3 promising areas for AI skills development
O'Reilly survey results and usage data reveal growing trends and topics in artificial intelligence.
Stairs and brick wall (source: Pixabay)
One of the key findings of a survey we released earlier this year (How Companies are Putting AI to Work through Deep Learning) was that the leading reason holding companies back from incorporating deep learning was their lack of access to skilled people. One-fifth of respondents pointed to a skills gap as one of the reasons they haven’t integrated deep learning, and at the time of the survey, 75% of respondents indicated their company had some combination of internal and external training programs to address this issue.
We’ve continued to monitor interest in topics relevant to building AI products and systems, specifically areas that also warrant investment in skills development. In this post, I’ll share results of related studies we’ve conducted. I’ll draw from two data sources:
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- We examine usage[1] across all content formats on the O’Reilly online learning platform, as well as demand via volume of search terms.
- We recently conducted a survey (full report forthcoming) on machine learning adoption, which included more than 6,000 respondents from North America.
I’ll use key portions of our upcoming AI Conference in San Francisco to describe how companies can address the topics and findings surfaced in these two recent studies.
Growing interest in key topics
Through the end of June 2018, we found double-digit growth in key topics associated with AI. Our online learning platform usage metrics encompass many content formats including books, videos, online training, interactive content, and other material:
Growth was strong across many topics associated with AI and machine learning. The chart below provides a sense of how much content usage (“relative popularity”) we’re seeing in some of these key topics: our users remain very interested in machine learning, particularly in deep learning.
It’s one thing to learn about an individual technology or a specific class of modeling techniques, but ultimately, organizations need to be able to design robust AI applications and products. This involves hardware, software infrastructure to manage data pipelines, and elegant user interfaces. For the upcoming AI Conference in San Francisco, we assembled training sessions, tutorials, and case studies on many of these important topics:
- Edge computing and hardware
- Platforms and infrastructure
- Deep learning models
- Computer vision
- Temporal data and time-series
- Text, language, and speech
- Interfaces and UX
We’ve also found that interest in machine learning compares favorably with other areas of technology. We track interest in topics by monitoring search volume on our online learning platform. Alongside Kubernetes and blockchain, machine learning has been one of the fast-growing, high-volume search topics year over year:
Emerging topics
As I noted in the first chart above, we are seeing growing interest in reinforcement learning and PyTorch. It’s important to point out that TensorFlow is still by far the most popular deep learning framework, but as with other surveys we are seeing that PyTorch is beginning to build a devoted following. Looking closely at interest in topics within data science and AI, we found that interest in reinforcement learning, PyTorch, and Keras have risen substantially this year:
The chart below provides a ranked list of industries that are beginning to explore using reinforcement learning and PyTorch:
We’ve had reinforcement learning tutorial sessions and presentations from the inception of our AI Conference. As tools and libraries get simpler and more tightly integrated with other popular components, I’m expecting to see more mainstream applications of reinforcement learning. We have assembled tutorial sessions and talks at the AI Conference on reinforcement learning and on popular tools for building deep learning applications (including PyTorch and TensorFlow):
Toward a holistic view of AI applications
There is growing awareness among major stakeholders about the importance of data privacy, ethics, and security. Users are beginning to seek more transparency and control over their data, regulators are beginning to introduce data privacy rules, and there is growing interest in ethics and privacy among data professionals.
There are an emerging set of tools and best practices for incorporating fairness, transparency, privacy, and security into AI systems. For our upcoming AI Conference in San Francisco, we have a wide selection of tutorials and sessions aimed at both technologists wanting to understand how to incorporate ethics and privacy into applications, and for managers needing to understand what these new tools and technologies are able to provide: