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The Future of AI Part 1

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It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".


The AI workplace and ArcGIS Deep Learning Workflow

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Welcome to part 4 of my AI and GeoAI Series that will cover the more technical aspects of GeoAI and ArcGIS. Previously, part 1 of this series covered the Future Impacts of AI on Mapping and Modernization which introduced the concept of GeoAI and why you should care about having an AI as a future coworker. Part 2 of the series, GIS, Artificial Intelligence, and Automation in the Workplace covered specific geospatial professions that will be drastically effected by introduction of GeoAI technology in the workplace. Part 3 addressed Teaming with the Machine - AI in the workplace the emergence of the new geospatial working relationship between information, humans, and artificial intelligence to be successful in an organizations mission. For part 4, we will address 3 specific GeoAI areas in ArcGIS that will help you with your journey to developing your Deep Learning workflows.


Future of AI Part 2

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This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Some of the classical approaches to AI include (non-exhaustive list) Search algorithms such as Breath-First, Depth-First, Iterative Deepening Search, A* algorithm, and the field of Logic including Predicate Calculus and Propositional Calculus. Local Search approaches were also developed for example Simulated Annealing, Hill Climbing (see also Greedy), Beam Search and Genetic Algorithms (see below). Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. A non-exhaustive list of examples of techniques include Linear Regression, Logistic Regression, K-Means, k-Nearest Neighbour (kNN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forests, XG Boost, Light Gradient Boosting Machine (LightGBM), CatBoost. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion.


Intelligence Primer

arXiv.org Artificial Intelligence

This primer explores the exciting subject of intelligence. Intelligence is a fundamental component of all living things, as well as Artificial Intelligence(AI). Artificial Intelligence has the potential to affect all of our lives and a new era for modern humans. This paper is an attempt to explore the ideas associated with intelligence, and by doing so understand the implications, constraints, and potentially the capabilities of future Artificial Intelligence. As an exploration, we journey into different parts of intelligence that appear essential. We hope that people find this useful in determining where Artificial Intelligence may be headed. Also, during the exploration, we hope to create new thought-provoking questions. Intelligence is not a single weighable quantity but a subject that spans Biology, Physics, Philosophy, Cognitive Science, Neuroscience, Psychology, and Computer Science. Historian Yuval Noah Harari pointed out that engineers and scientists in the future will have to broaden their understandings to include disciplines such as Psychology, Philosophy, and Ethics. Fiction writers have long portrayed engineers and scientists as deficient in these areas. Today, modern society, the emergence of Artificial Intelligence, and legal requirements all act as forcing functions to push these broader subjects into the foreground. We start with an introduction to intelligence and move quickly onto more profound thoughts and ideas. We call this a Life, the Universe and Everything primer, after the famous science fiction book by Douglas Adams. Forty-two may very well be the right answer, but what are the questions?


GPT-3 Creative Fiction

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What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.


Future of AI Part 5: The Cutting Edge of AI

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Edmond de Belamy is a Generative Adversarial Network portrait painting constructed in 2018 by Paris-based arts-collective Obvious and sold for $432,500 in Southebys in October 2018.


Explainable Artificial Intelligence: a Systematic Review

arXiv.org Artificial Intelligence

This has led to the development of a plethora of domain-dependent and context-specific methods for dealing with the interpretation of machine learning (ML) models and the formation of explanations for humans. Unfortunately, this trend is far from being over, with an abundance of knowledge in the field which is scattered and needs organisation. The goal of this article is to systematically review research works in the field of XAI and to try to define some boundaries in the field. From several hundreds of research articles focused on the concept of explainability, about 350 have been considered for review by using the following search methodology. In a first phase, Google Scholar was queried to find papers related to "explainable artificial intelligence", "explainable machine learning" and "interpretable machine learning". Subsequently, the bibliographic section of these articles was thoroughly examined to retrieve further relevant scientific studies. The first noticeable thing, as shown in figure 2 (a), is the distribution of the publication dates of selected research articles: sporadic in the 70s and 80s, receiving preliminary attention in the 90s, showing raising interest in 2000 and becoming a recognised body of knowledge after 2010. The first research concerned the development of an explanation-based system and its integration in a computer program designed to help doctors make diagnoses [3]. Some of the more recent papers focus on work devoted to the clustering of methods for explainability, motivating the need for organising the XAI literature [4, 5, 6].


AI Research Considerations for Human Existential Safety (ARCHES)

arXiv.org Artificial Intelligence

Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species. In negative terms, we ask what existential risks humanity might face from AI development in the next century, and by what principles contemporary technical research might be directed to address those risks. A key property of hypothetical AI technologies is introduced, called \emph{prepotence}, which is useful for delineating a variety of potential existential risks from artificial intelligence, even as AI paradigms might shift. A set of \auxref{dirtot} contemporary research \directions are then examined for their potential benefit to existential safety. Each research direction is explained with a scenario-driven motivation, and examples of existing work from which to build. The research directions present their own risks and benefits to society that could occur at various scales of impact, and in particular are not guaranteed to benefit existential safety if major developments in them are deployed without adequate forethought and oversight. As such, each direction is accompanied by a consideration of potentially negative side effects.


Top AI Resources Directory

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The Women in AI Podcast - Women at the forefront of AI discuss their work and diversity issues faced in STEM - Listen here. The DeepMind Podcast - A new series that we hope will answer the difficult questions in AI - Listen here. Lex Fridman's AI Podcast - a series of conversations about technology, science, and the human condition hosted by MIT's Lex Fridman - Listen here. The Eye on AI - Justin Gottschlich explains his group's efforts to automate software development - Listen along here. The NVIDIA AI Podcast - NVIDIA release new episodes every other week with guest speakers at the forefront of AI - Listen along here. Artificially Intelligent - Weekly discussions on the impacts of AI - Listen here. Underrated ML - Regular RE•WORK speaker, Sara Hooker & her brother, Sean Hooker have started their new podcast based on underrated ML papers - Listen here. Concerning AI - A series on AI hosted by Ted Sarvata & Brandon Sanders - Listen here.


On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

arXiv.org Artificial Intelligence

Developments in Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) currently lead lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans to perform. DLT acts in uncertain environments to create consensus over data among a group of participants. In recent articles, both technologies complement each other. Examples include the design of secure distributed ledgers or the creation of allied learning systems distributed across multiple nodes. This can lead to technological convergence, which in the past, has paved the way for major IT product innovations. Previous work highlights several potential benefits of the convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. We aim to contribute by conducting a systematic literature review on the previous work and by providing rigorously derived future research opportunities. Our analysis identifies how AI and DLT exchange data, and how to use these integration principles to build new systems. Based on that, we present open questions for future research. This work helps researchers active in AI or DLT to overcome current limitations in their field, and engineers to develop systems along with the convergence of these technologies.