelligence AI-Supported Systems, maintaining AI-supported system data management layers, such as:
    - AI Data Analytics Platforms for predictive analytics, utilizing AI-supported system deep learning frameworks
    - AI Market Intelligence Systems for trend analysis, employing AI-supported system MLOps practices
    - AI Performance Monitoring Systems for KPI tracking, implementing AI-supported system data pipelines
  - Enterprise Communication AI-Supported Systems, providing AI-supported system integration layers, such as:
    - Corporate AI Chatbots for enterprise support, leveraging AI-supported system NLP services
    - AI Meeting Assistants for meeting management, utilizing AI-supported system speech recognition
    - AI Documentation Systems for knowledge management, maintaining AI-supported system data retrieval
  - Business Process AI-Supported Systems, managing AI-supported system integration, such as:
    - AI-Based Decision Support Systems for business processes, implementing AI-supported system explainability mechanisms
    - AI Resource Planning Systems for enterprise management, requiring AI-supported system distributed storage
    - AI Workflow Optimization Systems for process automation, addressing AI-supported system regulatory requirements
- Customer-Facing AI-Supported Systems, providing AI-supported system application layers, such as:
  - AI Customer Service Systems, implementing AI-supported system user interfaces, such as:
    - Conversational AI Systems like customer support chatbots, demonstrating AI-supported system operational independence
    - AI-Based Virtual Assistants for customer interaction, varying in AI-supported system learning adaptability
    - AI Service Routing Systems for ticket management, requiring AI-supported system API gateways
  - AI Marketing Systems, maintaining AI-supported system data management layers, such as:
    - Product Recommendation Systems for e-commerce, implementing AI-supported system model execution
    - AI Campaign Management Systems for marketing automation, utilizing AI-supported system containerized deployment
    - AI Customer Segmentation Systems for targeted marketing, requiring AI-supported system feature stores
- Creative AI-Supported Systems, leveraging AI-supported system technical frameworks, such as:
  - Text Generation AI-Supported Systems, implementing AI-supported system model registrys, such as:
    - LLM-Supported Systems for content creation, demonstrating AI-supported system AI integration levels
    - AI Copywriting Systems for marketing content, employing AI-supported system continuous integration
    - AI Story Generation Systems for narrative creation, requiring AI-supported system GPU infrastructure
  - Visual AI-Supported Systems, managing AI-supported system risk management, such as:
    - AI Image Generation Systems for digital art, addressing AI-supported system model bias
    - AI-Supported Image Editing Applications for photo manipulation, implementing AI-supported system service mesh
    - AI Visual Design Systems for graphic creation, varying in AI-supported system architectural complexity
- Domain-Specific AI-Supported Systems, demonstrating AI-supported system application scope, such as:
  - Healthcare AI-Supported Systems, addressing AI-supported system regulatory requirements, such as:
    - AI Diagnostic Support Systems for medical analysis, implementing AI-supported system transparent decision-making
    - AI Treatment Planning Systems for care optimization, requiring AI-supported system audit trails
    - AI Medical Imaging Systems for radiological analysis, employing AI-supported system Computer Vision frameworks
    - AI Drug Discovery Systems for pharmaceutical research, demonstrating AI-supported system functional sophistication
  - Legal AI-Supported Systems, managing AI-supported system data privacy, such as:
    - Legal Contract AI-Supported Systems for document analysis, implementing AI-supported system data protection
    - Legal Research AI-Supported Systems for case analysis, maintaining AI-supported system AI governance
    - AI Compliance Systems for regulatory monitoring, utilizing AI-supported system model monitoring
    - AI Legal Assistant Systems for legal support, requiring AI-supported system MLOps knowledge
- Autonomous AI-Supported Systems, demonstrating AI-supported system operational independence, such as:
  - Vehicle Automation Systems, requiring AI-supported system high-bandwidth networks, such as:
    - Autonomous Vehicle Systems for navigation control, implementing AI-supported system distributed computing frameworks
    - AI Traffic Management Systems for fleet coordination, maintaining AI-supported system decision impact analysis
    - AI Vehicle Diagnostics Systems for maintenance prediction, utilizing AI-supported system inference optimization frameworks
  - Robotics AI-Supported Systems, implementing AI-supported system task diversity, such as:
    - AI Robotics Control Systems for industrial automation, demonstrating AI-supported system real-time processing
    - AI Collaborative Robot Systems for human-robot interaction, addressing AI-supported system safety requirements
    - AI Robot Vision Systems for environment perception, utilizing AI-supported system sensor fusion
- AI-Supported Development Systems, employing AI-supported system MLOps practices, such as:
  - AI Code Development Systems, implementing AI-supported system deployment automation, such as:
    - AI Code Generation Systems for software development, demonstrating AI-supported system AI expertise
    - AI Code Review Systems for code quality analysis, utilizing AI-supported system continuous integration
    - AI Code Optimization Systems for performance tuning, requiring AI-supported system integration skills
  - AI Testing Systems, maintaining AI-supported system data pipelines, such as:
    - AI Test Generation Systems for test case creation, implementing AI-supported system component assembly
    - AI Test Execution Systems for automated testing, utilizing AI-supported system cloud services
    - AI Bug Detection Systems for defect analysis, leveraging AI-supported system model execution
- AI-Supported Software Services, providing AI-supported system capability through AI-supported system service delivery, such as:
  - AI-Supported Software SaaSes, implementing AI-supported system multi-tenant architectures, such as:
    - AI-Supported Customer Analytics Service for customer behavior prediction, utilizing AI-supported system consumption tracking
    - AI-Supported Content Moderation Service for online platforms, employing AI-supported system scalable infrastructure
  - AI-Supported API Services, exposing AI-supported system functional capability through AI-supported system API gateways, such as:
    - AI-Supported Language Processing API for text analysis, implementing AI-supported system request throttling
    - AI-Supported Image Recognition API for visual content analysis, maintaining AI-supported system usage monitoring
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Counter-Example(s):
- a Database-Supported Software System that does not use AI techniques, relying only on structured querys without AI models.
- a Manual Software System with no intelligent automation, requiring direct human instructions for all operations.
- a Rule-Based Software System that uses only predefined logic without AI/ML capabilities, lacking learning ability.
- a Traditional Algorithm System that relies solely on deterministic algorithms, without adaptive behavior.
- a Static Decision System that makes choices based on fixed rules rather than AI-driven analysis and continuous learning.
See: AI Application, Intelligent System, Cognitive System, Smart System, AI-Supported System Architecture, AI Platform, MLOps System, AI Framework, AI-Centric System Architecture.

References

2024-12-16

Perplexity.ai
- Question: Write a definition of AI Supported Software Systems, including their core characteristics, key components, and implementation types. Explain how these systems utilize various AI Technology like machine learning, NLP, and computer vision. Describe their range from human-directed to autonomous systems, and from domain-specific to open-domain applications. Include information about their infrastructure requirements, technical frameworks, and functional capabilities. Discuss common features such as adaptive learning and predictive analytics. Detail different implementation categories like enterprise systems, customer-facing solutions, and development support systems. Explain their impact on operational efficiency, decision-making, and innovation.
- Answer: An AI Supported Software System is a sophisticated technological framework that integrates artificial intelligence capabilities to enhance, automate, and optimize software operations. These systems combine traditional software architecture with AI technologies to deliver intelligent, adaptive, and autonomous functionalities.
  - Core Components:
    - Infrastructure Layer:
      - Data Storage and Management systems for handling large training datasets.
      - Compute Resources including GPUs and TPUs for processing.
      - Networking infrastructure for efficient data flow.
      - Security and compliance frameworks.
    - Technical Components:
      - Machine Learning Frameworks for model development.
      - Data Processing Frameworks for data preparation.
      - MLOps Platforms for lifecycle management.
      - API interfaces for system integration.
    - Functional Components:
      - Learning capabilities for continuous improvement.
      - Reasoning and problem-solving modules.
      - Perception systems for data interpretation.
      - Language understanding for natural communication.
  - Key Features:
    - Core Capabilities:
      - Data Ingestion for handling high-volume data streams.
      - Adaptive Learning through machine learning algorithms.
      - Reactive Response to environmental changes.
      - Concurrent Processing of multiple system interactions.
    - Advanced Features:
      - Natural Language Dialogue for human-like interaction.
      - Smart Data Discovery for automated insights.
      - Visual Recognition for image analysis and video analysis.
      - Predictive Capabilities for forecasting.
  - Implementation Types:
    - By Integration Level:
      - AI-Assisted: Limited AI integration for specific tasks.
      - AI-Powered: AI as core system component.
      - Autonomous: Minimal human intervention required.
    - By Application Domain:
      - Enterprise AI Systems for business operations.
      - Customer-Facing AI Systems for service delivery.
      - Development Support Systems for software creation.
      - Domain-Specific AI Systems for specialized tasks.
- Citations:

[1] https://www.run.ai/guides/machine-learning-engineering/ai-infrastructure
[2] https://www.ibm.com/think/topics/ai-in-software-development
[3] https://www.eweek.com/artificial-intelligence/ai-software/
[4] https://lasoft.org/blog/types-of-ai-software/
[5] https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders?hl=en
[6] https://resources.concertidc.com/blog/understanding-the-tech-landscape-why-non-ai-solutions-still-matter
[7] https://ellow.io/components-of-ai/
[8] https://www.clariontech.com/platform-blog/powerful-ai-software-features-that-empower-your-business-success
[9] https://www.qarbon.it/blog/comparing-ai-software-development-and-non-ai-software-development
[10] https://caseguard.com/articles/the-five-basic-components-of-ai-new-software-development/

2023

(ChatGPT, 2023) ⇒ "What is an "AI-Supported Software System"? Summarize in 2-3 paragraphs.” prompt. ChatGPT, September 25 Version, OpenAI, 8 Oct. 2023. https://chat.openai.com.
- QUOTE: An AI-supported software system is a computer program or application that integrates artificial intelligence (AI) technologies and capabilities to enhance its functionality and provide more intelligent and efficient solutions to users. This type of software leverages machine learning algorithms, natural language processing, computer vision, and other AI techniques to process data, make predictions, automate tasks, and assist users in various ways.
  AI-supported software systems can be found across a wide range of industries and applications. For example, in customer service, AI chatbots are used to provide instant responses to customer inquiries. In healthcare, AI-supported diagnostic software can help doctors make more accurate diagnoses based on medical data. In finance, AI-powered trading algorithms can analyze market trends and execute trades autonomously. These systems often learn and adapt over time, becoming more proficient at their tasks as they are exposed to more data and user interactions.
  In essence, AI-supported software systems harness the power of artificial intelligence to augment human capabilities, improve decision-making, automate repetitive tasks, and provide more intelligent and personalized services across various domains, ultimately increasing efficiency and productivity.