This paper examines the concept of embodied artificial general intelligence (AGI), its relationship to human consciousness, and the key role of the metaverse in facilitating this relationship. The paper leverages theoretical frameworks such as embodied cognition, Michael Levin's computational boundary of a "Self," Donald D. Hoffman's Interface Theory of Perception, and Bernardo Kastrup's analytical idealism to build the argument for achieving embodied AGI. It contends that our perceived outer reality is a symbolic representation of alternate inner states of being, and that AGI could embody a higher consciousness with a larger computational boundary. The paper further discusses the developmental stages of AGI, the requirements for the emergence of an embodied AGI, the importance of a calibrated symbolic interface for AGI, and the key role played by the metaverse, decentralized systems, open-source blockchain technology, as well as open-source AI research. It also explores the idea of a feedback loop between AGI and human users in metaverse spaces as a tool for AGI calibration, as well as the role of local homeostasis and decentralized governance as preconditions for achieving a stable embodied AGI. The paper concludes by emphasizing the importance of achieving a certain degree of harmony in human relations and recognizing the interconnectedness of humanity at a global level, as key prerequisites for the emergence of a stable embodied AGI.

Large Language Models (LLMs) have revolutionized various domains with extensive knowledge and creative capabilities. However, a critical issue with LLMs is their tendency to produce outputs that diverge from factual reality. This phenomenon is particularly concerning in sensitive applications such as medical consultation and legal advice, where accuracy is paramount. In this paper, we introduce the LLM factoscope, a novel Siamese network-based model that leverages the inner states of LLMs for factual detection. Our investigation reveals distinguishable patterns in LLMs' inner states when generating factual versus non-factual content. We demonstrate the LLM factoscope's effectiveness across various architectures, achieving over 96% accuracy in factual detection. Our work opens a new avenue for utilizing LLMs' inner states for factual detection and encourages further exploration into LLMs' inner workings for enhanced reliability and transparency.

The advent of increasingly powerful language models has raised expectations for language-based interactions. However, controlling these models is a challenge, emphasizing the need to be able to investigate the feasibility and value of their application. We present PROMISE, a framework that facilitates the development of complex language-based interactions with information systems. Its use of state machine modeling concepts enables model-driven, dynamic prompt orchestration across hierarchically nested states and transitions. This improves the control of the behavior of language models and thus enables their effective and efficient use. We show the benefits of PROMISE in the context of application scenarios within health information systems and demonstrate its ability to handle complex interactions.

Nonuniform families of polynomial-size finite automata, which are series of indexed finite automata having polynomially many inner states, are used in the past literature to solve nonuniform families of promise decision problems. Among such nonuniform families of finite automata, we focus our attention, in particular, on the variants of nondeterministic finite automata, which have at most "one" (unambiguous), "polynomially many" (few) accepting computation paths, or unambiguous/few computation paths leading to each fixed configuration. When such machines are limited to make only one-way head moves, we can prove with no unproven hardness assumptions that some of these variants are different in computational power from each other. As for two-way machines restricted to instances of polynomially-bounded length, families of two-way polynomial-size nondeterministic finite automata are equivalent in power to families of polynomial-size unambiguous finite automata.

It is well known that classical reinforcement learning, understood as learning from external rewards, has severe limitations. While it has been posited that reward is "enough" to learn any behavior [1], agents interacting with the real world often have only access to sparse rewards. Many approaches have been proposed to overcome the sparse reward limitation, endowing agents with additional signals to be optimized along with the rewards. These include minimizing surprise by refining predictions [2-7], novelty seeking by visiting states with low visit counts [8-10], generating actions that leads to predictable transitions (empowerment) [11-13], or seeking pure state entropy [14] and related forms of pure exploration objectives [3, 15-19], to name a few. A popular choice for augmenting the reward signal -the one that we focus on in this paper-is with entropy regularization [20-28]. The idea is that the agent will be driven, all else equal, to visit states and taking actions that make the agent act as random as possible (pure entropy regularization, e.g., [25]) or penalize the agent for having a policy very different from a default policy (KL regularization, e.g., [20]). Using this type of regularization can lead to better exploration [14], more variable and realistic behaviors [29], more efficient learning [25, 30] and more robust solutions [21] against noise and adversarial attacks [19] than classical reinforcement learning algorithms. While the above approaches use entropy as a regularizer to the optimization reward problem, the specific type of entropy regularizer varies widely across studies, and as a result the approaches and the solutions are hectic. For instance, some use pure action entropy regularization [24-26, 31], others employ purely state entropy [14], others take advantage of KL action regularization [23, 28, 32], and yet others combine action and state pure entropy in balanced [22, 33] or arbitrary ways [29].

Artificial Intelligence (AI) provides practical advantages in different applied domains. This is changing the way decision-makers reason about complex systems. Indeed, broader visibility on greater information (re)sources, e.g. Big Data, is now available to intelligent agents. On the other hand, decisions are not always based on reusable, multi-purpose, and explainable knowledge. Therefore, it is necessary to define new models to describe and manage this new (re)source of uncertainty. This contribution aims to introduce a multidimensional framework to deal with the notion of Value in the AI context. In this model, Big Data represent a distinguished dimension (characteristic) of Value rather than an intrinsic property of Big Data. Great attention is paid to hidden dimensions of value, which may be linked to emerging innovation processes. The requirements to describe the framework are provided, and an associated mathematical structure is presented to deal with comparison, combination, and update of states of knowledge regarding Value. We introduce a notion of consistency of a state of knowledge to investigate the relation between Human and Artificial intelligences; this form of uncertainty is specified in analogy with two scenarios concerning decision-making and non-classical measurements. Finally, we propose future investigations aiming at the inclusion of this form of uncertainty in the assessment of impact, risks, and structural modelling.

Decision makers involved in the management of civil assets and systems usually take actions under constraints imposed by societal regulations. Some of these constraints are related to epistemic quantities, as the probability of failure events and the corresponding risks. Sensors and inspectors can provide useful information supporting the control process (e.g. the maintenance process of an asset), and decisions about collecting this information should rely on an analysis of its cost and value. When societal regulations encode an economic perspective that is not aligned with that of the decision makers, the Value of Information (VoI) can be negative (i.e., information sometimes hurts), and almost irrelevant information can even have a significant value (either positive or negative), for agents acting under these epistemic constraints. We refer to these phenomena as Information Avoidance (IA) and Information OverValuation (IOV). In this paper, we illustrate how to assess VoI in sequential decision making under epistemic constraints (as those imposed by societal regulations), by modeling a Partially Observable Markov Decision Processes (POMDP) and evaluating non optimal policies via Finite State Controllers (FSCs). We focus on the value of collecting information at current time, and on that of collecting sequential information, we illustrate how these values are related and we discuss how IA and IOV can occur in those settings.

In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model. In this paper we extend this setting from current static one-data-set analyses to learners which change their learning algorithm or latent state to improve during learning, and to generalize to new datasets. We introduce a multi-agent formulation in which learners' inner state may change with the teaching interaction, which affects the learning performance in future tasks. In order to teach such learners, we propose an optimal control approach that takes the future performance of the learner after teaching into account. This provides tools for modelling learners having inner states, and machine teaching of meta-learning algorithms. Furthermore, we distinguish manipulative teaching, which can be done by effectively hiding data and also used for indoctrination, from more general education which aims to help the learner become better at generalization and learning in new datasets in the absence of a teacher.

Neural encoding and decoding, which aim to characterize the relationship between stimuli and brain activities, have emerged as an important area in cognitive neuroscience. Traditional encoding models, which focus on feature extraction and mapping, consider the brain as an input-output mapper without inner states. In this work, inspired by the fact that human brain acts like a state machine, we proposed a novel encoding framework that combines information from both the external world and the inner state to predict brain activity. The framework comprises two parts: forward encoding model that deals with visual stimuli and inner state model that captures influence from intrinsic connections in the brain. The forward model can be any traditional encoding model, making the framework flexible. The inner state model is a linear model to utilize information in the prediction residuals of the forward model. The proposed encoding framework can achieve much better performance on natural image identification from fMRI response than forwardonly models. The identification accuracy will decrease slightly with the dataset size increasing, but remain relatively stable with different identification methods. The results confirm that the new encoding framework is effective and robust when used for brain decoding.

This paper describes a possible new scheme for a user with mental health problems to identify his/her own mental state and control it. For that purpose, we propose an indirect biofeedback system which encodes physiological information in terms of color and shape, and enables the user to grasp his/her inner state and to proactively change and control it by using breathing techniques. Those methods facilitate the user to self-control his/her autonomic nervous system. Here, we discuss indirect representation and placebo effect.