When Google announced the launch of its Bard chatbot last month, a competitor to OpenAI's ChatGPT, it came with some ground rules. An updated safety policy banned the use of Bard to "generate and distribute content intended to misinform, misrepresent or mislead." But a new study of Google's chatbot found that with little effort from a user, Bard will readily create that kind of content, breaking its maker's rules. Researchers from the Center for Countering Digital Hate, a UK-based nonprofit, say they could push Bard to generate "persuasive misinformation" in 78 of 100 test cases, including content denying climate change, mischaracterizing the war in Ukraine, questioning vaccine efficacy, and calling Black Lives Matter activists actors. "We already have the problem that it's already very easy and cheap to spread disinformation," says Callum Hood, head of research at CCDH. "But this would make it even easier, even more convincing, even more personal. So we risk an information ecosystem that's even more dangerous."

When you think of artificial intelligence (AI), you might think of Hollywood movies where intelligent machines become self-aware and take over the world. But the reality is far different. AI is becoming more prevalent in our everyday lives, whether we realize it or not. In web3, AI is being used to help us interact with decentralized applications (dApps). These dApps are powered by the Ethereum blockchain and use smart contracts to run.

Researchers at the University of York have found that current AI-generated music is inferior to human-composed music. In the study, 50 participants with a high level of musical knowledge were played excerpts of music--some from real human-composed works, and others generated by deep learning (DL), a type of artificial neural network, and non-DL algorithms. The study recruited participants who had experience in analyzing note content and stylistic success in music so that results were not just focused on expression in music. The listeners were asked to rate the excerpts along six musical criteria (stylistic success, aesthetic pleasure, repetition or self-reference, melody, harmony, and rhythm), but were not told the identity--human-composed or computer-generated--of what they were hearing. Co-author Dr. Tom Collins, from the School of Arts and Creative Technologies at the University of York, said, "On analysis, the ratings for human-composed excerpts are significantly higher and stylistically more successful than those for any of the systems responsible for computer-generated excerpts."

With artificial intelligence slowly taking over our society in the recents years, many brands and companies are now creating new ways to incorporate AI technology. Spotify has now introduced a new AI powered "DJ" that personalizes and curates new music and playlists for users. The greatest component of the new feature has to be the voice, which is powered by Sonantic: an AI voice platform under Spotify. The AI-powered DJ works by analyzing a user's listening habits, such as the artists, songs and genres and knowing what songs they love and hate. Then it will take that information to curate a personalized playlist that fits the user's preferences.

The increasing demand for high-quality 3D content creation has motivated the development of automated methods for creating 3D object models from a single image and/or from a text prompt. However, the reconstructed 3D objects using state-of-the-art image-to-3D methods still exhibit low correspondence to the given image and low multi-view consistency. Recent state-of-the-art text-to-3D methods are also limited, yielding 3D samples with low diversity per prompt with long synthesis time. To address these challenges, we propose DITTO-NeRF, a novel pipeline to generate a high-quality 3D NeRF model from a text prompt or a single image. Our DITTO-NeRF consists of constructing high-quality partial 3D object for limited in-boundary (IB) angles using the given or text-generated 2D image from the frontal view and then iteratively reconstructing the remaining 3D NeRF using inpainting latent diffusion model. We propose progressive 3D object reconstruction schemes in terms of scales (low to high resolution), angles (IB angles initially to outer-boundary (OB) later), and masks (object to background boundary) in our DITTO-NeRF so that high-quality information on IB can be propagated into OB. Our DITTO-NeRF outperforms state-of-the-art methods in terms of fidelity and diversity qualitatively and quantitatively with much faster training times than prior arts on image/text-to-3D such as DreamFusion, and NeuralLift-360.

This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of MNER and MRE lies in incorporating evident visual information to enhance textual semantics, where two issues inherently demand investigations. The first issue is modality-noise, where the task-irrelevant information in each modality may be noises misleading the task prediction. The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE by Multi-Modal representation learning with Information Bottleneck (MMIB). For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction. For the second issue, an alignment-regularizer is proposed, where a mutual information-based item works in a contrastive manner to regularize the consistent text-image representations. To our best knowledge, we are the first to explore variational IB estimation for MNER and MRE. Experiments show that MMIB achieves the state-of-the-art performances on three public benchmarks.

Recent decades have seen the rise of large-scale Deep Neural Networks (DNNs) to achieve human-competitive performance in a variety of artificial intelligence tasks. Often consisting of hundreds of millions, if not hundreds of billion parameters, these DNNs are too large to be deployed to, or efficiently run on resource-constrained devices such as mobile phones or IoT microcontrollers. Systems relying on large-scale DNNs thus have to call the corresponding model over the network, leading to substantial costs for hosting and running the large-scale remote model, costs which are often charged on a per-use basis. In this paper, we propose BiSupervised, a novel architecture, where, before relying on a large remote DNN, a system attempts to make a prediction on a small-scale local model. A DNN supervisor monitors said prediction process and identifies easy inputs for which the local prediction can be trusted. For these inputs, the remote model does not have to be invoked, thus saving costs, while only marginally impacting the overall system accuracy. Our architecture furthermore foresees a second supervisor to monitor the remote predictions and identify inputs for which not even these can be trusted, allowing to raise an exception or run a fallback strategy instead. We evaluate the cost savings, and the ability to detect incorrectly predicted inputs on four diverse case studies: IMDB movie review sentiment classification, Github issue triaging, Imagenet image classification, and SQuADv2 free-text question answering

Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs. However, creating an effective and robust ODCS agent is challenging. In this paper, we present a fully functional ODCS system, Ericson, which includes state-of-the-art question answering and information retrieval components, as well as intent inference and dialogue management models for proactive question refinement and recommendations. Our system was stress-tested in the Amazon Alexa Prize, by engaging in live conversations with thousands of Alexa users, thus providing empirical basis for the analysis of the ODCS system in real settings. Our interaction data analysis revealed that accurate intent classification, encouraging user engagement, and careful proactive recommendations contribute most to the users satisfaction. Our study further identifies limitations of the existing search techniques, and can serve as a building block for the next generation of ODCS agents.

Growing techniques have been emerging to improve the performance of passage retrieval. As an effective representation bottleneck pretraining technique, the contextual masked auto-encoder utilizes contextual embedding to assist in the reconstruction of passages. However, it only uses a single auto-encoding pre-task for dense representation pre-training. This study brings multi-view modeling to the contextual masked auto-encoder. Firstly, multi-view representation utilizes both dense and sparse vectors as multi-view representations, aiming to capture sentence semantics from different aspects. Moreover, multiview decoding paradigm utilizes both autoencoding and auto-regressive decoders in representation bottleneck pre-training, aiming to provide both reconstructive and generative signals for better contextual representation pretraining. We refer to this multi-view pretraining method as CoT-MAE v2. Through extensive experiments, we show that CoT-MAE v2 is effective and robust on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks.

The Structure from Motion (SfM) challenge in computer vision is the process of recovering the 3D structure of a scene from a series of projective measurements that are calculated from a collection of 2D images, taken from different perspectives. SfM consists of three main steps; feature detection and matching, camera motion estimation, and recovery of 3D structure from estimated intrinsic and extrinsic parameters and features. A problem encountered in SfM is that scenes lacking texture or with repetitive features can cause erroneous feature matching between frames. Semantic segmentation offers a route to validate and correct SfM models by labelling pixels in the input images with the use of a deep convolutional neural network. The semantic and geometric properties associated with classes in the scene can be taken advantage of to apply prior constraints to each class of object. The SfM pipeline COLMAP and semantic segmentation pipeline DeepLab were used. This, along with planar reconstruction of the dense model, were used to determine erroneous points that may be occluded from the calculated camera position, given the semantic label, and thus prior constraint of the reconstructed plane. Herein, semantic segmentation is integrated into SfM to apply priors on the 3D point cloud, given the object detection in the 2D input images. Additionally, the semantic labels of matched keypoints are compared and inconsistent semantically labelled points discarded. Furthermore, semantic labels on input images are used for the removal of objects associated with motion in the output SfM models. The proposed approach is evaluated on a data-set of 1102 images of a repetitive architecture scene. This project offers a novel method for improved validation of 3D SfM models.