While deep reinforcement learning has proven to be successful in solving control tasks, the ``black-box'' nature of an agent has received increasing concerns. We propose a prototype-based post-hoc \emph{policy explainer}, ProtoX, that explains a black-box agent by prototyping the agent's behaviors into scenarios, each represented by a prototypical state. When learning prototypes, ProtoX considers both visual similarity and scenario similarity. The latter is unique to the reinforcement learning context since it explains why the same action is taken in visually different states. To teach ProtoX about visual similarity, we pre-train an encoder using contrastive learning via self-supervised learning to recognize states as similar if they occur close together in time and receive the same action from the black-box agent.

As LLMs gain adoption in high-stakes domains like mental health, domain experts are increasingly consulted to provide input into policies governing their behavior. From an observation of 19 policymaking workshops with 9 experts over 15 weeks, we identified opportunities to better support rapid experimentation, feedback, and iteration for collaborative policy design processes. We present PolicyPad, an interactive system that facilitates the emerging practice of LLM policy prototyping by drawing from established UX prototyping practices, including heuristic evaluation and storyboarding. Using PolicyPad, policy designers can collaborate on drafting a policy in real time while independently testing policy-informed model behavior with usage scenarios. We evaluate PolicyPad through workshops with 8 groups of 22 domain experts in mental health and law, finding that PolicyPad enhanced collaborative dynamics during policy design, enabled tight feedback loops, and led to novel policy contributions. Overall, our work paves participatory paths for advancing AI alignment and safety.

While deep reinforcement learning has proven to be successful in solving control tasks, the black-box'' nature of an agent has received increasing concerns. We propose a prototype-based post-hoc \emph{policy explainer}, ProtoX, that explains a black-box agent by prototyping the agent's behaviors into scenarios, each represented by a prototypical state. When learning prototypes, ProtoX considers both visual similarity and scenario similarity. The latter is unique to the reinforcement learning context since it explains why the same action is taken in visually different states. To teach ProtoX about visual similarity, we pre-train an encoder using contrastive learning via self-supervised learning to recognize states as similar if they occur close together in time and receive the same action from the black-box agent.

This paper evaluates No-Code AutoML as a solution for challenges in AI product prototyping, characterized by unpredictability and inaccessibility to non-experts, and proposes a conceptual framework. This complexity of AI products hinders seamless execution and interdisciplinary collaboration crucial for human-centered AI products. Relevant to industry and innovation, it affects strategic decision-making and investment risk mitigation. Current approaches provide limited insights into the potential and feasibility of AI product ideas. Employing Design Science Research, the study identifies challenges and integrates no-code AutoML as a solution by presenting a framework for AI product prototyping with No-code AutoML. A case study confirms its potential in supporting non-experts, offering a structured approach to AI product development. The framework facilitates accessible and interpretable prototyping, benefiting academia, managers, and decision-makers. Strategic integration of no-code AutoML enhances efficiency, empowers non-experts, and informs early-stage decisions, albeit with acknowledged limitations.

The requirement of wireless data demands is increasingly high as the sixth-generation (6G) technology evolves. Reconfigurable intelligent surface (RIS) is promisingly deemed to be one of 6G techniques for extending service coverage, reducing power consumption, and enhancing spectral efficiency. In this article, we have provided some fundamentals of RIS deployment in theory and hardware perspectives as well as utilization of artificial intelligence (AI) and machine learning. We conducted an intelligent deployment of RIS (i-Dris) prototype, including dual-band auto-guided vehicle (AGV) assisted RISs associated with an mmWave base station (BS) and a receiver. The RISs are deployed on the AGV with configured incident/reflection angles. While, both the mmWave BS and receiver are associated with an edge server monitoring downlink packets for obtaining system throughput. We have designed a federated multi-agent reinforcement learning scheme associated with several AGV-RIS agents and sub-agents per AGV-RIS consisting of the deployment of position, height, orientation and elevation angles. The experimental results presented the stationary measurement in different aspects and scenarios. The i-Dris can reach up to 980 Mbps transmission throughput under a bandwidth of 100 MHz with comparably low complexity as well as rapid deployment, which outperforms the other existing works. At last, we highlight some opportunities and future issues in leveraging RIS-empowered wireless communication networks.

Science: Pure science happens in both academia and industry, working on everything from chemistry to mechanical design. These breakthroughs will eventually lead to batteries that have a higher energy density or lower weight, and your car company will want to test out this new technology to see if it can extend the range of your vehicles. Prototyping: Prototyping is the key step transforming new ideas into business value. For something like a battery, potentially years of development may occur before the technology is ready for production (luckily, software like ML tends to be faster). The prototyping phase starts with extensive analysis to ensure it is feasible for the new technology to make its way into your product.

As artificially intelligent machines get smarter, their complex algorithms are getting more opaque, resulting in a black box. We don't know how the algorithm works, how a specific decision is made. The gaps in knowledge have powerful implications. How can we as designers bridge this gap, bring transparency and trust in our relationship with machines? The discussion around explainable AI (XAI) has focused on the technical challenge of interpretability.

This new project is reminiscent of Hieroglyph, a project from Arizona State University that is similarly aimed at leveraging science fiction to make positive change in the real world. Like the Hieroglyph project, the Science Fiction Advisory Council will be launching with a short story collection. In July, XPRIZE plans to publish an online anthology of original science-fiction stories by members of the advisory council recounting the experiences of passengers on a fictional flight from Tokyo to San Francisco who are mysteriously transported 20 years into the future. The stories, published at Seat14C.com, will presumably include visions of some of the "preferred future states" that XPRIZE seeks to identify, and will be followed by quarterly meetings of the advisers as they build out their roadmaps for avoiding dystopia and reaching those better futures.

Conversational interfaces have reduced user experience down to a few lines of text. With bots, UX becomes conversational, products talk back, and persona's now go both ways. Every bot has a voice -- which means every bot needs a personality. If conversational computing means personality is the new user experience, how do we approach the design of these nuanced digital entities? Chatbots and voice assistants are for humans.