AI-Supported Software System

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An AI-Supported Software System is a software-based system that can solve AI-supported software tasks through AI-supported software capabilities requiring AI-supported software models and AI-supported software algorithms.

References

2025-01-03

[1] G. Melli, "AI-Supported Software System," GM-RKB (2023) – Definition and context of AI-supported software systems. https://www.gabormelli.com/RKB/AI-Supported_Software_System
[2] L. N. Kabilova et al., "Design of Intelligent Software Systems," Computers 11(5), 2022 – Definition of intelligent software systems and their growing role. https://archive.conscientiabeam.com/index.php/79/article/download/2967/6426
[3] IBM Data & AI Team, "Understanding the Different Types of AI," IBM (2023) – On narrow vs general AI, with examples like Siri/Alexa as Narrow AI. https://www.ibm.com/think/topics/artificial-intelligence-types
[4] B. Harper, "What is Hybrid AI?", Information Age (Oct 2024) – On combining rule-based reasoning with machine learning in hybrid AI systems. https://www.information-age.com/what-is-hybrid-ai-123512053/
[5] GeeksforGeeks, "Difference between Shallow and Deep Neural Networks," (2023) – Comparison of shallow vs deep models and their capacities. https://www.geeksforgeeks.org/deep-learning/difference-between-shallow-and-deep-neural-networks/
[6] Gaine Tech., "Probabilistic and Deterministic Results in AI Systems," (July 2023) – Explains differences between probabilistic AI (randomness, outputs as probabilities) and deterministic AI (consistent outputs). https://www.gaine.com/blog/probabilistic-and-deterministic-results-in-ai-systems
[7] N. Moore, "Single-Agent vs Multi-Agent Systems," DigitalOcean (Dec 2024) – Describes single vs multi-agent AI systems and their cooperative potential. https://www.digitalocean.com/resources/articles/single-agent-vs-multi-agent
[8] H. Idrees, "Edge AI vs. Cloud AI: Real-Time Intelligence vs. Centralized Processing," Medium (Oct 2024) – Discusses differences in deployment location (edge computing for low latency vs cloud for scale). https://medium.com/@hassaanidrees7/edge-ai-vs-cloud-ai-real-time-intelligence-vs-centralized-processing-df8c6e94fd11
[9] M. Kosinski, "What is Black Box AI?," IBM (Oct 2024) – Defines black-box AI and the challenge of interpreting complex models, versus the need for explainable AI. https://www.ibm.com/think/topics/black-box-ai
[10] Zilliz (Milvus) "AlphaGo and Reinforcement Learning," AI Reference (2023) – Case study of DeepMind's AlphaGo combining supervised and reinforcement learning to surpass human Go players. https://milvus.io/ai-quick-reference/what-is-alphago-and-how-did-it-use-reinforcement-learning
[11] Neptune.ai, "Self-Supervised Learning and Its Applications," (2023) – Notes that GPT models are trained by predicting the next word (self-supervised learning). https://neptune.ai/blog/self-supervised-learning
[12] ScribeHow, "Generative AI: Examples & Tools," (2023) – Highlights generative models like DALL-E for creating images from text and ChatGPT for language generation. https://scribehow.com/library/generative-ai-examples
[13] Kaggle/Toptal, "Ensemble Methods in Machine Learning," (2020) – Notes that ensembles can produce more accurate predictions, albeit with interpretability trade-offs. https://www.toptal.com/machine-learning/ensemble-methods-kaggle-machine-learn
[14] Silent Eight, "Continuous Learning Loops…", (Apr 2025) – Emphasizes continuous model updates (feedback loops) to keep AI systems aligned with changing data. https://www.silenteight.com/blog/continuous-learning-loops-the-key-to-keeping-ai-current-in-dynamic-environments
[15] Analytics Vidhya, "Adversarial Learning: Improving Model Robustness," (Feb 2023) – Confirms adversarial training improves model robustness to perturbed inputs. https://www.analyticsvidhya.com/blog/2023/02/exploring-the-use-of-adversarial-learning-in-improving-model-robustness/
[16] TuringPost, "Open-Source Tools for Bias Detection," (2023) – Lists tools like IBM AI Fairness 360 for identifying and mitigating bias in AI models. https://arunapattam.medium.com/navigating-the-ai-bias-exploring-tools-and-techniques-c42b0f26fd29
[17] World Economic Forum, "AI Value Alignment for Shared Human Goals," (2023) – Stresses the importance of aligning AI systems with human values and diverse ethical principles. https://www.weforum.org/publications/ai-value-alignment-guiding-artificial-intelligence-towards-shared-human-goals/

2024-12-16

[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

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