Artificial Intelligence and its Role in Near Future
arXiv:1804.01396
Abstract
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.
References in corpus (19)
- Deep Learning in Neural Networks: An Overview
- Continuous control with deep reinforcement learning
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
- Benchmarking Deep Reinforcement Learning for Continuous Control
- Deep Learning using Linear Support Vector Machines
- Deep Networks with Stochastic Depth
- A Berkeley View of Systems Challenges for AI
- DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
- Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots
- On the Performance of ConvNet Features for Place Recognition
- Differential Recurrent Neural Networks for Action Recognition
- Recurrent Network Models for Human Dynamics
- Strategic Dialogue Management via Deep Reinforcement Learning
- Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots
- On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
- A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots
- A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot
- Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data