As artificial intelligence (AI) becomes more widespread, one question that arises is how human-AI interaction might impact human-human interaction. Chatbots, for example, are increasingly used as social companions, but little is known about how their use impacts human relationships. A common hypothesis is that these companion bots are detrimental to social health by harming or replacing human interaction. To understand how companion bots impact social health, we studied people who used companion bots and people who did not. Contrary to expectations, companion bot users indicated that these relationships were beneficial to their social health, whereas nonusers viewed them as harmful. Another common assumption is that people perceive conscious, humanlike AI as disturbing and threatening. Among both users and nonusers, however, we found the opposite: perceiving companion bots as more conscious and humanlike correlated with more positive opinions and better social health benefits. Humanlike bots may aid social health by supplying reliable and safe interactions, without necessarily harming human relationships.

Abstract-- A wide variety of robotic hands have been designed to date. Yet, we do not know how users perceive these hands and feel about interacting with them. To inform hand design for social robots, we compiled a dataset of 73 robot hands and ran an online study, in which 160 users rated their impressions of the hands using 17 rating scales. Next, we developed 17 regression models that can predict user ratings (e.g., humanlike) from the design features of the hands (e.g., number of fingers). The models have less than a 10-point error in predicting the user ratings on a 0-100 scale. The shape of the fingertips, color scheme, and size of the hands influence the user ratings the most. We present simple guidelines to improve user impression of robot hands and outline remaining questions for future work. Figure 1: A collage of the 73 existing robotic hands that we evaluated in an online study.

In cognitive psychology, metamemory refers to the ability to self-monitor and control one's learning and memory. Metamemory is a field of study within metacognition, the study of the thinking about one's thinking. A new study reveals the creation of a new artificial intelligence (AI) machine-learning algorithm with an adaptable intelligence based on what it already knows--a step toward endowing machines with a more humanlike mind. "We believe that our study can contribute to the understanding of human metamemory and, furthermore, to the realization of artificial consciousness," wrote Professor Takaya Arita, Yusuke Yamato, and Reiji Suzuki at Nagoya University. The researchers show the evolution of artificial neural networks have a metamemory function based on the self-reference of memory that is similar to the metamemory model defined in 1980 by researchers Thomas Nelson and Louis Narens.

People tend to accept robots with humanlike characteristics up to a point. Then, things get strangely uncomfortable. Robots have appeared in film for more than 100 years, with the first depiction occurring in the silent film "The Master Mystery," starring magician-turned-wannabe-actor Harry Houdini. Previously referred to as "automatons" before "robot" became commonplace, these metal machines have been portrayed as delightful helpers à la C-3PO and WALL-E and as villains, like the T-800 from "Terminator" or VIKI from "I, Robot." Whether a robot is "good" or "bad" isn't the ultimate indicator of whether we fear them or not.

This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. To tackle the first issue we exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. The maps are used in an attention mechanism that promotes segmentation confidence masks, thus focusing the network on semantic classes in the image that are important for the current driving situation. Human-like driving is achieved using adversarial learning, by not only minimizing the imitation loss with respect to the human driver but by further defining a discriminator, that forces the driving model to produce action sequences that are human-like. Our models are trained and evaluated on the Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world driving data. Extensive experiments show that our driving models are more accurate and behave more human-like than previous methods.

The technological and scientific challenges involved in the development of autonomous vehicles (AVs) are currently of primary interest for many automobile companies and research labs. However, human-controlled vehicles are likely to remain on the roads for several decades to come and may share with AVs the traffic environments of the future. In such mixed environments, AVs should deploy human-like driving policies and negotiation skills to enable smooth traffic flow. To generate automated human-like driving policies, we introduce a model-free, deep reinforcement learning approach to imitate an experienced human driver's behavior. We study a static obstacle avoidance task on a two-lane highway road in simulation (Unity). Our control algorithm receives a stochastic feedback signal from two sources: a model-driven part, encoding simple driving rules, such as lane-keeping and speed control, and a stochastic, data-driven part, incorporating human expert knowledge from driving data. To assess the similarity between machine and human driving, we model distributions of track position and speed as Gaussian processes. We demonstrate that our approach leads to human-like driving policies.

Artificial intelligence can share our natural ability to make numeric snap judgments. Researchers observed this knack for numbers in a computer model composed of virtual brain cells, or neurons, called an artificial neural network. After being trained merely to identify objects in images -- a common task for AI -- the network developed virtual neurons that respond to specific quantities. These artificial neurons are reminiscent of the "number neurons" thought to give humans, birds, bees and other creatures the innate ability to estimate the number of items in a set (SN: 7/7/18, p. 7). This intuition is known as number sense.

In this paper, we analyze the performance of an agent developed according to a well-accepted appraisal theory of human emotion with respect to how it modulates play in the context of a social dilemma. We ask if the agent will be capable of generating interactions that are considered to be more human than machine-like. We conduct an experiment with 117 participants and show how participants rate our agent on dimensions of human-uniqueness (which separates humans from animals) and human-nature (which separates humans from machines). We show that our appraisal theoretic agent is perceived to be more human-like than baseline models, by significantly improving both human-nature and human-uniqueness aspects of the intelligent agent. We also show that perception of humanness positively affects enjoyment and cooperation in the social dilemma.

In January, Google's chief executive, Sundar Pichai, claimed in an interview that AI "is more profound than, I dunno, electricity or fire." Day-to-day developments, though, are more mundane. Recently Pichai stood onstage in front of a cheering audience and proudly showed a video in which a new Google program, Google Duplex, made a phone call and scheduled a hair salon appointment. The program performed those tasks well enough that a human at the other end of the call didn't suspect she was talking to a computer. Assuming the demonstration is legitimate, that's an impressive (if somewhat creepy) accomplishment.

Quick quiz: what do you visualize when you hear the word robot? Or perhaps one of the massive, spiderlike machines that assemble complex products ranging from automobiles to computers?