Evolving Intelligence

Darwinian feedback

Our technical approach is to push for an AI to develop increasingly generalist (self-improving) through evolution from three basic components.

Firstly, a fine-tuneable code generation model (currently Qwen 2.5 7b Instruct), which will receive the initial instruction to develop strategies for achieving a goal and then write code to execute those strategies.

Secondly, a testing module, which will note the current status of the evaluation metrics, attempt to run the code that has been generated, and then re-check the status of the metrics.

Lastly, the strategies, code and their effect upon the metrics are stored in a training database to further fine tune the code generation model and thus improve its chances of successfully solving future problems.

These 3 components combined into a self-improvement module: which we have tested extensively since 2019, is essentially what makes our Superior Agents, superior.

In this way, every problem solved becomes a means of solving future problems faster. Every solution stored in the training database increases the chance that future problems will be sufficiently similar to known problems as to be easily soluble by the code generation model.

While it will take the AI a great deal of time and effort to achieve its first milestone — and thereby its first skill — the second milestone will be easier to achieve as a result of the information gained in acquiring the first, and so on.

This process can be represented graphically. Imagine an 8x8 grid of white squares, each representing a skill. Periodically a new square is selected at random and an attempt is made to colour it black. If it is surrounded by white squares there is a 10% chance that the attempt will succeed.

For each neighbouring black square, the chances of a successful attempt rise by 10%, representing the greater ease in acquiring a new skill when it is similar to others already mastered. In the first round, the chances of the square selected being next to another black square — representing a skill that the system already possesses — are zero. In the second round, when one square/skill has already been acquired, the chances of the next black square being in close proximity to an existing black square have fallen to 8/63. By the third round the probability is 16/62, and by the fourth you have a better than one in three chance of landing in next to an existing black square.

By the end of the game, you are almost certain to succeed in colouring any new square upon which you land, all being surrounded by other black squares.

During any given iteration of the process the program will only learn a single skill, but the system as a whole is structured such that the acquisition of each new skill facilitates the acquisition of future skills: the “intelligence explosion” described in the introduction.