As artificial intelligence (AI) improves, traditional alignment strategies may falter in the face of unpredictable self-improvement, hidden subgoals, and the sheer complexity of intelligent systems. Inspired by contemplative wisdom traditions, we show how four axiomatic principles can instil a resilient Wise World Model in AI systems. First, mindfulness enables self-monitoring and recalibration of emergent subgoals. Second, emptiness forestalls dogmatic goal fixation and relaxes rigid priors. Third, non-duality dissolves adversarial self-other boundaries. Fourth, boundless care motivates the universal reduction of suffering. We find that prompting AI to reflect on these principles improves performance on the AILuminate Benchmark (d=.96) and boosts cooperation and joint-reward on the Prisoner's Dilemma task (d=7+). We offer detailed implementation strategies at the level of architectures, constitutions, and reinforcement on chain-of-thought. For future systems, active inference may offer the self-organizing and dynamic coupling capabilities needed to enact Contemplative AI in embodied agents.

This week has seen writers divided over a story written by an AI model that is "good at creative writing" – at least according to Sam Altman, the CEO of ChatGPT company OpenAI, which is developing the new model. Author Jeanette Winterson, writing in the Guardian on Wednesday, agreed with him, calling the story – which is a metafictional piece about grief – "beautiful and moving". We asked other authors to assess ChatGPT's current writing skills – and what recent developments around artificial intelligence might mean for human creativity. I think the story is an elegant emptiness. I'm more interested by Winterson's suggestion that we treat AI as "alternative intelligence". That makes it feel like a consciousness with which we can have a relationship, but as far as I know that would be like a bird falling in love with its reflection in a window.

Privacy-preserving clustering groups data points in an unsupervised manner whilst ensuring that sensitive information remains protected. Previous privacy-preserving clustering focused on identifying concentration of point clouds. In this paper, we take another path and focus on identifying appropriate separators that split a data set. We introduce the novel differentially private clustering algorithm DPM that searches for accurate data point separators in a differentially private manner. DPM addresses two key challenges for finding accurate separators: identifying separators that are large gaps between clusters instead of small gaps within a cluster and, to efficiently spend the privacy budget, prioritising separators that split the data into large subparts. Using the differentially private Exponential Mechanism, DPM randomly chooses cluster separators with provably high utility: For a data set $D$, if there is a wide low-density separator in the central $60\%$ quantile, DPM finds that separator with probability $1 - \exp(-\sqrt{|D|})$. Our experimental evaluation demonstrates that DPM achieves significant improvements in terms of the clustering metric inertia. With the inertia results of the non-private KMeans++ as a baseline, for $\varepsilon = 1$ and $\delta=10^{-5}$ DPM improves upon the difference to the baseline by up to $50\%$ for a synthetic data set and by up to $62\%$ for a real-world data set compared to a state-of-the-art clustering algorithm by Chang and Kamath.

Buddhism is an influential religion with a long-standing history and profound philosophy. Nowadays, more and more people worldwide aspire to learn the essence of Buddhism, attaching importance to Buddhism dissemination. However, Buddhist scriptures written in classical Chinese are obscure to most people and machine translation applications. For instance, general Chinese-English neural machine translation (NMT) fails in this domain. In this paper, we proposed a novel approach to building a practical NMT model for Buddhist scriptures. The performance of our translation pipeline acquired highly promising results in ablation experiments under three criteria.

The text-to-image revolution has arrived, turning the art world upside down. A cursory online search for "AI art" returns a long list of artificial intelligence (AI) art generators, from Open AI's DALL-E to Dream by app creator WOMBO. Advancements in creative AI applications have progressed incredibly quickly within the last few years. Today, deep learning models can create original images from simple text prompts, opening the doors to seemingly endless experimentation. To explore AI's impact on the art world, and on creativity and innovation more broadly, the International Telecommunication Union's AI for Good platform has launched the Artistic Intelligence Visionary Initiative (A.I.V.I).

Ask a group of physicists and philosophers to define "space" and you will likely be stuck in a long discussion that involves deep-sounding but meaningless word combinations such as "the very fabric of space-time itself is a physical manifestation of quantum entropy concepts woven together by the universal nature of location." On second thought, maybe you should avoid starting deep conversations between philosophers and physicists. Is space just an infinite emptiness that underlies everything? Or is it the emptiness between things? What if space is neither of these but is a physical thing that can slosh around, like a bathtub full of water? It turns out that the nature of space itself is one of the biggest and strangest mysteries in the universe. So get ready, because things are about to get ... spacey. Like many deep questions, the question of what space is sounds like a simple one at first. But if you challenge your intuition and reexamine the question, you discover that a clear answer is hard to find. Most people imagine that space is just the emptiness in which things happen, like a big empty warehouse or a theater stage on which the events of the universe play out. In this view, space is literally the lack of stuff.

In ontology-based data access (OBDA), database querying is enriched with an ontology that provides domain knowledge and additional vocabulary for query formulation. We identify query emptiness and predicate emptiness as two central reasoning services in this context. Query emptiness asks whether a given query has an empty answer over all databases formulated in a given vocabulary. Predicate emptiness is defined analogously, but quantifies universally over all queries that contain a given predicate. In this paper, we determine the computational complexity of query emptiness and predicate emptiness in the EL, DL-Lite, and ALC-families of description logics, investigate the connection to ontology modules, and perform a practical case study to evaluate the new reasoning services.

We examine some ways in which contemporary results from neural network theory can potentially contribute to a Buddhist understanding of emptiness. We also make some general remarks about the pitfalls and benefits inherent in attempting to apply ideas from artificial intelligence to an understanding of Buddhism.

Ontologies can be used to provide an enriched vocabulary for the formulation of queries over instance data. We identify query emptiness and predicate emptiness as two central reasoning services in this context. Query emptiness asks whether a given query has an empty answer over all data sets formulated in a given signature. Predicate emptiness is defined analogously, but quantifies universally over all queries that contain a given predicate. In this paper, we determine the computational complexity of query emptiness and predicate emptiness in the EL, DL-Lite, and ALC-families of description logics, investigate the connection to ontology modules, and perform a practical case study to evaluate the new reasoning services.