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Software Engineering for AI and Machine Learning

Rice University_Brandon Martin_083021A
[Rice University - Brandon Martin]


- Implementing AI/ML into Software Engineering

Software developers thrive on certainty. If you feed a program a set of inputs, you'll always get the same outputs. For most of software's history, software was built entirely on deterministic logic. What goes in determines what goes out. 

We even have a term for that: top-down programming. All algorithms follow a path, with branching that's also based on expected logic. When we debug code, we run down that same path over and over again, finding where behavior deviates from expectation, and wrangling it back on track. 

Certainty and deterministic logic work for a lot of software. But the real world doesn't work that way. By contrast, AI is probabilistic. Answers are never exact. Instead, AI uses models to predict behavior, and then generates that behavior. 

Perhaps the best way to describe this is how traditional software is updated vs. AI. Traditional software gets updates and patches. AI learns, evolving on its own, understanding and assimilating user feedback without manual intervention. This makes traditional software more precise, but AI more flexible.


- Software Engineering for AI and ML

Software engineering for AI and machine learning (AI-ML) is an emerging field that involves new technologies and processes for developing and evolving AI systems. 

Here are some roles in AI engineering:

  • AI engineers: Use machine learning techniques and AI to develop applications and systems to help organizations increase efficiency, cut costs, increase profits, and make better business decisions. 
  • ML engineers: Focus more on ML algorithms and models that can self-tune to better learn and make predictions from large data sets.

AI can also help software engineers in a number of ways: 

  • Automated code generation: AI can automatically generate codes, which can save time and reduce the risk of human error.
  • Algorithm analysis: Some algorithms can analyze requirements and automatically generate code that meets those requirements. This is particularly useful for generating boilerplate code or simple functions.


AI Engineering

Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as value propositions by companies in all industries in order to create or extend the services and products they offer. 

However, developing AI/ML systems has presented several engineering problems that are different from those that arise in, non-AI/ML software development. AI engineering is an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts. 

In contrast to the prevalent rush to develop capabilities and progress individual tools, AI Engineering asks a different set of questions: How can AI help humans achieve mission outcomes? What are the limits of AI systems in practice today? How can we ensure that ethical standards are upheld as AI systems are deployed? 

The rise in availability of computing power and massive datasets have led to the creation of new AI, models, and algorithms encompassing thousands of variables and capable of making rapid and impactful decisions. Too often, though, these capabilities work only in controlled environments and are difficult to replicate, verify, and validate in the real world.


- Implementing Software Engineering into AI/ML Systems

Software engineering is the process of designing, implementing, testing, and deploying software systems that incorporate AI and ML models. Software engineering involves requirements analysis, design, implementation, testing, deployment, and maintenance. 

Software developers specializing in AI/ML design, develop, and implement software applications that incorporate AI and ML technologies. 

Software engineering techniques such as use case modeling, requirement gathering, and user story creation can be valuable in building AI/ML applications. One of the most critical aspects of building AI/ML applications is the availability of high-quality data. 


[More to come ...]



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