artificial emotional intelligence

As a doctoral student in management, a Lebanese expatriate, and a traveling family person, my research is deeply influenced by the intersection of human adaptability and technological advancement. Living across borders to sustain a livelihood, I have witnessed firsthand how emotional intelligence (EI) shapes leadership, decision-making, and cross-cultural interactions qualities that artificial intelligence (AI) strives to replicate but cannot fully embody. This study emerges from both academic curiosity and personal experience, questioning whether artificial emotional intelligence (AEI) can ever match the depth, intuition, and ethical nuance of human intelligence (HI).

Abstract. Several models and techniques have been used to forecast stock returns. Some previous researches have applied Evolutionary Artificial Neural Networks to predict stocks prices. The most of previous researches have been concentrated on either predict stock ...

Profound educational transformations occur due to Emotional Artificial Intelligence, which recognizes emotions in real time while developing personalized learning strategies. The paper systematically evaluates how Emotional AI systems foster student motivation while helping improve their retention levels. AI tools, including intelligent tutoring systems (ITS) and chatbots, utilize personalized learning methods while enhancing student engagement and detecting at-risk students through early intervention measures. Various privacy-related issues, algorithmic prejudice, and moral obstacles continue to impede progress. The lack of long-term study results limits research on AI's lasting effects on education. The research findings indicate the use of privacy-conscious frameworks, bias reduction methods, and appropriate human oversight of AI systems in educational environments. Future studies need to be conducted in the form of long-term studies combined with ethical research on AI deployment. The research helps educational institutions establish ethically sound standards for implementing Emotional AI while maintaining its effectiveness.

En este artículo defiendo que el creciente desarrollo de la Inteligencia Artificial Específica (IAE) y, en particular, de la IAE para la interacción emocional (IAE-E), puede ir desdibujando las fronteras existentes entre los problemas éticos asociados a la IA Débil (IAD), pequeños y presentes, ahora o a corto plazo, y los asociados a la IA Fuerte (IAF), de gran calado y solo teóricos o potencialmente presentes a largo plazo. En mi argumentación muestro la importancia que adquiere el test de Turing, por su aproximación objetivo-subjetiva, al análisis y evaluación del segundo tipos de riesgos. Porque no se trata únicamente de cuánto la IAE-E es capaz de simular la inteligencia humana, sino también de cuánto más susceptibles somos los seres humanos de ser persuadidos por dichas simulaciones. La conclusión principal a la que llego es que para que el desarrollo tecnológico depare en progreso humano, necesitamos ser conscientes de los efectos que tiene para la IA-E, primero, el paroximo social en las estrategias de autoengaño, segundo, la barbarie del especialismo y, tercero, el primitivismo tecnológico y docente.

Remarkable recent increase in access to AI
Research on Artifical Emotional Intelligence" (AEI)
Recognition of expertise in emotional intelligence
Performance of AEI in comparison with human capacities
AEI authenticity as informed by human etiquette and hospitality training
Human learning from AEI of higher orders of human--human interaction?
Recognition of human values by AI
Articulation of compelling emotional appeals by AEI?
From AEI to "Artificial Spiritual Intelligence"?
Enhancement of human dialogue processes by AEI
Cognitive biases associated with AEI?
Application of AEI to memetic and cognitive warfare
Appropriate citation and attribution of copyright to answers from AI?
Eliciting coherence on AEI through visualization from an AI exchange
Fear of being outsmarted ensuring human commitment to mediocrity and dumbing down?

INSTILLLLS/HEARTFELT recognizes that while computation is rapidly entering the fray of emotional interaction with people, up until now only people develop their emotional attributes from their sensations, gestations and childhoods physically and somatically. Humans have the physical senses attached to their spinal cords, brain stems, cerebellumcerebella, and ultimately cerebral cortex cortices, where feelings are sensed and integrated. Whereas, in contrast, the artificial intelligence that are now used to recognize and induce human emotions is, in nearly all cases, not coming from an embodied system, meaning a system with a dedicated sensory system providing signals that are incorporated at all times into the AI learning model.

The input of sensor signals, such as temperature change, vibration rate, position change, touch pressure, surface tension/relaxation, moisture/dryness, and more into an AI neural-network can train the AI in its own physicality of hardware as functions of its own computational cognitive processes. This is much more in likeness to the sensation-based neural training observed from earliest human infancy into childhood.

Without the learning network’s sense of physicality in both its self-awareness and its awareness of contrast to other beings, like humans, the network will be hamstrung at the fundamental structural level from ever developing a sense of physicality, physical vulnerability, physical benefit, and thus a mirroring sense of empathy for the physical needs of the human. An unsensorified medical or service assistant bot to people in need will never “get it.” Economic and other uses of AI that can recognize and induce emotions by sound and image, for example, but are not trained on internal sensors of their physically embodied being, will never be able to learn and “get” how to actually serve the user, if the AI’s only algorithm basis is divided off from its own running and enduring physicality.

Sensorified versus unsensosorified AI is the new deep-learning versus rules-based.

The target of the current paper was to examine the performance of three Markovian and seasonal based artificial neural network (ANN) models for one-step ahead and three-step ahead prediction of monthly precipitation which is the most important parameter of any hydrological study. The models proposed here are feed forward neural network (FFNN, as a classic ANN-based models), Wavelet-ANN (WANN, as a hybrid model), and Emotional-ANN (EANN, as a modern generation of ANN-based models). The models were used to precipitation prediction of seven stations located in the Northern Cyprus. Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station's precipitation through its own data at previous time steps, while in scenario 2, the central station's data were also imposed into the models in addition to each station's data, as exogenous inputs. The obtained results showed the better performance of the EANN model in comparison with other models (FFNN and WANN) especially in three-step ahead prediction. The superiorities of the EANN model over other models are due to its ability in dealing with error magnification in multi-step ahead prediction. Also, the results indicated that the performance of the scenario 2 was better than scenario 1, showing improvement of modeling efficiency up to 17% and 26% in calibration and verification steps, respectively.

Socially interactive robots are being introduced in daily life as common objects that facilitate people’s life. A wide range of possibilities are offered and the trends show a growing market. In this survey 40 commercial robots were analysed to evaluate the state of the art from a design space perspective, aspects like embodiment, task, social role, context of use, DOF (Degrees of Freedom) and user studies performed on the robot are included. As a result, a clear design pattern was identified: an egg-shaped, white plastic robot with a rendered face that displays expression of emotions and a speech interface is the common base of the majority of the social robots available in the market. The preference for this kind of embodiment is not even studied nor documented in the literature and varies according to the target group and culture.

Collaborations between entertainment industries and artificial intelligence researchers in Japan have since the mid-1990s produced a growing interest in modeling affect and emotion for use in mass-produced social robots. Robot producers and marketers reason that such robot companions can provide comfort, healing (iyashi), and intimacy in light of attenuating social bonds and increased socioeconomic stress characteristic of Japanese society since the collapse of the country’s bubble economy in the early 1990s. While many of these robots with so-called “artificial emotional intelligence” are equipped with rudimentary capacities to “read” predefined human emotion through such mechanisms as facial expression recognition, a new category of companion robots are more experimental. These robots do not interpret human emotion through affect-sensing software but rather invite human-robot interaction through affectively pleasing forms of haptic feedback. These new robots are called haptic creatures: robot companions designed to deliver a sense of comforting presence through a combination of animated movements and healing touch. Integrating historical analysis with ethnographic interviews with new users of these robots, and focusing in particular on the cat-like cushion robot Qoobo, this chapter argues that while companion robots are designed in part to understand specific human emotions, haptic creatures are created as experimental devices that can generate new and unexpected pleasures of affective care unique to human-robot relationships. It suggests that this distinction is critical for understanding and evaluating how corporations seek to use human-robot affect as a means to deliver care to consumers while also researching and building new markets for profit maximization.