The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity. Beyond digital content creation, GAI's capability in analyzing complex data distributions offers great potential for wireless communications, particularly amidst a rapid expansion of new physical layer communication technologies. For example, the diffusion model can learn input signal distributions and use them to improve the channel estimation accuracy, while the variational autoencoder can model channel distribution and infer latent variables for blind channel equalization. Therefore, this paper presents a comprehensive investigation of GAI's applications for communications at the physical layer, ranging from traditional issues, including signal classification, channel estimation, and equalization, to emerging topics, such as intelligent reflecting surfaces and joint source channel coding. We also compare GAI-enabled physical layer communications with those supported by traditional AI, highlighting GAI's inherent capabilities and unique contributions in these areas. Finally, the paper discusses open issues and proposes several future research directions, laying a foundation for further exploration and advancement of GAI in physical layer communications.

In the evolving landscape of education, robotics has emerged as a powerful tool for fostering creativity, critical thinking, and problem-solving skills among students of all ages. This innovative approach to learning seamlessly integrates STEM (Science, Technology, Engineering, and Mathematics) concepts, creating an engaging and immersive learning experience. Educational robotics transcends traditional classroom settings, transforming learning into a hands-on, experiential endeavor. Students are actively involved in the design, construction, and programming of robots, allowing them to apply theoretical concepts to practical applications. This hands-on approach fosters deeper understanding and retention of knowledge, making learning more meaningful and enjoyable. In this paper, the potential of simulation robotics is evaluated as a hands on interactive learning experience that goes beyond traditional robotic classroom methods.

Recent Offline Reinforcement Learning methods have succeeded in learning high-performance policies from fixed datasets of experience. A particularly effective approach learns to first identify and then mimic optimal decision-making strategies. Our work evaluates this method's ability to scale to vast datasets consisting almost entirely of sub-optimal noise. A thorough investigation on a custom benchmark helps identify several key challenges involved in learning from high-noise datasets. We re-purpose prioritized experience sampling to locate expert-level demonstrations among millions of low-performance samples. This modification enables offline agents to learn state-of-the-art policies in benchmark tasks using datasets where expert actions are outnumbered nearly 65:1.

This holiday season, put less of an emphasis on things, and give some more thoughtful gifts. For instance, if you or someone you love wants to learn how to code, now's a great time to give the gift of knowledge. That's because The 2023 Complete Python Certification Boot Camp Bundle is on sale for the holiday season for just $19.99. This bundle includes 12 courses on Python and more from some of the web's top instructors, including Dr. Chris Mall (4.6/5-star instructor rating) and ZENVA Academy (4.4/5-star rating). It's suitable for absolute beginners, with courses covering the foundations of Python and slowly elevating towards more advanced subject matter.

The advancements in Artificial Intelligence (AI) technologies such as ChatGPT have gained popularity in recent days. The integration of ChatGPT in educational contexts has already created attractions due to a wide range of applications. However, the automatic generation of human-like texts also poses potential risks to academic integrity, especially when faced with writing-intensive language courses. Considering the ongoing debates, this study aims to investigate the academic policies and guidelines established by US universities regarding the use of ChatGPT in teaching and learning. The data sources include academic policies, statements, guidelines as well as relevant resources that were provided by the top 50 universities in the United States, according to U.S. News. Thematic analysis and qualitative analysis were employed in the analysis and showed that most top 50 universities were open but cautious towards the integration of generative AI in teaching and learning and also expressed their concerns on ethical usage, accuracy, and data privacy. Most universities also provided a variety of resources and guidelines, including syllabus templates/samples, workshops and discussions, shared articles, and one-on-one consultations, with focuses on general technical introduction, ethical concerns, pedagogical applications, preventive strategies, data privacy, limitations, and detective tools. The findings will inform future policy-making regarding the integration of ChatGPT in college-level education and influence the provision of supportive resources by universities for the appropriate application of ChatGPT in education.

Supervised imitation learning, also known as behavioral cloning, suffers from distribution drift leading to failures during policy execution. One approach to mitigate this issue is to allow an expert to correct the agent's actions during task execution, based on the expert's determination that the agent has reached a `point of no return.' The agent's policy is then retrained using this new corrective data. This approach alone can enable high-performance agents to be learned, but at a substantial cost: the expert must vigilantly observe execution until the policy reaches a specified level of success, and even at that point, there is no guarantee that the policy will always succeed. To address these limitations, we present FIRE (Failure Identification to Reduce Expert Burden in intervention-based learning), a system that can predict when a running policy will fail, halt its execution, and request a correction from the expert. Unlike existing approaches that learn only from expert data, our approach learns from both expert and non-expert data, akin to adversarial learning. We demonstrate experimentally for a series of challenging manipulation tasks that our method is able to recognize state-action pairs that lead to failures. This permits seamless integration into an intervention-based learning system, where we show an order-of-magnitude gain in sample efficiency compared with a state-of-the-art inverse reinforcement learning method and dramatically improved performance over an equivalent amount of data learned with behavioral cloning.

The thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023) is due to kick-off on Sunday 10 December and run until Saturday 16 December. There is a bumper programme of events, including invited talks, orals, posters, tutorials, workshops, and socials, not to mention AIhub's session on science communication. There are seven invited talks this year. For this year's conference, there will be a total of 14 tutorials. These will be held on Monday 11 December, in person only.

With the recent developments in large language models (LLMs) and their widespread availability through open source models and/or low-cost APIs, several exciting products and applications are emerging, many of which are in the field of STEM educational technology for K-12 and university students. There is a need to evaluate these powerful language models on several benchmarks, in order to understand their risks and limitations. In this short paper, we summarize and analyze the performance of Bard, a popular LLM-based conversational service made available by Google, on the standardized Physics GRE examination.

In this work, we present Digital Life Project, a framework utilizing language as the universal medium to build autonomous 3D characters, who are capable of engaging in social interactions and expressing with articulated body motions, thereby simulating life in a digital environment. Our framework comprises two primary components: 1) SocioMind: a meticulously crafted digital brain that models personalities with systematic few-shot exemplars, incorporates a reflection process based on psychology principles, and emulates autonomy by initiating dialogue topics; 2) MoMat-MoGen: a text-driven motion synthesis paradigm for controlling the character's digital body. It integrates motion matching, a proven industry technique to ensure motion quality, with cutting-edge advancements in motion generation for diversity. Extensive experiments demonstrate that each module achieves state-of-the-art performance in its respective domain. Collectively, they enable virtual characters to initiate and sustain dialogues autonomously, while evolving their socio-psychological states. Concurrently, these characters can perform contextually relevant bodily movements. Additionally, a motion captioning module further allows the virtual character to recognize and appropriately respond to human players' actions. Homepage: https://digital-life-project.com/

Natural gradient descent has a remarkable property that in the small learning rate limit, it displays an invariance with respect to network reparameterizations, leading to robust training behavior even for highly covariant network parameterizations. We show that optimization algorithms with this property can be viewed as discrete approximations of natural transformations from the functor determining an optimizer's state space from the diffeomorphism group if its configuration manifold, to the functor determining that state space's tangent bundle from this group. Algorithms with this property enjoy greater efficiency when used to train poorly parameterized networks, as the network evolution they generate is approximately invariant to network reparameterizations. More specifically, the flow generated by these algorithms in the limit as the learning rate vanishes is invariant under smooth reparameterizations, the respective flows of the parameters being determined by equivariant maps. By casting this property a natural transformation, we allow for generalizations beyond equivariance with respect to group actions; this framework can account for non-invertible maps such as projections, creating a framework for the direct comparison of training behavior across non-isomorphic network architectures, and the formal examination of limiting behavior as network size increases by considering inverse limits of these projections, should they exist. We introduce a simple method of introducing this naturality more generally and examine a number of popular machine learning training algorithms, finding that most are unnatural.