Scientists and tech industry leaders, including high-level executives at Microsoft and Google, issued a new warning Tuesday about the perils that artificial intelligence poses to humankind. "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war," the statement said. Sam Altman, CEO of ChatGPT maker OpenAI, and Geoffrey Hinton, a computer scientist known as the godfather of artificial intelligence, were among the hundreds of leading figures who signed the statement, which was posted on the Center for AI Safety's website. Worries about artificial intelligence systems outsmarting humans and running wild have intensified with the rise of a new generation of highly capable AI chatbots such as ChatGPT. It has sent countries around the world scrambling to come up with regulations for the developing technology, with the European Union blazing the trail with its AI Act expected to be approved later this year.

If you want to really hurt someone's feelings in the year 2023, just call them an AI. An all-star cast of celebrities and public figures have recently been the victim of such jokes: the NBA player Jordan Poole ("AI Steph Curry"), Raquel Leviss from the reality-TV show Vanderpump Rules ("what would happen if you asked chat GBT [sic] to create an American girl"), Transportation Secretary Pete Buttigieg ("our first A.I. cabinet member?"). That these slights span the three pillars of American life--sports, politics, Bravo--suggests that no one, or rather nothing, is safe. Such digs have popped up all over social media; on Twitter alone, insults like these have been levied against TV shows, songs, sports uniforms, commencement speeches, White House press releases, proposed legislation, and lots of news articles. That AI has become an attack is a result of the huge moment for AI we're in.

A necklace'made from the tooth of a megalodon shark' is revealed in new images from the wreckage of RMS Titanic. The stunning artefact – which has not been worn since the ship's sinking in April 1912 – was identified in footage taken last summer by Guernsey-based firm Magellan Ltd. The footage was shot during efforts to capture the first digital scans of the shipwreck, which present the wreck almost as if it's been retrieved from the water. Other objects surrounding the necklace have not been identified, although it appears to be surrounded by small ring-shaped beads. Magellan Ltd, which is working with Atlantic Productions on a documentary about last year's expedition, is prohibited from taking them from the sea floor, however.

Welcome to our May 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we learn how to mitigate biases in machine learning, explore tradeoffs in school redistricting, and find out how machine learning algorithms fared in predicting the winner of this year's Eurovision Song Contest. In this blogpost, Max Springer examines the notion of fairness in hierarchical clustering. Max and colleagues demonstrate that it's possible to incorporate fairness constraints or demographic information into the optimization process to reduce biases in ML models without significantly sacrificing performance. Joar Skalse and Alessandro Abate won the AAAI 2023 outstanding paper award for their work, Misspecification in Inverse Reinforcement Learning, in which they study the question of how robust the inverse reinforcement learning problem is to misspecification of the underlying behavioural model.

I asked some of the best AI journalists in the business to share their top tips on how to talk about AI with confidence. My colleagues and I spend our days obsessing over the tech, listening to AI folks and then translating what they say into clear, relatable language with important context. I'd say we know a thing or two about what we're talking about. Here are seven things to pay attention to when talking about AI. "The tech industry is not great at explaining itself clearly, despite insisting that large language models will change the world. If you're struggling, you aren't alone," says Nitasha Tiku, the Washington Post's tech culture reporter.

During the 2020 pandemic, members of the reddit & discord r/robotics community rallied to organize an online showcase for members of our community. What was originally envisioned as a small, intimate afternoon video call turned out to be a two day event of participants from across the world. All times are recorded in Eastern Daylight Time (EDT), UTC-4. Check out the full program in our website for more details.

Despite a growing interest in diffusion-based language models, existing work has not shown that these models can attain nontrivial likelihoods on standard language modeling benchmarks. In this work, we take the first steps towards closing the likelihood gap between autoregressive and diffusion-based language models, with the goal of building and releasing a diffusion model which outperforms a small but widely-known autoregressive model. We pursue this goal through algorithmic improvements, scaling laws, and increased compute. On the algorithmic front, we introduce several methodological improvements for the maximum-likelihood training of diffusion language models. We then study scaling laws for our diffusion models and find compute-optimal training regimes which differ substantially from autoregressive models. Using our methods and scaling analysis, we train and release Plaid 1B, a large diffusion language model which outperforms GPT-2 124M in likelihood on benchmark datasets and generates fluent samples in unconditional and zero-shot control settings.

In this work, we define a diffusion-based generative model capable of both music synthesis and source separation by learning the score of the joint probability density of sources sharing a context. Alongside the classic total inference tasks (i.e., generating a mixture, separating the sources), we also introduce and experiment on the partial generation task of source imputation, where we generate a subset of the sources given the others (e.g., play a piano track that goes well with the drums). Additionally, we introduce a novel inference method for the separation task based on Dirac likelihood functions. We train our model on Slakh2100, a standard dataset for musical source separation, provide qualitative results in the generation settings, and showcase competitive quantitative results in the source separation setting. Our method is the first example of a single model that can handle both generation and separation tasks, thus representing a step toward general audio models.

Since the introduction of the SemEval 2020 Task 11 (Martino et al., 2020a), several approaches have been proposed in the literature for classifying propaganda based on the rhetorical techniques used to influence readers. These methods, however, classify one span at a time, ignoring dependencies from the labels of other spans within the same context. In this paper, we approach propaganda technique classification as a Multi-Instance Multi-Label (MIML) learning problem (Zhou et al., 2012) and propose a simple RoBERTa-based model (Zhuang et al., 2021) for classifying all spans in an article simultaneously. Further, we note that, due to the annotation process where annotators classified the spans by following a decision tree, there is an inherent hierarchical relationship among the different techniques, which existing approaches ignore. We incorporate these hierarchical label dependencies by adding an auxiliary classifier for each node in the decision tree to the training objective and ensembling the predictions from the original and auxiliary classifiers at test time. Overall, our model leads to an absolute improvement of 2.47% micro-F1 over the model from the shared task winning team in a cross-validation setup and is the best performing non-ensemble model on the shared task leaderboard.

Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and dialogues. Most existing benchmarks treat both modalities the same as a frame-independent visual understanding task, while neglecting the intrinsic attributes in multimodal dialogues, such as scene and topic transitions. In this paper, we present Video-grounded Scene&Topic AwaRe dialogue (VSTAR) dataset, a large scale video-grounded dialogue understanding dataset based on 395 TV series. Based on VSTAR, we propose two benchmarks for video-grounded dialogue understanding: scene segmentation and topic segmentation, and one benchmark for video-grounded dialogue generation. Comprehensive experiments are performed on these benchmarks to demonstrate the importance of multimodal information and segments in video-grounded dialogue understanding and generation.