Catalin Mitelut

Catalin C. Mitelut

A central goal in neuroscience is to provide explanations for how animal nervous systems can generate actions and cognitive states such as consciousness while artificial intelligence (AI) and machine learning (ML) seek to provide models that are increasingly better at prediction. Despite many decades of research we have made limited progress on providing neuroscience explanations yet there is an increased use of AI and ML methods in neuroscience for prediction of behavior and even cognitive states. Here we propose emulator theory (ET) and neural emulators as circuit- and scale-independent predictive models of biological brain activity and emulator theory (ET) as an alternative research paradigm in neuroscience. ET proposes that predictive models trained solely on neural dynamics and behaviors can generate functionally indistinguishable systems from their sources. That is, compared to the biological organisms which they model, emulators may achieve indistinguishable behavior and cognitive states - including consciousness - without any mechanistic explanations. We posit ET via several conjectures, discuss the nature of endogenous and exogenous activation of neural circuits, and discuss neural causality of phenomenal states. ET provides the conceptual and empirical framework for prediction-based models of neural dynamics and behavior without explicit representations of idiosyncratically evolved nervous systems.

Catalin Mitelut, Ben Smith, Peter Vamplew

The rapid advancement of artificial intelligence (AI) systems suggests that artificial general intelligence (AGI) systems may soon arrive. Many researchers are concerned that AIs and AGIs will harm humans via intentional misuse (AI-misuse) or through accidents (AI-accidents). In respect of AI-accidents, there is an increasing effort focused on developing algorithms and paradigms that ensure AI systems are aligned to what humans intend, e.g. AI systems that yield actions or recommendations that humans might judge as consistent with their intentions and goals. Here we argue that alignment to human intent is insufficient for safe AI systems and that preservation of long-term agency of humans may be a more robust standard, and one that needs to be separated explicitly and a priori during optimization. We argue that AI systems can reshape human intention and discuss the lack of biological and psychological mechanisms that protect humans from loss of agency. We provide the first formal definition of agency-preserving AI-human interactions which focuses on forward-looking agency evaluations and argue that AI systems - not humans - must be increasingly tasked with making these evaluations. We show how agency loss can occur in simple environments containing embedded agents that use temporal-difference learning to make action recommendations. Finally, we propose a new area of research called "agency foundations" and pose four initial topics designed to improve our understanding of agency in AI-human interactions: benevolent game theory, algorithmic foundations of human rights, mechanistic interpretability of agency representation in neural-networks and reinforcement learning from internal states.

Ari Pakman, Yueqi Wang, Catalin Mitelut et al.

Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces. For these models, posterior inference methods can be inaccurate and/or very slow. In this work we introduce deep network architectures trained with labeled samples from any generative model of clustered datasets. At test time, the networks generate approximate posterior samples of cluster labels for any new dataset of arbitrary size. We develop two complementary approaches to this task, requiring either O(N) or O(K) network forward passes per dataset, where N is the dataset size and K the number of clusters. Unlike previous approaches, our methods sample the labels of all the data points from a well-defined posterior, and can learn nonparametric Bayesian posteriors since they do not limit the number of mixture components. As a scientific application, we present a novel approach to neural spike sorting for high-density multielectrode arrays.