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Perhaps Artificial Intelligence (AI) can come to our rescue. Yes, that's right, we might be able to harness the beneficial uses of AI to cope with our relentless tsunami of disinformation and misinformation. We might be wise to try doing so. Every avenue of potential solution would seem worthy of pursuit. There is no question that humans can readily leverage AI to generate disinformation and misinformation.

From Ex Machina to I, Robot, humanoid robots have been a staple feature in science fiction blockbusters throughout the years. Now, lifelike robots are becoming more and more popular in the real world, with many able to produce human speech and facial expressions with eerie precision. This week, a robot described as the'world's most advanced humanoid robot' hit the headlines, after it tried to dispel fears of a robot takeover. Ameca, a humanoid robot developed by Cornwall-based Engineered Arts, said: 'There's no need to worry, robots will never take over the world. We're here to help and serve humans, not replace them.'

What are we going to do about the massive glut of disinformation and misinformation? It all is demonstrably getting worse and worse, with each passing day. Perhaps Artificial Intelligence (AI) can come to our rescue. Yes, that's right, we might be able to harness the beneficial uses of AI to cope with our relentless tsunami of disinformation and misinformation. We might be wise to try doing so. Every avenue of potential solution would seem worthy of pursuit. As an aside, I'd like to immediately acknowledge and note that AI is undoubtedly going to also be a part of the problem too. There is no question that humans can readily leverage AI to generate disinformation and misinformation. Furthermore, AI can insidiously be used to make disinformation and misinformation appear to be amazingly valid and fool humans into believing that the presented information is alluringly accurate and factual. A decidedly sad face side of what AI brings to the table. We will come back to this downside conundrum toward the end of this discussion. For now, let's put on our smiley faces and explore how AI is beneficial to bringing disinformation and misinformation to its mighty knees.

While debate rages as to the merit and validity of machine-generated imagery, a photography website has fired the first shots in the man-machine war by banning images generated using AI services like Midjourney and DALL•E. PurplePort, a popular portfolio and networking website for models, photographers and imaging creatives, announced a blanket ban on "100% machine-generated images" so that the platform can remain focused on "human-generated and human-focused art". In an update titled'Artmageddon: The rise of the machines, and banning machine-generated images', owner and photographer Russ Freeman made the website's position explicitly clear. "Due to the rise of machine-generated images, we have decided to ban this type of image. Uploading images generated using services (such as Midjourney / DALL•E / Craiyon / Stable Diffusion / etc), where you type a phrase or description of the desired image and a machine algorithm (often called A.I) creates an image for you, is banned from PurplePort until further notice."

Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmarks for the 'right' reasons; and (b) to understand what those reasons would even be. We investigate the behavior of reading comprehension models with respect to two linguistic 'skills': coreference resolution and comparison. We propose a definition for the reasoning steps expected from a system that would be 'reading slowly', and compare that with the behavior of five models of the BERT family of various sizes, observed through saliency scores and counterfactual explanations. We find that for comparison (but not coreference) the systems based on larger encoders are more likely to rely on the 'right' information, but even they struggle with generalization, suggesting that they still learn specific lexical patterns rather than the general principles of comparison.

We introduce the task of entity-centric query refinement. Given an input query whose answer is a (potentially large) collection of entities, the task output is a small set of query refinements meant to assist the user in efficient domain exploration and entity discovery. We propose a method to create a training dataset for this task. For a given input query, we use an existing knowledge base taxonomy as a source of candidate query refinements, and choose a final set of refinements from among these candidates using a search procedure designed to partition the set of entities answering the input query. We demonstrate that our approach identifies refinement sets which human annotators judge to be interesting, comprehensive, and non-redundant. In addition, we find that a text generation model trained on our newly-constructed dataset is able to offer refinements for novel queries not covered by an existing taxonomy. Our code and data are available at https://github.

As political attitudes have diverged ideologically in the United States, political speech has diverged lingusitically. The ever-widening polarization between the US political parties is accelerated by an erosion of mutual understanding between them. We aim to make these communities more comprehensible to each other with a framework that probes community-specific responses to the same survey questions using community language models CommunityLM. In our framework we identify committed partisan members for each community on Twitter and fine-tune LMs on the tweets authored by them. We then assess the worldviews of the two groups using prompt-based probing of their corresponding LMs, with prompts that elicit opinions about public figures and groups surveyed by the American National Election Studies (ANES) 2020 Exploratory Testing Survey. We compare the responses generated by the LMs to the ANES survey results, and find a level of alignment that greatly exceeds several baseline methods. Our work aims to show that we can use community LMs to query the worldview of any group of people given a sufficiently large sample of their social media discussions or media diet.

Multiple-objective optimization (MOO) aims to simultaneously optimize multiple conflicting objectives and has found important applications in machine learning, such as minimizing classification loss and discrepancy in treating different populations for fairness. At optimality, further optimizing one objective will necessarily harm at least another objective, and decision-makers need to comprehensively explore multiple optima (called Pareto front) to pinpoint one final solution. We address the efficiency of finding the Pareto front. First, finding the front from scratch using stochastic multi-gradient descent (SMGD) is expensive with large neural networks and datasets. We propose to explore the Pareto front as a manifold from a few initial optima, based on a predictor-corrector method. Second, for each exploration step, the predictor solves a large-scale linear system that scales quadratically in the number of model parameters and requires one backpropagation to evaluate a second-order Hessian-vector product per iteration of the solver. We propose a Gauss-Newton approximation that only scales linearly, and that requires only first-order inner-product per iteration. This also allows for a choice between the MINRES and conjugate gradient methods when approximately solving the linear system. The innovations make predictor-corrector possible for large networks. Experiments on multi-objective (fairness and accuracy) misinformation detection tasks show that 1) the predictor-corrector method can find Pareto fronts better than or similar to SMGD with less time; and 2) the proposed first-order method does not harm the quality of the Pareto front identified by the second-order method, while further reduce running time.

Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism. Recent studies have shown that explicitly modeling the input graph structure can significantly improve the performance. However, the vanilla structural encoder cannot capture all specialized information in a single forward pass for all decoding steps, resulting in inaccurate semantic representations. Meanwhile, the input graph is flatted as an unordered sequence in the cross attention, ignoring the original graph structure. As a result, the obtained input graph context vector in the decoder may be flawed. To address these issues, we propose a Structure-Aware Cross-Attention (SACA) mechanism to re-encode the input graph representation conditioning on the newly generated context at each decoding step in a structure aware manner. We further adapt SACA and introduce its variant Dynamic Graph Pruning (DGP) mechanism to dynamically drop irrelevant nodes in the decoding process. We achieve new state-of-the-art results on two graph-to-text datasets, LDC2020T02 and ENT-DESC, with only minor increase on computational cost.