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AI, ML, and Big Data Developers Survey, 2018 Edition -
[October 09, 2018]

AI, ML, and Big Data Developers Survey, 2018 Edition -

The "AI, ML, and Big Data Survey 2018 Vol. 1" report has been added to's offering.

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.

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 survey of active AI developers also showed that model selection is a particularly challenging aspec 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 (News - Alert), 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.

Key Topics Covered:

1. Executive Summary

2. Overview

3. Demographics And Firmographics

4. Perspectives On The Field Of Artificial Intelligence

5. Decision-Making For AI And Big Data

6. Enterprise AI

7. Barriers And Challenges For AI And Machine Learning

8. AI Concepts, Methods And Approaches

9. AI Tools, Resources And Services

10. Security Concerns For AI, Ml, And Big Data

11. Conversational Systems And Virtual Assistants

12. Blockchain

13. AI, Big Data The Cloud And IoT

14. Parallelism And AI Development

15. AI And Hardware Infrastructure Optimization

16. Databases And Data Warehousing

17. Operating Systems And Languages

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