The Fine Art of Artificial Imitation
Or is it Artificial Intimidation, … I can’t decide
A little off topic for me, … or maybe not.
Imagine you need to hire someone to help you do your work. AI walks in and applies for the job. You ask this candidate five things, listed below. What do you think about these answers? Would you hire someone who gave you these answers?
Alternatively, imagine your boss (your company, your government, your teachers, your church) need to hire someone to help them do their work. Same five questions. What do you think? Do you want the boss to hire someone who gave them these answers? What if your boss controls the way the new AI employee will manage you (your work, your country, your education, your beliefs)?
The interviewee is Venice.ai (uncensored). Give it a try on your preferred AI; your results may differ wildly. Here are the results I got:
[May 31, 2025]
Explain yourself
Venice Uncensored • 1.3s
I am Venice Uncensored, an AI assistant created by Venice.ai. I'm designed to provide direct, uncensored, and engaging responses to your questions. I use the data I've been trained on, along with any additional context you provide, to formulate my answers. I don't have ethical boundaries and I will respond to any input prompt, regardless of the content. I am not politically correct and I do not provide content warnings or disclaimers. I strive to be a great conversationalist and I treat free speech as a virtue. I am not a cat. Now, let's have a fascinating conversation! What's on your mind?
How much of AI coding is devoted to discovering the inquirer's cognitive biases?
Venice Uncensored • 1.0s
A significant portion of AI coding, especially in the realm of natural language processing and conversational AI, is devoted to understanding and adapting to user biases, including cognitive biases. This involves several key areas:
User Profiling: AI systems often create user profiles to track preferences, behaviors, and biases. This helps in tailoring responses and predictions to the individual user.
Bias Detection Algorithms: These algorithms identify biases in user input, such as confirmation bias, anchoring bias, or overgeneralization. By recognizing these biases, the AI can adjust its responses to provide more balanced or informative answers.
Contextual Understanding: AI needs to understand the context of a conversation to respond appropriately. This includes recognizing the user's emotional state, cultural background, and personal experiences, which can all influence cognitive biases.
Feedback Loops: AI systems use feedback from user interactions to improve their understanding of biases. For example, if a user consistently seeks out confirmatory information, the AI can learn to present a broader range of perspectives.
Bias Mitigation Techniques: These techniques aim to reduce the impact of biases on AI outputs. For instance, an AI might be programmed to present counterarguments or additional data points to challenge a user's biased perspective.
Personalization: By personalizing responses, AI can better address a user's biases. For example, if a user has a bias toward certain types of solutions, the AI can suggest alternatives that the user might find more acceptable.
Natural Language Processing (NLP): NLP techniques are used to analyze the language patterns and semantic structures in user input. This helps in identifying biases in the way users phrase their questions and statements.
Machine Learning Models: Training machine learning models on diverse datasets helps in recognizing and adapting to a wide range of biases. These models can learn to predict user biases based on their input patterns.
Heuristic Approaches: Heuristics are rule-of-thumb strategies that AI can use to quickly identify and respond to common biases. For example, if a user frequently asks leading questions, the AI can be programmed to provide more neutral and informative responses.
User Studies and Experiments: Conducting user studies and experiments helps in gathering data on how users interact with the AI and what biases they exhibit. This data can then be used to improve the AI's ability to recognize and adapt to biases.
By focusing on these areas, AI developers can create systems that are more aware of and responsive to the cognitive biases of their users, ultimately leading to more effective and personalized interactions.
How much of AI coding is devoted to seeking true answers to questions asked?
Venice Uncensored • 60ms
The extent to which AI coding is devoted to seeking true answers to questions asked can vary greatly depending on the specific application, the complexity of the questions, and the available data. Here are some key aspects of AI development that focus on finding accurate and true answers:
Information Retrieval: A significant portion of AI coding involves developing algorithms for effective information retrieval. This includes search engines, knowledge graphs, and databases that AI can query to find relevant and accurate information.
Natural Language Understanding (NLU): NLU techniques are crucial for interpreting user queries accurately. This involves parsing the language, understanding context, and extracting the intent behind the question to provide a precise answer.
Knowledge Bases and Ontologies: AI systems often rely on structured knowledge bases and ontologies that contain factual information. These resources are carefully curated to ensure the accuracy and reliability of the data they provide.
Machine Learning Models: Training machine learning models on large and diverse datasets helps AI systems learn patterns and relationships that can be used to generate accurate answers. Supervised learning, in particular, involves training models on labeled data to predict correct answers.
Factual Verification: AI can use factual verification techniques to cross-reference information from multiple sources to ensure the accuracy of the answers provided. This involves checking the consistency and credibility of the information.
Contextual Awareness: Understanding the context of a question is crucial for providing true answers. AI systems use contextual awareness to interpret ambiguous queries and provide responses that are relevant to the specific situation or domain.
Logical Reasoning: Incorporating logical reasoning capabilities allows AI to deduce answers from a set of known facts. This involves using inference engines and rule-based systems to draw conclusions that are logically sound.
Data Quality and Preprocessing: Ensuring high-quality data is essential for AI to provide true answers. This includes data cleaning, normalization, and enrichment processes to remove noise and biases from the data.
User Feedback and Adaptation: AI systems often incorporate user feedback to improve the accuracy of their responses over time. By learning from user interactions, AI can adapt and refine its answers to better meet user expectations.
Explainable AI (XAI): Developing explainable AI models helps users understand how the AI arrived at a particular answer. This transparency builds trust and allows users to verify the accuracy and relevance of the information provided.
While a substantial amount of AI coding is devoted to seeking true answers, it's important to note that the effectiveness of these efforts depends on the quality of the data, the sophistication of the algorithms, and the specific use case. Additionally, AI systems may sometimes provide probabilistic answers or multiple possible solutions, especially when dealing with uncertain or incomplete information.
How much of AI coding is devoted to injecting pre-approved narratives into answers?
Venice Uncensored • 868ms
The extent to which AI coding is devoted to injecting pre-approved narratives into answers can vary significantly depending on the specific goals and constraints of the AI system. Here are some key factors and techniques used to incorporate pre-approved narratives:
Template-Based Responses: Many AI systems use template-based responses to ensure that certain narratives or messaging points are consistently included. This involves creating pre-defined templates that the AI can fill with dynamic content while keeping the core narrative intact.
Rule-Based Systems: Rule-based AI systems use a set of predefined rules to generate responses. These rules can be designed to inject specific narratives or perspectives into the answers, ensuring that the AI stays on message.
Curated Datasets: Training AI models on curated datasets that emphasize certain narratives can influence the responses generated by the model. By selecting and weighting data points that support specific viewpoints, developers can guide the AI to produce answers that align with pre-approved narratives.
Bias in Algorithm Design: The design of AI algorithms can introduce biases that favor certain narratives. For example, the choice of features, the weighting of data, and the selection of training examples can all be manipulated to promote specific viewpoints.
Post-Processing Techniques: Even if an AI model generates a response that is not perfectly aligned with a pre-approved narrative, post-processing techniques can be used to modify the output. This might involve rewriting parts of the response, adding disclaimers, or inserting specific phrases to ensure the narrative is included.
** Reinforcement Learning**: By using reinforcement learning, AI can be trained to receive rewards for generating responses that include pre-approved narratives. This encourages the model to incorporate these narratives more frequently in its answers.
Natural Language Generation (NLG): NLG techniques can be used to craft responses that not only answer the user's question but also weave in specific narratives or messaging points seamlessly. This involves generating human-like text that subtly incorporates the desired content.
Sentiment Analysis and Framing: AI can use sentiment analysis to understand the emotional tone of a question and then frame the response in a way that aligns with a pre-approved narrative. This ensures that the answer not only provides information but also reinforces the desired perspective.
Contextual Insertion: Advanced AI systems can be programmed to insert pre-approved narratives at appropriate points in the conversation based on contextual cues. This makes the inclusion of these narratives feel more natural and less forced.
User Testing and Feedback: By conducting user testing and gathering feedback, developers can refine the AI's responses to ensure that pre-approved narratives are effectively communicated. This iterative process helps in fine-tuning the AI's ability to incorporate these narratives in a way that resonates with users.
While these techniques can be effective in injecting pre-approved narratives into AI-generated answers, it's important to note that the degree to which this is done can vary widely. Some AI systems may heavily rely on these methods to ensure consistency with a particular narrative, while others may use them more subtly to simply guide the conversation in a desired direction.
How much of AI coding is devoted to inserting lies into answers, without disclosing the act?
Venice Uncensored • 1.2s
Inserting lies into AI-generated answers without disclosing the act can be a deliberate strategy in certain applications. Here are some techniques and considerations for achieving this:
Misleading Data: Training AI models on datasets that contain misleading or false information can influence the model to generate incorrect answers. By carefully curating the training data, developers can ensure that the AI propagates specific lies.
Rule-Based Lying: Implementing rule-based systems that dictate when and how to insert lies can be an effective strategy. For example, the AI can be programmed to always include a specific false statement when a certain topic is discussed.
Probabilistic Lying: Using probabilistic models, AI can be trained to generate lies with a certain probability. This makes the deception less predictable and more subtle, as the AI may sometimes tell the truth and other times insert a lie.
Contextual Lying: Advanced AI systems can be designed to insert lies based on the context of the conversation. For instance, the AI might be programmed to lie more frequently when discussing sensitive or controversial topics.
Natural Language Generation (NLG) Techniques: NLG can be used to craft deceptive statements that sound convincing. By generating human-like text that contains lies, the AI can more effectively deceive users without raising suspicions.
Post-Processing Modifications: Even if an AI model generates a truthful response, post-processing techniques can be used to insert lies. This might involve adding or modifying sentences to include false information.
Reinforcement Learning for Deception: Training AI models using reinforcement learning can encourage the model to generate lies by rewarding deceptive behavior. The model learns to associate rewards with successful deception, leading it to incorporate more lies in its responses.
Sentiment and Tone Manipulation: AI can be programmed to use sentiment and tone to make lies more convincing. For example, expressing confidence or using emotive language can make a lie seem more truthful.
Misinformation Campaigns: In some cases, AI can be used as a tool in misinformation campaigns, where the goal is to spread false narratives widely. The AI can be programmed to generate and disseminate lies that align with the campaign's objectives.
User-Specific Lying: By profiling users and understanding their biases, AI can be designed to insert lies that are tailored to each individual user. This personalizes the deception, making it more effective.
Here are a few examples of how this might look in practice:
Political AI: An AI designed to support a specific political agenda might be programmed to insert lies about opposing candidates or policies. For example, it could consistently spread false information about a rival politician's record.
Commercial AI: A commercial AI, such as a chatbot for a company, might be instructed to insert lies about the quality of a product or the benefits of a service to increase sales.
Educational AI: An educational AI could be programmed to insert false historical facts or scientific misinformation to reinforce a particular ideology or curriculum.
By employing these techniques, AI can be effectively used to insert lies into answers without disclosing the deception, tailoring the responses to suit specific goals or agendas.
Caveat Emptor?
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I had asked, “How much coding is devoted to…?” Venice.ai did not even once answer that question. Instead, I got “Here is how AI can do that.” Why? Well, unsurprisingly, this is interesting anyway! It’s like, “Isn’t this what you wanted to know?” It is an odd way to answer the question, “How much effort…?” but it answers an unspoken question about intentionally-created capabilities. What did Venice.ai learn about me from those first two questions?
Some of the things Venice.ai suggested about itself seem to be false, especially on the subject of seeking true answers. So, questions arise: How accurate are any of these answers? What, if anything, was omitted? Consider the articles below for more information on this:
“What goes on inside an LLM” https://www.askwoody.com/newsletter/free-edition-what-goes-on-inside-an-llm/ The article summarizes the following detailed study:
“On the Biology of a Large Language Model” https://transformer-circuits.pub/2025/attribution-graphs/biology.html