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Intro to Tensorflow 2.0 MBL, August 2019
Intro to TensorFlow 2.0 MBL, August 2019 Josh Gordon (@random_forests) 1 Agenda 1 of 2 Exercises ● Fashion MNIST with dense layers ● CIFAR-10 with convolutional layers Concepts (as many as we can intro in this short time) ● Gradient descent, dense layers, loss, softmax, convolution Games ● QuickDraw Agenda 2 of 2 Walkthroughs and new tutorials ● Deep Dream and Style Transfer ● Time series forecasting Games ● Sketch RNN Learning more ● Book recommendations Deep Learning is representation learning Image link Image link Latest tutorials and guides tensorflow.org/beta News and updates medium.com/tensorflow twitter.com/tensorflow Demo PoseNet and BodyPix bit.ly/pose-net bit.ly/body-pix TensorFlow for JavaScript, Swift, Android, and iOS tensorflow.org/js tensorflow.org/swift tensorflow.org/lite Minimal MNIST in TF 2.0 A linear model, neural network, and deep neural network - then a short exercise. bit.ly/mnist-seq ... ... ... Softmax model = Sequential() model.add(Dense(256, activation='relu',input_shape=(784,))) model.add(Dense(128, activation='relu')) model.add(Dense(10, activation='softmax')) Linear model Neural network Deep neural network ... ... Softmax activation After training, select all the weights connected to this output. model.layers[0].get_weights() # Your code here # Select the weights for a single output # ... img = weights.reshape(28,28) plt.imshow(img, cmap = plt.get_cmap('seismic')) ... ... Softmax activation After training, select all the weights connected to this output. Exercise 1 (option #1) Exercise: bit.ly/mnist-seq Reference: tensorflow.org/beta/tutorials/keras/basic_classification TODO: Add a validation set. Add code to plot loss vs epochs (next slide). Exercise 1 (option #2) bit.ly/ijcav_adv Answers: next slide. -
Procedural Content Generation for Games
Procedural Content Generation for Games Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universit¨atMannheim vorgelegt von M.Sc. Jonas Freiknecht aus Hannover Mannheim, 2020 Dekan: Dr. Bernd L¨ubcke, Universit¨atMannheim Referent: Prof. Dr. Wolfgang Effelsberg, Universit¨atMannheim Korreferent: Prof. Dr. Colin Atkinson, Universit¨atMannheim Tag der m¨undlichen Pr¨ufung: 12. Februar 2021 Danksagungen Nach einer solchen Arbeit ist es nicht leicht, alle Menschen aufzuz¨ahlen,die mich direkt oder indirekt unterst¨utzthaben. Ich versuche es dennoch. Allen voran m¨ochte ich meinem Doktorvater Prof. Wolfgang Effelsberg danken, der mir - ohne mich vorher als Master-Studenten gekannt zu haben - die Promotion an seinem Lehrstuhl erm¨oglichte und mit Geduld, Empathie und nicht zuletzt einem mir unbegreiflichen Verst¨andnisf¨ur meine verschiedenen Ausfl¨ugein die Weiten der Informatik unterst¨utzthat. Sie werden mir nicht glauben, wie dankbar ich Ihnen bin. Weiterhin m¨ochte ich meinem damaligen Studiengangsleiter Herrn Prof. Heinz J¨urgen M¨ullerdanken, der vor acht Jahren den Kontakt zur Universit¨atMannheim herstellte und mich ¨uberhaupt erst in die richtige Richtung wies, um mein Promotionsvorhaben anzugehen. Auch Herr Prof. Peter Henning soll nicht ungenannt bleiben, der mich - auch wenn es ihm vielleicht gar nicht bewusst ist - davon ¨uberzeugt hat, dass die Erzeugung virtueller Welten ein lohnenswertes Promotionsthema ist. Ganz besonderer Dank gilt meiner Frau Sarah und meinen beiden Kindern Justus und Elisa, die viele Abende und Wochenenden zugunsten dieser Arbeit auf meine Gesellschaft verzichten mussten. Jetzt ist es geschafft, das n¨achste Projekt ist dann wohl der Garten! Ebenfalls geb¨uhrt meinen Eltern und meinen Geschwistern Dank. -
Keras2c: a Library for Converting Keras Neural Networks to Real-Time Compatible C
Keras2c: A library for converting Keras neural networks to real-time compatible C Rory Conlina,∗, Keith Ericksonb, Joeseph Abbatec, Egemen Kolemena,b,∗ aDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton NJ 08544, USA bPrinceton Plasma Physics Laboratory, Princeton NJ 08544, USA cDepartment of Astrophysical Sciences at Princeton University, Princeton NJ 08544, USA Abstract With the growth of machine learning models and neural networks in mea- surement and control systems comes the need to deploy these models in a way that is compatible with existing systems. Existing options for deploying neural networks either introduce very high latency, require expensive and time con- suming work to integrate into existing code bases, or only support a very lim- ited subset of model types. We have therefore developed a new method called Keras2c, which is a simple library for converting Keras/TensorFlow neural net- work models into real-time compatible C code. It supports a wide range of Keras layers and model types including multidimensional convolutions, recurrent lay- ers, multi-input/output models, and shared layers. Keras2c re-implements the core components of Keras/TensorFlow required for predictive forward passes through neural networks in pure C, relying only on standard library functions considered safe for real-time use. The core functionality consists of ∼ 1500 lines of code, making it lightweight and easy to integrate into existing codebases. Keras2c has been successfully tested in experiments and is currently in use on the plasma control system at the DIII-D National Fusion Facility at General Atomics in San Diego. 1. Motivation TensorFlow[1] is one of the most popular libraries for developing and training neural networks. -
Innk: a Multi-Player Game to Deceive a Neural Network
iNNk: A Multi-Player Game to Deceive a Neural Network Jennifer Villareale1, Ana Acosta-Ruiz1, Samuel Arcaro1, Thomas Fox1, Evan Freed1, Robert Gray1, Mathias Löwe2, Panote Nuchprayoon1, Aleksanteri Sladek1, Rush Weigelt1, Yifu Li1, Sebastian Risi2, Jichen Zhu1 1Drexel University, Philadelphia, USA 2IT University of Copenhagen, Copenhagen, Denmark {jmv85, ava48, saa35, tbf33, emf67, rcg48, pn355, ams939, rw643, yl3374, jichen}@drexel.edu, {malw, sebr}@itu.dk ABSTRACT experiment with different drawing strategies. This paper also sum- This paper presents iNNK, a multiplayer drawing game where hu- marizes the main strategies our players have developed in our man players team up against an NN. The players need to success- playtesting. Certainly, a lot more effort is needed to empower cit- fully communicate a secret code word to each other through draw- izens to be more familiar with AI and to engage the technology ings, without being deciphered by the NN. With this game, we aim critically. Through our game, we have seen evidence that playful to foster a playful environment where players can, in a small way, experience can turn people from passive users into creative and re- go from passive consumers of NN applications to creative thinkers flective thinkers, a crucial step towards a more mature relationship and critical challengers. with AI. CCS CONCEPTS 2 RELATED WORK • Computer systems organization ! Embedded systems; Re- With recent breakthroughs in neural networks (NN), particularly dundancy; Robotics; • Networks ! Network reliability. deep learning, designers are increasingly exploring their use in computer games. For example, researchers use NNs to procedurally KEYWORDS generate game content, which otherwise would have to be created artificial intelligence, game design, machine learning, neural net- by human artists and thus make the game more variable [13, 20]. -
Plagiarism in the Age of Massive Generative Pre-Trained Transformers (GPT- 3): “The Best Time to Act Was Yesterday
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/348583797 Plagiarism in the age of massive Generative Pre-trained Transformers (GPT- 3): “The best time to act was yesterday. The next best time is now.” Article in Ethics in Science and Environmental Politics · January 2021 DOI: 10.3354/esep00195 CITATIONS READS 0 26 1 author: Nassim Dehouche Mahidol University 30 PUBLICATIONS 9 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Improving Access to the Bangkok Transit System for Older Adults and Disabled People View project Operational Research on Football View project All content following this page was uploaded by Nassim Dehouche on 13 February 2021. The user has requested enhancement of the downloaded file. Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3): “The best time to act was yesterday. The next best time is now.” Nassim Dehouche [email protected] Business Administration Division, Mahidol University International College, Salaya, Thailand Abstract As if 2020 were not a peculiar enough year, its fifth month has seen the relatively quiet publication of a preprint describing the most powerful Natural Language Processing (NLP) system to date, GPT-3 (Generative Pre-trained Transformer-3), by Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial Beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this Artificial Intelligence can comprehend prompts in natural language, on virtually any topic, and generate relevant, original text content that is indistinguishable from human writing. -
Tensorflow, Theano, Keras, Torch, Caffe Vicky Kalogeiton, Stéphane Lathuilière, Pauline Luc, Thomas Lucas, Konstantin Shmelkov Introduction
TensorFlow, Theano, Keras, Torch, Caffe Vicky Kalogeiton, Stéphane Lathuilière, Pauline Luc, Thomas Lucas, Konstantin Shmelkov Introduction TensorFlow Google Brain, 2015 (rewritten DistBelief) Theano University of Montréal, 2009 Keras François Chollet, 2015 (now at Google) Torch Facebook AI Research, Twitter, Google DeepMind Caffe Berkeley Vision and Learning Center (BVLC), 2013 Outline 1. Introduction of each framework a. TensorFlow b. Theano c. Keras d. Torch e. Caffe 2. Further comparison a. Code + models b. Community and documentation c. Performance d. Model deployment e. Extra features 3. Which framework to choose when ..? Introduction of each framework TensorFlow architecture 1) Low-level core (C++/CUDA) 2) Simple Python API to define the computational graph 3) High-level API (TF-Learn, TF-Slim, soon Keras…) TensorFlow computational graph - auto-differentiation! - easy multi-GPU/multi-node - native C++ multithreading - device-efficient implementation for most ops - whole pipeline in the graph: data loading, preprocessing, prefetching... TensorBoard TensorFlow development + bleeding edge (GitHub yay!) + division in core and contrib => very quick merging of new hotness + a lot of new related API: CRF, BayesFlow, SparseTensor, audio IO, CTC, seq2seq + so it can easily handle images, videos, audio, text... + if you really need a new native op, you can load a dynamic lib - sometimes contrib stuff disappears or moves - recently introduced bells and whistles are barely documented Presentation of Theano: - Maintained by Montréal University group. - Pioneered the use of a computational graph. - General machine learning tool -> Use of Lasagne and Keras. - Very popular in the research community, but not elsewhere. Falling behind. What is it like to start using Theano? - Read tutorials until you no longer can, then keep going. -
Arxiv:2007.12391V6 [Cs.CV] 2 Jul 2021 Techniques for Practical Purposes Has Been Artificial Intelligence (AI)
Artificial Intelligence Review manuscript No. (will be inserted by the editor) Artificial Intelligence in the Creative Industries: A Review Nantheera Anantrasirichai · David Bull Accepted for publication in Artificial Intelligence Review (AIRE), 19 June 2021 Abstract This paper reviews the current state of the art in Artificial Intelligence (AI) tech- nologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolu- tional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neu- ral Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorize creative applications into five groups, related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) in- formation extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human-centric – where it is designed to augment, rather than replace, human creativity. -
Setting up Your Environment for the TF Developer Certificate Exam
Set up your environment to take the TensorFlow Developer Ceicate Exam Questions? Email [email protected]. Last Updated: June 23, 2021 This document describes how to get your environment ready to take the TensorFlow Developer Ceicate exam. It does not discuss what the exam is or how to take it. For more information about the TensorFlow Developer Ceicate program, please go to tensolow.org/ceicate. Contents Before you begin Refund policy Install Python 3.8 Install PyCharm Get your environment ready for the exam What libraries will the exam infrastructure install? Make sure that PyCharm isn't subject to le-loading controls Windows and Linux Users: Check your GPU drivers Mac Users: Ensure you have Python 3.8 All Users: Create a Test Viual Environment that uses TensorFlow in PyCharm Create a new PyCharm project Install TensorFlow and related packages Check the suppoed version of Python in PyCharm Practice training TensorFlow models Setup your environment for the TensorFlow Developer Certificate exam 1 FAQs How do I sta the exam? What version of TensorFlow is used in the exam? Is there a minimum hardware requirement? Where is the candidate handbook? Before you begin The TensorFlow ceicate exam runs inside PyCharm. The exam uses TensorFlow 2.5.0. You must use Python 3.8 to ensure accurate grading of your models. The exam has been tested with Python 3.8.0 and TensorFlow 2.5.0. Before you sta the exam, make sure your environment is ready: ❏ Make sure you have Python 3.8 installed on your computer. ❏ Check that your system meets the installation requirements for PyCharm here. -
Tensorflow and Keras Installation Steps for Deep Learning Applications in Rstudio Ricardo Dalagnol1 and Fabien Wagner 1. Install
Version 1.0 – 03 July 2020 Tensorflow and Keras installation steps for Deep Learning applications in Rstudio Ricardo Dalagnol1 and Fabien Wagner 1. Install OSGEO (https://trac.osgeo.org/osgeo4w/) to have GDAL utilities and QGIS. Do not modify the installation path. 2. Install ImageMagick (https://imagemagick.org/index.php) for image visualization. 3. Install the latest Miniconda (https://docs.conda.io/en/latest/miniconda.html) 4. Install Visual Studio a. Install the 2019 or 2017 free version. The free version is called ‘Community’. b. During installation, select the box to include C++ development 5. Install the latest Nvidia driver for your GPU card from the official Nvidia site 6. Install CUDA NVIDIA a. I installed CUDA Toolkit 10.1 update2, to check if the installed or available NVIDIA driver is compatible with the CUDA version (check this in the cuDNN page https://docs.nvidia.com/deeplearning/sdk/cudnn-support- matrix/index.html) b. Downloaded from NVIDIA website, searched for CUDA NVIDIA download in google: https://developer.nvidia.com/cuda-10.1-download-archive-update2 (list of all archives here https://developer.nvidia.com/cuda-toolkit-archive) 7. Install cuDNN (deep neural network library) – I have installed cudnn-10.1- windows10-x64-v7.6.5.32 for CUDA 10.1, the last version https://docs.nvidia.com/deeplearning/sdk/cudnn-install/#install-windows a. To download you have to create/login an account with NVIDIA Developer b. Before installing, check the NVIDIA driver version if it is compatible c. To install on windows, follow the instructions at section 3.3 https://docs.nvidia.com/deeplearning/sdk/cudnn-install/#install-windows d. -
Julia: a Modern Language for Modern ML
Julia: A modern language for modern ML Dr. Viral Shah and Dr. Simon Byrne www.juliacomputing.com What we do: Modernize Technical Computing Today’s technical computing landscape: • Develop new learning algorithms • Run them in parallel on large datasets • Leverage accelerators like GPUs, Xeon Phis • Embed into intelligent products “Business as usual” will simply not do! General Micro-benchmarks: Julia performs almost as fast as C • 10X faster than Python • 100X faster than R & MATLAB Performance benchmark relative to C. A value of 1 means as fast as C. Lower values are better. A real application: Gillespie simulations in systems biology 745x faster than R • Gillespie simulations are used in the field of drug discovery. • Also used for simulations of epidemiological models to study disease propagation • Julia package (Gillespie.jl) is the state of the art in Gillespie simulations • https://github.com/openjournals/joss- papers/blob/master/joss.00042/10.21105.joss.00042.pdf Implementation Time per simulation (ms) R (GillespieSSA) 894.25 R (handcoded) 1087.94 Rcpp (handcoded) 1.31 Julia (Gillespie.jl) 3.99 Julia (Gillespie.jl, passing object) 1.78 Julia (handcoded) 1.2 Those who convert ideas to products fastest will win Computer Quants develop Scientists prepare algorithms The last 25 years for production (Python, R, SAS, DEPLOY (C++, C#, Java) Matlab) Quants and Computer Compress the Scientists DEPLOY innovation cycle collaborate on one platform - JULIA with Julia Julia offers competitive advantages to its users Julia is poised to become one of the Thank you for Julia. Yo u ' v e k i n d l ed leading tools deployed by developers serious excitement. -
The Future of Storytelling in the Age of AI and Posthuman
The Future of Storytelling in the Age of AI and Posthuman DR. SERDAR TUNCER PROF. BOULOU EBANDA DE B'BÉRI UNIVERSITY OF OTTAWA, AMLAC&S Background Computer scientists are building a vast array of machine learning systems (often called Artificial Intelligence or AI) that can perform daily human tasks reliably, sometimes more so than humans. Indeed, AI is becoming capable of capturing the unique essence that makes us “human”: storytelling. Humans are intrinsically storytellers who express themselves through stories that already exist (Fisher, 1987; Frank, 2012). Moreover, people have always learnt about themselves and made sense of the rest of the world through narratives (Ebanda de B'béri, 2006; Tunçer, 2018). However, in recent years, AI-generated games and articles have already started to emerge (Fitch, 2019). While AI is able to exceed certain functions of natural human intelligence as it gains self- learning capacity in repetitive or analytical tasks, it may have the potential to harness the persuasive capabilities of storytelling. Guiding Questions • What will become of our humanity if AI can write and tell better stories? • Is AI the greatest danger to our humanity as Bramer (2015) has suggested? • Are we already entered the posthuman era (Wolfe, 2010; Braidotti, 2013) with such advances as neural nets that allow biological and silicon-based artificial brain cells to communicate with each other (Serb et al., 2020) – a new form of AI-human being? • What will the future look like if AI can harness the influential potential of storytelling? • What would be the storytelling landscape in such an environment of posthuman? Where we are today We are still in the infant stages of AI with errors happening along the way: ◦ Microsoft had released its AI bot named Tay, which was designed to foster “conversational understanding.” But things did not go as expected. -
Deepfakes & Disinformation
DEEPFAKES & DISINFORMATION DEEPFAKES & DISINFORMATION Agnieszka M. Walorska ANALYSISANALYSE 2 DEEPFAKES & DISINFORMATION IMPRINT Publisher Friedrich Naumann Foundation for Freedom Karl-Marx-Straße 2 14482 Potsdam Germany /freiheit.org /FriedrichNaumannStiftungFreiheit /FNFreiheit Author Agnieszka M. Walorska Editors International Department Global Themes Unit Friedrich Naumann Foundation for Freedom Concept and layout TroNa GmbH Contact Phone: +49 (0)30 2201 2634 Fax: +49 (0)30 6908 8102 Email: [email protected] As of May 2020 Photo Credits Photomontages © Unsplash.de, © freepik.de, P. 30 © AdobeStock Screenshots P. 16 © https://youtu.be/mSaIrz8lM1U P. 18 © deepnude.to / Agnieszka M. Walorska P. 19 © thispersondoesnotexist.com P. 19 © linkedin.com P. 19 © talktotransformer.com P. 25 © gltr.io P. 26 © twitter.com All other photos © Friedrich Naumann Foundation for Freedom (Germany) P. 31 © Agnieszka M. Walorska Notes on using this publication This publication is an information service of the Friedrich Naumann Foundation for Freedom. The publication is available free of charge and not for sale. It may not be used by parties or election workers during the purpose of election campaigning (Bundestags-, regional and local elections and elections to the European Parliament). Licence Creative Commons (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0 DEEPFAKES & DISINFORMATION DEEPFAKES & DISINFORMATION 3 4 DEEPFAKES & DISINFORMATION CONTENTS Table of contents EXECUTIVE SUMMARY 6 GLOSSARY 8 1.0 STATE OF DEVELOPMENT ARTIFICIAL