New Jersey Political Briefing

Memorandum detailing New Jersey political figures and landscape for Ferraro campaign.https://ir.lawnet.fordham.edu/vice_presidential_campaign_materials_1984/1060/thumbnail.jp

Memorandum: New Jersey Political Briefing

Details New Jersey political scene and figures.https://ir.lawnet.fordham.edu/vice_presidential_campaign_materials_1984/1053/thumbnail.jp

How Liability Insurers Protect Patients and Improve Safety

Forty years after the publication of the first systematic study of adverse medical events, there is greater access to information about adverse medical events and increasingly widespread acceptance of the view that patient safety requires more than vigilance by well-intentioned medical professionals. In this essay, we describe some of the ways that medical liability insurance organizations contributed to this transformation, and we catalog the roles that those organizations play in promoting patient safety today. Whether liability insurance in fact discourages providers from improving safety or encourages them to protect patients from avoidable harms is an empirical question that a survey like this one cannot resolve. But, as we show, insurers make serious efforts to reduce their losses by encouraging and helping health care providers to do better in at least six ways. (1) Insurers identify subpar providers in ways that provide the opportunity for other institutions to act. (2) Insurers provide incentives for providers by charging premiums that are based on risk and by refusing to insure providers who are too high risk. (3) Insurers accumulate data for root cause analysis. (4) Insurers conduct loss prevention inspections of medical facilities. (5) Insurers educate providers about legal oversight and steps that they can take to manage their risks. (6) Finally, insurers provide financial and human capital support to patient safety organizations

Embodied Active Learning of Relational State Abstractions for Bilevel Planning

State abstraction is an effective technique for planning in robotics environments with continuous states and actions, long task horizons, and sparse feedback. In object-oriented environments, predicates are a particularly useful form of state abstraction because of their compatibility with symbolic planners and their capacity for relational generalization. However, to plan with predicates, the agent must be able to interpret them in continuous environment states (i.e., ground the symbols). Manually programming predicate interpretations can be difficult, so we would instead like to learn them from data. We propose an embodied active learning paradigm where the agent learns predicate interpretations through online interaction with an expert. For example, after taking actions in a block stacking environment, the agent may ask the expert: "Is On(block1, block2) true?" From this experience, the agent learns to plan: it learns neural predicate interpretations, symbolic planning operators, and neural samplers that can be used for bilevel planning. During exploration, the agent plans to learn: it uses its current models to select actions towards generating informative expert queries. We learn predicate interpretations as ensembles of neural networks and use their entropy to measure the informativeness of potential queries. We evaluate this approach in three robotic environments and find that it consistently outperforms six baselines while exhibiting sample efficiency in two key metrics: number of environment interactions, and number of queries to the expert. Code: https://tinyurl.com/active-predicatesComment: Conference on Lifelong Learning Agents (CoLLAs) 202

Few-Shot Bayesian Imitation Learning with Logical Program Policies

Humans can learn many novel tasks from a very small number (1--5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples. We represent policies as logical combinations of programs drawn from a domain-specific language (DSL), define a prior over policies with a probabilistic grammar, and derive an approximate Bayesian inference algorithm to learn policies from demonstrations. In experiments, we study five strategy games played on a 2D grid with one shared DSL. After a few demonstrations of each game, the inferred policies generalize to new game instances that differ substantially from the demonstrations. Our policy learning is 20--1,000x more data efficient than convolutional and fully convolutional policy learning and many orders of magnitude more computationally efficient than vanilla program induction. We argue that the proposed method is an apt choice for tasks that have scarce training data and feature significant, structured variation between task instances.Comment: AAAI 202

The Roles of Symbols in Neural-based AI: They are Not What You Think!

We propose that symbols are first and foremost external communication tools used between intelligent agents that allow knowledge to be transferred in a more efficient and effective manner than having to experience the world directly. But, they are also used internally within an agent through a form of self-communication to help formulate, describe and justify subsymbolic patterns of neural activity that truly implement thinking. Symbols, and our languages that make use of them, not only allow us to explain our thinking to others and ourselves, but also provide beneficial constraints (inductive bias) on learning about the world. In this paper we present relevant insights from neuroscience and cognitive science, about how the human brain represents symbols and the concepts they refer to, and how today's artificial neural networks can do the same. We then present a novel neuro-symbolic hypothesis and a plausible architecture for intelligent agents that combines subsymbolic representations for symbols and concepts for learning and reasoning. Our hypothesis and associated architecture imply that symbols will remain critical to the future of intelligent systems NOT because they are the fundamental building blocks of thought, but because they are characterizations of subsymbolic processes that constitute thought.Comment: 28 page