Curved architecture
Curved architecture (source: epicantus via Pixabay)

We’re at an exciting point with artificial intelligence (AI). Years of research are yielding tangible results, specifically in the area of deep learning. New projects and related technologies are blossoming. Enthusiasm is high.

Yet the path toward real and practical application of AI and deep learning remains unclear for many organizations. Business and technology leaders are searching for clarity. Where do I start? How can I train my teams to perform this work? How do I avoid the pitfalls?

We conducted a survey[1] to help leaders better understand how organizations are applying AI through deep learning and where they’re encountering the biggest obstacles. We identified four notable survey findings that apply to organizations.

1. There’s an AI skills gap

Of particular note is an AI skills gap revealed in the survey. 28% of respondents are using deep learning now and 54% say it will play a key role in their future projects. Who will do this work? AI talent is scarce, and the increase in AI projects means the talent pool will likely get smaller in the near future.

2. Companies are addressing the AI skills gap through training

Deep learning remains a relatively new technique, one that hasn’t been part of the typical suite of algorithms employed by industrial data scientists. So, it’s no surprise that the main factor holding companies back from trying deep learning is the skills gap. To overcome this gap, a majority (75%) of respondents said their company is using some form of in-house or external training program. Almost half (49%) of respondents said their company offered “in-house on-the-job training.” 35% indicated their company used either formal training from a third party or from individual training consultants or contractors.

3. Initial deep learning projects often focus on safe upgrades

The rise of deep learning can be traced to its success in computer vision, speech technologies, and game playing, but our survey shows developers and data scientists are more likely to use it to work with structured or semistructured data. Why? There are good reasons. Upgrading familiar applications with deep learning is a safer investment than starting something new, businesses have a lot of structured and semistructured data already, and the number of businesses that can currently make use of computer vision (to say nothing of gaming) is limited. That said, our respondents see value in vision technology, and new deep learning applications for vision will grow in tandem with text and semistructured data.

4. TensorFlow is the most popular deep learning tool

Most respondents (73%) said they’ve begun playing with deep learning software. TensorFlow is by far the most popular tool among our respondents, with Keras in second place, and PyTorch in third. Other frameworks like MXNet, CNTK, and BigDL have growing audiences as well. We expect all of these frameworks—including those that are less popular now—to continue to add users and use cases.

Looking for more insight? Download our free report, "How companies are putting AI to work through deep learning," for full findings from our AI and deep learning survey.

We'll also explore these and related AI topics at Artificial Intelligence Conference in New York, April 29-May 2, 2018.


Article image: Curved architecture (source: epicantus via Pixabay).