In recent decades, AI has become a pervasive technology, affecting companies across industries and throughout the world. These innovations arise from research, and the research objectives in the AI field are influenced by many factors. Together, these factors shape patterns in what the research accomplishes, as well as who benefits from it -- and who doesn't. In an effort to document the factors influencing AI research, researchers at Stanford, the University of California, Berkeley, the University of Washington, and University College Dublin & Lero surveyed 100 highly cited studies submitted to two prominent AI conferences, NeurIPS and ICML. They claim that in the papers they analyzed, which were published in 2008, 2009, 2018, and 2019, the dominant values were operationalized in ways that centralize power, disproportionally benefiting corporations while neglecting society's least advantaged.

IBM Corp. and Heifer International announced today that blockchain-based IBM Food Trust and artificial intelligence platform IBM Watson are being used to help small-scale coffee and cocoa farmers in Honduras get higher crop yields and access new markets. Food Trust uses blockchain technology to track products from farm to point of sale, providing transparency along the entire supply chain. It is currently being used by coffee farmers in the Cooperativa Regional Agroforestal Nuevas Ideas Limitada, a farmer cooperative, and cocoa farmers that are part of Heifer's Chocolate4All project. According to a study published by Heifer International small-scale farmers operate at an average of 46% to 59% loss because of excessive middlemen. Increased transparency enables end buyers more opportunity to understand whom they are buying from and thus can create a competitive advantage for small-scale coffee and cocoa farmers.

This paper is a review of a particular approach to the method of maximum entropy as a general framework for inference. The discussion emphasizes the pragmatic elements in the derivation. An epistemic notion of information is defined in terms of its relation to the Bayesian beliefs of ideally rational agents. The method of updating from a prior to a posterior probability distribution is designed through an eliminative induction process. The logarithmic relative entropy is singled out as the unique tool for updating that (a) is of universal applicability; (b) that recognizes the value of prior information; and (c) that recognizes the privileged role played by the notion of independence in science. The resulting framework -- the ME method -- can handle arbitrary priors and arbitrary constraints. It includes MaxEnt and Bayes' rule as special cases and, therefore, it unifies entropic and Bayesian methods into a single general inference scheme. The ME method goes beyond the mere selection of a single posterior, but also addresses the question of how much less probable other distributions might be, which provides a direct bridge to the theories of fluctuations and large deviations.

However, Our contributions in this work are: instead of computing the curvature matrix, we show that, under some regularity conditions, - We show that under some regularity conditions, a diagonal the Laplace approximation can be easily constructed Laplace approximation can be constructed without using the gradient second moment. This computing anything besides what is already being quantity is already estimated by many exponential computed by widely used optimizers; moving average variants of Adagrad such as Adam and RMSprop, but is traditionally discarded - We qualitatively compare this approximation with after training. We show that our method (L2M) methods such as deep ensembles (Lakshminarayanan does not require changes in models or optimization, et al., 2017), MC Dropout (Gal & Ghahramani, 2016), can be implemented in a few lines of code Hamiltonian Monte Carlo (HMC) (Cobb & Jalaian, to yield reasonable results, and it does not require 2020), among others; any extra computational steps besides what is already - We also show that our approximation is orthogonal being computed by optimizers, without introducing to methods such as ensembling (Lakshminarayanan any new hyperparameter. We hope our et al., 2017) and does not require changing training method can open new research directions on using procedures, estimating new quantities, or adding new quantities already computed by optimizers for hyperparameters.

This work was developed aiming to employ Statistical techniques to the field of Music Emotion Recognition, a well-recognized area within the Signal Processing world, but hardly explored from the statistical point of view. Here, we opened several possibilities within the field, applying modern Bayesian Statistics techniques and developing efficient algorithms, focusing on the applicability of the results obtained. Although the motivation for this project was the development of a emotion-based music recommendation system, its main contribution is a highly adaptable multivariate model that can be useful interpreting any database where there is an interest in applying regularization in an efficient manner. Broadly speaking, we will explore what role a sound theoretical statistical analysis can play in the modeling of an algorithm that is able to understand a well-known database and what can be gained with this kind of approach.

Guideline-based treatment for sepsis and septic shock is difficult because sepsis is a disparate range of life-threatening organ dysfunctions whose pathophysiology is not fully understood. Early intervention in sepsis is crucial for patient outcome, yet those interventions have adverse effects and are frequently overadministered. Greater personalization is necessary, as no single action is suitable for all patients. We present a novel application of reinforcement learning in which we identify optimal recommendations for sepsis treatment from data, estimate their confidence level, and identify treatment options infrequently observed in training data. Rather than a single recommendation, our method can present several treatment options. We examine learned policies and discover that reinforcement learning is biased against aggressive intervention due to the confounding relationship between mortality and level of treatment received. We mitigate this bias using subspace learning, and develop methodology that can yield more accurate learning policies across healthcare applications.

Everything became imbued with a sense of vitality and life and vividness. If I picked up a pebble from the beach, it would move. It would glisten and gleam and sparkle and be absolutely captivating," says neuroscientist Anil Seth. "Somebody looking at me would see me staring at a stone for hours." Or what seemed like hours to Seth. A researcher at the United Kingdom's University of Sussex, he studies how the brain helps us perceive the world within and without, and is intrigued by what psychedelics such as LSD can tell us about how the brain creates these perceptions. So, a few years ago, he decided to try some, in controlled doses and with trusted people by his side. He had a notebook to keep track of his experiences. "I didn't write very much in the notebook," he says, laughing. Instead, while on LSD, he reveled in a sense of well-being and marveled at the "fluidity of time and space." He found himself staring at clouds and seeing them change into faces of people he was thinking of.

Manifold hypothesis states that data points in high-dimensional space actually lie in close vicinity of a manifold of much lower dimension. In many cases this hypothesis was empirically verified and used to enhance unsupervised and semi-supervised learning. Here we present new approach to manifold hypothesis checking and underlying manifold dimension estimation. In order to do it we use two very different methods simultaneously - one geometric, another probabilistic - and check whether they give the same result. Our geometrical method is a modification for sparse data of a well-known box-counting algorithm for Minkowski dimension calculation. The probabilistic method is new. Although it exploits standard nearest neighborhood distance, it is different from methods which were previously used in such situations. This method is robust, fast and includes special preliminary data transformation. Experiments on real datasets show that the suggested approach based on two methods combination is powerful and effective.

Artificial Intelligence (AI) systems are increasingly deployed in the public sector. Existing public procurement processes and standards are in urgent need of innovation to address potential risks and harms to citizens. Read our primer based on our research and on input from leading experts in the public sector, data science, civil society, policy, social science, and the law to learn about pathways forward. The COVID-19 pandemic has underlined how biases can manifest in many different aspects of public use technology. For example, federal COVID-19 funding allocation algorithms have favored high-income communities over low-income communities due to historical biases prevalent in the training data. AI solutions that can be implemented fast are typically provided by private companies. As more and more aspects of public service are infused with AI systems and other technologies provided by private companies, we see a growing network of privately owned infrastructure. As government entities outsource critical technological infrastructure (such as data storage and cloud-based systems for data sharing and analysis) to private companies under the guise of modernizing public services, we see a trend towards losing control over critical infrastructure and decreasing accountability to the public that relies on it.

IMAGE: As a fellow of the 4th Intercontinental Academia (ICA): Intelligence and Artificial Intelligence, Regenstrief Institute Research Scientist Suranga Kasthurirathne, PhD, is studying the role of operationalizing artificial intelligence (AI) within... view more INDIANAPOLIS -- Regenstrief research scientist and Indiana University School of Medicine faculty member Suranga Kasthurirathne, PhD, has been selected as a fellow of the 4th Intercontinental Academia (ICA): Intelligence and Artificial Intelligence. He and the other outstanding early and midcareer researchers chosen as fellows will work together on cross-disciplinary projects while being mentored by some of the most renowned scientists from around the world, including Nobel Prize winners. Through its fellowship program, the ICA seeks to create a global network of future research leaders. Each fellow proposes a project. Dr. Kasthurirathne's focuses on the role of operationalizing artificial intelligence (AI) within learning health systems.