From Apes to Apps - How Darwin Is the Key to the Evolution of AI
Darwin’s theory of evolution puts forward that organisms evolve via a process of natural selection, where those who are fittest survive while others naturally phase out.
At TurinTech, we believe that this process of evolution is not just for natural organisms. We are convinced that nature’s way of selecting the most optimal characteristics of organisms and applying those characteristics to offspring is also applicable to code. As a result, backed by over a decade of research, we developed Artemis for evolutionary code optimization. Artemis and its evolutionary code optimization capabilities enable users to extract and mutate the most performant sections of their code. But just as natural selection doesn’t work in isolation, Artemis Intelligence combines evolutionary algorithms with multi-agent collaboration, contextual optimization, and real-time validation. This ensures that code doesn’t just evolve—it transforms into a production-ready, enterprise-grade solution.
Understanding the Problem
The challenge with institutional codebases is that they are massive, inefficient, and old. Often, multiple developers have worked on these codebases over time, so anything undocumented can be riddled with confusion. This tech debt accumulates over time, driving businesses to direct valuable resources and time to untangle spaghetti code.
There are a few ways to tackle the problem of suboptimal code:
Manual Code Optimization
This is where developers work through code changes at a granular level, locating suboptimal code snippets and updating them manually. This is an extremely cumbersome process; Developers spend time understanding codebases, checking if the code compiles, manually implementing changes, and checking if these changes still preserve the original functionality without introducing bugs.
Can GenAI Help?
In the current Generative AI (GenAI) landscape, there’s a lot that Large Language Models (LLMs) can do for code. We’ve seen a surge of GenAI coding tools, and they largely fit into three categories:
1. Code generation, where GenAI-based tools take natural language queries and convert them to code
2. Code completion, where GenAI-based tools autocomplete sections of code
3. Code review, where GenAI-based tools test and review code
The challenge in using GenAI tools for coding in any of the above applications is that due to their probabilistic nature, the output's accuracy and validity cannot be guaranteed. This is the case in both generating new code and working on old code bases. For code to be production-quality, they are expected to display some essential characteristics:
Reliability: Code does what it is supposed to do, without any major failures.
Clarity and consistency: Code is clear to read and understand, and follows consistent stylistics and standards.
Robustness: Code can handle a variety of different application scenarios, including those that occur less commonly.
Efficiency: Code is productive, and wastes minimum time, energy, and resources.
In the current generative AI landscape, code produced by generative tools is often far from ideal and does not meet these quality criteria. Since LLMs are known to hallucinate, they can easily introduce bugs and inaccuracies to code. As a result, if not properly quality-checked, LLMs can aggravate your tech debt problem, instead of resolving it.
Artemis: The What and the Why
Artemis is a GenAI-based code optimization platform that aims to solve the challenges of code optimization. On the one hand, Artemis expedites the manual code optimization process by automating the workflows around optimizing code. On the other hand, Artemis provides additional mechanisms to validate LLM output, ensuring that you are not generating more inefficient code.
Artemis works in a few easy steps:
1. Import your codebase
Import your codebase from a Git repository or a local folder.
2. Extract inefficient snippets
Use a combination of profilers, parsers, and LLMs to extract inefficient code snippets from your imported codebase:
3. Derive alternative snippets
Use a combination of LLMs and proprietary evolutionary optimisation algorithms to generate better code snippets
4. Validate whether the code performs as expected
Run validations to determine whether your code is up to standard
Well, What’s Powering Artemis?
The Intelligence Engine.
This is where we think evolution is not just for organisms. Just as natural selection gradually works to generate stronger organisms and weed out the weak, evolutionary algorithms can be used to mutate snippets of code to their strongest form.
Artemis Intelligence brings together a suite of tools and techniques, including evolutionary algorithms, reinforcement learning, and LLMs, to understand a codebase and evolve the codebase to its most optimal form.
In code, this type of mutation can be carried out in a few ways. Artemis Intelligence particularly considers three possible pathways: (1) Evolving a single piece of code until it is optimised, (2) combining two strong pieces of code to create one single optimised code snippet, and (3) combining several snippets of code to generate multiple optimal versions of optimal code.
Depending on the needs of a codebase, users can ask Artemis Intelligence to select from a set of proprietary evolutionary algorithms and apply them to the codebase. This evolves the codebase to its strongest form. See some sample logs below:
While Artemis Intelligence recommends the best and the most optimal version of code, it also provides the user the space to decide which of the optimisations to retain. This human-in-the-loop setup ensures that your codebase will not go rogue and mutate into something that is more harmful than useful (Green Goblin flashbacks, anyone?).
Further, Artemis Intelligence learns from your codebase and your preferences. This means that the more you use Artemis Intelligence to improve your codebase, the better it will be at generating optimisations that are most relevant and useful to you.
Artemis Intelligence also comes with fine-tuned prompts, built-in scores, and validation steps, so that all the mutated snippets that are generated are checked for validity and accuracy.
This end-to-end code optimisation workflow takes away the burden of manual code optimization and enables users to harness cutting-edge techniques to optimise code with a few clicks. We are certain that Artemis and its Darwin-inspired code optimisation process will dramatically improve your software development workflows.
Are you ready to start the (r)evolution?
