Unintended Consequences: AI Cognitive Devolution?

Over the past decade, Artificial Intelligence (AI) has become an integral part of our daily lives, influencing numerous industries and revolutionizing how we interact with technology. A crucial aspect of AI development is the wealth of knowledge and data available that allows it to provide 'smart' answers.

With critical conversations ongoing about the ethics of such training (respect for original work ownership, etc.) there is a forming risk of a pattern problem here. Take a LinkedIn post. Many of us like to articulate some thoughts around something we have been involved with or working on, and frame those thoughts in a way for others to digest. Hopefully this provides a rich, insightful experience that others draw from, who then go on to write more novel content that builds on the ideas and concepts that were raised.

But if AI services are grabbing that content and making it extremely easy to very quickly post repackaging of that content, there is a real risk that people will simply post less. If people post less, the derivative posts from a new article will not be building on what was discussed with more novel content, it will be AI repackaged forms of the same content and, given the pattern of a lot of how AI works the credibility of information contained may well degrade while it's doing it.

So where can we look for some early problem indicators?

The Role of Stack Overflow in AI Development

Stack Overflow has been a vital resource for AI development, particularly in the realm of natural language processing (NLP) and language models. The vast amount of code snippets, troubleshooting discussions, and programming best practices shared on the platform have allowed AI models to learn from human expertise and gain a deeper understanding of human languages and problem-solving strategies.

The Process of AI Learning

At the core of AI's capabilities lies machine learning, a process in which algorithms are trained on vast datasets to recognize patterns and make predictions. Stack Overflow's data has been a treasure trove for training AI models in various fields, enabling them to provide helpful responses and assist users effectively. By leveraging the collective knowledge of developers worldwide, AI has been able to stay current and up-to-date on the latest programming trends and methodologies.

The Decline of Stack Overflow Usage

While Stack Overflow has been a valuable resource, its decline in usage has been a cause for concern. Several other factors could also be contributing to this trend, such as the rise of alternative platforms, a shift in developer preferences, or the emergence of new knowledge-sharing channels.

But, assuming AI services are also contributing, the concern is two-fold - Stack Overflow consumers use it less, as it's easier to get an answer from a Co-Pilot service, but this misses the debate and evolution of responses. As a result, contributors use it less - they value it for the debate and because of the credibility they get by being an active participant, neither of which holds as much value any more.

Unfortunately, as fewer people actively participate in Stack Overflow, the volume and quality of data available to train AI models decreases.

The "Dumbing Down" of AI

As AI models heavily rely on data to refine their knowledge and understanding, the decline in Stack Overflow usage could lead to a gradual "dumbing down" of AI. Outdated or incomplete information could persist within AI models, causing them to generate less accurate or relevant responses. Moreover, AI's ability to adapt to rapidly changing technologies and programming practices might be hindered, making them less useful for developers seeking assistance.

Mitigating the Issue

The conversations around ethical use of AI are key to rethinking how we take advantage of all of it's wonders, while avoiding a short term harvest that destroys our ability to grow more knowledge.

Some may roll their eyes at 'Respect for Original Work' concerns, but it needs to be understood that discussion on topics such as this is also about ensuring longevity of the pattern of sharing, which we have all come to rely on heavily since the Internet Age.

Restrictions to knowledge sharing is a devolution in cognitive ability, and that devolution would impact the very systems (AI) that are poised to take leadership in the knowledge share economy.

Conclusion

The decline in Stack Overflow usage poses a potential threat to AI's intelligence and effectiveness. To prevent the "dumbing down" of AI, developers, researchers, and the broader tech community must recognize the importance of active participation in knowledge-sharing platforms and embrace strategies to keep AI models ethical, up-to-date and reliable. By working together, we can ensure that AI continues to be a powerful tool for developers and users alike, enriching our technological landscape for years to come.