Risk Modeling of Time-Varying Covariates Using an Ensemble of Survival Trees: Predicting Future Cancer Events

The challenge of survival prediction is ubiquitous in medicine, but only a handful of methods are available for survival prediction based on time-varying data. Here we propose a novel method for this problem, using a random forest of survival trees for left-truncated and right-censored data. We demonstrate the advantage of our method on prediction of breast cancer and prostate gland cancer risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age. We analyze electronic medical records of 20,317 healthy individuals who underwent routine checkups and identified those who later developed cancer. In cross-validation, our method predicted future prostate and breast cancers six months before diagnosis with an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, outperforming standard random forest, random survival forest, cox-regression model, dynamic deep-hit and a single survival tree. Our work proposes a new framework for survival risk prediction in time-varying data and our results suggest that computational analysis of data on healthy individuals can improve the detection of those at risk of future cancer development

When Robots Self-Disclosure Personal Information, Do Users Reciprocate?

Human-robot interaction consists of a rich set of behaviors between humans and robots often requiring the exchange of personal and sensitive information between them. From a conceptual framework, this paper discusses whether a robot who self-discloses personal information when conversing with a user will prompt the user to reciprocate and self-dis- close personal and sensitive information to the robot. Additionally, the paper discusses various factors which may influence whether self-disclosure of personal information between human and robot occurs and briefly discusses aspects of a conceptual representational system necessary for HRI enabling the robot to self-disclose to a user

A Hazard-Aware Metric for Ordinal Multi-Class Classification in Pathology

Artificial Intelligence (AI) for decision support and diagnosis in pathology could provide immense value to society, improving patient outcomes and alleviating workload demands on pathologists. However, this potential cannot be realized until sufficient methods for testing and evaluation of such AI systems are developed and adopted. We present a novel metric for evaluation of multi-class classification algorithms for pathology, Error Severity Index (ESI), to address the needs of pathologists and pathology lab managers in evaluating AI systems

Building Intelligent Systems by Combining Machine Learning and Automated Commonsense Reasoning

We present an approach to building systems that emulate human-like intelligence. Our approach uses machine learning technology (including generative AI systems) to extract knowledge from pictures, text, etc., and represents it as (pre-defined) predicates. Next, we use the s(CASP) automated commonsense reasoning system to check the consistency of this extracted knowledge and reason over it in a manner very similar to how a human would do it. We have used our approach for building systems for visual question answering, task-specific chatbots that can ``understand" human dialogs and interactively talk to them, and autonomous driving systems that rely on commonsense reasoning. Essentially, our approach emulates how humans process knowledge where they use sensing and pattern recognition to gain knowledge (Kahneman's System 1 thinking, akin to using a machine learning model), and then use reasoning to draw conclusions, generate response, or take actions (Kahneman's System 2 thinking, akin to automated reasoning)

Awareness and Acceptance of Emerging Technology and Quadruped Robots in Dementia Care: A Survey Study

The rapid increase in the number of persons with Alzheimer’s Disease or related dementia has led many researchers to develop supplemental care to assist caregivers. One such form of care comes in the form of a quadruped robot that can interact with its environment to provide additional care. However, before such technology is fully implemented, researchers must understand how aware the public is of such technology for dementia care and how they perceive it. In this study, we surveyed 16 adults, all but one of which have been affected by dementia either directly or indirectly. We asked them questions regarding their attitude towards technology in healthcare and the quadruped robot that was demoed for them. It was found that people positively accept these robotic forms of dementia care, even if they do not have a comprehensive understanding of them. Furthermore, regarding the quadruped robot, people do perceive it positively but are not as confident in its ability to provide adequate care. They also have reservations about using robots to care for persons with dementia, mostly because of the lack of a “human touch,” and are afraid that robots might replace human caregivers altogether. From these results, researchers must do their best to not only develop the technology to be as robust as possible but keep the public informed of their research to bridge the gap between this revolutionary technology and its end users

Quantum Machine Learning in Climate Change and Sustainability: A Short Review

Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challenges and current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research

Generative AI and Discovery of Preferences for Single-Use Plastics Regulations

Given the heightened global awareness and attention to the negative externalities of plastics use, many state and local governments are considering legislation that will limit single-use plastics for consumers and retailers under extended producer responsibility laws. Considering the growing momentum of these single-use plastics regulations globally, there is a need for reliable and cost-effective measures of the public response to this rulemaking for inference and prediction. Automated computational approaches such as generative AI could enable real-time discovery of consumer preferences for regulations but have yet to see broad adoption in this domain due to concerns about evaluation costs and reliability across large-scale social data. In this study, we leveraged the zero and few-shot learning capabilities of GPT-4 to classify public sentiment towards regulations with increasing complexity in expert prompting. With a zero-shot approach, we achieved a 92% F1 score (s.d. 1%) and 91% accuracy (s.d. 1%), which resulted in three orders of magnitude lower research evaluation cost at 0.138 pennies per observation. We then use this model to analyze 5,132 tweets related to the policy process of the California SB-54 bill, which mandates user fees and limits plastic packaging. The policy study reveals a 12.4% increase in opposing public sentiment immediately after the bill was enacted with no significant changes earlier in the policy process. These findings shed light on the dynamics of public engagement with lower cost models for research evaluation. We find that public opposition to single-use plastics regulations becomes evident in social data only when a bill is effectively enacted

Bridging Cognitive Architectures and Generative Models with Vector Symbolic Algebras

Recent developments in generative models have demonstrated that with the right data set, techniques, computational infrastructure, and network architectures, it is possible to generate seemingly intelligent outputs, without explicitly reckoning with underlying cognitive processes. The ability to generate novel, plausible behaviour could be a boon to cognitive modellers. However, insights for cognition are limited, given that generative models' blackbox nature does not provide readily interpretable hypotheses about underlying cognitive mechanisms. On the other hand, cognitive architectures make very strong hypotheses about the nature of cognition, explicitly describing the subjects and processes of reasoning. Unfortunately, the formal framings of cognitive architectures can make it difficult to generate novel or creative outputs. We propose to show that cognitive architectures that rely on certain Vector Symbolic Algebras (VSAs) are, in fact, naturally understood as generative models. We discuss how memories of VSA representations of data form distributions, which are necessary for constructing distributions used in generative models. Finally, we discuss the strengths, challenges, and future directions for this line of work

The Observable Mind: Enabling an Autonomous Agent Sharing Its Conscious Contents Using a Cognitive Architecture

We enable an autonomous agent sharing its artificial mind to its audiences like humans. This supports the autonomous human robot interactions relying on a cognitive architecture, LIDA, which explains and predicts how minds work and is used as the controllers of intelligent autonomous agents. We argue that LIDA’s cognitive representations and processes may serve as the source of the mind content its agent shares out, autonomously. We proposed a new description (sub) model into LIDA, letting its agent describing its conscious contents. Through this description, the agent’s mind is more observable so we can understand the agent’s entity and intelligence more directly. Also, this helps the agent explains its behaviors to its audiences so engage into its living society better. We built an initial LIDA agent embedding with this description model. The agent shares its conscious content autonomously, reasonably explaining its behaviors

Bi-cultural Investigation of Collisions in Social Navigation

Imagine a service robot developed in the United States (US) being deployed in a public space in Israel. Due to the cultural differences, the robot from a ``contact-averse'' culture (i.e., the US) might find it difficult to find its way when navigating the crowd, as people from a ``contact-tolerant'' culture (i.e., Israel) - where a subtle touch between strangers is not uncommon - will always move closer to the robot than it would expect; conversely, an ``Israeli'' robot may be found too aggressive in US social spaces. Currently, these cultural differences hinder the ability to plug-and-play social robots in different cultures due to the requirement of extensive extra engineering effort. This paper presents a comparison of the results from an existing study conducted in the US with the same study design that was deployed in Israel. This comparison shows the clear, identifiable criteria that a socially aware robot will need to consider when navigating a new culture. More generally, the results from this paper offer a first step to identifying the cultural differences in social robot navigation so we can structure solutions to be compatible with these cultures and with novel ones, with minimum adaptation