dr. a. j. anceno’s Post

WHAT SEPARATES HUMAN INTELLIGENCE FROM ARTIFICIAL INTELLIGENCE ? Prima facie, we know that artificial intelligence requires separate denotations for processing while human intelligence has an inbuilt denotation functionality. It all boils down to our inability to formalize self reference satisfactorily. The key question is whether we can mimic cognitive methods of self reference on machines ? This book tries to address this problem. https://lnkd.in/gp3egFCb

Wow, the possibility of quantum mind? Interesting, this is a good line of research. But what is more interesting about this article is that it rules out the possibility of AGI. According to this article, embodied intelligence is a conditio sine qua non. Not only that, many of the most important mind processes are also not necessarily based on induction, deduction, or abduction as the machines are.

Tangles: A structural approach to artificial intelligence in the empirical sciences https://lnkd.in/e9H4iMrQ A short part is available here: https://lnkd.in/ecDk7NgE

RUSTIC TEAPOT Today's topics ( https://conta.cc/3SCwl0a ) - artificial intelligence, CPSC structure, REACH, headlights, lead, pools

https://lnkd.in/gsQr4_N3 Anthropic making waves with Claude-3. Check out some of the examples :- https://lnkd.in/gDrM7V_B I loved the one where Claude-3 Reinvented Quantum Algorithm from scratch

Fcdnet: Fuzzy Cognition-based Dynamic Fusion Network for Multimodal Sentiment Analysis https://lnkd.in/d6mstJya The Fcdnet dynamically integrates #sentiment #scores across different modalities using a fuzzy cognition fusion mechanism (FCM), significantly enhancing the accuracy of identifying divergent sentiments across modalities. Additionally, a disparity balancing module (DBM) is proposed to normalize the representations between different modality features by penalizing the similarity of sentiments with different #degrees and rewarding the #separability of sentiments with same degree. #Experimental results demonstrate that Fcdnet outperforms state-of-the-art methods on public datasets, validating the superiority and effectiveness. Marc Polo (Ph.D) Miriam Diez Bosch PhD Josep Maria Picola Meix (Ph. D.) Víctor Curto (Ph.D) Blanquerna - Universitat Ramon Llull

🌟 **Thrilled to Share a Breakthrough in Precision Measurement!** 🌌 I'm excited to introduce **Hyper-Resolution Binary Analysis**—a pioneering approach to binary data representation. This innovative method allows for nearly a million decimal points between the traditional binary states of 0 and 1. I am eager to apply this technique using data from the **CERN LHC** to discover anomalies and perform detailed interpolation analysis. The implications are vast, significantly enhancing our capacity to model and predict quantum states with unmatched precision. This could open new doors in quantum science and beyond! Stay tuned for updates as we explore these uncharted territories. 🚀 #Innovation #DataAnalysis #QuantumScience #CERN

A new preprint from my group that revives the theory of continous attractors that may be surprising to you! Despite their mathematical fragility, we show that they are functionally *robust*. No wonder we see approximate continuous attractors in neuroscience often! Ságodi, Á., Martín-Sánchez, G., Sokół, P., & Park, I. M. (2024). Back to the Continuous Attractor. arXiv. 2408.00109 (previous version is under review) https://lnkd.in/dwXDSXAh Dynamical systems inferred from data, task-trained neuralODE and RNNs, finite size effects on theoretical models of ring attractors, lack of expressive power of RNNs, all lead to some need to formalize the notion of approximate continuous attractors. I believe we made some fresh theroetical progress in this regrad.

RUSTIC TEAPOT Today's topics ( https://conta.cc/3SCwl0a ) - artificial intelligence, CPSC structure, REACH, headlights, lead, pools

https://lnkd.in/eSB7n4MG Artificial Intelligence with human-like reasoning, commonsense reasoning, computational creativity, conceptual representation and categorization,  integration of typicality, probability and cognitive heuristics, combinatorial and transformational intelligence and the adequacies and drawbacks of using the human brain as a model of intelligence.