018
E-learning – Maths Hurdles to AI development (ENGLISH, 23:13min)
English
Summary
These sources explore the limitations of current artificial intelligence and the mathematical obstacles hindering its progress toward human-level understanding.
Target audience: Anyone interested in future human-level AI development and its mathematical challenges.
Audio-only?
- Short play, audio-only:
ai:Pod 018 – AI Personas: Limitations and Divergence from Reality – Audio-only (19:09min) - Long play, audio-only:
ai:Pod 018 – E-learning – Yann LeCun: Mathematical Obstacles and Future Directions in AI (23:13min)
Story: How AI leading expert Yann LeCun proposes to develop a more human-level, self-learning AI – with respect to sources on freedom and ethics.
Duration: 23:13min
Audience: All interested in mathematics, UX design ethics and AI development
The NotebookLMs
Interactive research with Voice chat
Below are 3 omplementary Google NotebookLMs to similar topics. Test and learn!
- (1) ai:Pod 018 – E-learning – Yann LeCun: Mathematical Obstacles and Future Directions in AI
The sources discuss various perspectives on the future of Artificial Intelligence, particularly the challenges in achieving human-level intelligence and ensuring responsible development. - (2) ai:Pod 018 – Maths Hurdles to Human-Level AI
The sources highlight key mathematical hurdles that must be overcome to achieve more advanced AI capabilities, advocating for new approaches like learning “world models” from sensory input and using Inference by Optimization with architectures like JEPA.
Additionally, the text explores Hugo Mercier and Dan Sperber’s theory that human reason is primarily social and argumentative, raising questions about designing collaborative AI and the importance of defining constraints for AI development to align with human values. - (3) ai:Pod 018 – AI Personas: Limitations and Divergence from Reality
The sources highlight how current AI methods are inefficient and lack robust “world models”, which are deep, intuitive understandings of how the world works, unlike humans who learn through rich sensory experiences. A key example of this limitation is the unreliability of AI-generated user personas, which are shown to diverge from real user data and exhibit biases.
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Video explainer based on 018: a Dual AI podcast testing
I created this video teaser using HeyGen’s legacy “Dual podcast” GenAI function (first half of 2025).
This lab test serves me as a tangible proof that socially unintelligent talking robots (despite being polished and likable GenAI products) reveal their limitations even when given literature to learn and repeat.
They demonstrate exactly what the discourse tries to explain: we need a new approach toward more socially intelligent, human-level, Like Yann LeCun coined it: AI World Models.
Still in for a tongue in cheek?
(English, 04:53min)
Interactive Mindmap to help you research
Why our digital AI Assistants still don’t “Get” us as well as we do?
(The learning limitations of current AI math)
Explore our latest episodes that tackle AI’s serious limitations of cognitive intelligence:
- 018 – AI Personas Limitations and Divergence from Reality
- 018 – Mathematical hurdles explained in detail by Yann LeCun
Both episodes critically examine why traditional AI learning methods—supervised learning, reinforcement learning, and even the much-hyped large language models with their sequential prediction approach—fall short of human cognitive capabilities.
The UX Research Problem
Our discussions reveal how AI-generated personas in UX research present significant accuracy issues. These digital stand-ins simply cannot represent real users authentically, hampered by inherent biases and a fundamental lack of real-world understanding.
What’s Missing: World Models
What’s the missing piece? “World models”—the mental frameworks that help humans grasp common sense and physical dynamics. In ai:Pod 018, Aiko and Blaise converse about promising alternatives like Energy-Based Models and Joint Embedding Predictive Architectures that might build more robust world models in AI systems.
The Social Nature of Intelligence
Perhaps most importantly, our hosts explore how human reasoning is inherently social—and what this means for developing truly collaborative AI. They emphasize that ethical considerations and thoughtful constraints aren’t limitations but essential ingredients for beneficial AI development.
The verdict? Despite technological advances, real user data remains irreplaceable in UX research, and new paradigms in AI development—especially for fields like e-learning—must carefully consider both technical approaches and ethical implications.
aiPod 018 – Mathematical hurdles
Aiko or Blaise AI hosting personas will be responding in real-time through their text-to-speech, curated knowledge and NotebookLM’s speech-to-speech LIVE technology.
Interactive podcasts are generated by AI sources and instructions I’ve provided to Google’s NotebooKLM, Gemini 2.0. Flash (Thinking Experimental), and Anthropic’s Claude 3.7 Sonnet.
Non interactive AI podcast, Audio only
(To ask live questions via your mic to AI personas Aiko & Blaise, access the Google NotebookLM in the Chrome browser, click on ‘Interactive mode/ BETA’)
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