Future of Artificial Intelligence

Future of Artificial Intelligence

Investigating the Hardware and Machine Learning Technologies that would Realize the AI Agents of the Future

RELEASE DATE
06-Apr-2016
REGION
Europe
Research Code: NF69-01-00-00-00
SKU: CI00231-EU-MT_18832
$15,000.00
In stock
SKU
CI00231-EU-MT_18832
$15,000.00
DownloadLink

Pay by invoice

ENQUIRE NOW

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
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?
More Information
No Index No
Podcast No
Author Robin Varghese
Industries Cross Industries
WIP Number NF69-01-00-00-00
Is Prebook No