AI-Supported Software System
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An AI-Supported Software System is a information processing software system that can solve AI-supported tasks (which require AI capabilitieses).
- AKA: AI-Enhanced System, Intelligent Software System.
- Context:
- Input(s): domain data, task specifications, user requirements
- Output(s): task results, AI-generated content, system responses
- Performance Measure(s): task accuracy, response quality, system reliability
- ...
- It can (typically) be supported by AI Technologies like ML technologies, NLP technologies, Computer Vision technologies, GenAI technologies, etc.
- It can (typically) implement AI Processing Layer with model execution, model registry, and feature store.
- It can (typically) maintain Data Management Layer with data ingestion, data storage, and data retrieval.
- It can (typically) provide Integration Layer with API gateway, message queue, and service mesh.
- It can (typically) include Application Layer with user interface, business logic, and monitoring.
- It can (typically) require Infrastructure Layer with compute resources, network fabric, and storage systems.
- ...
- It can (often) be based on AI Platforms or AI Frameworks.
- It can (often) follow Initial Development with requirements analysis and architecture design.
- It can (often) implement Data Pipeline with data collection and preprocessing.
- It can (often) perform Model Development with training, testing, and optimization.
- It can (often) manage System Integration with component assembly and interface implementation.
- It can (often) handle Deployment with release management and monitoring.
- It can (often) conduct Maintenance with model retraining and performance tracking.
- ...
- It can require System Resources such as GPU infrastructure, high-bandwidth networks, and distributed storage.
- It can involve Risk Management including data privacy, model bias, and decision impact.
- It can need capabilities like AI expertise, MLOps knowledge, and integration skills.
- It can address Regulatory Requirements such as AI governance and data protection.
- It can consider Cost Factors including development costs and operational expenses.
- ...
- It can range from being an Human-Directed AI-Supported System to being an Autonomous AI-Supported System, depending on its operational independence.
- It can range from being an AI-Assisted Software System to being an AI-Powered Software System, depending on its AI integration level.
- It can range from being a Domain-Specific AI System to being an Open-Domain AI System, depending on its application scope.
- It can range from being a Simple AI System to being a Complex AI System, depending on its architectural complexity.
- It can range from being a Single-Task AI System to being a Multi-Task AI System, depending on its task diversity.
- It can range from being a Static AI System to being an Evolving AI System, depending on its learning adaptability.
- …
- Examples:
- Enterprise AI Systems, such as:
- Business Intelligence AI Systems, such as:
- Enterprise Communication AI Systems, such as:
- Business Process AI Systems, such as:
- Customer-Facing AI Systems, such as:
- AI Customer Service Systems, such as:
- AI Marketing Systems, such as:
- AI Experience Systems, such as:
- Creative AI Systems, such as:
- Text Generation AI Systems, such as:
- Visual AI Systems, such as:
- Multimedia AI Systems, such as:
- Domain-Specific AI Systems, such as:
- Healthcare AI Systems, such as:
- Legal AI Systems, such as:
- Financial AI Systems, such as:
- Educational AI Systems, such as:
- Autonomous AI Systems, such as:
- Vehicle Automation Systems, such as:
- Robotics AI Systems, such as:
- Facility AI Systems, such as:
- AI Development Support Systems, such as:
- AI Code Development Systems, such as:
- AI Testing Systems, such as:
- AI DevOps Systems, such as:
- …
- Enterprise AI Systems, such as:
- Counter-Example(s):
- a Database-Supported Software System that does not use AI techniques.
- a Manual Software System with no intelligent automation.
- a Rule-Based Software System that uses only predefined logic without AI/ML capabilities.
- a Traditional Algorithm System that relies solely on deterministic algorithms.
- a Static Decision System that makes choices based on fixed rules rather than AI-driven analysis.
- See: AI Application, Intelligent System, Cognitive System, Smart System.
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:
- Functional Components:
- Infrastructure Layer:
- 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:
- Core Capabilities:
- 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:
- By Integration Level:
- Core Components:
- 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.
- 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.