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.

Scientists say they have discovered a giant volcano hidden in plain sight on Mars. The volcano, temporarily named the Noctis, spans 280 miles wide and was discovered alongside a buried ice glacier to the east of Mars, near the red-planet's equator, scientists revealed at the 55th Lunar and Planetary Science Conference held in Texas on Wednesday. Scientists said the 29,600-foot-high volcano was active from ancient through recent times and with possible remnants of glacier ice near its base. They say its discovery points to an exciting new place to search for life and a potential destination for future robotic and human exploration. The findings were detailed in a new study by the SETI Institute and the Mars Institute based at NASA Ames Research Centre. Scientists have discovered a gigantic volcano on Mars that spans 280 miles wide and and nearly 30,000 feet high near the red-planet's equator, (NASA/USGS Mars globe.

Knowledge-based support vector machines (KBSVMs) incorporate advice from domain experts, which can improve generalization significantly. A major limitation that has not been fully addressed occurs when the expert advice is imperfect, which can lead to poorer models. We propose a model that extends KBSVMs and is able to not only learn from data and advice, but also simultaneously improves the advice. The proposed approach is particularly effective for knowledge discovery in domains with few labeled examples. The proposed model contains bilinear constraints, and is solved using two iterative approaches: successive linear programming and a constrained concave-convex approach. Experimental results demonstrate that these algorithms yield useful refinements to expert advice, as well as improve the performance of the learning algorithm overall.

Fox News' William La Jeunesse reports on wait times as long as 26 years to enter the U.S. legally. The U.S. government has started requiring migrants without passports to submit to facial recognition technology to take domestic flights under a change that prompted confusion this week among immigrants and advocacy groups in Texas. It is not clear exactly when the change took effect, but several migrants with flights out of South Texas on Tuesday told advocacy groups that they thought they were being turned away. The migrants included people who had used the government's online appointment system to pursue their immigration cases. Advocates were also concerned about migrants who had crossed the U.S.-Mexico border illegally before being processed by Border Patrol agents and released to pursue their immigration cases.

In recent years, a rich variety of shrinkage priors have been proposed that have great promise in addressing massive regression problems. In general, these new priors can be expressed as scale mixtures of normals, but have more complex forms and better properties than traditional Cauchy and double exponential priors. We first propose a new class of normal scale mixtures through a novel generalized beta distribution that encompasses many interesting priors as special cases. This encompassing framework should prove useful in comparing competing priors, considering properties and revealing close connections. We then develop a class of variational Bayes approximations through the new hierarchy presented that will scale more efficiently to the types of truly massive data sets that are now encountered routinely.

Our analysis is relied on a notion of extended restricted eigenvalue for the design matrix X. Our second set of results applies to a general class of Gaussian design matrix X with i.i.d rows N (0, Σ), for which we provide a surprising phenomenon: the extended Lasso can recover exact signed supports of both β

This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model the observed graph as a sample from a manifold endowed with a vector field, and we design an algorithm that separates and recovers the features of this process: the geometry of the manifold, the data density and the vector field. The algorithm is motivated by our analysis of Laplacian-type operators and their continuous limit as generators of diffusions on a manifold. We illustrate the recovery algorithm on both artificially constructed and real data.

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.

This work introduces Divide-Factor-Combine (DFC), a parallel divide-andconquer framework for noisy matrix factorization.

This paper proposes a novel boosting algorithm called VadaBoost which is motivated by recent empirical Bernstein bounds. VadaBoost iteratively minimizes a cost function that balances the sample mean and the sample variance of the exponential loss. Each step of the proposed algorithm minimizes the cost efficiently by providing weighted data to a weak learner rather than requiring a brute force evaluation of all possible weak learners. Thus, the proposed algorithm solves a key limitation of previous empirical Bernstein boosting methods which required brute force enumeration of all possible weak learners. Experimental results confirm that the new algorithm achieves the performance improvements of EBBoost yet goes beyond decision stumps to handle any weak learner. Significant performance gains are obtained over AdaBoost for arbitrary weak learners including decision trees (CART).