Empowering AI with a mission
Something that I’ve been thinking about lately on applying AI to business. We’ve probably all seen those videos where the developer gives his AI system a goal for Super Mario and the AI figures out the most interesting glitches and exploits the game in a way that we would never be able to. There are also times when the AI thought that the best way to beat it was just to pause the game forever. Interesting result but not what we were looking for.
How about a web application? Can we define a goal, or a series of goals, for the AI to follow? Do we even need to build an application? Could we use the AI for marketing by giving it a budget and having it go for it, similar to AI driven Google Ads?
To give an AI a goal for a web application, you first need to define the objective or task that the AI should accomplish. This can be achieved by breaking down the task into smaller sub-tasks and defining metrics for success.
Once you have defined the goal, you can train the AI using machine learning algorithms on a dataset of examples to enable it to learn how to achieve the task. The AI will then use this knowledge to make decisions and take actions to optimize the web application towards the defined goal.
To enable the AI to self-manage the web application, you can integrate it with the application’s codebase and allow it to make decisions and take actions based on the data it collects from the application. This can include optimizing the user experience, improving conversion rates, reducing errors, and other tasks that are important for achieving the defined goal.
It is important to note that self-management of a web application by an AI requires continuous monitoring and tuning to ensure that the AI is making the right decisions and achieving the desired outcomes. This can be done by providing the AI with feedback on its performance and using this feedback to adjust its behavior and decision-making processes.
The effectiveness of an AI at self-managing a web application depends on various factors, including the quality and amount of data it has access to, the complexity of the application, and the accuracy of the machine learning algorithms used to train the AI.
If the AI has access to a large amount of high-quality data, and the algorithms used to train it are well-designed and optimized, it can be highly effective at self-managing a web application. The AI can learn from its experience and make informed decisions that lead to improved performance, such as better user engagement, increased conversions, and reduced errors.
However, if the data is of poor quality, or the algorithms are not well-designed, the AI may not be effective at self-managing the web application. It may make incorrect decisions that lead to poor performance or even harm the application’s functionality or user experience.
The effectiveness of an AI at self-managing a web application can be affected by external factors, such as changes in user behavior or the competitive landscape. Therefore, it is important to continuously monitor and update the AI to ensure that it remains effective and responsive to changes in the environment.
It really is interesting to note that you can let AI experiment with your application and see how well it does compared to normal testing. It’s a cost effective way to experiment. Frightening? Sure. Exciting? Absolutely.