AI, ML, and Big Data Survey 2018 Vol. 1

  • ID: 4649604
  • Report
  • 132 pages
  • Evans Data Corp
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Legacy Systems and Poor Quality of Tools are Top Barriers to AI Adoption

This series focuses on tools, methodologies, and concerns related to implementing machine learning, deep learning, image recognition, pattern recognition and other forms of artificial intelligence as well as efficiently storing, handling, and analyzing large datasets and databases from a wide range of sources.

Artificial intelligence is permeating software development in many ways and many industries, which necessitates a thorough knowledge of how developers are doing this. Big Data, often related, is also becoming a reality for more and more companies; this report provides valuable insight into developer opinions on these topics.

This volume includes research and analysis covering topics such as Developer Demographics and Firmographics, Perspectives on the field of AI, Decision-Making for AI & Big Data, Enterprise AI, Barriers and Challenges for AI Data Analytics, AI Concept and Approaches, AI Methods, analytics tools, and services, security concerns for AI, ML, and Big Data, Conversational Systems & Virtual Assistants, Blockchain, Big Data the Cloud and IoT, Parallelism & AI development, Databases & Data Warehousing, and operating systems and languages.

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EXECUTIVE SUMMARY

OVERVIEW
Objectives of the Survey
Survey Methodology
Research Design
Relative Rankings
The Sample – Software Developers
The EDC Panel
Multi-Client Survey Series
Tactical Survey Reports
Custom Surveys
Targeted Analytics

DEMOGRAPHICS AND FIRMOGRAPHICS
Developer Segment
Job Role
Industry
Company Size
Company's Length of Time in Business
Location Relative to Company Headquarters

PERSPECTIVES ON THE FIELD OF ARTIFICIAL INTELLIGENCE
Industry Expectations for AI and ML
Changing Focus on Various Fields within AI and Big Data
Development Focus for AI and ML Projects
Development Focus by Developer Segment

DECISION-MAKING FOR AI AND BIG DATA
Plans for Using AI and Machine Learning in Development
Involvement in Big Data and Advanced Analytics Projects
Involvement in Selecting Big Data or Machine Learning Solutions
Industries Addressed by Big Data and Analytics Solutions

ENTERPRISE AI
Is AI for New or Existing Projects?
New or Existing Projects by Developer Segment
Involvement with Specific AI/ML Tasks
Process of Record for the Development of Models
Organizational Approach to AI and ML
Type of Vertical Apps Augmented by AI and ML
Will AI Replace Existing Business Processes?
Selling AI/ML Solutions in the European Union
Challenging EU Regulations for AI or ML Apps
Challenging EU Regulations by Developer Segment
Perspectives on the EU Regulatory Framework for AI
Perspectives on EU Framework by Developer Segment

BARRIERS AND CHALLENGES FOR AI AND MACHINE LEARNING
Barriers to Adopting Artificial Intelligence and Machine Learning
Challenges to AI and ML Development Ranked
Common Problems with AI Development
Common Problems with AI Development by Developer Segment

AI CONCEPTS, METHODS AND APPROACHES
Artificial Intelligence and Machine Learning Adoption
Engagement with Various Deep Learning Methodologies
Number of Data Sources Used in Modeling
Use of Image Recognition
Image Recognition Domains Targeted for AI
Machine Learning Frameworks and Libraries Used
Machine Learning vs. Rules Based Implementations
Data Feeds for Predictive Analytics
Average Data Ingestion Rates
Working with Data

AI TOOLS, RESOURCES AND SERVICES
Plans for Using Analysis Tools for Particular Tasks
Most Important Considerations for AI or Big Data Tools
Data Analysis Tools Used
Importance of Data Visualization
Data Visualization Tools Used
Information Sources for Big Data and Machine Learning
Most important Vendor Resources for AI, ML or Big Data
Types of APIs Used for AI or ML
Rationale for Integrating Vendor’s APIs into your AI Apps
Biggest Barrier to using a Vendor’s API in your AI App
Code Resources for AI and ML
Useful Code Samples for AI and ML

SECURITY CONCERNS FOR AI, ML, AND BIG DATA
Traditional Security Mechanisms and Big Data
Traditional Security Mechanisms and Big Data by Company Size
Primary Determinants of Security Standards
Primary Determinants of Security Standards by Company Size
Most Important Types of Data to Analyze for Data Security

CONVERSATIONAL SYSTEMS AND VIRTUAL ASSISTANTS
Speech Recognition in Applications
Development of Conversational Systems or Virtual Assistants
Development of Conversational Systems by Company Size
Target Audience for Conversational Systems or Virtual Assistants
Type of Apps Targeted with Conversational Systems or Virtual Assistants
Use of Text Classification Algorithms for Conversational Systems or Virtual
Assistants
Text Classification Method for Conversational Systems
User Interface Used for Conversational Systems or Virtual Assistants
Use Case for Conversational Systems or Virtual Assistants
Industries Targeted by Conversational Systems or Virtual Assistants

BLOCKCHAIN
Plans for Implementing Blockchain in AI or Big Data Projects
Biggest Barrier to Implementing Blockchain in AI or Big Data Projects
Biggest Barrier by Plans for Implementing Blockchain
Evaluation of Blockchain
Most Important Blockchain Benefits

AI, BIG DATA THE CLOUD AND IOT
Where are Big Data Applications Implemented?
Top Three Reasons for Using Cloud to Deploy Big Data Apps
Use of Cloud-based Backend for AI Needs
Projects That Intersect With IoT Projects
Machine Learning in Connected Device Projects
IoT Project Benefits from Machine Learning

PARALLELISM AND AI DEVELOPMENT
Use of Parallelism
Use of Parallelism by Company Size
Use of Parallelism by Developer Segment
Issues with Parallel Programming
Task or Data Parallelism

AI AND HARDWARE INFRASTRUCTURE OPTIMIZATION
Importance of Hardware to Big Data and Machine Learning Projects
Performance vs. Efficiency in Compute Intensive Projects
AI Use in Software Defined Networking
SDN and NVS Functions
Use of Tools or Techniques to Optimize AI Projects for Hardware
Architectures
Preferred Processor Types for AI Optimization
Distribution of AI Workloads on Processors
Hardware Architectures Preferred for Optimization
Number of GPUs Used in Training Efforts
Plans for a Heterogeneous Hardware Approach
Optimization of Specific Processors
AI/ML Platforms

DATABASES AND DATA WAREHOUSING
Growth in Data Stores
Size of Data Sets
Types of Database Technologies in Use
Non-Relational Databases in Use
Biggest Advantage of NoSQL
Use Cases for In-Memory Databases

OPERATING SYSTEMS AND LANGUAGES
Languages Used with Big Data
Technologies Used with Big Data
Languages Used with AI
Technologies Used with AI
Application Languages Preferred for AI and Machine Learning
Primary Host Operating System
Primary Host Operating System by Company Size

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Having to transition from existing legacy systems is the top barrier to incorporation of artificial intelligence or machine learning into organizations today, cited by 18% of developers actively working with AI or ML in the researcher's recently released AI, ML and Big Data Development Survey. However, the quality of existing tools was cited by almost the same number (17.4%). Other factors, such as budget or the cost of materials, regulatory or governance issues, and corporate policy restrictions were also cited by nearly as many AI developers as the top two barriers.

The June 2018 survey of active AI developers also showed that model selection is a particularly challenging aspect of AI or ML implementation along with optimizing for specific parameters and increasing algorithm accuracy. Data ingestion is the phase of AI-related development that proves most vexing to fully a third of AI developers while algorithm development is the top problem area for nearly a quarter.

“Legacy systems that are already in place and the current state of specialized tools are fairly expected issues to come up as software technology evolves to embrace artificial intelligence and machine learning,” said Janel Garvin, CEO of Evans Data Corp, “But what we also saw here was a close list of problems cited in addition to those two, and that close range is illustrative of a new but quickly maturing market.”

Additional insights from the worldwide survey of AI practitioners focus on AI in the large enterprise, hardware, parallelism, algorithms, and other focal areas crafted into data research on questions contributed by some of the largest software companies in the world.

The new Artificial Intelligence, Machine Learning and Big Data Survey provides over 130 pages of data, analysis and graphs with an industry standard margin of error of 5%. Topics covered include: Demographics and Firmographics, Perspectives on AI, Enterprise AI, Parallelism, AI Concepts and Approaches, Tools and Processes, Security Concerns, Conversational Systems, Blockchain, Infrastructure Optimization, and more.

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