Intelligent chatbots generate responses through statistical analysis of vast datasets on which they have been trained rather than producing predetermined answers. They leverage patterns and algorithms to create dynamic and contextually relevant responses. This allows them to adapt their replies based on the input and context of the conversation, resulting in a more interactive and human-like experience.

Depends on the quality of data used to train the chatbot Models. Foundation Models are rather adaptive and tend to learn from new instances and scenarios. The algorithms chatbots operates on are the determinants in the sense that the larger the data of the pre trained model, the more the quality and the spontaneouscity of its response.

So for me it's yes and no.

Depending on the Algorithm employed and the type of data (quality and quantity)

AI’s data well is drying up — what now?

"The vast improvements in artificial intelligence have largely been a matter of scaling — researchers build a bigger neural network and they train it on more data. But progress might be about to hit a roadblock. A study published this year projected that by 2028, the size of a typical training-data set will equal all of the text that is publicly available online. Put simply, AI is about to run out of training data. Researchers now face a dilemma: find and use training data from untapped sources, such as instant messages, or change course to focus on smaller, more efficient models..."

https://www.nature.com/articles/d41586-024-03990-2

Based on the law of diminishing marginal utility,

There is always bound to be a plateau, just didn't expect it to be this soon 2028.

New Developers are only just learning to utilize AI models for coding/programming with great efficiency. According to Google CEO about 60% of code is now done by AI.

I still think AI and IoT integration and optimization might still have about a decade to go before experiencing a flatline.

My thoughts.

>"These algorithms are not “preloaded” with specific views or worldviews of their creators, but rather are trained on data that reflects the diversity of thoughts, ideas and perspectives present in society."

That's not exactly correct. After initial training the models are being further tuned to be so called "safe". Part of that being "safe" involves training the models into avoiding expressing views that are being at that moment considered unfashionable among US coastal elites and squash any undesired diversity of thought. Interestingly, at least initially there had been claims of jailbreaking those models to express position that were logically consistent but going against fashionable political opinions, suggesting that this preloading has been so far superficial.

Dear Researchers, Scientists, Friends,

I still consider the question "Can the responses of intelligent chatbots be predetermined, or are they rather a statistical result of the data on which they were trained? Can the responses of intelligent chatbots available online be 'arbitrarily programmed into algorithms,' biased, and predetermined by their creators, or are they rather a statistical result of the data on which they were trained?" to be extremely important and relevant in the dynamically evolving landscape of conversational artificial intelligence. Continuing the discussion and conducting further research in this area is crucial for a fuller understanding of the nature of the responses generated by these systems. The relevance of this question stems from the increasing impact of chatbots on various aspects of our lives and the need to ensure their transparency and minimize potential biases. Based on my research and observations, I agree with the prevailing view that advanced language models largely rely on the statistical analysis of vast datasets. Nevertheless, I believe it is worth deepening research into how subtle choices in model architecture, data preprocessing, and fine-tuning methods can introduce certain forms of bias or preferences, even if they are not intentionally 'programmed.' New research areas that are becoming pressing include methods for identifying and mitigating hidden biases in training data and models, studying the impact of different neural network architectures on the response tendencies of chatbots, and developing metrics for evaluating the neutrality and objectivity of generated content. The determinants shaping chatbot responses are not only the training data itself but also the learning algorithms, model parameters, and even interactions with users during the deployment phase. I would like to thank all researchers and scientists for their previous contributions to this important discussion. I am open to scientific collaboration that will allow us to better understand the complex nature of intelligent chatbots and work towards their more ethical and responsible development. I invite you to continue exchanging thoughts on this fascinating topic.

I invite you to discuss this issue and to engage in scientific cooperation,

Best regards,

Dariusz Prokopowicz

AI chatbots are already biasing research — we must establish guidelines for their use now

The academic community has looked at how artificial-intelligence tools help researchers to write papers, but not how they distort the literature scientists choose to cite...

https://www.nature.com/articles/d41586-025-02810-5