A British computer scientist who earned the nickname "the Godfather of AI" warned that the dangers of artifical intelligence made famous in films like "The Terminator" could become more reality than fiction. "I think in five years' time, it may well be able to reason better than us," Geoffrey Hinton, a British computer scientist and cognitive psychologist, said during an interview with "60 Minutes," according to a report from Yahoo News. Hinton, who became well known for his work on the framework for AI, urged caution in the continued development of AI technology, questioning whether humans can fully understand the technology that is currently seeing rapid development. "I think we're moving into a period when for the first time ever, we have things more intelligent than us," Hinton said. Hinton argued that while humans develop the algorithm AI tools use to learn, they have little understanding of how that learning actually takes place.

Active learning aims to construct an effective training set by iteratively curating the most informative unlabeled data for annotation, which is practical in low-resource tasks. Most active learning techniques in classification rely on the model's uncertainty or disagreement to choose unlabeled data. However, previous work indicates that existing models are poor at quantifying predictive uncertainty, which can lead to over-confidence in superficial patterns and a lack of exploration. Inspired by the cognitive processes in which humans deduce and predict through causal information, we propose a novel Explainable Active Learning framework (XAL) for low-resource text classification, which aims to encourage classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Specifically, besides using a pre-trained bi-directional encoder for classification, we employ a pre-trained uni-directional decoder to generate and score the explanation. A ranking loss is proposed to enhance the decoder's capability in scoring explanations. During the selection of unlabeled data, we combine the predictive uncertainty of the encoder and the explanation score of the decoder to acquire informative data for annotation. As XAL is a general framework for text classification, we test our methods on six different classification tasks. Extensive experiments show that XAL achieves substantial improvement on all six tasks over previous AL methods. Ablation studies demonstrate the effectiveness of each component, and human evaluation shows that the model trained in XAL performs surprisingly well in explaining its prediction.

Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations. In this paper, we define a new task of Knowledge-aware Language Model Attribution (KaLMA) that improves upon three core concerns on conventional attributed LMs. First, we extend attribution source from unstructured texts to Knowledge Graph (KG), whose rich structures benefit both the attribution performance and working scenarios. Second, we propose a new ``Conscious Incompetence" setting considering the incomplete knowledge repository, where the model identifies the need for supporting knowledge beyond the provided KG. Third, we propose a comprehensive automatic evaluation metric encompassing text quality, citation quality, and text citation alignment. To implement the above innovations, we build a dataset in biography domain BioKaLMA via a well-designed evolutionary question generation strategy, to control the question complexity and necessary knowledge to the answer. For evaluation, we develop a baseline solution and demonstrate the room for improvement in LLMs' citation generation, emphasizing the importance of incorporating the "Conscious Incompetence" setting, and the critical role of retrieval accuracy.

As vehicular communication and networking technologies continue to advance, infrastructure-based roadside perception emerges as a pivotal tool for connected automated vehicle (CAV) applications. Due to their elevated positioning, roadside sensors, including cameras and lidars, often enjoy unobstructed views with diminished object occlusion. This provides them a distinct advantage over onboard perception, enabling more robust and accurate detection of road objects. This paper presents MSight, a cutting-edge roadside perception system specifically designed for CAVs. MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction. Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency, revealing a range of potential applications to enhance CAV safety and efficiency. Presently, MSight operates 24/7 at a two-lane roundabout in the City of Ann Arbor, Michigan.

The 26th European Conference on Artificial Intelligence (ECAI 2023) took place from 30 September – 4 October in Krakow, Poland. On the final day of the conference, the outstanding paper awards were announced. There were two winners in the ECAI 2023 Outstanding Paper category, and one winner in the Outstanding Paper for AI in Social Good category. Abstract: Learning effective strategies in sparse reward tasks is one of the fundamental challenges in reinforcement learning. This becomes extremely difficult in multi-agent environments, as the concurrent learning of multiple agents induces the non-stationarity problem and sharply increased joint state space.

Large Language Models (LLMs) are quickly becoming ubiquitous, but the implications for social science research are not yet well understood. This paper asks whether LLMs can help us analyse large-N qualitative data from open-ended interviews, with an application to transcripts of interviews with Rohingya refugees in Cox's Bazaar, Bangladesh. We find that a great deal of caution is needed in using LLMs to annotate text as there is a risk of introducing biases that can lead to misleading inferences. We here mean bias in the technical sense, that the errors that LLMs make in annotating interview transcripts are not random with respect to the characteristics of the interview subjects. Training simpler supervised models on high-quality human annotations with flexible coding leads to less measurement error and bias than LLM annotations. Therefore, given that some high quality annotations are necessary in order to asses whether an LLM introduces bias, we argue that it is probably preferable to train a bespoke model on these annotations than it is to use an LLM for annotation.

Rap legend Tupac Shakur, soulful English singer-songwriter Amy Winehouse and many other beloved, 'once in a lifetime' talents have been tragically robbed of a full lifetime to share their gifts with the world. So we put the image-making artificial intelligence (AI) Midjourney to work to help imagine what these stars might have looked like at age 80. The results were unusual and uncanny, as might be expected of a machine manifesting snaps from an alternate dimension of what could have been. Scroll down to see if you recognize these famous figures in their AI-generated old age. The results might surprise you.

When I spoke to my phone, my face appeared on the screen, and I said, 'Hi, my name is Robert, and I'm looking forward to telling you about my life.' I was talking to an AI avatar of myself, designed to allow people to'live on' after death so that relatives can talk to them and learn about their lives. My wife's reaction to my AI clone was absolute horror, as she simply said, 'My God, why?' The clone comes courtesy of a'digital afterlife' service, Hereafter.AI, part of a wave of AI-powered'grief tech' created by programmer James Vlahos after his father died of cancer in 2016. The service creates a'Legacy Avatar' that can live on after your death (Rob Waugh/Hereafter) Vlahos programmed a'Dadbot' while his father was still alive, recording his responses to questions - and Hereafter's service now uses AI to make it easier to interact. Science has unearthed several distinct patterns around when people tend to die.

Despite the remarkable advances in language modeling, current mainstream decoding methods still struggle to generate texts that align with human texts across different aspects. In particular, sampling-based methods produce less-repetitive texts which are often disjunctive in discourse, while search-based methods maintain topic coherence at the cost of increased repetition. Overall, these methods fall short in achieving holistic alignment across a broad range of aspects. In this work, we frame decoding from a language model as an optimization problem with the goal of strictly matching the expected performance with human texts measured by multiple metrics of desired aspects simultaneously. The resulting decoding distribution enjoys an analytical solution that scales the input language model distribution via a sequence-level energy function defined by these metrics. And most importantly, we prove that this induced distribution is guaranteed to improve the perplexity on human texts, which suggests a better approximation to the underlying distribution of human texts. To facilitate tractable sampling from this globally normalized distribution, we adopt the Sampling-Importance-Resampling technique. Experiments on various domains and model scales demonstrate the superiority of our method in metrics alignment with human texts and human evaluation over strong baselines.

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow through an expert interview.