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We are witnessing a progressive shift if not a total handover of control from humans to machines, but a rebalancing of agency in many domains. The rise of AI systems, especially those with autonomous decision making capabilities, is changing the dynamics of control in many profound ways. In this chapter, we examine why this shift matters, how it isn’t just technological, but philosophical too. As machines take on ...
Compression is fundamental to intelligence. A model that can represent its training data as a short code has discovered regularities that enable generalization. Large neural networks may learn functions far simpler than their parameter counts suggest, but it is challenging to construct codes that realize this simplicity. Parameter based methods such as quantization produce code lengths that scale with model size, ...
Decoding continuous three dimensional (3D) motor imagery (MI) using non invasive electroencephalography (EEG) based brain computer interfaces (BCIs) remains challenging due to signal variability and residual decoding errors. Deep learning architectures such as convolutional neural network long short term memory (CNN LSTM) models can capture spatial and temporal dynamics for continuous kinematic decoding; however, ...
Robotic manipulation is inherently multi frame: local actions may be simple in an end effector frame, while transport, upright object handling, and whole body coordination are better represented in a base aligned frame. However, modern diffusion based visuomotor policies typically commit to a single predefined action frame, forcing one denoiser to model action distributions that are often unnecessarily complex in ...
Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the first step is faithfulness: a requirement that causally ...
Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision making, communication, and more. In recent years, it has become increasingly recognized as a cornerstone of capable, transparent AI systems. Yet while LLMs have made significant progress across diverse real world tasks, it is not yet clear when, how, or to what extent they can exhibit or be endowed w...
Existing studies of LLM as judge scoring bias work predominantly at the input output level: they perturb inputs, measure score deltas, and propose prompt level mitigations. We argue that the same biases admit a representation level account in the judge's hidden state, complementary to the input output view and operationally useful in ways it does not afford. We report three findings, across seven judges, seven bia...
This paper proposes a human centered artificial intelligence (HCAI) framework for AI assisted lexicography. While generative AI offers significant opportunities to enhance lexicographic work, it also raises concerns regarding the future role of lexicographers and the preservation of linguistic and cultural diversity. Drawing on HCAI principles and previous applications in other language professions, the paper iden...
The rapid development of brain computer interfaces has made memory uploading, brain controlling of some basic functions of computers, and the treatment of diseases or disabilities like paralysis and blindness possible. However, human consciousness cannot be fully replicated. This paper analyzes why brain computer interface (BCI) cannot completely replicate human consciousness via a literature review method from th...
Recent work in humanoid whole body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation? The answer is not obvious, as manipulation involves complex, contact rich dynamics and requires delicate regulation of contact modes and forces. We prese...
Bipedal robots are challenging to control because they operate close to instability, where small variations in foot terrain contact can rapidly destabilize locomotion. On rigid terrain, bipedal robots mitigate this fragility by using well established contact mechanics and control strategies. On flowable surfaces such as granular slopes, foot contact can induce large surface deformations and solid fluid like transi...
For children with severe physical and cognitive disabilities (e.g. iatrogenic neurologic injury, congenital myelopathy, or quadriplegic cerebral palsy), there are limited therapeutic options. Scientific and clinical developments in implantable brain computer interface (BCI) technology are in clinical trials in adults. The ethical issues around implantable BCI research in adults have recently been examined, but to ...
Brain–computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed window decoding that may result in redundant observation or unreliable predictions due to insufficient evidence. Adaptive temporal decision making (ATDM) addresses this accuracy–time trade off by progressively accumulating EEG evidence and deciding when to stop. However, existing EEG encoders are mainly d...
Abstract Objective. Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with motor impairments. These signals remain inherently susceptible to physiological artifacts (e.g., eye blinking, swallowing), which pose persistent challenges. Although Transformer based a...
Brain–computer interface (BCI) systems based on motor imagery hold significant clinical value for individuals who have lost voluntary movement, but most studies test BCI assistive devices like powered wheelchairs under ideal conditions where motor imagery signals are not interfered by simultaneous cognitive load. This work records electroencephalography (EEG) from 13 participants across four tasks: baseline rest, ...
Abstract/Description: This study investigates the dynamics of sensorimotor rhythms within non invasive brain computer interface (BCI) environments. Inspired by advancements in neural signal processing, this research utilizes open source telemetry to characterize how specific signal variances correlate with intentional thought sequences. The project demonstrates an analytical approach to interpreting neural data in...
Categoría: Neurotecnología / BCI (Brain Computer Interfaces) Resumen Ejecutivo (Abstract) La búsqueda de la extensión de la vida humana ha trascendido la medicina regenerativa para entrar en el terreno de la integración cíborg. Esta propuesta analiza la viabilidad teórica de un sistema de interfaz cerebro máquina (BCI) de alta densidad capaz de mantener la continuidad de la conciencia mediante la integración de un...
This dataset contains electroencephalography (EEG) recordings collected for five class imagined alphabet decoding using an OpenBCI system. EEG data were recorded from 20 healthy adult participants during an eyes closed alphabet imagery task. The five target alphabet classes are A, K, N, P, and Z. Each participant completed one recording session for each alphabet class, and each session contained 25 repeated trials...
This conceptual paper develops the notion of substrate–world decoupling: the possibility that conscious processing remains locally sustained by a biological brain while the subject’s perception, action, and operational life unfold through an external robotic body or a virtual avatar.The argument is grounded in complete locked in syndrome and in existing neurotechnological research. Bidirectional brain–computer int...
Abstract/Description: This study investigates the dynamics of sensorimotor rhythms within non invasive brain computer interface (BCI) environments. Inspired by advancements in neural signal processing, this research utilizes open source telemetry to characterize how specific signal variances correlate with intentional thought sequences. The project demonstrates an analytical approach to interpreting neural data in...
