Towards Common-Sense Reasoning with Advanced NLP Architectures
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A Comprehensive Workplace Environment Based on a Deep
International Journal on Advances in Software, vol 11 no 3 & 4, year 2018, http://www.iariajournals.org/software/ 358 A Comprehensive Workplace Environment based on a Deep Learning Architecture for Cognitive Systems Using a multi-tier layout comprising various cognitive channels, reflexes and reasoning Thorsten Gressling Veronika Thurner Munich University of Applied Sciences ARS Computer und Consulting GmbH Department of Computer Science and Mathematics Munich, Germany Munich, Germany e-mail: [email protected] e-mail: [email protected] Abstract—Many technical work places, such as laboratories or All these settings share a number of commonalities. For test beds, are the setting for well-defined processes requiring both one thing, within each of these working settings a human being high precision and extensive documentation, to ensure accuracy interacts extensively with technical devices, such as measuring and support accountability that often is required by law, science, instruments or sensors. For another thing, processing follows or both. In this type of scenario, it is desirable to delegate certain a well-defined routine, or even precisely specified interaction routine tasks, such as documentation or preparatory next steps, to protocols. Finally, to ensure that results are reproducible, some sort of automated assistant, in order to increase precision and reduce the required amount of manual labor in one fell the different steps and achieved results usually have to be swoop. At the same time, this automated assistant should be able documented extensively and in a precise way. to interact adequately with the human worker, to ensure that Especially in scenarios that execute a well-defined series of the human worker receives exactly the kind of support that is actions, it is desirable to delegate certain routine tasks, such required in a certain context. -
Lecture 9 Recurrent Neural Networks “I’M Glad That I’M Turing Complete Now”
Lecture 9 Recurrent Neural Networks “I’m glad that I’m Turing Complete now” Xinyu Zhou Megvii (Face++) Researcher [email protected] Nov 2017 Raise your hand and ask, whenever you have questions... We have a lot to cover and DON’T BLINK Outline ● RNN Basics ● Classic RNN Architectures ○ LSTM ○ RNN with Attention ○ RNN with External Memory ■ Neural Turing Machine ■ CAVEAT: don’t fall asleep ● Applications ○ A market of RNNs RNN Basics Feedforward Neural Networks ● Feedforward neural networks can fit any bounded continuous (compact) function ● This is called Universal approximation theorem https://en.wikipedia.org/wiki/Universal_approximation_theorem Cybenko, George. "Approximation by superpositions of a sigmoidal function." Mathematics of Control, Signals, and Systems (MCSS) 2.4 (1989): 303-314. Bounded Continuous Function is NOT ENOUGH! How to solve Travelling Salesman Problem? Bounded Continuous Function is NOT ENOUGH! How to solve Travelling Salesman Problem? We Need to be Turing Complete RNN is Turing Complete Siegelmann, Hava T., and Eduardo D. Sontag. "On the computational power of neural nets." Journal of computer and system sciences 50.1 (1995): 132-150. Sequence Modeling Sequence Modeling ● How to take a variable length sequence as input? ● How to predict a variable length sequence as output? RNN RNN Diagram A lonely feedforward cell RNN Diagram Grows … with more inputs and outputs RNN Diagram … here comes a brother (x_1, x_2) comprises a length-2 sequence RNN Diagram … with shared (tied) weights x_i: inputs y_i: outputs W: all the -
Build Your Trust Advantage, Leadership in the Era of Data
Global C-suite Study 20th Edition Build Your Trust Advantage Leadership in the era of data and AI everywhere This report is IBM’s fourth Global C-suite Study and the 20th Edition in the ongoing IBM CxO Study series developed by the IBM Institute for Business Value (IBV). We have now collected data and insights from more than 50,000 interviews dating back to 2003. This report was authored in collaboration with leading academics, futurists, and technology visionaries. In this report, we present our key findings of CxO insights, experiences, and sentiments based on analysis as described in the research methodology on page 44. Build Your Trust Advantage | 1 Build Your Trust Advantage Leadership in the era of data and AI everywhere Global C-suite Study 20th Edition Our latest study draws on input from 13,484 respondents across 6 C-suite roles, 20 industries, and 98 countries. 2,131 2,105 2,118 2,924 2,107 2,099 Chief Chief Chief Chief Chief Chief Executive Financial Human Information Marketing Operations Officers Officers Resources Officers Officers Officers Officers 3,363 Europe 1,910 Greater China 3,755 North America 858 Japan 915 Middle East and Africa 1,750 Asia Pacific 933 Latin America 2 | Global C-suite Study Table of contents Executive summary 3 Introduction 4 Chapter 1 Customers: How to win in the trust economy 8 Action guide 19 Chapter 2 Enterprises: How to build the human-tech partnership 20 Action guide 31 Chapter 3 Ecosystems: How to share data in the platform era 32 Action guide 41 Conclusion: Return on trust 42 Acknowledgments 43 Related IBV studies 43 Research methodology 44 Notes and sources 45 Build Your Trust Advantage | 3 Executive summary More than 13,000 C-suite executives worldwide their data scientists, to uncover insights from data. -
A.I. Technologies Applied to Naval CAD/CAM/CAE
A.I. Technologies Applied to Naval CAD/CAM/CAE Jesus A. Muñoz Herrero, SENER, Madrid/Spain, [email protected] Rodrigo Perez Fernandez, SENER, Madrid/Spain, [email protected] Abstract Artificial Intelligence is one of the most enabling technologies of digital transformation in the industry, but it is also one of the technologies that most rapidly spreads in our daily activity. Increasingly, elements and devices that integrate artificial intelligence features, appear in our everyday lives. These characteristics are different, depending on the devices that integrate them, or the aim they pursue. The methods and processes that are carried out in Marine Engineering cannot be left out of this technology, but the peculiarities of the profession and the people that take part in it must be taken into account. There are many aspects in which artificial intelligence can be applied in the field of our profession. The management and access to all the information necessary for the correct and efficient execution of a naval project is one of the aspects where this technology can have a very positive impact. Access all the rules, rules, design guides, good practices, lessons learned, etc., in a fast and intelligent way, understanding the natural language of the people, identifying the most appropriate to the process that is being carried out and above all. Learning as we go through the design, is one of the characteristics that will increase the application of this technology in the professional field. This article will describe the evolution of this technology and the current situation of it in the different areas of application. -
Neural Turing Machines Arxiv:1410.5401V2 [Cs.NE]
Neural Turing Machines Alex Graves [email protected] Greg Wayne [email protected] Ivo Danihelka [email protected] Google DeepMind, London, UK Abstract We extend the capabilities of neural networks by coupling them to external memory re- sources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to- end, allowing it to be efficiently trained with gradient descent. Preliminary results demon- strate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples. 1 Introduction Computer programs make use of three fundamental mechanisms: elementary operations (e.g., arithmetic operations), logical flow control (branching), and external memory, which can be written to and read from in the course of computation (Von Neumann, 1945). De- arXiv:1410.5401v2 [cs.NE] 10 Dec 2014 spite its wide-ranging success in modelling complicated data, modern machine learning has largely neglected the use of logical flow control and external memory. Recurrent neural networks (RNNs) stand out from other machine learning methods for their ability to learn and carry out complicated transformations of data over extended periods of time. Moreover, it is known that RNNs are Turing-Complete (Siegelmann and Sontag, 1995), and therefore have the capacity to simulate arbitrary procedures, if properly wired. Yet what is possible in principle is not always what is simple in practice. We therefore enrich the capabilities of standard recurrent networks to simplify the solution of algorithmic tasks. This enrichment is primarily via a large, addressable memory, so, by analogy to Turing’s enrichment of finite-state machines by an infinite memory tape, we 1 dub our device a “Neural Turing Machine” (NTM). -
Memory Augmented Matching Networks for Few-Shot Learnings
International Journal of Machine Learning and Computing, Vol. 9, No. 6, December 2019 Memory Augmented Matching Networks for Few-Shot Learnings Kien Tran, Hiroshi Sato, and Masao Kubo broken down when they have to learn a new concept on the Abstract—Despite many successful efforts have been made in fly or to deal with the problems which information/data is one-shot/few-shot learning tasks recently, learning from few insufficient (few or even a single example). These situations data remains one of the toughest areas in machine learning. are very common in the computer vision field, such as Regarding traditional machine learning methods, the more data gathered, the more accurate the intelligent system could detecting a new object with only a few available captured perform. Hence for this kind of tasks, there must be different images. For those purposes, specialists define the approaches to guarantee systems could learn even with only one one-shot/few-shot learning algorithms that could solve a task sample per class but provide the most accurate results. Existing with only one or few known samples (e.g., less than 20 solutions are ranged from Bayesian approaches to examples per category). meta-learning approaches. With the advances in Deep learning One-shot learning/few-shot learning is a challenging field, meta-learning methods with deep networks could be domain for neural networks since gradient-based learning is better such as recurrent networks with enhanced external memory (Neural Turing Machines - NTM), metric learning often too slow to quickly cope with never-before-seen classes, with Matching Network framework, etc. -
Ilearn Goals
Combining AI and collective intelligence to create a GPS for knowledge CRI Paris We experiment at frontiers of learning, life and digital From babies to lifelong learning LMD students Learning by doing Interdisciplinarity Sustainable Development goals A changing job market Too much! Fake news Recruting for skills on the digital job market Can we reinvent learning with artificial intelligence? What can you (almost) do with AI today? Deep Learning Classification Generation Who are these persons? www.thispersondoesnotexist.com Based on GAN (Generative Adversarial Networks) A.I. image generation From text to image From text to image Text generation Automatic Q&A generation from Wikipedia Debating with A.I. February 12th, 2019 IBM Project Debater vs. World Debating Champion Motion: “We should subsidise preschool” ● 15 mins to prepare arguments ● 4-minute opening statement ● 4-minute rebuttal ● 2-minute summary Predictive tools Pneumonia detection from thorax radiographies (2017) Deepfake A.I. lip reading Conversational interfaces May 2018 Google Duplex iLearn goals Map all learning resources available on the Internet Build learning profiles of our users Provide them maps of their own learnings Match learners with adapted resources Match learners with mentors or co-learners iLearn Knowledge maps + = + Collective Artificial intelligence intelligence Learning groups iLearn iLearn Tags an online Users improve document as a qualification Concepts extraction useful learning (concepts and resource difficulty) Artificial Collective Intelligence Intelligence -
Training Robot Policies Using External Memory Based Networks Via Imitation
Training Robot Policies using External Memory Based Networks Via Imitation Learning by Nambi Srivatsav A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science Approved September 2018 by the Graduate Supervisory Committee: Heni Ben Amor, Chair Siddharth Srivastava HangHang Tong ARIZONA STATE UNIVERSITY December 2018 ABSTRACT Recent advancements in external memory based neural networks have shown promise in solving tasks that require precise storage and retrieval of past information. Re- searchers have applied these models to a wide range of tasks that have algorithmic properties but have not applied these models to real-world robotic tasks. In this thesis, we present memory-augmented neural networks that synthesize robot naviga- tion policies which a) encode long-term temporal dependencies b) make decisions in partially observed environments and c) quantify the uncertainty inherent in the task. We extract information about the temporal structure of a task via imitation learning from human demonstration and evaluate the performance of the models on control policies for a robot navigation task. Experiments are performed in partially observed environments in both simulation and the real world. i TABLE OF CONTENTS Page LIST OF FIGURES . iii CHAPTER 1 INTRODUCTION . 1 2 BACKGROUND .................................................... 4 2.1 Neural Networks . 4 2.2 LSTMs . 4 2.3 Neural Turing Machines . 5 2.3.1 Controller . 6 2.3.2 Memory . 6 3 METHODOLOGY . 7 3.1 Neural Turing Machine for Imitation Learning . 7 3.1.1 Reading . 8 3.1.2 Writing . 9 3.1.3 Addressing Memory . 10 3.2 Uncertainty Estimation . 12 4 EXPERIMENTAL RESULTS . -
Learning Operations on a Stack with Neural Turing Machines
Learning Operations on a Stack with Neural Turing Machines Anonymous Author(s) Affiliation Address email Abstract 1 Multiple extensions of Recurrent Neural Networks (RNNs) have been proposed 2 recently to address the difficulty of storing information over long time periods. In 3 this paper, we experiment with the capacity of Neural Turing Machines (NTMs) to 4 deal with these long-term dependencies on well-balanced strings of parentheses. 5 We show that not only does the NTM emulate a stack with its heads and learn an 6 algorithm to recognize such words, but it is also capable of strongly generalizing 7 to much longer sequences. 8 1 Introduction 9 Although neural networks shine at finding meaningful representations of the data, they are still limited 10 in their capacity to plan ahead, reason and store information over long time periods. Keeping track of 11 nested parentheses in a language model, for example, is a particularly challenging problem for RNNs 12 [9]. It requires the network to somehow memorize the number of unmatched open parentheses. In 13 this paper, we analyze the ability of Neural Turing Machines (NTMs) to recognize well-balanced 14 strings of parentheses. We show that even though the NTM architecture does not explicitely operate 15 on a stack, it is able to emulate this data structure with its heads. Such a behaviour was unobserved 16 on other simple algorithmic tasks [4]. 17 After a brief recall of the Neural Turing Machine architecture in Section 3, we show in Section 4 how 18 the NTM is able to learn an algorithm to recognize strings of well-balanced parentheses, called Dyck 19 words. -
Neural Shuffle-Exchange Networks
Neural Shuffle-Exchange Networks − Sequence Processing in O(n log n) Time Karlis¯ Freivalds, Em¯ıls Ozolin, š, Agris Šostaks Institute of Mathematics and Computer Science University of Latvia Raina bulvaris 29, Riga, LV-1459, Latvia {Karlis.Freivalds, Emils.Ozolins, Agris.Sostaks}@lumii.lv Abstract A key requirement in sequence to sequence processing is the modeling of long range dependencies. To this end, a vast majority of the state-of-the-art models use attention mechanism which is of O(n2) complexity that leads to slow execution for long sequences. We introduce a new Shuffle-Exchange neural network model for sequence to sequence tasks which have O(log n) depth and O(n log n) total complexity. We show that this model is powerful enough to infer efficient algorithms for common algorithmic benchmarks including sorting, addition and multiplication. We evaluate our architecture on the challenging LAMBADA question answering dataset and compare it with the state-of-the-art models which use attention. Our model achieves competitive accuracy and scales to sequences with more than a hundred thousand of elements. We are confident that the proposed model has the potential for building more efficient architectures for processing large interrelated data in language modeling, music generation and other application domains. 1 Introduction A key requirement in sequence to sequence processing is the modeling of long range dependencies. Such dependencies occur in natural language when the meaning of some word depends on other words in the same or some previous sentence. There are important cases, e.g., to resolve coreferences, when such distant information may not be disregarded. -
Advances in Neural Turing Machines
Advances in Neural Turing Machines [email protected] truyentran.github.io Truyen Tran Deakin University @truyenoz letdataspeak.blogspot.com Source: rdn consulting CafeDSL, Aug 2018 goo.gl/3jJ1O0 30/08/2018 1 https://twitter.com/nvidia/status/1010545517405835264 30/08/2018 2 (Real) Turing machine It is possible to invent a single machine which can be used to compute any computable sequence. If this machine U is supplied with the tape on the beginning of which is written the string of quintuples separated by semicolons of some computing machine M, then U will compute the same sequence as M. Wikipedia 30/08/2018 3 Can we learn from data a model that is as powerful as a Turing machine? 30/08/2018 4 Agenda Brief review of deep learning Neural Turing machine (NTM) Dual-controlling for read and write (PAKDD’18) Dual-view in sequences (KDD’18) Bringing variability in output sequences (NIPS’18 ?) Bringing relational structures into memory (IJCAI’17 WS+) 30/08/2018 5 Deep learning in a nutshell 1986 2016 http://blog.refu.co/wp-content/uploads/2009/05/mlp.png 30/08/2018 2012 6 Let’s review current offerings Feedforward nets (FFN) BUTS … Recurrent nets (RNN) Convolutional nets (CNN) No storage of intermediate results. Message-passing graph nets (MPGNN) Little choices over what to compute and what to use Universal transformer ….. Little support for complex chained reasoning Work surprisingly well on LOTS of important Little support for rapid switching of tasks problems Enter the age of differentiable programming 30/08/2018 7 Searching for better -
ELEC 576: Understanding and Visualizing Convnets & Introduction
ELEC 576: Understanding and Visualizing Convnets & Introduction to Recurrent Neural Networks Ankit B. Patel Baylor College of Medicine (Neuroscience Dept.) Rice University (ECE Dept.) 10-11-2017 Understand & Visualizing Convnets Deconvolutional Net [Zeiler and Fergus] Feature Visualization [Zeiler and Fergus] Activity Maximization [Simonyan et al.] Deep Dream Visualization • To produce human viewable images, need to • Activity maximization (gradient ascent) • L2 regularization • Gaussian blur • Clipping • Different scales Image Example [Günther Noack] Dumbbell Deep Dream AlexNet VGGNet GoogleNet Deep Dream Video Class: goldfish, Carassius auratus Texture Synthesis [Leon Gatys, Alexander Ecker, Matthias Bethge] Generated Textures [Leon Gatys, Alexander Ecker, Matthias Bethge] Deep Style [Leon Gatys, Alexander Ecker, Matthias Bethge] Deep Style [Leon Gatys, Alexander Ecker, Matthias Bethge] Introduction to Recurrent Neural Networks What Are Recurrent Neural Networks? • Recurrent Neural Networks (RNNs) are networks that have feedback • Output is feed back to the input • Sequence processing • Ideal for time-series data or sequential data History of RNNs Important RNN Architectures • Hopfield Network • Jordan and Elman Networks • Echo State Networks • Long Short Term Memory (LSTM) • Bi-Directional RNN • Gated Recurrent Unit (GRU) • Neural Turing Machine Hopfield Network [Wikipedia] Elman Networks [John McCullock] Echo State Networks [Herbert Jaeger] Definition of RNNs RNN Diagram [Richard Socher] RNN Formulation [Richard Socher] RNN Example [Andrej