Humanitarian Action in the Digital Age

Description

As humanitarian practitioner, you are interested in technology but you feel it should be used more responsibly? You are worried people tend to jump on opportunities without properly considering risks? This MOOC is for you!

This course will first introduce you to the basics of computer and data science and help you understand what happens underneath the surface when using a computer, the internet or social media. You will then dive into some more complex technologies like artificial intelligence, cryptography or distributed ledgers. Equipped with this knowledge, you will then explore with world-leading experts how to use technology responsibly, covering topics such as accountability, ethics, data protection, the do no harm principle and inclusion.

Source officielle

https://www.edx.org/course/humanitarian-action-in-the-digital-age

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Séances de ce MOOC (90)

Concepts associés (44)

Cours associés (41)

Publications associées (8)

Mechanical intelligence for learning embodied sensor-object relationships

Ahalya Prabhakar

Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsupervised learning problem. Current machine learning methods process new sensory data using prior knowledge defined by either domain knowledge or datasets. When datasets are not available, data acquisition is needed, though automating exploration in support of learning is still an unsolved problem. Here we develop a method that enables agents to efficiently collect data for learning a predictive sensor model-without requiring domain knowledge, human input, or previously existing data-using ergodicity to specify the data acquisition process. This approach is based entirely on data-driven sensor characteristics rather than predefined knowledge of the sensor model and its physical characteristics. We learn higher quality models with lower energy expenditure during exploration for data acquisition compared to competing approaches, including both random sampling and information maximization. In addition to applications in autonomy, our approach provides a potential model of how animals use their motor control to develop high quality models of their sensors (sight, sound, touch) before having knowledge of their sensor capabilities or their surrounding environment. Information-based search strategies are relevant for the learning of interacting agents dynamics and usually need predefined data. The authors propose a method to collect data for learning a predictive sensor model, without requiring domain knowledge, human input, or previously existing data.

NATURE PORTFOLIO2022

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. Existing solutions that attempt to automate the data cleaning procedure treat data cleaning as a separate offline process that takes place before analysis begins, while also focusing on a specific use-case. In addition, when the analysis involves complex non-relational data such as graphs, data cleaning becomes more challenging as it involves expensive operations. Therefore, offline, specialized cleaning tools exhibit long running times or fail to process large datasets. At the same time, applying data cleaning before analysis starts assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning data that is unnecessary for the analysis. Therefore, from a user's perspective, one is forced to use a different, potentially inefficient tool for each category of errors.In this thesis we aim for coverage and efficiency of data cleaning. We design and build data cleaning systems that employ high-level abstractions to (a) represent and optimize different cleaning operations for data of various formats, and (b) allow for real-time data cleaning that is relevant to data analysis.We introduce CleanM, a language that can express multiple types of cleaning operations. CleanM goes through a three-level translation process for optimization purposes; a different family of optimizations is applied in each abstraction level. Thus, CleanM can express complex data cleaning tasks, optimize them in a unified way, and deploy them in a scaleout fashion.To further reduce the data-to-insight time, we propose an approach that performs probabilistic repair of denial constraint violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning into the analysis by relaxing query results. Daisy executes analytical query-workloads over dirty data by weaving cleaning operators into the query plan.To cover complex data such as graphs, we optimize the building block of data cleaning operations on graph data, that is subgraph matching. Subgraph matching constitutes the main bottleneck when cleaning graph data as it is an NP-complete problem. To optimize subgraph matching, we present a scale-up, radix-based algorithm that starts from an arbitrary partitioning of the graph and coordinates parallel pattern matching to eliminate redundant work among the workers. To address load imbalance, we employ a work stealing technique, specific to the subgraph matching problem. Worker threads steal work from straggler threads by using a heuristic that maximizes the work stolen, while at the same time preserves the order of evaluating candidate vertices.Overall, instead of using offline, specialised tools, this thesis designs abstractions that optimize different cleaning primitives over heterogeneous data, while also integrating data cleaning tasks seamlessly into data analysis. Specifically, we provide (a) a declarative high-level interface backed by an optimizable query calculus and (b) the required optimizations at the underlying data cleaning layers while also taking into consideration the exploratory analysis that users perform.

EPFL2021

Probabilistic Deep Learning on Spheres for Weather/Climate Applications

Wentao Feng

The climate and weather are modeled by running computer simulations. In a data-driven approach, scientists tailor the simulation to resemble reality (partly through an understanding of the physical processes, partly through their parameterization). With the availability of large data and compute, we can hope for machines to help in the automatic discovery of principles from data. The integration of machine learning (deep learning) within simulations opens up the exploration of new tradeoffs between accuracy and computational cost.

2020

EPFL2021