Vision-language models enable open-world classification of objects without the need for any retraining. While this zero-shot paradigm marks a significant advance, even today's best models exhibit skewed performance when objects are dissimilar from their typical depiction. Real world objects such as pears appear in a variety of forms -- from diced to whole, on a table or in a bowl -- yet standard VLM classifiers map all instances of a class to a \it{single vector based on the class label}. We argue that to represent this rich diversity within a class, zero-shot classification should move beyond a single vector. We propose a method to encode and account for diversity within a class using inferred attributes, still in the zero-shot setting without retraining. We find our method consistently outperforms standard zero-shot classification over a large suite of datasets encompassing hierarchies, diverse object states, and real-world geographic diversity, as well finer-grained datasets where intra-class diversity may be less prevalent. Importantly, our method is inherently interpretable, offering faithful explanations for each inference to facilitate model debugging and enhance transparency. We also find our method scales efficiently to a large number of attributes to account for diversity -- leading to more accurate predictions for atypical instances. Finally, we characterize a principled trade-off between overall and worst class accuracy, which can be tuned via a hyperparameter of our method. We hope this work spurs further research into the promise of zero-shot classification beyond a single class vector for capturing diversity in the world, and building transparent AI systems without compromising performance.

Cluster analysis is a popular unsupervised learning tool used in many disciplines to identify heterogeneous sub-populations within a sample. However, validating cluster analysis results and determining the number of clusters in a data set remains an outstanding problem. In this work, we present a global criterion called the Distinguishability criterion to quantify the separability of identified clusters and validate inferred cluster configurations. Our computational implementation of the Distinguishability criterion corresponds to the Bayes risk of a randomized classifier under the 0-1 loss. We propose a combined loss function-based computational framework that integrates the Distinguishability criterion with many commonly used clustering procedures, such as hierarchical clustering, k-means, and finite mixture models. We present these new algorithms as well as the results from comprehensive data analysis based on simulation studies and real data applications.

Meta's drive to integrate artificial intelligence into its products yielded strong financial results for the second quarter in a row. The company plans to spend even more on AI in the coming months, though, and its share price slumped more than 12% as the company reported earnings Wednesday. A weak sales forecast and higher spending guidance rattled investors. Revenue at the world's largest social media business increased 27% to 36.46bn during the first quarter in contrast to analyst expectations of 36.16bn. Earnings per share more than doubled to 4.71, surpassing expectations on Wall Street of 4.32.

Artificial Intelligence (AI) – models that process large and diverse datasets and make predictions from them – can have many uses in nature conservation, such as remote monitoring (like the use of camera traps to study animals or plants) or data analysis. Some of these are controversial because AI can be trained to be biased, but others are valuable research tools. Biologist Daniele Silvestro has developed an AI tool that can help identify conservation and restoration priorities. We asked him to tell us more about how it works and what it offers. Artificial intelligence (AI) is a term indicating a broad family of models used to process large and diverse datasets and make predictions from them. We built a model using biodiversity datasets as well as socioeconomic data.

Tesla reported a 55 percent drop in profit amid fierce competition in the electric vehicle market, but shares rallied on plans to accelerate the production of more affordable models. The Austin, Texas-based company on Tuesday reported profits of 1.1bn in the first quarter, down from 2.51bn a year ago. But shares of Tesla soared by 11 percent after CEO Elon Musk said that production of new, more affordable vehicles would begin in the second half of next year "if not late this year". The models "will use new aspects of the next generation platform as well as aspects of our current platform", Musk said on a conference call with analysts. Musk did not elaborate on the new vehicles, saying more details would be released in August.

The world is facing the collapse of the 1948 international order established in the wake of World War II, amid the brutal wars in Gaza and Ukraine, while authoritarian policies continue to spread, Amnesty International has warned. The report accused the world's most powerful governments, including China, Russia and the United States, of leading the global disregard for international rules and values enshrined in the Universal Declaration of Human Rights of December 1948. The war in Gaza, which began on October 7, was a "descent into hell", Secretary-General Agnes Callamard wrote in her preface to the report, where "the'never again' moral and legal lessons [of 1948] were torn into a million pieces". Noting that Hamas had committed "horrific crimes" in its assault on communities in southern Israel on October 7, Callamard said Israel's "campaign of retaliation" had become a "campaign of collective punishment". Amnesty said while Israel continued to disregard international human rights law, the US, its foremost ally, and other countries including the United Kingdom and Germany were guilty of "grotesque double standards" given their willingness to back Israeli and US authorities over Gaza while condemning war crimes by Russia in Ukraine.

As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent's unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for climate change adaptation and mitigation, its effectiveness and potential are contingent upon overcoming significant challenges such as data scarcity, infrastructure gaps, and limited local AI development. This position paper explores the role of AI in climate change adaptation and mitigation in Africa. It advocates for a collaborative approach to build capacity, develop open-source data repositories, and create context-aware, robust AI-driven climate solutions that are culturally and contextually relevant.

The awareness of multi-cultural human values is critical to the ability of language models (LMs) to generate safe and personalized responses. However, this awareness of LMs has been insufficiently studied, since the computer science community lacks access to the large-scale real-world data about multi-cultural values. In this paper, we present WorldValuesBench, a globally diverse, large-scale benchmark dataset for the multi-cultural value prediction task, which requires a model to generate a rating response to a value question based on demographic contexts. Our dataset is derived from an influential social science project, World Values Survey (WVS), that has collected answers to hundreds of value questions (e.g., social, economic, ethical) from 94,728 participants worldwide. We have constructed more than 20 million examples of the type "(demographic attributes, value question) $\rightarrow$ answer" from the WVS responses. We perform a case study using our dataset and show that the task is challenging for strong open and closed-source models. On merely $11.1\%$, $25.0\%$, $72.2\%$, and $75.0\%$ of the questions, Alpaca-7B, Vicuna-7B-v1.5, Mixtral-8x7B-Instruct-v0.1, and GPT-3.5 Turbo can respectively achieve $<0.2$ Wasserstein 1-distance from the human normalized answer distributions. WorldValuesBench opens up new research avenues in studying limitations and opportunities in multi-cultural value awareness of LMs.

Modular deep learning is the state-of-the-art solution for lifting the curse of multilinguality, preventing the impact of negative interference and enabling cross-lingual performance in Multilingual Pre-trained Language Models. However, a trade-off of this approach is the reduction in positive transfer learning from closely related languages. In response, we introduce a novel method called language arithmetic, which enables training-free post-processing to address this limitation. Inspired by the task arithmetic framework, we apply learning via addition to the language adapters, transitioning the framework from a multi-task to a multilingual setup. The effectiveness of the proposed solution is demonstrated on three downstream tasks in a MAD-X-based set of cross-lingual schemes, acting as a post-processing procedure. Language arithmetic consistently improves the baselines with significant gains in the most challenging cases of zero-shot and low-resource applications. Our code and models are available at https://github.com/mklimasz/language-arithmetic .

The global decline in bee populations poses significant risks to agriculture, biodiversity, and environmental stability. To bridge the gap in existing data, we introduce ApisTox, a comprehensive dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB, providing an extensive, consistent, and curated collection that surpasses the previous datasets. ApisTox incorporates a wide array of data, including toxicity levels for chemicals, details such as time of their publication in literature, and identifiers linking them to external chemical databases. This dataset may serve as an important tool for environmental and agricultural research, but also can support the development of policies and practices aimed at minimizing harm to bee populations. Finally, ApisTox offers a unique resource for benchmarking molecular property prediction methods on agrochemical compounds, facilitating advancements in both environmental science and cheminformatics. This makes it a valuable tool for both academic research and practical applications in bee conservation.