6,580 research outputs found
A review on Neural Turing Machine
One of the major objectives of Artificial Intelligence is to design learning
algorithms that are executed on a general purposes computational machines such
as human brain. Neural Turing Machine (NTM) is a step towards realizing such a
computational machine. The attempt is made here to run a systematic review on
Neural Turing Machine. First, the mind-map and taxonomy of machine learning,
neural networks, and Turing machine are introduced. Next, NTM is inspected in
terms of concepts, structure, variety of versions, implemented tasks,
comparisons, etc. Finally, the paper discusses on issues and ends up with
several future works
A Monte Carlo Algorithm for Universally Optimal Bayesian Sequence Prediction and Planning
The aim of this work is to address the question of whether we can in
principle design rational decision-making agents or artificial intelligences
embedded in computable physics such that their decisions are optimal in
reasonable mathematical senses. Recent developments in rare event probability
estimation, recursive bayesian inference, neural networks, and probabilistic
planning are sufficient to explicitly approximate reinforcement learners of the
AIXI style with non-trivial model classes (here, the class of resource-bounded
Turing machines). Consideration of the effects of resource limitations in a
concrete implementation leads to insights about possible architectures for
learning systems using optimal decision makers as components.Comment: Submitted to MDPI Algorithms Special Issue "Algorithmic Complexity in
Physics & Embedded Artificial Intelligences
Advances in Natural Language Question Answering: A Review
Question Answering has recently received high attention from artificial
intelligence communities due to the advancements in learning technologies.
Early question answering models used rule-based approaches and moved to the
statistical approach to address the vastly available information. However,
statistical approaches are shown to underperform in handling the dynamic nature
and the variation of language. Therefore, learning models have shown the
capability of handling the dynamic nature and variations in language. Many deep
learning methods have been introduced to question answering. Most of the deep
learning approaches have shown to achieve higher results compared to machine
learning and statistical methods. The dynamic nature of language has profited
from the nonlinear learning in deep learning. This has created prominent
success and a spike in work on question answering. This paper discusses the
successes and challenges in question answering question answering systems and
techniques that are used in these challenges.Comment: arXiv admin note: text overlap with arXiv:1609.04667 by other author
A Roadmap towards Machine Intelligence
The development of intelligent machines is one of the biggest unsolved
challenges in computer science. In this paper, we propose some fundamental
properties these machines should have, focusing in particular on communication
and learning. We discuss a simple environment that could be used to
incrementally teach a machine the basics of natural-language-based
communication, as a prerequisite to more complex interaction with human users.
We also present some conjectures on the sort of algorithms the machine should
support in order to profitably learn from the environment
Artificial Intelligence and its Role in Near Future
AI technology has a long history which is actively and constantly changing
and growing. It focuses on intelligent agents, which contain devices that
perceive the environment and based on which takes actions in order to maximize
goal success chances. In this paper, we will explain the modern AI basics and
various representative applications of AI. In the context of the modern
digitalized world, AI is the property of machines, computer programs, and
systems to perform the intellectual and creative functions of a person,
independently find ways to solve problems, be able to draw conclusions and make
decisions. Most artificial intelligence systems have the ability to learn,
which allows people to improve their performance over time. The recent research
on AI tools, including machine learning, deep learning and predictive analysis
intended toward increasing the planning, learning, reasoning, thinking and
action taking ability. Based on which, the proposed research intends towards
exploring on how the human intelligence differs from the artificial
intelligence. Moreover, we critically analyze what AI of today is capable of
doing, why it still cannot reach human intelligence and what are the open
challenges existing in front of AI to reach and outperform human level of
intelligence. Furthermore, it will explore the future predictions for
artificial intelligence and based on which potential solution will be
recommended to solve it within next decades
Learning Numeracy: Binary Arithmetic with Neural Turing Machines
One of the main problems encountered so far with recurrent neural networks is
that they struggle to retain long-time information dependencies in their
recurrent connections. Neural Turing Machines (NTMs) attempt to mitigate this
issue by providing the neural network with an external portion of memory, in
which information can be stored and manipulated later on. The whole mechanism
is differentiable end-to-end, allowing the network to learn how to utilise this
long-term memory via stochastic gradient descent. This allows NTMs to infer
simple algorithms directly from data sequences. Nonetheless, the model can be
hard to train due to a large number of parameters and interacting components
and little related work is present. In this work we use NTMs to learn and
generalise two arithmetical tasks: binary addition and multiplication. These
tasks are two fundamental algorithmic examples in computer science, and are a
lot more challenging than the previously explored ones, with which we aim to
shed some light on the real capabilities on this neural model
(Yet) Another Theoretical Model of Thinking
This paper presents a theoretical, idealized model of the thinking process
with the following characteristics: 1) the model can produce complex thought
sequences and can be generalized to new inputs, 2) it can receive and maintain
input information indefinitely for the generation of thoughts and later use,
and 3) it supports learning while executing. The crux of the model lies within
the concept of internal consistency, or the generated thoughts should always be
consistent with the inputs from which they are created. Its merit, apart from
the capability to generate new creative thoughts from an internal mechanism,
depends on the potential to help training to generalize better. This is
consequently enabled by separating input information into several parts to be
handled by different processing components with a focus mechanism to fetch
information for each. This modularized view with the focus binds the model with
the computationally capable Turing machines. And as a final remark, this paper
constructively shows that the computational complexity of the model is at
least, if not surpass, that of a universal Turing machine
Open Problems in Universal Induction & Intelligence
Specialized intelligent systems can be found everywhere: finger print,
handwriting, speech, and face recognition, spam filtering, chess and other game
programs, robots, et al. This decade the first presumably complete mathematical
theory of artificial intelligence based on universal
induction-prediction-decision-action has been proposed. This
information-theoretic approach solidifies the foundations of inductive
inference and artificial intelligence. Getting the foundations right usually
marks a significant progress and maturing of a field. The theory provides a
gold standard and guidance for researchers working on intelligent algorithms.
The roots of universal induction have been laid exactly half-a-century ago and
the roots of universal intelligence exactly one decade ago. So it is timely to
take stock of what has been achieved and what remains to be done. Since there
are already good recent surveys, I describe the state-of-the-art only in
passing and refer the reader to the literature. This article concentrates on
the open problems in universal induction and its extension to universal
intelligence.Comment: 32 LaTeX page
Neural GPUs Learn Algorithms
Learning an algorithm from examples is a fundamental problem that has been
widely studied. Recently it has been addressed using neural networks, in
particular by Neural Turing Machines (NTMs). These are fully differentiable
computers that use backpropagation to learn their own programming. Despite
their appeal NTMs have a weakness that is caused by their sequential nature:
they are not parallel and are are hard to train due to their large depth when
unfolded.
We present a neural network architecture to address this problem: the Neural
GPU. It is based on a type of convolutional gated recurrent unit and, like the
NTM, is computationally universal. Unlike the NTM, the Neural GPU is highly
parallel which makes it easier to train and efficient to run.
An essential property of algorithms is their ability to handle inputs of
arbitrary size. We show that the Neural GPU can be trained on short instances
of an algorithmic task and successfully generalize to long instances. We
verified it on a number of tasks including long addition and long
multiplication of numbers represented in binary. We train the Neural GPU on
numbers with upto 20 bits and observe no errors whatsoever while testing it,
even on much longer numbers.
To achieve these results we introduce a technique for training deep recurrent
networks: parameter sharing relaxation. We also found a small amount of dropout
and gradient noise to have a large positive effect on learning and
generalization
Intelligent Biohybrid Neurotechnologies: Are They Really What They Claim?
In the era of intelligent biohybrid neurotechnologies for brain repair, new
fanciful terms are appearing in the scientific dictionary to define what has so
far been unimaginable. As the emerging neurotechnologies are becoming
increasingly polyhedral and sophisticated, should we talk about evolution and
rank the intelligence of these devices?Comment: Number of pages: 15 Words in abstract: 49 Words in main text: 3265
Number of figures: 5 Number of references: 25 Keywords: artificial
intelligence, biohybrid system, closed-loop control, functional brain repai
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