Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at say-can.github.io. (a) Large Language Models (LLMs) (b) SayCan

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Stuart Bretschneider is associate dean and chair of the Department of Public Administration, Maxwell School of Citizenship and Public Affairs, Syracuse University. He also holds one of the University's Laura J. and L. Douglas Meredith Professorships for Teaching Excellence. His research focuses on how public organizations make use of information technology and the effects of those technologies on public organizations; how public organizations employ forecasting technology and organize to carry out forecasting activities; and how sector differences affect administrative processes. HeTh is case study reports an innovative e-government experiment by a local government in Seoul, South Korea-Gangnam-gu. A new local political leadership in Gangnam made strategic use of e-government applications to exert greater political control over the local civil service bureaucracy. Th e authors fi nd that e-government applications possess political properties that can be applied eff ectively by the political leadership as instruments to improve control over the government bureaucracy as well as to enhance essential government accountability and transparency. Th e political circumstances underlying e-government development as well as its impact on local government are reported, along with key variables associated with this innovation and directions for future research.

E-government has been touted by many as a technological answer to improve citizen participation, government accountability, and transparency by facilitating a greater level of communication and flow of public information between citizens and the government. This article examines how political environment, government structure, and the nature of individual e-government applications influence the likelihood of adoption. Using data obtained from multiple sources, logistic regressions are conducted on a sample of six e-government applications that possess varying degrees of communicative and organizational impacts on the government to observe how different factors influence their adoption. Findings include a general disinclination for adopting e-government applications with high communicative impact; however, such disinclination dissipated when there was a high level of political competition in the area and perceived demand for online communication; active traditional channels of political communication, such as political parties and accessibility to local council members, reduced the likelihood of adoption; the preferences of the elected mayors coincided with the perceptions of nonelected officials who favor e-government applications that would reduce the workload while disfavoring applications that would increase it.