A Review on Neural Turing Machine
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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 -
Much Has Been Written About the Turing Test in the Last Few Years, Some of It
1 Much has been written about the Turing Test in the last few years, some of it preposterously off the mark. People typically mis-imagine the test by orders of magnitude. This essay is an antidote, a prosthesis for the imagination, showing how huge the task posed by the Turing Test is, and hence how unlikely it is that any computer will ever pass it. It does not go far enough in the imagination-enhancement department, however, and I have updated the essay with a new postscript. Can Machines Think?1 Can machines think? This has been a conundrum for philosophers for years, but in their fascination with the pure conceptual issues they have for the most part overlooked the real social importance of the answer. It is of more than academic importance that we learn to think clearly about the actual cognitive powers of computers, for they are now being introduced into a variety of sensitive social roles, where their powers will be put to the ultimate test: In a wide variety of areas, we are on the verge of making ourselves dependent upon their cognitive powers. The cost of overestimating them could be enormous. One of the principal inventors of the computer was the great 1 Originally appeared in Shafto, M., ed., How We Know (San Francisco: Harper & Row, 1985). 2 British mathematician Alan Turing. It was he who first figured out, in highly abstract terms, how to design a programmable computing device--what we not call a universal Turing machine. All programmable computers in use today are in essence Turing machines. -
Turing Test Does Not Work in Theory but in Practice
Int'l Conf. Artificial Intelligence | ICAI'15 | 433 Turing test does not work in theory but in practice Pertti Saariluoma1 and Matthias Rauterberg2 1Information Technology, University of Jyväskylä, Jyväskylä, Finland 2Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands Abstract - The Turing test is considered one of the most im- Essentially, the Turing test is an imitation game. Like any portant thought experiments in the history of AI. It is argued good experiment, it has two conditions. In the case of control that the test shows how people think like computers, but is this conditions, it is assumed that there is an opaque screen. On actually true? In this paper, we discuss an entirely new per- one side of the screen is an interrogator whose task is to ask spective. Scientific languages have their foundational limita- questions and assess the nature of the answers. On the other tions, for example, in their power of expression. It is thus pos- side, there are two people, A and B. The task of A and B is to sible to discuss the limitations of formal concepts and theory answer the questions, and the task of the interrogator is to languages. In order to represent real world phenomena in guess who has given the answer. In the case of experimental formal concepts, formal symbols must be given semantics and conditions, B is replaced by a machine (computer) and again, information contents; that is, they must be given an interpreta- the interrogator must decide whether it was the human or the tion. They key argument is that it is not possible to express machine who answered the questions. -
THE TURING TEST RELIES on a MISTAKE ABOUT the BRAIN For
THE TURING TEST RELIES ON A MISTAKE ABOUT THE BRAIN K. L. KIRKPATRICK Abstract. There has been a long controversy about how to define and study intel- ligence in machines and whether machine intelligence is possible. In fact, both the Turing Test and the most important objection to it (called variously the Shannon- McCarthy, Blockhead, and Chinese Room arguments) are based on a mistake that Turing made about the brain in his 1948 paper \Intelligent Machinery," a paper that he never published but whose main assumption got embedded in his famous 1950 paper and the celebrated Imitation Game. In this paper I will show how the mistake is a false dichotomy and how it should be fixed, to provide a solid foundation for a new understanding of the brain and a new approach to artificial intelligence. In the process, I make an analogy between the brain and the ribosome, machines that translate information into action, and through this analogy I demonstrate how it is possible to go beyond the purely information-processing paradigm of computing and to derive meaning from syntax. For decades, the metaphor of the brain as an information processor has dominated both neuroscience and artificial intelligence research and allowed the fruitful appli- cations of Turing's computation theory and Shannon's information theory in both fields. But this metaphor may be leading us astray, because of its limitations and the deficiencies in our understanding of the brain and AI. In this paper I will present a new metaphor to consider, of the brain as both an information processor and a producer of physical effects. -
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. -
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. -
The Turing Test and Other Design Detection Methodologies∗
Detecting Intelligence: The Turing Test and Other Design Detection Methodologies∗ George D. Montanez˜ 1 1Machine Learning Department, Carnegie Mellon University, Pittsburgh PA, USA [email protected] Keywords: Turing Test, Design Detection, Intelligent Agents Abstract: “Can machines think?” When faced with this “meaningless” question, Alan Turing suggested we ask a dif- ferent, more precise question: can a machine reliably fool a human interviewer into believing the machine is human? To answer this question, Turing outlined what came to be known as the Turing Test for artificial intel- ligence, namely, an imitation game where machines and humans interacted from remote locations and human judges had to distinguish between the human and machine participants. According to the test, machines that consistently fool human judges are to be viewed as intelligent. While popular culture champions the Turing Test as a scientific procedure for detecting artificial intelligence, doing so raises significant issues. First, a simple argument establishes the equivalence of the Turing Test to intelligent design methodology in several fundamental respects. Constructed with similar goals, shared assumptions and identical observational models, both projects attempt to detect intelligent agents through the examination of generated artifacts of uncertain origin. Second, if the Turing Test rests on scientifically defensible assumptions then design inferences become possible and cannot, in general, be wholly unscientific. Third, if passing the Turing Test reliably indicates intelligence, this implies the likely existence of a designing intelligence in nature. 1 THE IMITATION GAME gin et al., 2003), this paper presents a novel critique of the Turing Test in the spirit of a reductio ad ab- In his seminal paper on artificial intelligence (Tur- surdum. -
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