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The AI Index 2021 Annual Report

arXiv.org Artificial Intelligence

Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.


Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

arXiv.org Artificial Intelligence

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [...] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge." Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and evolution.


Open Problems in Cooperative AI

arXiv.org Artificial Intelligence

Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such as driving on highways, scheduling meetings, and working collaboratively--to our global challenges--such as peace, commerce, and pandemic preparedness. Arguably, the success of the human species is rooted in our ability to cooperate. Since machines powered by artificial intelligence are playing an ever greater role in our lives, it will be important to equip them with the capabilities necessary to cooperate and to foster cooperation. We see an opportunity for the field of artificial intelligence to explicitly focus effort on this class of problems, which we term Cooperative AI. The objective of this research would be to study the many aspects of the problems of cooperation and to innovate in AI to contribute to solving these problems. Central goals include building machine agents with the capabilities needed for cooperation, building tools to foster cooperation in populations of (machine and/or human) agents, and otherwise conducting AI research for insight relevant to problems of cooperation. This research integrates ongoing work on multi-agent systems, game theory and social choice, human-machine interaction and alignment, natural-language processing, and the construction of social tools and platforms. However, Cooperative AI is not the union of these existing areas, but rather an independent bet about the productivity of specific kinds of conversations that involve these and other areas. We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.


What is AI? Everything you need to know about Artificial Intelligence

ZDNet

It depends who you ask. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if it had been done by a human, would have to apply intelligence in order to accomplish it. That's obviously a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. Modern definitions of what it means to create intelligence are slightly more specific. Francois Chollet, AI researcher at Google and creator of the machine-learning software library Keras, has said intelligence is tied to a system's ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios. "Intelligence is the efficiency with which you acquire new skills at tasks you didn't previously prepare for," he said. "Intelligence is not skill itself, it's not what you can do, it's how well and how efficiently you can learn new things." It's a definition under which modern AI-powered systems, such as virtual assistants, would be characterised as having demonstrated'narrow AI'; the ability to generalise their training when carrying out a limited set of tasks, such as speech recognition or computer vision. Typically, AI systems demonstrate at least some of the following behaviours associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. AI is ubiquitous today, used to recommend what you should buy next online, to understanding what you say to virtual assistants, such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud.


Over a Decade of Social Opinion Mining

arXiv.org Artificial Intelligence

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.


Another reminder that bias, testing, diversity is needed in machine learning: Twitter's image-crop AI may favor white men, women's chests

#artificialintelligence

Concerns about bias or unfair results in AI systems have come to the fore in recent years as the technology has infiltrated hiring, insurance, law enforcement, advertising, and other aspects of society. Prejudiced code may be a source of indignation on social media but it affects people's access to opportunities and resources in the real world. It's something that needs to be dealt with on a national and international level. A variety of factors go into making insufficiently neutral systems, such as unrepresentative training data, lack of testing on diverse subjects at scale, lack of diversity among research teams, and so on. But among those who developed Twitter's cropping algorithm, several expressed frustration about the assumptions being made about their work. Ferenc Huszár, former Twitter employee, one of the co-authors of Twitter's image pruning research, and now a senior lecturer on machine-learning at University of Cambridge, acknowledged there's reason to look into the results people have been reporting though cautioned against jumping to conclusions about negligence or lack of oversight. Some of the outrage was based on a small number of reported failure cases. While these failures look very bad, there's work to be done to determine the degree to which they are associated w/ race or gender.


Google 'exploring' why picture of Churchill went missing from search

The Independent - Tech

Google has said it is exploring why a picture of Winston Churchill went missing from a search list of former UK prime ministers, amid controversy over the legacy of the wartime leader. The company apologised on Sunday morning for the disappearance of the picture from its "knowledge graph" listing, adding that many photos of Churchill could still be found on its search engine. In a statement made on Twitter, Google's search liaison team said: "We're aware an image for Sir Winston Churchill is missing from his Knowledge Graph entry on Google. This was not purposeful and will be resolved." The problem, which was fixed at around midday on Sunday, was allegedly not specific to Churchill, with a similar problems occurring with images of former prime ministers Harold Wilson, Ramsay MacDonald and Stanley Baldwin.


Zoom faces lawsuit over Facebook data controversy

The Independent - Tech

Video conference app Zoom illegally shared personal data with Facebook, even if users did not have a Facebook account, a lawsuit claims. The app has experienced a surge in popularity as millions of people around the world are forced to work from home as part of coronavirus containment measures. The lawsuit, which was filed in a California federal court on Monday, states that the company failed to inform users that their data was being sent to Facebook "and possibly other third parties". It states: "Had Zoom informed its users that it would use inadequate security measures and permit unauthorised third-party tracking of their personal information, users... would not have been willing to use the Zoom App." The allegations come amid a flurry of questions surrounding Zoom's privacy policies, with the Electronic Frontier Foundation recently warning that the app allows administrators to track the activities of attendees.


Knowledge Graphs

arXiv.org Artificial Intelligence

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.


The 2018 Survey: AI and the Future of Humans

#artificialintelligence

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.