AI PC

Sarah Bankins, Paul Formosa

ABSTRACT The psychological contract refers to the implicit and subjective beliefs regarding a reciprocal exchange agreement, predominantly examined between employees and employers. While contemporary contract research is investigating a wider range of exchanges employees may hold, such as with team members and clients, it remains silent on a rapidly emerging form of workplace relationship: employees’ increasing engagement with technically, socially, and emotionally sophisticated forms of artificially intelligent (AI) technologies. In this paper we examine social robots (also termed humanoid robots) as likely future psychological contract partners for human employees, given these entities transform notions of workplace technology from being a tool to being an active partner. We first overview the increasing role of robots in the workplace, particularly through the advent of sociable AI, and synthesize the literature on human–robot interaction. We then develop an account of a human-social robot psychological contract and zoom in on the implications of this exchange for the enactment of reciprocity. Given the future-focused nature of our work we utilize a thought experiment, a commonly used form of conceptual and mental model reasoning, to expand on our theorizing. We then outline potential implications of human-social robot psychological contracts and offer a range of pathways for future research.

Yanheng He, Jiahe Jin, Shijie Xia, Jiadi Su, Run-Ze Fan, Haoyang Zou, Xiangkun Hu, Pengfei Liu

Imagine a world where AI can handle your work while you sleep - organizing your research materials, drafting a report, or creating a presentation you need for tomorrow. However, while current digital agents can perform simple tasks, they are far from capable of handling the complex real-world work that humans routinely perform. We present PC Agent, an AI system that demonstrates a crucial step toward this vision through human cognition transfer. Our key insight is that the path from executing simple"tasks"to handling complex"work"lies in efficiently capturing and learning from human cognitive processes during computer use. To validate this hypothesis, we introduce three key innovations: (1) PC Tracker, a lightweight infrastructure that efficiently collects high-quality human-computer interaction trajectories with complete cognitive context; (2) a two-stage cognition completion pipeline that transforms raw interaction data into rich cognitive trajectories by completing action semantics and thought processes; and (3) a multi-agent system combining a planning agent for decision-making with a grounding agent for robust visual grounding. Our preliminary experiments in PowerPoint presentation creation reveal that complex digital work capabilities can be achieved with a small amount of high-quality cognitive data - PC Agent, trained on just 133 cognitive trajectories, can handle sophisticated work scenarios involving up to 50 steps across multiple applications. This demonstrates the data efficiency of our approach, highlighting that the key to training capable digital agents lies in collecting human cognitive data. By open-sourcing our complete framework, including the data collection infrastructure and cognition completion methods, we aim to lower the barriers for the research community to develop truly capable digital agents.

Yuh-Shyan Chen, Kuang-Hung Cheng, Chih-Shun Hsu, Honglei Zhang

In this paper, we present a new AI (Artificial Intelligence) edge platform, called “MiniDeep”, which provides a standalone deep learning platform based on the cloud-edge architecture. This AI-Edge platform provides developers with a whole deep learning development environment to set up their deep learning life cycle processes, such as model training, model evaluation, model deployment, model inference, ground truth collecting, data pre-processing, and training data management. To the best of our knowledge, such a whole deep learning development environment has not been built before. MiniDeep uses Amazon Web Services (AWS) as the backend platform of a deep learning tuning management model. In the edge device, the OpenVino enables deep learning inference acceleration at the edge. To perform a deep learning life cycle job, MiniDeep proposes a mini deep life cycle (MDLC) system which is composed of several microservices from the cloud to the edge. MiniDeep provides Train Job Creator (TJC) for training dataset management and the models’ training schedule and Model Packager (MP) for model package management. All of them are based on several AWS cloud services. On the edge device, MiniDeep provides Inference Handler (IH) to handle deep learning inference by hosting RESTful API (Application Programming Interface) requests/responses from the end device. Data Provider (DP) is responsible for ground truth collection and dataset synchronization for the cloud. With the deep learning ability, this paper uses the MiniDeep platform to implement a recommendation system for AI-QSR (Quick Service Restaurant) KIOSK (interactive kiosk) application. AI-QSR uses the MiniDeep platform to train an LSTM (Long Short-Term Memory)-based recommendation system. The LSTM-based recommendation system converts KIOSK UI (User Interface) flow to the flow sequence and performs sequential recommendations with food suggestions. At the end of this paper, the efficiency of the proposed MiniDeep is verified through real experiments. The experiment results have demonstrated that the proposed LSTM-based scheme performs better than the rule-based scheme in terms of purchase hit accuracy, categorical cross-entropy, precision, recall, and F1 score.

Fadi N. Sibai, Abu Asaduzzaman, A. El-Moursy

To better design AI processors, it is critical to characterize artificial intelligence (AI) workloads and contrast them to normal personal computer (PC) workloads. In this work, we profiled the AIBench and PassMark PerformanceTest benchmarks with the Intel oneAPI VTune Profiler on a multi-core computer. We captured and contrasted the various CPU and platform metrics and event counts for these two distinct benchmarks. Using the Orange 3.0 data mining tool, and based on the captured profile metrics and event counts, we then trained and tested 9 machine learning (ML) models to classify the CPIs and elapsed times of the various tests of these two benchmarks, including inference and training tests in AIBench, and CPU, memory, graphics, and disk tests in PassMark. The linear regression machine learning model emerged as the best clocks per instruction (CPI) classifier, while the neural network model with 4 hidden layers was the best elapsed time classifier. This machine learning classification can help in predicting the CPI and elapsed time and distinguish between AI and standard PC workloads based on the profiled application(s) and captured profile metrics and event counts. The stressed computer units identified by this detailed profiling work and exercised by the benchmark tests can also guide future AI processor design improvements.

Garth Long, Arisha Kumar, Ria Cheruvu, Paula Ramos, Dmitriy Pastushenkov, Zhuoqing Wu, R. Lo

Imagine a new kind of tabletop gaming experience, where a narrator is describing a complex fantasy world, and players gathered around the table can see the events of their world unfolding in real-time, on their PC devices. In this talk, we will show attendees how to execute multi-modal Generative AI (Gen AI) models, running on a PC, in real-time to create immersive scenery, followed by an interactive live demonstration. This talk walks through the optimization of Gen AI modalities, chaining audio transcription and diffusion models, together in real-time. We compress these models with the OpenVINO™ Toolkit and leverage the Intel® Core™ Ultra processor to split these workloads across CPU, integrated GPU, and Neural Processing Unit (NPU), getting the best performance for each model. We also cover exciting new developments with temporal-consistent and depth estimation approaches towards high-resolution, 3D, pop-up, scenery generation. This talk equips participants with hands-on tools to resolve challenges with real-time, high-quality gaming scenery generation on their PC for gaming.