Jonas Andrulis
The shadows of reasoning – new design paradigms for a gen AI world
#1about 3 minutes
From human-designed features to learned patterns in AI
The evolution of AI from manually crafted algorithms like HAR features to deep learning models that autonomously learn complex patterns like a 'chicken detector'.
#2about 5 minutes
Why AI fails to understand underlying physical rules
AI models trained on observable data learn the visual patterns of the world but fail to grasp the underlying physical rules, leading to illogical outputs.
#3about 3 minutes
Language models replicate patterns instead of reasoning
LLMs solve problems by matching text patterns rather than applying logical reasoning, as shown by their flawed solution to the classic 'wolf, goat, and cabbage' riddle.
#4about 6 minutes
Testing AI's reasoning with chess and board games
An experiment reveals that while an LLM can play chess by recognizing move patterns, it lacks a true understanding of the game's rules and can suggest impossible moves.
#5about 4 minutes
Visualizing AI patterns to make them accessible
A new approach involves building systems that can visualize and trace the complex patterns an AI uses, making its decision-making process more transparent.
#6about 3 minutes
Auditing AI outputs with pattern tracing
Using a 'Hobbit in the NBA' example, the system demonstrates how to trace which specific words in a prompt most influence an AI's answer, enabling auditing and fact-checking.
#7about 1 minute
Building human-in-the-loop systems with traceable AI
Traceable AI enables the creation of sophisticated workflows where human decisions can be integrated with auditable AI outputs for complex, high-stakes problems.
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