[A version of this post appears on the O’Reilly Radar.]
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.
In this post, I share slides and notes from a keynote Roger Chen and I gave at the Artificial Intelligence conference in London in October 2018. We presented an overview of the state of automation technologies: we tried to highlight the state of the key building block technologies and we described how these tools might evolve in the near future.
To assess the state of adoption of machine learning (ML) and AI, we recently conducted a survey that garnered more than 11,000 respondents. As I pointed out in previous posts, we learned many companies are still in the early stages of deploying machine learning:
Companies cite “lack of data” and “lack of skilled people” as the main factors holding back adoption. In many instances, “lack of data” is literally the state of affairs: companies have yet to collect and store the data needed to train the ML models they desire. The “skills gap” is real and persistent. Developers have taken heed of this growth in demand. In our own online learning platform, we are seeing strong growth in usage of content across AI topics, including 77% growth in consumption of content pertaining to deep learning:
Continue reading “Assessing progress in automation technologies”