Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summaries. However, recent unsupervised models are extractive, which remove words from texts and thus they are less flexible than abstractive summarization. In this work, we devise an abstractive model based on reinforcement learning without ground-truth summaries. We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality. To further enhance the summary quality, we develop a multi-summary learning mechanism that generates multiple summaries with varying lengths for a given text, while making the summaries mutually enhance each other. Experimental results show that the proposed model substantially outperforms both abstractive and extractive models, yet frequently generating new words not contained in input texts.

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? In this paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values.

Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on multiple factors such as context, use case, type of the AI system, and so on. In this paper, we elaborate that the AI system is prone to biases at every stage of its lifecycle, from inception to its usage, and that all stages require due attention for mitigating AI bias. We need a standardised approach to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot research topic, a holistic strategy for AI fairness is generally missing. Most researchers focus only on a few facets of AI model-building. Peer review shows excessive focus on biases in the datasets, fairness metrics, and algorithmic bias. In the process, other aspects affecting AI fairness get ignored. The solution proposed: We propose a comprehensive approach in the form of a novel seven-layer model, inspired by the Open System Interconnection (OSI) model, to standardise AI fairness handling. Despite the differences in the various aspects, most AI systems have similar model-building stages. The proposed model splits the AI system lifecycle into seven abstraction layers, each corresponding to a well-defined AI model-building or usage stage. We also provide checklists for each layer and deliberate on potential sources of bias in each layer and their mitigation methodologies. This work will facilitate layer-wise standardisation of AI fairness rules and benchmarking parameters.

Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and-play module RelAttn to adapt any general summarizers to the content-controllable summarization task. RelAttn first identifies the relevant content in the source documents, and then makes the model attend to the right context by directly steering the attention weight. We further apply an unsupervised online adaptive parameter searching algorithm to determine the degree of control in the zero-shot setting, while such parameters are learned in the few-shot setting. By applying the module to three backbone summarization models, experiments show that our method effectively improves all the summarizers, and outperforms the prefix-based method and a widely used plug-and-play model in both zero- and few-shot settings. Tellingly, more benefit is observed in the scenarios when more control is needed.

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

This is likely the final photo that NASA's Mars InSight lander will ever send back to Earth. The robot has been snapping pics and gathering data about the Martian environment since landing on the planet in November 2018 -- and it's been steadily accumulating dust on its solar panels that entire time. As NASA predicted earlier this year, the layer of debris has finally become too thick for the solar panels to operate. The InSight Twitter account officially said goodbye on December 19th with a final image from the surface of Mars. "My power's really low, so this may be the last image I can send," the tweet reads.

Law enforcement officials investigating Elon Musk's Neuralink over its animal trial program are also scrutinizing the US Department of Agriculture's oversight of the company's operations, after the agency failed to act on violations at other research organizations, according to several people familiar with the matter. Reuters reported on 5 December that the USDA's watchdog, the Office of the Inspector General, is investigating Neuralink, a medical device company that is developing brain implants, over potential animal-welfare violations. A federal prosecutor in the civil division at the US Attorney's Office for the Northern District of California requested the investigation, people familiar with the matter said. Reuters was unable to determine what potential violations are being investigated. The 5 December report identified four experiments in recent years involving 86 pigs and two monkeys that were marred by human errors.

NASA's InSight Mars lander is covered in dust in its final selfie, taken on April 24. The following month its robotic arm was put into resting position, aka "retirement pose." NASA's InSight Mars lander is covered in dust in its final selfie, taken on April 24. The following month its robotic arm was put into resting position, aka "retirement pose." The end has long been in sight for InSight, the NASA lander that's been stationed on Mars since 2018.

This White Paper assesses the progress of three pillars of U.S. leadership in AI innovation and trustworthy AI that carry the force of law: (i) the AI in Government Act of 2020; (ii) the Executive Order on "AI Leadership"; and (iii) the Executive Order on "AI in Government." Collectively, these Executive Orders and the AI in Government Act have been critical to defining the U.S. national strategy on AI and envisioning an ecosystem where the U.S. government leads in AI and promotes trustworthy AI. We systematically examined the implementation status of each requirement and performed a comprehensive search across 200 federal agencies to assess implementation of key requirements to identify regulatory authorities pertaining to AI and to enumerate AI use cases. While much progress has been made, our findings are sobering. America's AI innovation ecosystem is threatened by weak and inconsistent implementation of these legal requirements.

It was 2018, and the world as we knew it--or rather, how we knew it--teetered on a precipice. Against a rising drone of misinformation, The New York Times, the BBC, Good Morning America, and just about everyone else sounded the alarm over a new strain of fake but highly realistic videos. Using artificial intelligence, bad actors could manipulate someone's voice and face in recorded footage almost like a virtual puppet and pass the product off as real. In a famous example engineered by BuzzFeed, Barack Obama seemed to say, "President Trump is a total and complete dipshit." Synthetic photos, audio, and videos, collectively dubbed "deepfakes," threatened to destabilize society and push us into a full-blown "infocalypse."