Navigating the Future of AI-Augmented Software Engineering: Part 3
AI Summary
Summary: AI Augmented Software Engineering - Part Three
AI for Operations
- Focus on conventional software development techniques to build AI-augmented systems.
- AI-augmented systems include both AI and non-AI components.
Types of AI Systems
- Generative AI has gained popularity, but there are other types such as visual AI and augmented reality.
- Examples include societal scale infotainment systems like YouTube and mission-critical systems like self-driving cars.
Societal Scale Systems
- Platforms like Facebook, YouTube, and Instagram are used widely and incorporate AI for various functions.
Mission Critical Systems
- Systems where timely and accurate responses are crucial.
- Examples include Palantir’s AIP for battle management and Scale’s Donovan for rapid data analysis.
Characteristics of AI-Enabled Systems
- Intentionally non-deterministic to mimic human behavior.
- Emergent behavior arises from interactions of simpler elements.
- Extreme dependence on data quality, quantity, and depth.
- Testing of data is as important as testing of code.
- Designing for explainability and fairness is crucial.
Building AI Augmented Systems
- Rethinking the entire software development process is necessary.
- Assured AI is a promising career path, especially in cybersecurity and defense.
Political and Ethical Considerations
- The U.S. aims for ethical use of AI in defense, minimizing mistakes.
- Other countries may not prioritize avoiding collateral damage.
Testing and Safety
- Shift-left testing: detecting problems early to reduce costs.
- Continuous runtime testing is essential for AI systems to adapt on the fly.
- Large language models like Auto GPT are being explored for testing processes.
Conclusion
- AI-enabled systems for enterprise use will likely be adopted faster than for defense due to safety and security concerns.