Contrary to what mass media might have you believe, artificial intelligence (AI) is not a new concept. AI was first mathematically conceptualized in 1950 by Alan Turing, a British polymath. Turing proposed that machines could use available information and logic to solve problems and make decisions the same way humans do. Although no tangible program came out of Turing's speculations, Allen Newell, Cliff Shaw, and Herbert Simon soon proved that AI was not simply science fiction. In 1955, Newell, Shaw, and Simon created the first "artificial intelligence" program, Logic Theorist.

No human drives a car in a vacuum; she/he must negotiate with other road users to achieve their goals in social traffic scenes. A rational human driver can interact with other road users in a socially-compatible way through implicit communications to complete their driving tasks smoothly in interaction-intensive, safety-critical environments. This paper aims to review the existing approaches and theories to help understand and rethink the interactions among human drivers toward social autonomous driving. We take this survey to seek the answers to a series of fundamental questions: 1) What is social interaction in road traffic scenes? 2) How to measure and evaluate social interaction? 3) How to model and reveal the process of social interaction? 4) How do human drivers reach an implicit agreement and negotiate smoothly in social interaction? This paper reviews various approaches to modeling and learning the social interactions between human drivers, ranging from optimization theory and graphical models to social force theory and behavioral & cognitive science. We also highlight some new directions, critical challenges, and opening questions for future research.

Large language models generate complex, open-ended outputs: instead of outputting a class label they write summaries, generate dialogue, or produce working code. In order to asses the reliability of these open-ended generation systems, we aim to identify qualitative categories of erroneous behavior, beyond identifying individual errors. To hypothesize and test for such qualitative errors, we draw inspiration from human cognitive biases -- systematic patterns of deviation from rational judgement. Specifically, we use cognitive biases as motivation to (i) generate hypotheses for problems that models may have, and (ii) develop experiments that elicit these problems. Using code generation as a case study, we find that OpenAI's Codex errs predictably based on how the input prompt is framed, adjusts outputs towards anchors, and is biased towards outputs that mimic frequent training examples. We then use our framework to elicit high-impact errors such as incorrectly deleting files. Our results indicate that experimental methodology from cognitive science can help characterize how machine learning systems behave.

Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. Research in the area of natural language understanding (NLU) in artificial intelligence claims to have been making great strides in this area, however, the lack of conceptual clarity/consistency in how 'understanding' is used in this and other disciplines makes it difficult to discern how close we actually are. In this interdisciplinary research thesis, I integrate insights from cognitive science/psychology, philosophy of mind, and cognitive linguistics, and evaluate it against a critical review of current approaches in NLU to explore the basic requirements--and remaining challenges--for developing artificially intelligent systems with human-like capacities for language use and comprehension.

According to the "Digital Virtual Human Depth Industry Report", by 2030, the overall market size of China's digital virtual human will reach 270 billion. The digital virtual human has the appearance of a human being, and even the fineness of the skin is close to that of a real person. It has human behavior and can be expressed through language, facial expressions or body movements; it has human thoughts and can interact with human beings in real time, which is almost the same as human beings. The mainstream technology-driven routes of virtual digital humans are divided into AI-driven and human-driven digital human. Human-driven digital people are driven by real people. The main principle is that the real person communicates with the user in real time according to the user video sent by the video surveillance system, and at the same time, the expression and action of the real person are presented on the virtual digital human image through the motion capture collection system, so as to interact with the user.

Mars rovers Opportunity and Spirit departed Earth in 2003. Upon successfully touching down on the red planet, they were only expected to last about 90 days. The scientists and engineers at NASA were flabbergasted that the pair survived for many years. In his latest documentary, "Good Night Oppy," director Ryan White examines the doting relationship between the control room crew members – people from across the globe – and their robotic progeny. It's a story of gumption: When a machine gets mired in quicksand 140 million miles away, how do you rescue it?

Word order choices during sentence production can be primed by preceding sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is driven by multiple different sources. Using a Hindi corpus of text productions, we model lexical priming with an n-gram cache model and we capture more abstract syntactic priming with an adaptive neural language model. We permute the preverbal constituents of corpus sentences, and then use a logistic regression model to predict which sentences actually occurred in the corpus against artificially generated meaning-equivalent variants. Our results indicate that lexical priming and lexically-independent syntactic priming affect complementary sets of verb classes. By showing that different priming influences are separable from one another, our results support the hypothesis that multiple different cognitive mechanisms underlie priming.

We test the hypothesis that discourse predictability influences Hindi syntactic choice. While prior work has shown that a number of factors (e.g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature. Inspired by prior work on syntactic priming, we investigate how the words and syntactic structures in a sentence influence the word order of the following sentences. Specifically, we extract sentences from the Hindi-Urdu Treebank corpus (HUTB), permute the preverbal constituents of those sentences, and build a classifier to predict which sentences actually occurred in the corpus against artificially generated distractors. The classifier uses a number of discourse-based features and cognitive features to make its predictions, including dependency length, surprisal, and information status. We find that information status and LSTM-based discourse predictability influence word order choices, especially for non-canonical object-fronted orders. We conclude by situating our results within the broader syntactic priming literature.

To achieve desirable performance, current AI systems often require huge amounts of training data. This is especially problematic in domains where collecting data is both expensive and time-consuming, e.g., where AI systems require having numerous interactions with humans, collecting feedback from them. In this work, we substantiate the idea of $\textit{cognitive models as simulators}$, which is to have AI systems interact with, and collect feedback from, cognitive models instead of humans, thereby making their training process both less costly and faster. Here, we leverage this idea in the context of moral decision-making, by having reinforcement learning (RL) agents learn about fairness through interacting with a cognitive model of the Ultimatum Game (UG), a canonical task in behavioral and brain sciences for studying fairness. Interestingly, these RL agents learn to rationally adapt their behavior depending on the emotional state of their simulated UG responder. Our work suggests that using cognitive models as simulators of humans is an effective approach for training AI systems, presenting an important way for computational cognitive science to make contributions to AI.

This paper proposes a perceptual visual analysis regarding the personality of virtual humans. Many studies have presented findings regarding the way human beings perceive virtual humans with respect to their faces, body animation, motion in the virtual environment and etc. We are interested in investigating the way people perceive visual manifestations of virtual humans' personality traits when they are interactive and organized in groups. Many applications in games and movies can benefit from the findings regarding the perceptual analysis with the main goal to provide more realistic characters and improve the users' experience. We provide experiments with subjects and obtained results indicate that, although is very subtle, people perceive more the extraversion (the personality trait that we measured), into the crowds of virtual humans, when interacting with virtual humans behaviors, than when just observing as a spectator camera.