Trends and Developments in Artificial Intelligence Challenges to the Intellectual Property Rights Framework
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Distant Music An autoethnographic study of global songwriting in the digital age. SHEILA SIMMENES SUPERVISOR Michael Rauhut University of Agder, 2020 Faculty of Fine Arts Department of Popular music 2 Abstract This thesis aims to explore the path to music creation and shed a light on how we may maneuver songwriting and music production in the digital age. As a songwriter in 2020, my work varies from being in the same physical room using digital communication with collaborators from different cultures and sometimes even without ever meeting face to face with the person I'm making music with. In this thesis I explore some of the opportunities and challenges of this process and shine a light on tendencies and trends in the field. As an autoethnographic research project, this thesis offers an insight into the songwriting process and continuous development of techniques by myself as a songwriter in the digital age, working both commercially and artistically with international partners across the world. The thesis will aim to supplement knowledge on current working methods in songwriting and will hopefully shine a light on how one may improve as a songwriter and topliner in the international market. We will explore the tools and possibilities available to us, while also reflecting upon what challenges and complications one may encounter as a songwriter in the digital age. 3 4 Acknowledgements I wish to express my gratitude to my supervisor Michael Rauhut for his insight, expertise and continued support during this work; associate professor Per Elias Drabløs for his kind advice and feedback; and to my artistic supervisor Bjørn Ole Rasch for his enthusiasm and knowledge in the field of world music and international collaborations. -
Lecture Note on Deep Learning and Quantum Many-Body Computation
Lecture Note on Deep Learning and Quantum Many-Body Computation Jin-Guo Liu, Shuo-Hui Li, and Lei Wang∗ Institute of Physics, Chinese Academy of Sciences Beijing 100190, China November 23, 2018 Abstract This note introduces deep learning from a computa- tional quantum physicist’s perspective. The focus is on deep learning’s impacts to quantum many-body compu- tation, and vice versa. The latest version of the note is at http://wangleiphy.github.io/. Please send comments, suggestions and corrections to the email address in below. ∗ [email protected] CONTENTS 1 introduction2 2 discriminative learning4 2.1 Data Representation 4 2.2 Model: Artificial Neural Networks 6 2.3 Cost Function 9 2.4 Optimization 11 2.4.1 Back Propagation 11 2.4.2 Gradient Descend 13 2.5 Understanding, Visualization and Applications Beyond Classification 15 3 generative modeling 17 3.1 Unsupervised Probabilistic Modeling 17 3.2 Generative Model Zoo 18 3.2.1 Boltzmann Machines 19 3.2.2 Autoregressive Models 22 3.2.3 Normalizing Flow 23 3.2.4 Variational Autoencoders 25 3.2.5 Tensor Networks 28 3.2.6 Generative Adversarial Networks 29 3.3 Summary 32 4 applications to quantum many-body physics and more 33 4.1 Material and Chemistry Discoveries 33 4.2 Density Functional Theory 34 4.3 “Phase” Recognition 34 4.4 Variational Ansatz 34 4.5 Renormalization Group 35 4.6 Monte Carlo Update Proposals 36 4.7 Tensor Networks 37 4.8 Quantum Machine Leanring 38 4.9 Miscellaneous 38 5 hands on session 39 5.1 Computation Graph and Back Propagation 39 5.2 Deep Learning Libraries 41 5.3 Generative Modeling using Normalizing Flows 42 5.4 Restricted Boltzmann Machine for Image Restoration 43 5.5 Neural Network as a Quantum Wave Function Ansatz 43 6 challenges ahead 45 7 resources 46 BIBLIOGRAPHY 47 1 1 INTRODUCTION Deep Learning (DL) ⊂ Machine Learning (ML) ⊂ Artificial Intelli- gence (AI). -
AI Computer Wraps up 4-1 Victory Against Human Champion Nature Reports from Alphago's Victory in Seoul
The Go Files: AI computer wraps up 4-1 victory against human champion Nature reports from AlphaGo's victory in Seoul. Tanguy Chouard 15 March 2016 SEOUL, SOUTH KOREA Google DeepMind Lee Sedol, who has lost 4-1 to AlphaGo. Tanguy Chouard, an editor with Nature, saw Google-DeepMind’s AI system AlphaGo defeat a human professional for the first time last year at the ancient board game Go. This week, he is watching top professional Lee Sedol take on AlphaGo, in Seoul, for a $1 million prize. It’s all over at the Four Seasons Hotel in Seoul, where this morning AlphaGo wrapped up a 4-1 victory over Lee Sedol — incidentally, earning itself and its creators an honorary '9-dan professional' degree from the Korean Baduk Association. After winning the first three games, Google-DeepMind's computer looked impregnable. But the last two games may have revealed some weaknesses in its makeup. Game four totally changed the Go world’s view on AlphaGo’s dominance because it made it clear that the computer can 'bug' — or at least play very poor moves when on the losing side. It was obvious that Lee felt under much less pressure than in game three. And he adopted a different style, one based on taking large amounts of territory early on rather than immediately going for ‘street fighting’ such as making threats to capture stones. This style – called ‘amashi’ – seems to have paid off, because on move 78, Lee produced a play that somehow slipped under AlphaGo’s radar. David Silver, a scientist at DeepMind who's been leading the development of AlphaGo, said the program estimated its probability as 1 in 10,000. -
Accelerators and Obstacles to Emerging ML-Enabled Research Fields
Life at the edge: accelerators and obstacles to emerging ML-enabled research fields Soukayna Mouatadid Steve Easterbrook Department of Computer Science Department of Computer Science University of Toronto University of Toronto [email protected] [email protected] Abstract Machine learning is transforming several scientific disciplines, and has resulted in the emergence of new interdisciplinary data-driven research fields. Surveys of such emerging fields often highlight how machine learning can accelerate scientific discovery. However, a less discussed question is how connections between the parent fields develop and how the emerging interdisciplinary research evolves. This study attempts to answer this question by exploring the interactions between ma- chine learning research and traditional scientific disciplines. We examine different examples of such emerging fields, to identify the obstacles and accelerators that influence interdisciplinary collaborations between the parent fields. 1 Introduction In recent decades, several scientific research fields have experienced a deluge of data, which is impacting the way science is conducted. Fields such as neuroscience, psychology or social sciences are being reshaped by the advances in machine learning (ML) and the data processing technologies it provides. In some cases, new research fields have emerged at the intersection of machine learning and more traditional research disciplines. This is the case for example, for bioinformatics, born from collaborations between biologists and computer scientists in the mid-80s [1], which is now considered a well-established field with an active community, specialized conferences, university departments and research groups. How do these transformations take place? Do they follow similar paths? If yes, how can the advances in such interdisciplinary fields be accelerated? Researchers in the philosophy of science have long been interested in these questions. -
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean Google Research [email protected] Abstract The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Introduction The past decade has seen a remarkable series of advances in machine learning (ML), and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas [LeCun et al. 2015]. Major areas of significant advances include computer vision [Krizhevsky et al. 2012, Szegedy et al. 2015, He et al. 2016, Real et al. 2017, Tan and Le 2019], speech recognition [Hinton et al. -
VIRAL ART Exhibition Guide
VIRAL ART Exhibition Guide VIRAL ART is an exhibition focusing on this last year and the universal pandemic (COVID-19) which has affected many different aspects of life, one of which is evident in the artistic world. COVID-19 has forced many artists to open up to new avenues of expression in many different disciplines; visual art, music, performing arts, photography, fashion, and literature. Some of the main goals for our exhibition are to bring light to how the pandemic has affected all corners of life, especially in regard to artistic capability/expression, and further, illustrate how certain artists captured the essence and experience of COVID-19. Introduction by Taylor Weaver I wanted to start by saying how grateful I am to have been able to lead this exhibition project with the help of Mirea Suarez. I am so proud of each and every one of the girls and hope you can see how hard they have all worked to make this possible. So welcome to our exhibition which we have titled Viral Art! I would like to note that some of the pieces, such as music are slightly more interactive, for example one of the music pieces is a live DJ set and is an hour and a half in total in the event you are interested in watching more – we will only briefly show certain pieces We would like to preface this exhibition by giving a small dedication to those whose lives have been affected by COVID-19. We thought it would be most appropriate to say a few words to front line workers such as those in the healthcare industry who have dedicated their lives to fighting this virus, essential workers such as those in the food (and or) agriculture industry who prepared meals for those in need during these stressful times, Research Methods Fall 2020 Taylor Weaver, Mirea Suarez, Cristina Ponce de Leon, Sophie Schaesberg, Sydney Levno, Annamaria Borvice, Marta Belogolova VIRAL ART Exhibition Guide volunteers, and of course, to all who lost their lives to COVID-19 or people who have lost family members. -
Television Advertising Insights
Lockdown Highlight Tous en cuisine, M6 (France) Foreword We are delighted to present you this 27th edition of trends and to the forecasts for the years to come. TV Key Facts. All this information and more can be found on our This edition collates insights and statistics from dedi cated TV Key Facts platform www.tvkeyfacts.com. experts throughout the global Total Video industry. Use the link below to start your journey into the In this unprecedented year, we have experienced media advertising landscape. more than ever how creative, unitive, and resilient Enjoy! / TV can be. We are particularly thankful to all participants and major industry players who agreed to share their vision of media and advertising’s future especially Editors-in-chief & Communications. during these chaotic times. Carine Jean-Jean Alongside this magazine, you get exclusive access to Coraline Sainte-Beuve our database that covers 26 countries worldwide. This country-by-country analysis comprises insights for both television and digital, which details both domestic and international channels on numerous platforms. Over the course of the magazine, we hope to inform you about the pandemic’s impact on the market, where the market is heading, media’s social and environmental responsibility and all the latest innovations. Allow us to be your guide to this year’s ACCESS OUR EXCLUSIVE DATABASE ON WWW.TVKEYFACTS.COM WITH YOUR PERSONAL ACTIVATION CODE 26 countries covered. Television & Digital insights: consumption, content, adspend. Australia, Austria, Belgium, Brazil, Canada, China, Croatia, Denmark, France, Finland, Germany, Hungary, India, Italy, Ireland, Japan, Luxembourg, The Netherlands, Norway, Poland, Russia, Spain, Sweden, Switzerland, UK and the US. -
Equivalence Between Minimal Generative Model Graphs and Directed Information Graphs
Equivalence Between Minimal Generative Model Graphs and Directed Information Graphs Christopher J. Quinn Negar Kiyavash Todd P. Coleman Department of Electrical and Department of Industrial and Department of Electrical and Computer Engineering Enterprise Systems Engineering Computer Engineering University of Illinois University of Illinois University of Illinois Urbana, Illinois 61801 Urbana, Illinois 61801 Urbana, Illinois 61801 Email: [email protected] Email: [email protected] Email: [email protected] Abstract—We propose a new type of probabilistic graphical own past [4], [5]. This improvement is measured in terms of model, based on directed information, to represent the causal average reduction in the encoding length of the information. dynamics between processes in a stochastic system. We show the Clearly such a definition of causality has the notion of timing practical significance of such graphs by proving their equivalence to generative model graphs which succinctly summarize interde- ingrained in it. Therefore, we expect a graphical model defined pendencies for causal dynamical systems under mild assumptions. based on directed information to deal with processes as its This equivalence means that directed information graphs may be nodes. used for causal inference and learning tasks in the same manner In this work, we formally define directed information Bayesian networks are used for correlative statistical inference graphs, an alternative type of graphical model. In these graphs, and learning. nodes represent processes, and directed edges correspond to whether there is directed information from one process to I. INTRODUCTION another. To demonstrate the practical significance of directed Graphical models facilitate design and analysis of interact- information graphs, we validate their ability to capture causal ing multivariate probabilistic complex systems that arise in interdependencies for a class of interacting processes corre- numerous fields such as statistics, information theory, machine sponding to a causal dynamical system. -
A Hierarchical Generative Model of Electrocardiogram Records
A Hierarchical Generative Model of Electrocardiogram Records Andrew C. Miller ∗ Sendhil Mullainathan School of Engineering and Applied Sciences Department of Economics Harvard University Harvard University Cambridge, MA 02138 Cambridge, MA 02138 [email protected] [email protected] Ziad Obermeyer Brigham and Women’s Hospital Harvard Medical School Boston, MA 02115 [email protected] Abstract We develop a probabilistic generative model of electrocardiogram (EKG) tracings. Our model describes multiple sources of variation in EKGs, including patient- specific cardiac cycle morphology and between-cycle variation that leads to quasi- periodicity. We use a deep generative network as a flexible model component to describe variation in beat-specific morphology. We apply our model to a set of 549 EKG records, including over 4,600 unique beats, and show that it is able to discover interpretable information, such as patient similarity and meaningful physiological features (e.g., T wave inversion). 1 Introduction An electrocardiogram (EKG) is a common non-invasive medical test that measures the electrical activity of a patient’s heart by recording the time-varying potential difference between electrodes placed on the surface of the skin. The resulting data is a multivariate time-series that reflects the depolarization and repolarization of the heart muscle that occurs during each heartbeat. These raw waveforms are then inspected by a physician to detect irregular patterns that are evidence of an underlying physiological problem. In this paper, we build a hierarchical generative model of electrocardiogram signals that disentangles sources of variation. We are motivated to build a generative model of EKGs for multiple reasons. -
Bias Correction of Learned Generative Models Using Likelihood-Free Importance Weighting
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting Aditya Grover1, Jiaming Song1, Alekh Agarwal2, Kenneth Tran2, Ashish Kapoor2, Eric Horvitz2, Stefano Ermon1 1Stanford University, 2Microsoft Research, Redmond Abstract A learned generative model often produces biased statistics relative to the under- lying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions. When the likelihood ratio is unknown, it can be estimated by training a probabilistic classifier to distinguish samples from the two distributions. We show that this likelihood-free importance weighting method induces a new energy-based model and employ it to correct for the bias in existing models. We find that this technique consistently improves standard goodness-of-fit metrics for evaluating the sample quality of state-of-the-art deep generative mod- els, suggesting reduced bias. Finally, we demonstrate its utility on representative applications in a) data augmentation for classification using generative adversarial networks, and b) model-based policy evaluation using off-policy data. 1 Introduction Learning generative models of complex environments from high-dimensional observations is a long- standing challenge in machine learning. Once learned, these models are used to draw inferences and to plan future actions. For example, in data augmentation, samples from a learned model are used to enrich a dataset for supervised learning [1]. In model-based off-policy policy evaluation (henceforth MBOPE), a learned dynamics model is used to simulate and evaluate a target policy without real-world deployment [2], which is especially valuable for risk-sensitive applications [3]. -
Probabilistic PCA and Factor Analysis
Generative Models for Dimensionality Reduction: Probabilistic PCA and Factor Analysis Piyush Rai Machine Learning (CS771A) Oct 5, 2016 Machine Learning (CS771A) Generative Models for Dimensionality Reduction: Probabilistic PCA and Factor Analysis 1 K Generate latent variables z n 2 R (K D) as z n ∼ N (0; IK ) Generate data x n conditioned on z n as 2 x n ∼ N (Wz n; σ ID ) where W is the D × K \factor loading matrix" or \dictionary" z n is K-dim latent features or latent factors or \coding" of x n w.r.t. W Note: Can also write x n as a linear transformation of z n, plus Gaussian noise 2 x n = Wz n + n (where n ∼ N (0; σ ID )) This is \Probabilistic PCA" (PPCA) with Gaussian observation model 2 N Want to learn model parameters W; σ and latent factors fz ngn=1 When n ∼ N (0; Ψ), Ψ is diagonal, it is called \Factor Analysis" (FA) Generative Model for Dimensionality Reduction D Assume the following generative story for each x n 2 R Machine Learning (CS771A) Generative Models for Dimensionality Reduction: Probabilistic PCA and Factor Analysis 2 Generate data x n conditioned on z n as 2 x n ∼ N (Wz n; σ ID ) where W is the D × K \factor loading matrix" or \dictionary" z n is K-dim latent features or latent factors or \coding" of x n w.r.t. W Note: Can also write x n as a linear transformation of z n, plus Gaussian noise 2 x n = Wz n + n (where n ∼ N (0; σ ID )) This is \Probabilistic PCA" (PPCA) with Gaussian observation model 2 N Want to learn model parameters W; σ and latent factors fz ngn=1 When n ∼ N (0; Ψ), Ψ is diagonal, it is called \Factor Analysis" (FA) Generative Model for Dimensionality Reduction D Assume the following generative story for each x n 2 R K Generate latent variables z n 2 R (K D) as z n ∼ N (0; IK ) Machine Learning (CS771A) Generative Models for Dimensionality Reduction: Probabilistic PCA and Factor Analysis 2 where W is the D × K \factor loading matrix" or \dictionary" z n is K-dim latent features or latent factors or \coding" of x n w.r.t. -
Chapter 1 Eligibility for Patent and Industrial Applicability (Main Paragraph of Article 29(1) of the Patent Act)
Note: When any ambiguity of interpretation is found in this provisional translation, the Japanese text shall prevail. Part III Chapter 1 Eligibility for Patent and Industrial Applicability Chapter 1 Eligibility for Patent and Industrial Applicability (Main Paragraph of Article 29(1) of the Patent Act) 1. Overview The main paragraph of Article 29(1) of the Patent Act defines that any person who has made an invention which is industrially applicable may obtain a patent therefor. Article 2(1) of the Patent Act defines "invention" as "the highly advanced creation of technical ideas utilizing the laws of nature". An invention which does not comply with this definition shall not be patented. An invention for which a patent is sought shall be industrially applicable even if the patent complies with this definition, since the purpose of the Patent Act is the development of industry (Article 1). The main paragraph of Article 29(1) of the Patent Act provides the two following points as the patentability requirements: (i) A statutory "invention" (hereinafter, referred to as "eligibility for a patent" in this chapter) (see 2.) (ii) An "industrially applicable invention" (hereinafter, referred to as "industrial applicability" in this chapter) (see 3.) This chapter explains determination on eligibility for a patent and industrial applicability. In this chapter, an invention complying with the requirements of eligibility for a patent is referred to as a statutory "invention". The word "invention" in the expression "claimed invention" does not mean that the invention complies with the requirements of eligibility for a patent. 2. Determination on Requirements of Eligibility for Patent The subject of determination on the requirements of eligibility for a patent is a claimed invention.