Virtual Personal Assistants (VPA), also known as Intelligent Digital Agents or Conversational (or Dialog) Systems, are playing a significant role in both personal lives and increasingly also in the business world. The fundamental technologies of VPA have been advanced by Artificial Intelligence and have empowered intelligent and intuitive product offerings in the market. This drives the user’s expectations of using VPA to enhance digital interactions and potentially make their lives more efficient. These expectations then boost the demand for the industry, bring the technologies to the next level, and thus form a virtuous cycle, resulting in creating more advanced solutions at cheaper prices.
This report analyses the current state of VPA development and explores the key challenges and opportunities for this potentially transformative set of technologies.
Artificial Intelligence (AI) is set to reshape the world in a similar way that the Internet did two decades ago and this will have a major impact on the semiconductor industry. AI’s influence coupled with the rapid rise of cloud computing and the slowing of Moore’s Law is leading to a fragmentation of computer architecture. CPU designs are no longer keeping pace with improvements predicted by Moore’s Law (i.e. double CPU performance for half the price every 18-24 months). CPUs are serial devices and many of today’s AI applications can be more efficiently executed on parallel devices such as GPUs. As a result, GPUs and a range of AI-specific processors have the potential to displace the traditional CPU in the next few years with profound implications for industry stalwarts such as Intel. This PowerPoint presentation compares and contrasts alternative processor architectures being developed for both the server market and edge devices (automotive, mobile and IoT) and profiles key players.
Table of Content:
– Introduction
– Key Trends Driving the AI Chip Market
– AI Training and Inference
– Key Players Profiled
– Promising Start-ups
– Applications and Approaches – Software vs Hardware
– Conclusions – For the Cloud & for the Edge
In this PowerPoint-style report, Counterpoint profiles one of the leading players in artificial intelligence in China, iFLYTECH. The company has been a leader in voice processing for many years but is now developing a broader set of competences in artificial intelligence. While it is widely know in China, it generates the vast majority of its revenues in the domestic Chinese market. This represents both a threat and an opportunity to the industry and, more specifically, iFLYTECH itself.
Table of Contents:
Executive Summary
Section I: iFLYTEK Introduction
1.1 A Brief History of iFLYTEK
1.2 Technology Expansion Overview
1.3 Current Achievements and Statistics
Section II: Core Competitive Technologies
2.1 Speech Synthesis
2.2 Speech Recognition
2.3 Semantic Understanding
2.4 Microphone Array
Section III: Major B2C Product Offerings
3.1 Translation
3.2 Mobile
3.3 Automobile
3.4 Home Technology
Section IV: Major B2B Product Offerings in Vertical Markets
4.1 Education
4.2 Healthcare
4.3 Customer Service
Section V: Corporate Strategy
5.1 From Speech Leader to A.I. Ecosystem Builder
5.2 Open AIUI (Artificial Intelligence User Interface)
Overview:
Face Recognition is one of the important biometrics techniques, along with fingerprint, Retina scan, and Iris scan, etc. It has been a perennial popular research challenge in personal identification and human machine interaction since its inception in 1960s, and in recent years there has been a marked escalation in accuracy thanks to the development of convolutional neural networks (CNN) and deep learning schemes in Artificial Intelligence (A.I.).
This report takes a close look at China’s face recognition technology, as its massive algorithms currently top the global rivals in Labeled Faces in the Wild (LFW) – an internationally well-known test for examining face recognition accuracy.
Table of Contents:
- Executive Summary
- Background of Face Recognition Technology
- Major Biometrics Technologies
- Breakthrough in Face Recognition Accuracy
- Elements of Face Recognition
- China’s Face Recognition Technology
- Overview
- Algorithm Level
- Application Level
- Top Application Industries
- Top Vertical Application Cases
- Conclusion
Number of Pages: 35
Author: Wei Sun (analyst@counterpointresearch.com)
Published Date: Dec 2017
Overview:
This report starts with exploring the five tribes of machine learning, the “big bang” of modern deep learning algorithms and the progress made over the years trained either by supervised or unsupervised learning. Post that, it examines current global A.I. developments by country in terms of research and development intensity, funding, patents, A.I. companies and country policies in support of A.I. It then illustrates another five enabling technologies in artificial intelligence, three value creation driving forces of A.I. with their market leaders and identifies the level of applicability of the six enabling technologies in A.I. in the industry of Internet, consumer electronics, financial services, automobile, healthcare and education.
Table of contents:
- Executive Summary
- Evolution of Artificial Intelligence (A.I.)
- Current A.I. Developments by Country
- Top Enabling Technologies
- Three Value Creation Driving Forces
- Major Vertical Businesses and Trends
Number of Pages: 66
Author: Peter Richardson and Wei Sun
Published Date: First published Dec 2017, updated March 2019
While 2015 and 2016 were, in many ways, breakthrough years for Artificial Intelligence (AI) and Machine/Deep Learning in particular, most of the focus has been on developing better algorithms. AI, however, can also benefit greatly from better hardware which is becoming a big differentiator in the performance of AI networks.
GPUs and AI-specific processors have the potential to displace the traditional CPU in the next few years with profound implications for industry stalwarts such as Intel. This report compares and contrasts the various alternative processor architectures for both the server market and client-side devices, and includes short profiles of leading processor vendors and their technologies.
Table of Contents:
– Snapshot
– Strategic Planning Assumptions
– Introduction
– Machine Learning
– Processor Options for Machine Learning
– AI Chip Applications
– Key Players
– General Purpose OR Specialized AI chips?
– Viewpoint
This report can be read in conjunction with the presentation:
- AI at the Edge: Deep Learning Sparks Innovation for Chip Vendors
