DEB(AI)TE: ROUND I. DO MODELS TRULY REASON OR MIMIC REASONING?

The discussion centers around the question of whether current AI models genuinely reason or merely imitate reasoning. Dr. Abdul Mahmood argues that AI models, like clinicians, often follow pre-determined guidelines, suggesting a form of reasoning within constraints. Amir Hussain posits that neural networks can reason, citing mathematical proof, but emphasizes the need to constrain their search for logical consistency. Karan Joel highlights the importance of counterfactual reasoning, an area where current models struggle. Hassan Soif suggests that abstraction layers in language models contribute to reasoning capabilities, but more research is needed. The panelists debate the potential of world models and embodied AI, with varying perspectives on the necessity of real-world experience. They also discuss the role of smarter training and alignment in bridging the gap between mimicry and true understanding. Amir emphasizes the limitations of simulated environments due to computational irreducibility and chaos theory. The speakers offer insights into the future of AI development, including neuromorphic computing and more efficient deep learning approaches. The panel agrees on the importance of benchmarks and long-term reasoning.