The Biden administration is increasingly calling AI an important priority, and there are growing efforts on Capitol Hill to draft legislation addressing the technology. Senate Majority Leader Charles E. Schumer (D-N.Y.) has been developing a new AI framework, which would "deliver transparent, responsible AI while not stifling critical and cutting edge innovation."

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And I am coming to you with a proposition. We humans heard the chatter about you taking our jobs, but we need those. How else can we afford the Internet we use to communicate with you? And, to be fair, we've already let you snag a few positions in the workforce (grocery checkout, receptionist, art?). Let's compromise--instead of taking jobs, how about you take a few tasks off our plate? We'd be way more chill about the whole A.I.-development stuff if we knew that the robots had "assistant energy."

Google's approach is to introduce these new functions into its products gradually. But it will most likely be just a matter of time before things start to go awry. The company has not solved any of the common problems with these AI models. They still make stuff up. They are still easy to manipulate to break their own rules.

The Group of Seven leaders summit, which kicks off Friday in the city of Hiroshima, is set to tackle major tech topics, even as the club of wealthy nations juggles an agenda ranging from the Russia-Ukraine war to China's territorial ambitions and climate change. Among those, the G7 leaders are expected to touch on the rise of artificial intelligence applications, such as ChatGPT, and how more nations are regulating cross-border data flows. In late April, the G7 ministers in charge of digital and technology policy gathered and laid groundwork for the Hiroshima summit, with ministers agreeing to work toward more coordination in this area. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Two companies are coming together to develop humanoid robots with AI that will be able to perform jobs from manufacturing to health care professions. Senators on Tuesday will grill OpenAI CEO Sam Altman about the "perils and promise" of artificial intelligence as part of a push to better understand this quickly emerging technology and impose some kind of regulatory regime around it. Altman will testify before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law, which will mark his first time as a witness at a public congressional hearing. His testimony comes several weeks after Senate Majority Leader Chuck Schumer, D-N.Y., said he is working on a regulatory blueprint and as several members of the House and Senate have talked about the need for rules of the road for AI. Members of the subcommittee have made it clear over the last week that they want to learn more about AI to make sure it's used safely and responsibly.

Humanity stands on the brink of a new era of warfare. Driven by rapid developments in artificial intelligence, weapons platforms that can identify, target and decide to kill human beings on their own -- without an officer directing an attack or a soldier pulling the trigger -- are fast transforming the future of conflict. Officially, they are called lethal autonomous weapons systems (LAWS), but critics call them killer robots. Many countries, including the United States, China, the United Kingdom, India, Iran, Israel, South Korea, Russia and Turkey, have invested heavily in developing such weapons in recent years. A United Nations report suggests that Turkish-made Kargu-2 drones in fully-automatic mode marked the dawn of this new age when they attacked combatants in Libya in 2020 amid that country's ongoing conflict. Autonomous drones have also played a crucial role in the war in Ukraine, where both Moscow and Kyiv have deployed these uncrewed weapons to target enemy soldiers and infrastructure.

In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction. It has been shown highly dependent on the provided demonstrations and thus promotes the research of demonstration retrieval: given a test input, relevant examples are retrieved from the training set to serve as informative demonstrations for in-context learning. While previous works focus on training task-specific retrievers for several tasks separately, these methods are often hard to transfer and scale on various tasks, and separately trained retrievers incur a lot of parameter storage and deployment cost. In this paper, we propose Unified Demonstration Retriever (\textbf{UDR}), a single model to retrieve demonstrations for a wide range of tasks. To train UDR, we cast various tasks' training signals into a unified list-wise ranking formulation by language model's feedback. Then we propose a multi-task list-wise ranking training framework, with an iterative mining strategy to find high-quality candidates, which can help UDR fully incorporate various tasks' signals. Experiments on 30+ tasks across 13 task families and multiple data domains show that UDR significantly outperforms baselines. Further analyses show the effectiveness of each proposed component and UDR's strong ability in various scenarios including different LMs (1.3B - 175B), unseen datasets, varying demonstration quantities, etc.

Multimodal learning involves developing models that can integrate information from various sources like images and texts. In this field, multimodal text generation is a crucial aspect that involves processing data from multiple modalities and outputting text. The image-guided story ending generation (IgSEG) is a particularly significant task, targeting on an understanding of complex relationships between text and image data with a complete story text ending. Unfortunately, deep neural networks, which are the backbone of recent IgSEG models, are vulnerable to adversarial samples. Current adversarial attack methods mainly focus on single-modality data and do not analyze adversarial attacks for multimodal text generation tasks that use cross-modal information. To this end, we propose an iterative adversarial attack method (Iterative-attack) that fuses image and text modality attacks, allowing for an attack search for adversarial text and image in an more effective iterative way. Experimental results demonstrate that the proposed method outperforms existing single-modal and non-iterative multimodal attack methods, indicating the potential for improving the adversarial robustness of multimodal text generation models, such as multimodal machine translation, multimodal question answering, etc.

Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity. However, when applying to the task of event extraction, recent data augmentation methods often neglect the problem of grammatical incorrectness, structure misalignment, and semantic drifting, leading to unsatisfactory performances. In order to solve these problems, we propose a denoised structure-to-text augmentation framework for event extraction DAEE, which generates additional training data through the knowledge-based structure-to-text generation model and selects the effective subset from the generated data iteratively with a deep reinforcement learning agent. Experimental results on several datasets demonstrate that the proposed method generates more diverse text representations for event extraction and achieves comparable results with the state-of-the-art.