Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

Recent progress in logic programming, particularly answer set programming, has enabled us to teach it to undergraduate and high school students. We developed an online answer set programming environment with simple interface and self contained file system. It is expected to make the teaching of answer set programming more effective and help us to reach more students.

In this article, we describe the hardware and software challenges involved in realizing Claytronics, a form of programmable matter made out of very large numbers-potentially millions-of submillimeter sized spherical robots. The goal of the claytronics project is to create ensembles of cooperating submillimeter  robots, which work together to form dynamic 3D physical objects. For example, claytronics might be used in telepresense to mimic, with high-fidelity and in 3-dimensional solid form, the look, feel, and motion of the person at the other end of the telephone call. To achieve this long-range vision we are investigating hardware mechanisms for constructing submillimeter robots, which can be manufactured en masse using photolithography. We also propose the creation of a new media type, which we call pario. The idea behind pario is to render arbitrary moving, physical 3-dimensional objects that you can see, touch, and even hold in your hands. In parallel with our hardware effort, we are developing novel distributed programming languages and algorithms to control the ensembles, LDP and Meld. Pario may fundamentally change how we communicate with others and interact with the world around us. Our research results to date suggest that there is a viable path to implementing both the hardware and software necessary for claytronics, which is a form of programmable matter that can be used to implement pario. While we have made significant progress, there is still much research ahead in order to turn this vision into reality.