In spatial statistics, a common objective is to predict the values of a spatial process at unobserved locations by exploiting spatial dependence. In geostatistics, Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes. However, when considering non-linear prediction for non-Gaussian and categorical data, the Kriging prediction is not necessarily optimal, and the associated variance is often overly optimistic. We propose to use deep neural networks (DNNs) for spatial prediction. Although DNNs are widely used for general classification and prediction, they have not been studied thoroughly for data with spatial dependence. In this work, we propose a novel neural network structure for spatial prediction by adding an embedding layer of spatial coordinates with basis functions. We show in theory that the proposed DeepKriging method has multiple advantages over Kriging and classical DNNs only with spatial coordinates as features. We also provide density prediction for uncertainty quantification without any distributional assumption and apply the method to PM$_{2.5}$ concentrations across the continental United States.

Voted and approved by state assembly and the senate, New York will suspend implementation of AI facial recognition technology in schools for two years on Wednesday. State Governor Andrew Cuomo has signed the legislation into law. The decision is the aftermath of a lawsuit filed in June by the New York Civil Liberties Union on behalf of student parents, whose school district adopted the technology earlier this year. Facial recognition technology remains the most controversial AI deployment in the United States: cities like San Francisco, Somerville, and Oakland have already banned the technology in 2019. Moreover, a letter was sent to the US Privacy and Civil Liberties Board (PCLOB) in January, requesting the US government to halt relevant applications while waiting for further review.

As I've noted in the past ICT related legislation tends to be considerably over broad in scope ay the best of times, and prosecuters have tried very hard to open it up further with case law. Whilst some judges do pull things in a bit, to many alow prosecutorial over reach go to far. A rule of thumb for legislation should be to reset any proposed legislation from ICT and see what equivalent legislation exists for non ICT situations. Thus any ICT legislation should be similarly restrained in scope. After all it is not illegal to walk up to somebodies door and knock politely, if you've made a nusance of yourself there are civil remidies. However ICT legislation makes the equivalent online activity actually a criminal activity from the get go, and it's frequently treated as something worse than armed robbery.

Co-CEO at Fluree, the scalable semantic graph database backed by blockchain technology. As AI continues to permeate the online world, it opens up a Pandora's box of unintended consequences. That's because unleashing AI on the current version of the internet and letting it feed on potentially inauthentic data can lead to devastation. Our increasing reliance on machine learning opens the floodgates for hackers and other bad actors to manipulate data and exploit algorithms in dangerous ways. From entering counterfeit products into the supply chain to changing software source code to meddling with voter registration databases, data tampering is already being used as a powerful weapon.

It took a global pandemic and the death of George Floyd to put deep-seated social inequities, especially systemic racism, front and center for intense public debate. We may or may not be on the cusp of a redressing social injustice by reordering our legacy political and economic systems. Either way, a singular piece of technology – artificial intelligence (AI) -- is destined to profoundly influence which way we go from here. This is not just my casual observation. Those in power fully recognize how AI can be leveraged to preserve status-quo political and economic systems, with all of its built-in flaws, more or less intact.

International negotiations to regulate artificial intelligence-based weapons are encountering difficulties, with Japan, Germany and others backing international rules on regulation but maintaining a cautious stance on a treaty to prohibit killer robots. Behind their muted approach is a fear that countries that develop autonomous weapons would shun such a treaty anyway, diminishing the significance of international efforts toward any regulation. Therefore, countries differ over how to attain this objective while agreeing on the need to prevent lethal autonomous weapons from running out of control. Germany hosted an online meeting in early April amid the COVID-19 pandemic to facilitate talks on the control of killer robots, as promoted by the U.N. Convention on Certain Conventional Weapons (CCW). Representatives of more than 60 countries and regions, including the United States and Israel, both developers of AI weapons, the European Union and the United Nations, as well as nongovernmental organizations, logged in to participate in the forum.

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes.

The popular convergence theory of the EM algorithm explains that the observed incomplete data log-likelihood L and the complete data log-likelihood Q are positively correlated, and we can maximize L by maximizing Q. The Deterministic Annealing EM (DAEM) algorithm was hence proposed for avoiding locally maximal Q. This paper provides different conclusions: 1) The popular convergence theory is wrong; 2) The locally maximal Q can affect the convergent speed, but cannot block the global convergence; 3) Like marriage competition, unfair competition between two components may vastly decrease the globally convergent speed; 4) Local convergence exists because the sample is too small, and unfair competition exists; 5) An improved EM algorithm, called the Channel Matching (CM) EM algorithm, can accelerate the global convergence. This paper provides an initialization map with two means as two axes for the example of a binary Gaussian mixture studied by the authors of DAEM algorithm. This map can tell how fast the convergent speeds are for different initial means and why points in some areas are not suitable as initial points. A two-dimensional example indicates that the big sample or the fair initialization can avoid global convergence. For more complicated mixture models, we need further study to convert the fair marriage principle to specific methods for the initializations.

Facebook has agreed to pay $650 million – $100 million more than before – to settle a long-running class-action lawsuit over its use of facial recognition technology. "We are focused on settling as it is in the best interest of our community and our shareholders to move past this matter," Facebook said in a statement. Three Illinois residents sued Facebook under a state law, the Biometric Information Privacy Act, which allows residents who have had their faces scanned for data without written consent to sue. The lawsuit, which was certified as a class action, involved gathering facial data for a Facebook feature that suggests the name of people in users' photos and could have exposed Facebook to billions in damages. The problem with AI? Study says it's too white and male, calls for more women, minorities Facial recognition software is courting more controversy in the wake of nationwide protests over police brutality.

Fully appreciating the overarching scope of CS requires weaving more than ethics into the reigning curricula.