Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Building Citywide Systems for Quality: A Guide and Case Studies for Afterschool Leaders

This guide is intended to help cities strengthen and sustain quality afterschool programs by using an emerging practice known as a quality improvement system (QIS). The guide explains how to start building a QIS or how to further develop existing efforts and features case studies of six communities' QIS

Programmable Agents

We build deep RL agents that execute declarative programs expressed in formal language. The agents learn to ground the terms in this language in their environment, and can generalize their behavior at test time to execute new programs that refer to objects that were not referenced during training. The agents develop disentangled interpretable representations that allow them to generalize to a wide variety of zero-shot semantic tasks

Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1

Crossing Organizational Boundaries in Palliative Care: The Promise and Reality of Community Partnerships

This report presents the first of a series of findings from the Community-Oriented Palliative Care Initiative (COPCI), an innovative program testing new approaches to caring for individuals with progressive, life threatening illness. Developed and supported by the United Hospital Fund, the project was designed to initiate collaborations among health care and social service organizations, with the goal of reaching seriously ill individuals and their caregivers earlier in the course of illness and providing a broad range of coordinated services. Six such networks of diverse partners received a total of $2.1 million in grants over the two-year period from mid-2000 into 2002.The urgency to provide alternatives to current standard practice is underscored by the number of individuals affected: in New York City alone, in the year 2000, some 46,000 people died of diseases typically marked by a lengthy course from diagnosis to death. While many could have benefited from appropriate and timely palliative care services, most did not receive them.The Fund reasoned that networks including not only hospitals and hospices but also social services agencies and other community resources could collectively respond, earlier and more fully, to the complex combination of medical, social, psychological, and spiritual needs that typify the months and years leading to death. Local expertise and resources should determine the structure of each network, the partners involved, and the specific model for service delivery. Drawing on the experiences of the six pioneering projects, this report focuses on the challenges of creating such new networks