In a special award session, the best papers of the conference were announced. The AAAI-2023 outstanding paper award went to Joar Skalse and Alessandro Abate for their work Misspecification in Inverse Reinforcement Learning. The AAAI-2023 outstanding student paper award was given to Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation, authored by Yulu Gan, Yan Bai, Yihang Lou, Xianzheng Ma, Renrui Zhang, Nian Shi, and Lin Luo. There were also 12 distinguished paper award winners, the details of which can be found here. As well as these best paper awards, a number of prestigious AAAI awards were presented at the conference. These included the AAAI Award for Artificial Intelligence for the Benefit of Humanity, which was won by Tuomas Sandholm. You can find out more about this prize, and the others awarded, here. There will be plenty more content to come as we continue to cover the conference, and hear from participants about their work. You can find our conference coverage here, and this collection will be updated as soon as we add new content.

The rapid rise of ChatGPT has spawned equal proportions of hype, horror, and hope about the potential of artificial intelligence. Here's a sample of the surging headlines about AI from just one day, most triggered by the ChatGPT phenomenon (emphasis added): How Artificial Intelligence Can Boost Diversity & Inclusion (Forbes.com) Can Artificial Intelligence Help Detect Lung Cancer? And my favorite: "Can pigeons match wits with artificial intelligence?" ChatGPT, an AI application that allows machines to write and respond in uncannily humanlike ways, reached 100 million users in about two months. This makes it the fastest-growing app in history, according to a report from UBS cited by Reuters.

OpenAI's ChatGPT isn't just good at generating coherent text responses to natural language prompts -- it can also play a role in human-to-robot interactions and use sensor feedback to write code for robot actions. Microsoft recently conducted research to "see if ChatGPT can think beyond text, and reason about the physical world to help with robotics tasks." The aim was to see if people can use ChatGPT to instruct robots without learning programming languages or understanding robotic systems. In Depth: These experts are racing to protect AI from hackers. "The key challenge here is teaching ChatGPT how to solve problems considering the laws of physics, the context of the operating environment, and how the robot's physical actions can change the state of the world," a team from Microsoft Autonomous Systems and Robotics Research note in a blogpost.

With ChatGPT, we are seeing a resurgence of Chatbots. I have previously written on the enterprise applications of ChatGPT, and similar Large Language Models (LLMS) -- article. We can only expect this adoption to grow in different verticals, e.g. Customer Support, Health, Banking, Dating; leading to the inevitable harvesting of queries posed by the users as a'source of personal data' for Adverting, Phishing, etc. scenarios. While most users are sufficiently aware of the privacy risks to not share explicit Personally Identifiable Information (PII), e.g., credit card numbers, bank account details, health conditions; We propose a solution in the form of a user (chat client) module that intercepts the user (natural language) query and leverages the same "generative model" of ChatGPT to a generate a privacy preserving variant of the original user query.

Suffering a bit of anxiety over what recent breakthroughs in artificial intelligence might mean for humanity? So is Twitter, Tesla, and SpaceX CEO Elon Musk. "Having a bit of AI existential angst today," the billionaire tweeted over the weekend, just a few hours after starting the day on a much lighter "hope you have a good Sunday" note to followers. Honestly, in the grand scheme of Musk tweets, this one is a bit more relatable than most. AI broke into the public sphere in a major way towards the end of last year, with OpenAI's ChatGPT chatbot swiftly shaping up to be the fastest-growing app in consumer history.

China led the Artificial Intelligence (AI) race a few years ago with abundant data, innovative entrepreneurs, and supportive policies. However, today the country has fallen behind in tech innovation, lagging far behind the United States. The ChatGPT, an advanced experimental chatbot, created by the American startup OpenAI with the help of Microsoft, is leaving China's tech entrepreneurs shocked and demoralized. Many are asking fundamental questions about China's innovation environment, with some suggesting that censorship, geopolitical tensions, and government control of the private sector have made China less innovation-friendly. The Chinese government is notorious for censorship, and its obsession with controlling online content is perhaps its most significant obstacle to technological advancements.

Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e.g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations. Specifically, we employ GPT-3 Codex as the LM. In the parsing stage, with only a few in-context exemplars, Codex is able to identify the part of the task input that cannot be answerable by the original programming language, correctly generate API calls to prompt Codex to solve the unanswerable part, and identify where to place the API calls while being compatible with the original grammar. In the execution stage, Codex can perform versatile functionalities (e.g., commonsense QA, information extraction) given proper prompts in the API calls. Binder achieves state-of-the-art results on WikiTableQuestions and TabFact datasets, with explicit output programs that benefit human debugging. Note that previous best systems are all finetuned on tens of thousands of task-specific samples, while Binder only uses dozens of annotations as in-context exemplars without any training. Our code is available at https://github.com/HKUNLP/Binder .

To perform household tasks, assistive robots receive commands in the form of user language instructions for tool manipulation. The initial stage involves selecting the intended tool (i.e., object grounding) and grasping it in a task-oriented manner (i.e., task grounding). Nevertheless, prior researches on visual-language grasping (VLG) focus on object grounding, while disregarding the fine-grained impact of tasks on object grasping. Task-incompatible grasping of a tool will inevitably limit the success of subsequent manipulation steps. Motivated by this problem, this paper proposes GraspCLIP, which addresses the challenge of task grounding in addition to object grounding to enable task-oriented grasp prediction with visual-language inputs. Evaluation on a custom dataset demonstrates that GraspCLIP achieves superior performance over established baselines with object grounding only. The effectiveness of the proposed method is further validated on an assistive robotic arm platform for grasping previously unseen kitchen tools given the task specification. Our presentation video is available at: https://www.youtube.com/watch?v=e1wfYQPeAXU.

Large language models are not detailed models of human linguistic processing. They are, however, extremely successful at their primary task: providing a model for language. For this reason and because there are no animal models for language, large language models are important in psycholinguistics: they are useful as a practical tool, as an illustrative comparative, and philosophically, as a basis for recasting the relationship between language and thought. This is a commentaryon Bowers et al. (2022). Neural-network models of language are optimized to solve practical problems such as machine translation.

In the field of dream research, the study of dream content typically relies on the analysis of verbal reports provided by dreamers upon awakening from their sleep. This task is classically performed through manual scoring provided by trained annotators, at a great time expense. While a consistent body of work suggests that natural language processing (NLP) tools can support the automatic analysis of dream reports, proposed methods lacked the ability to reason over a report's full context and required extensive data pre-processing. Furthermore, in most cases, these methods were not validated against standard manual scoring approaches. In this work, we address these limitations by adopting large language models (LLMs) to study and replicate the manual annotation of dream reports, using a mixture of off-the-shelf and bespoke approaches, with a focus on references to reports' emotions. Our results show that the off-the-shelf method achieves a low performance probably in light of inherent linguistic differences between reports collected in different (groups of) individuals. On the other hand, the proposed bespoke text classification method achieves a high performance, which is robust against potential biases. Overall, these observations indicate that our approach could find application in the analysis of large dream datasets and may favour reproducibility and comparability of results across studies.