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Future of Artificial Intelligence
Future of Artificial Intelligence
Investigating the Hardware and Machine Learning Technologies that would Realize the AI Agents of the Future
06-Apr-2016
Europe
Description
With improvements in data acquisition and computing power, artificial intelligence (AI) is becoming more of a reality each day. Companies are constantly developing self-evolving machine learning approaches and hardware enhancements to support AI applications. This study explores new innovations in machine learning and computing that will enable the application of AI in augmenting real-life scenarios and the impact this will have on the world. For businesses constantly striving to automate their services in a more intuitive and intelligent way to sustain and be competitive in tomorrow’s market, this study provides a comprehensive range of use cases across various business verticals. For companies looking to penetrate the AI industry, this study highlights the key technological directions the big movers of the industry are pursuing to mass commercialize AI solutions in an economical and utilitarian manner.
The following are key questions this study answers:
1. What are the important segments that summarize the complex AI ecosystem?
2. What are the key machine learning problems pursued by the industry and the approaches adopted by the companies to solve them?
3. What are the innovations in the hardware side that will accelerate innovation and applications in AI?
4. What are the big participants in the industry doing to enhance and mass commercialize the AI applications of the future?
5. What factors determine whether or not an organization is ready to adopt AI?
6. How will the adoption of AI affect industries, personal lives, and economies?
7. What should the actors defining employability do to avoid loss of employment because of AI adoption?
8. How will AI applications be deployed in the future?
Table of Contents
Key Findings
Research Scope
Research Methodology
Research Background
Defining AI
AI Systems Overview
Evolution of AI
Human Brain versus Machines—Comparison of Learning
Human Brain, Computers, and Supercomputers—Computing Powers
Dissecting AI
Key ML Problems
ML Styles
Application of ML
Data Mining and ML
Computer Vision and ML
Learning in Robots
RL in Robotics
NLP Tasks
Voice Assistance System for Businesses from MindMeld
Machines Learning the Brain’s Way—Deep Neural Networks
A Technical Insight into Deep Learning Algorithms
Deep Learning Applications
Evolution of ML and its Applications
Shortfalls of Current Computing Hardware Approaches
Analog Replaces Digital—New Opportunities in Neuromorphic Computing
Neuromorphic Chips Modeled on Neurons
FPAA—Driving Innovation in Analog ICs
Promising Memory Technologies for Neuromorphic Computing
Quantum Computing
AI Deployments—Cloud versus On-device Intelligence
Intelligence Becomes Distributed—from Cloud to Edge Computing
Digitalization—Necessary for a Successful AI Strategy
AI Disrupting Business Functions
AI Impacting Organizations and Management
AI Readiness Map
Big Companies Acquiring AI Capabilities
Google’s AI Acquisition Library
Google Brain—Helping Google Leapfrog from Search to AI
AI as a Service—Evolving the Google Way
Microsoft’s Project Adam
Microsoft’s Project Adam (continued)
IBM’s Massive AI System—Watson
IBM Sells Intelligent Outputs, Not Intelligence—Intelligence-as-a-Service
IBM Watson’s Cognitive API Portfolio
IBM’s Neuromorphic Chip—TrueNorth
Qualcomm: Bringing On-device Intelligence to Mobile Low-power Devices
Industry Collaborates at Quantum Artificial Intelligence Lab (QuAIL)
Facebook Revolutionizing Social Network Using Deep Learning
What Are the Big Participants Doing Right?
Technological Revolutions versus Job Trends
Technology Revolutions and the Economy
AI and Automation—Job Threat Mapping
AI Shifting and Disrupting the Labor Ecosystem
Transforming the Actors Defining Employability
AI Disrupting the Middle Class
Are Actors Defining Employability Ready for the Future?
What Capabilities Does AI Bring to the Table?
AI-driven Industry Transformation—Agriculture
AI-driven Industry Transformation—Healthcare
AI-driven Industry Transformation—Manufacturing
AI-driven Industry Transformation—Automotive
AI-driven Industry Transformation—Social Media
AI-driven Industry Transformation—Financial Services
Cloud Robotics and AI
AI in Home Automation
Like Humans, Machines Need Plenty of Data to Learn
How Will AI Systems Be Realized?
Future of AI—How Research Efforts Will Evolve
The Last Word—Key Takeaways
Wellsprings of ML
Key DNN Algorithms
Key DNN Algorithms (continued)
Key DNN Algorithms (continued)
Related Research
| No Index | No |
|---|---|
| Podcast | No |
| Author | Robin Varghese |
| Industries | Cross Industries |
| WIP Number | NF69-01-00-00-00 |
| Is Prebook | No |