I have questions about Denis Villeneuve's Dune: Part Two. If the Fremen have lasers, why don't they just shoot the sand harvesters and run away? Why don't they use their sandworms until the last battle? Wouldn't it make more sense to fight the other great houses on Arrakis itself, where they have sandworms, rather than board ships off-world to go off to war? If Paul (Timothée Chalamet) has to invade the galaxy at the end, why bother marrying the daughter of the emperor he just deposed?

In Tanzania, farmers are using an AI-assisted app that works in their native language of Swahili to detect a devastating cassava disease before it spreads. In South Africa, computer scientists have built machine learning models to analyze the impact of racial segregation in housing. And in Nairobi, Kenya, AI classifies images from thousands of surveillance cameras perched on lampposts in the bustling city's center. The projected benefit of AI adoption on Africa's economy is tantalizing. Estimates suggest that four African countries alone--Nigeria, Ghana, Kenya, and South Africa--could rake in up to 136 billion worth of economic benefits by 2030 if businesses there begin using more AI tools.

Latent tree graphical models are natural tools for expressing long range and hierarchical dependencies among many variables which are common in computer vision, bioinformatics and natural language processing problems. However, existing models are largely restricted to discrete and Gaussian variables due to computational constraints; furthermore, algorithms for estimating the latent tree structure and learning the model parameters are largely restricted to heuristic local search. We present a method based on kernel embeddings of distributions for latent tree graphical models with continuous and non-Gaussian variables. Our method can recover the latent tree structures with provable guarantees and perform local-minimum free parameter learning and efficient inference. Experiments on simulated and real data show the advantage of our proposed approach.

Now, the African Union--made up of 55 member nations--is preparing an ambitious AI policy that envisions an Africa-centric path for the development and regulation of this emerging technology. But debates on when AI regulation is warranted and concerns about stifling innovation could pose a roadblock, while a lack of AI infrastructure could hold back the technology's adoption. "We're seeing a growth of AI in the continent; it's really important there be set rules in place to govern these technologies," says Chinasa T. Okolo, a fellow in the Center for Technology Innovation at Brookings, whose research focuses on AI governance and policy development in Africa. Some African countries have already begun to formulate their own legal and policy frameworks for AI. Seven have developed national AI policies and strategies, which are currently at different stages of implementation.

Variational Bayesian matrix factorization (VBMF) efficiently approximates the posterior distribution of factorized matrices by assuming matrix-wise independence of the two factors. A recent study on fully-observed VBMF showed that, under a stronger assumption that the two factorized matrices are column-wise independent, the global optimal solution can be analytically computed. However, it was not clear how restrictive the column-wise independence assumption is. In this paper, we prove that the global solution under matrix-wise independence is actually column-wise independent, implying that the column-wise independence assumption is harmless. A practical consequence of our theoretical finding is that the global solution under matrix-wise independence (which is a standard setup) can be obtained analytically in a computationally very efficient way without any iterative algorithms. We experimentally illustrate advantages of using our analytic solution in probabilistic principal component analysis.

We analyze the statistical performance of a recently proposed convex tensor decomposition algorithm. Conventionally tensor decomposition has been formulated as non-convex optimization problems, which hindered the analysis of their performance. We show under some conditions that the mean squared error of the convex method scales linearly with the quantity we call the normalized rank of the true tensor. The current analysis naturally extends the analysis of convex low-rank matrix estimation to tensors. Furthermore, we show through numerical experiments that our theory can precisely predict the scaling behaviour in practice.

We consider the problem of learning rules from natural language text sources. These sources, such as news articles and web texts, are created by a writer to communicate information to a reader, where the writer and reader share substantial domain knowledge. Consequently, the texts tend to be concise and mention the minimum information necessary for the reader to draw the correct conclusions. We study the problem of learning domain knowledge from such concise texts, which is an instance of the general problem of learning in the presence of missing data. However, unlike standard approaches to missing data, in this setting we know that facts are more likely to be missing from the text in cases where the reader can infer them from the facts that are mentioned combined with the domain knowledge.

We derive algorithms for generalised tensor factorisation (GTF) by building upon the well-established theory of Generalised Linear Models. Our algorithms are general in the sense that we can compute arbitrary factorisations in a message passing framework, derived for a broad class of exponential family distributions including special cases such as Tweedie's distributions corresponding to β- divergences. By bounding the step size of the Fisher Scoring iteration of the GLM, we obtain general updates for real data and multiplicative updates for non-negative data. The GTF framework is, then extended easily to address the problems when multiple observed tensors are factorised simultaneously. We illustrate our coupled factorisation approach on synthetic data as well as on a musical audio restoration problem.

The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science. Leveraging ideas from survival and event history analysis, we introduce a continuous-time regression modeling framework for network event data that can incorporate both time-dependent network statistics and time-varying regression coefficients. We also develop an efficient inference scheme that allows our approach to scale to large networks. On synthetic and real-world data, empirical results demonstrate that the proposed inference approach can accurately estimate the coefficients of the regression model, which is useful for interpreting the evolution of the network; furthermore, the learned model has systematically better predictive performance compared to standard baseline methods.

Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set diversity analysis, and document retrieval.