Why an open mind on open science could reshape human knowledge

In the year 1610, Galileo observed a ring-like shape around a distant planet (Saturn). After realising the significance of his discovery, Galileo wanted to record it to be able to claim it as his own contribution once it was announced. To do that, he wrote a letter to a colleague stating the following: "smaismrmilmepoetaleumibunenugttauiras"

Thoughts on the future of human knowledge and machine intelligence

Throughout history, nations and armies have brawled for knowledge. The burning of the Library of Alexandria, the destruction of Xianyang Palace’s archives, the secret investigations of the Dead Sea Scrolls, the destruction of the Mayan Codex, and many other examples illustrate the continuous human quest for owning knowledge or eliminating it from an enemy

The unspoken global race for artificial intelligence

Two men walk into a bar, the first one says: “robots will conquer our civilisation and make us their servants within ten years”, the second one responds: “No, the principle of artificial intelligence (AI) is a far-fetched goal that will never see light”. The bartender smiles, analyses their facial expressions, assigns a sentiment score to their sentences, evaluates their historical drinking trends, and decides to pour the first one a glass of gin and tonic, and the second one a glass of Scotch. Here is the spoiler: both men are lying; and the bartender is a robot. Not a funny joke, but a reality that is shadowing all conventional discussions about the future prospects of AI. In order to avoid such binary discussions about the goodness and possibilities of machine intelligence, and to eliminate the ‘hype’ surrounding the topic, this article aims to unveil the slowly cooking, quietly simmering, unspoken truths of the inevitable global arms race of AI

Cybersecurity Law: Legal Jurisdiction and Authority

Cybersecurity threats affect all aspects of society; critical infrastructures (such as networks, corporate systems, water supply systems, and intelligent transportation systems) are especially prone to attacks and can have tangible negative consequences on society. However, these critical cyber systems are generally governed by multiple jurisdictions, for instance the Metro in the Washington, D.C. area is managed by the states of Virginia and Maryland, as well as the District of Columbia (DC) through Washington Metropolitan Area Transit Authority (WMATA). Additionally, the water treatment infrastructure managed by DC Water consists of waste water input from Fairfax and Arlington counties, and the district (i.e. DC). Additionally, cyber attacks usually launch from unknown sources, through unknown switches and servers, and end up at the destination without much knowledge on their source or path. Certain infrastructures are shared amongst multiple countries, another idiosyncrasy that exacerbates the issue of governance. This law paper however, is not concerned with the general governance of these infrastructures, rather with the ambiguity in the relevant laws or doctrines about which authority would prevail in the context of a cyber threat or a cyber-attack, with a focus on federal vs. state issues, international law involvement, federal preemption, technical aspects that could affect lawmaking, and conflicting responsibilities in cases of cyber crime. A legal analysis of previous cases is presented, as well as an extended discussion addressing different sides of the argument.Comment: This report is developed for partial fulfillment of the requirements for the degree of Juris Masters of Law at GMU's Antonin Scalia Law Schoo

Pandemics and big data tyrannies

Tyranny typically occurs when citizens share their data (willingly) to achieve safety but end up losing control of it, writes Feras A. Batarseh

ExClaim: Explainable Neural Claim Verification Using Rationalization

With the advent of deep learning, text generation language models have improved dramatically, with text at a similar level as human-written text. This can lead to rampant misinformation because content can now be created cheaply and distributed quickly. Automated claim verification methods exist to validate claims, but they lack foundational data and often use mainstream news as evidence sources that are strongly biased towards a specific agenda. Current claim verification methods use deep neural network models and complex algorithms for a high classification accuracy but it is at the expense of model explainability. The models are black-boxes and their decision-making process and the steps it took to arrive at a final prediction are obfuscated from the user. We introduce a novel claim verification approach, namely: ExClaim, that attempts to provide an explainable claim verification system with foundational evidence. Inspired by the legal system, ExClaim leverages rationalization to provide a verdict for the claim and justifies the verdict through a natural language explanation (rationale) to describe the model's decision-making process. ExClaim treats the verdict classification task as a question-answer problem and achieves a performance of 0.93 F1 score. It provides subtasks explanations to also justify the intermediate outcomes. Statistical and Explainable AI (XAI) evaluations are conducted to ensure valid and trustworthy outcomes. Ensuring claim verification systems are assured, rational, and explainable is an essential step toward improving Human-AI trust and the accessibility of black-box systems.Comment: Published at 2022 IEEE 29th ST

ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems

This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water supply management using AI and Cybersecurity experimentation. The main goal of ACWA is to address pressing challenges in the water and agricultural domains by utilising cutting-edge AI and data-driven technologies. These challenges include Cyberbiosecurity, resources management, access to water, sustainability, and data-driven decision-making, among others. To address such issues, ACWA consists of multiple topologies, sensors, computational nodes, pumps, tanks, smart water devices, as well as databases and AI models that control the system. Moreover, we present ACWA simulator, which is a software-based water digital twin. The simulator runs on fluid and constituent transport principles that produce theoretical time series of a water distribution system. This creates a good validation point for comparing the theoretical approach with real-life results via the physical ACWA testbed. ACWA data are available to AI and water domain researchers and are hosted in an online public repository. In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios

Rationalization for Explainable NLP: A Survey

Recent advances in deep learning have improved the performance of many Natural Language Processing (NLP) tasks such as translation, question-answering, and text classification. However, this improvement comes at the expense of model explainability. Black-box models make it difficult to understand the internals of a system and the process it takes to arrive at an output. Numerical (LIME, Shapley) and visualization (saliency heatmap) explainability techniques are helpful; however, they are insufficient because they require specialized knowledge. These factors led rationalization to emerge as a more accessible explainable technique in NLP. Rationalization justifies a model's output by providing a natural language explanation (rationale). Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users. Since rationalization is a relatively new field, it is disorganized. As the first survey, rationalization literature in NLP from 2007-2022 is analyzed. This survey presents available methods, explainable evaluations, code, and datasets used across various NLP tasks that use rationalization. Further, a new subfield in Explainable AI (XAI), namely, Rational AI (RAI), is introduced to advance the current state of rationalization. A discussion on observed insights, challenges, and future directions is provided to point to promising research opportunities

Improving irrigation efficiency will be insufficient to meet future water demand in the Nile Basin

The Nile River Basin covers an area of approximately 3.2 million km2 and is shared by 11 countries. Rapid population growth is expected in the region. The irrigation requirements of Nile riparian countries of existing (6.4 million ha) and additional planned (3.8 million ha, 2050) irrigation schemes were calculated, and the likely water savings through improved irrigation efficiency were evaluated. We applied SPARE:WATER to calculate irrigation demands on the basis of the well-known FAO56 Crop Irrigation Guidelines. Egypt (67 km3 yr-1) and Sudan (19 km3 yr-1) consume the highest share of the 84 km3 yr-1 total (2011). Assuming today’s poor irrigation infrastructure, the total consumption was predicted to increase to 123 km3 yr-1 (2050), an amount far exceeding the total annual yield of the Nile Basin. Therefore, a key challenge for water resources management in the Nile Basin is balancing the increasing irrigation water demand basin-wide with the available water supply. We found that water savings from improved irrigation technology will not be able to meet the additional needs of planned areas. Under a theoretical scenario of maximum possible efficiency, the deficit would still be 5 km3 yr-1. For more likely efficiency improvement scenarios, the deficit ranged between 23 and 29 km3 yr-1. Our results suggest that that improving irrigation efficiency may substantially contribute to decreasing water stress on the Nile system but would not completely meet the demand. Study Region: The Nile River Basin covers an area of approximately 3.2 million km2 and is shared by 11 countries. Rapid population growth is expected in the region. Study Focus: Record population growth is expected for the study region. Therefore, the irrigation requirements of Nile riparian countries of existing (6.4 million ha) and additional planned (3.8 million ha, 2050) irrigation schemes were calculated, and likely water savings through improved irrigation efficiency were evaluated. We applied a spatial decision support system (SPARE:WATER) to calculate the irrigation demands on the basis of the well-known FAO56 Crop Irrigation Guidelines. New Hydrological Insights for the Region: Egypt (67 km3yr-1) and Sudan (19 km3yr-1) consume the highest share of 84 km3yr-1 (2011). Assuming today’s poor irrigation infrastructure, the total demand were predicted to increase to 123 km3yr-1 (2050), an amount far exceeding the total annual yield of the Nile Basin. Therefore, a key challenge for water resources management in the Nile Basin is balancing the increasing irrigation water demand and available water supply. We found that water savings from improved irrigation technology will not be able to meet the additional needs of planned areas. Under a theoretical scenario of maximum possible efficiency, the deficit would still be 5 km3yr-1. For more likely efficiency improvement scenarios, the deficit ranges between 23 and 29 km3yr-1. Our results suggest that improving irrigation efficiency may substantially contribute to decreasing water stress on the Nile system but would not completely meet the demand