k i n o k a s t . e u

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aiPod 019 – audio-only (31:59min)
aiPod 019 – Reflexive AI Trajectories 2025-2027 (NotebookLM)

Story: AI Personas, Indispensable Human Skills, Abilities, and Inclusive Design

Duration: 31:59min
Audience: AI and UX design future skills

Visit the Interactive mode Audio Overview: NotebookLM (with your Google account)

Target audience:
All concerned with the future and potential human-level, learning AI, and personal professional development.

Blog

Summary

AI 2025-2027: A Critical Turning Point

The aiPod 019 discusses a pivotal (societal and technological) moment we’re approaching where technical advances, safety concerns, and human adaptability will determine whether AI becomes our greatest ally (i.e. in our present & future UX design practice) or most significant challenge.

Definitions (Wikipedia):
https://en.wikipedia.org/wiki/Reflexivity_(social_theory)

Here’s a more detailed breakdown:

In Research (especially Qualitative):

Self-Awareness:
Reflexivity involves acknowledging the researcher’s own background, beliefs, and values, and how these might influence their research. 

Critical Reflection:
It’s about critically examining the researcher’s position within the research process and how their perspective shapes the research. 

Contextual Awareness:
Reflexivity also involves understanding the broader social, political, and cultural context in which the research is being conducted. 

Transparency:
Reflexive researchers are transparent about their biases and how they might impact the research findings. 

In General, Reflexivity refers to:

Self-Examination:
Reflexivity is the ability to examine one’s own feelings, reactions, and motives, and how these influence their actions and thinking. 

Openness:
It encourages openness to new perspectives and challenges existing assumptions. 

Growth and Adaptation:
Practicing reflexivity can lead to personal and community growth and adaptation to changing circumstances. 

Example in Sociology:
Reflexivity in sociology addresses how a person’s experiences shape their identity and behavior, and how those behaviors, in turn, affect their experiences

AI Personas development faces difficulties in creating truly representative user frameworks, indicating challenges in designing AI that authentically understands diverse human needs.

AI Progress (AI 2027) points to a potential breakthrough in superhuman AI by 2027, triggering an arms race between the US and China and fundamentally reshaping global power dynamics.

AI Safety and Alignment emerges as a growing challenge as AI systems become more powerful, with significant technical and philosophical hurdles in ensuring AI behaves according to human values.

Economic and Societal Impact focuses on workplace transformation, where AI-driven automation is reshaping employment patterns and requiring new approaches to defining value.

Key Technical Concepts highlights current limitations in AI models, including mathematical obstacles and the automation of AI research itself, suggesting both opportunities and risks in how systems evolve.

Human Reasoning from a social perspective becomes crucial for AI development, as understanding the nuances of how humans think collectively could improve human-AI collaboration.

Human Role in AI World emphasizes that people will remain essential in defining value, ensuring ethical AI use, and adapting to automation—suggesting successful AI integration depends on grounding development in real-world user research.

aiPod 019 – Reflexive AI Trajectories 2025-2027

Aiko or Blaise are AI conversational (fictitious) personas. They answer your spoken questions or comments (in English) in real-time through their text-to-speech Google NotebookLM’s LIVE technology, sourcing only curated knowledge (no live internet source).
Just click on the Interactive mode Button to interrupt standard Audio Overview playback and ask your questions!
The NotebookLM Audio Overview panel is at the top right of your screen.

Listen to this AI podcast, Audio only

Notice on the use of AI and genAI text and speech information

In an attempt to be helpful, AI and generative AI (genAI) text or speech can occasionally produce responses that are incorrect or misleading.

This is known as “hallucinating” information, and it’s a byproduct of some of the current limitations of frontier Generative AI models. For example, in some subject areas, AI and generative AI (genAI) text or speech might not have been trained on the most-up-to-date information and may get confused when prompted about current events. Another example is that AI and generative AI (genAI) text or speech can display quotes that may look authoritative or sound convincing, but are not grounded in fact. In other words, AI and generative AI (genAI) text or speech can write things that might look correct but are very mistaken.

Users should not rely on AI and generative AI (genAI) text or speech as a singular source of truth and should carefully scrutinize any high-stakes advice given by AI and generative AI (genAI) text or speech.

When working with web search results, users should review cited sources. Original websites may contain important context or details not included in the AI and generative AI (genAI) text or speech synthesis. Additionally, the quality of responses depends on the underlying sources referenced, so checking original content helps you identify any information that might be misinterpreted without the full context.

You can use the thumbs down button to let us know if a particular response was unhelpful, or write to me at jerome-bertrand at kinokast.eu with your thoughts or suggestions.

kinokast.eu – 2025