Social robots (SRs) should autonomously interact with humans, while exhibiting proper social behaviors associated to their role. By contributing to health-care, education, and companionship, SRs will enhance life quality. However, personalization and sustaining user engagement remain a challenge for SRs, due to their limited understanding of human mental states. Accordingly, we leverage a recently introduced mathematical dynamic model of human perception, cognition, and decision-making for SRs. Identifying the parameters of this model and deploying it in behavioral steering system of SRs allows to effectively personalize the responses of SRs to evolving mental states of their users, enhancing long-term engagement and personalization. Our approach uniquely enables autonomous adaptability of SRs by modeling the dynamics of invisible mental states, significantly contributing to the transparency and awareness of SRs. We validated our model-based control system in experiments with 10 participants who interacted with a Nao robot over three chess puzzle sessions, 45 - 90 minutes each. The identified model achieved a mean squared error (MSE) of 0.067 (i.e., 1.675% of the maximum possible MSE) in tracking beliefs, goals, and emotions of participants. Compared to a model-free controller that did not track mental states of participants, our approach increased engagement by 16% on average. Post-interaction feedback of participants (provided via dedicated questionnaires) further confirmed the perceived engagement and awareness of the model-driven robot. These results highlight the unique potential of model-based approaches and control theory in advancing human-SR interactions.

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ignore context-focused content, or use counterfactual thinking to remove the model's bias of potential relations in entities, but the relation reasoning process will still be hindered by irrelevant content. Therefore, how to preserve relevant content and remove noisy segments from sentences is a crucial task. In addition, retained content needs to be fluent enough to maintain semantic coherence and interpretability. In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. To solve the problem that the gold rationales are not labeled, RE2 applies an optimizable binary mask to each token in the sentence, and adjust the rationales that need to be selected according to the relation label. Experiments on four datasets show that RE2 surpasses baselines.

The recent blizzard of warnings about artificial intelligence and how it is transforming learning, upending legal, financial and organizational functions, and reshaping social and cultural interaction, have mostly left out the role it is already playing in governance. Governments in the US at every level are attempting the transition from a programmatic model of service delivery to a citizen-focused model. Los Angeles, the US's second largest city, is a pioneer in the field, unveiling technologies to help streamline bureaucratic functions from police recruitment to paying parking tickets to filling potholes or locating resources at the library. For now, AI advances are limited to automation. When ChatGPT was asked recently about how it might change how people deal with government, it responded that "the next generation of AI, which includes ChatGPT, has the potential to revolutionize the way governments interact with their citizens."

The vilification of AI is prominent in certain groups, with many thinking AI will replace human beings. Thankfully, the reality is that AI gives us humans countless benefits. The misconception of AI being sentient or a machine version of a human comes from science fiction and similar media. It is usually portrayed as a robot gaining sentience and pursuing shenanigans such as eliminating the human populace. In contrast, it is just a hyper-capable program that can better combine data with intelligent algorithms to produce an outcome. Currently, more than half of all corporate recruitment firms, such as Lensa, use AI to magnify the industry's potential for recruitment.

Intelligence is something we humans thrive on. We think of ourselves as the most intelligent beings, certainly on this planet but possibly in the entire universe. However, it is a notoriously difficult task to define what "intelligence" really is. Among various definitions, perspectives, and outlooks, the standard consensus is that a being is intelligent if "it" can respond to events and stimuli around it and be able to manipulate either the surroundings or itself to make things better for itself. This definition suits artificial intelligence nicely since it can be adapted to non-living beings almost readily. Artificial intelligence, more commonly known by its abbreviation "AI," is the field of study that analyses this process of understanding or gaining intelligence; it is also concerned with building systems or agents that display such intelligent behaviour.

As you know, we Homo Sapiens the most intelligent being in this planet earth. Many thousands of years we have passed trying to realize how we think. That is how we manage a matter to perceive, predict and utilize a world far larger and more complex than it self. Every activity is accomplished by a human is assigned as intelligence. If we talk more precisely combination of many diverse abilities such as problem solving, reasoning, perception, creativity, planning, critical thinking, emotional knowledge and ability in handling language.

Welcome to eSimplicity's Tech Talks blog series! Tech Talks is a series launched by eSimplicity's technical writing interns to discuss various topics within the tech industry. From personal experiences within the company to emergent innovative technologies, eSimplicity aims to gauge diverse perspectives and shed light on engaging topics within the tech sector! In a recent interview, eSimplicity's Lead AI Scientist Bin Shao shared with us his thoughts on the prominence of artificial intelligence, as well as its place in the future. Bin has over 20 years of professional experience in the areas of artificial intelligence, machine learning, computer vision and cybersecurity.

The human brain is capable of incredible things, but it's also extremely flawed at times. Science has shown that we tend to make all sorts of mental mistakes, called "cognitive biases", that can affect both our thinking and actions. These biases can lead to us extrapolating information from the wrong sources, seeking to confirm existing beliefs, or failing to remember events the way they actually happened! To be sure, this is all part of being human--but such cognitive biases can also have a profound effect on our endeavors, investments, and life in general. For this reason, today's infographic from DesignHacks.co is particularly handy.

This work provides a starting point for researchers interested in gaining a deeper understanding of the big picture of artificial intelligence (AI). To this end, a narrative is conveyed that allows the reader to develop an objective view on current developments that is free from false promises that dominate public communication. An essential takeaway for the reader is that AI must be understood as an umbrella term encompassing a plethora of different methods, schools of thought, and their respective historical movements. Consequently, a bottom-up strategy is pursued in which the field of AI is introduced by presenting various aspects that are characteristic of the subject. This paper is structured in three parts: (i) Discussion of current trends revealing false public narratives, (ii) an introduction to the history of AI focusing on recurring patterns and main characteristics, and (iii) a critical discussion on the limitations of current methods in the context of the potential emergence of a strong(er) AI. It should be noted that this work does not cover any of these aspects holistically; rather, the content addressed is a selection made by the author and subject to a didactic strategy.

Jul 19 · 9 min read Artificial Intelligence (AI) is such a buzz word these days and one thing about buzz words is… 'They often get lost in translation'. But I think it's time we all take a deep breath, exhale, pause… And realize that AI is a well-founded discipline in its own right. Machine Learning and Deep Learning do not define Artificial Intelligence. AI is a much broader field than ML, which is a Statistical subset of AI and DL, which is a specialized subset of ML involving Neural networks computation… ML and DL are Subsets of a much broader field called AI… So what exactly is Artificial Intelligence? To answer this question we must consider the four historical approaches to AI.