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“On the flight home we reflected on what an amazing time we had!

Overcoming barriers to AI adoption

When asked what held back adoption of deep learning, the same set of respondents cited “lack of skilled people,” “data-related challenges,” and “compute resources” as the main obstacles they faced.

This is precisely what we found in our upcoming survey, “Artificial Intelligence in the Enterprise”: for example, respondents from the financial services sector were already using AI in “customer service” or “finance and accounting.” It’s also no surprise that even though deep learning is often associated with computer vision and speech technologies, we are beginning to see it used in areas where companies already have existing data sets and machine learning applications (specifically areas that involve text and time series).

Related training programs, tutorials, and sessions to explore at the Artificial Intelligence Conference in New York City include: There have been numerous articles written about artificial general intelligence, but the reality is that, at least for now, many of the AI systems that have captured press coverage have very specific and narrow capabilities.

At the Artificial Intelligence Conference, we’ll have sessions and case studies from many industries, including: As I noted, respondents who work at organizations with more mature AI practices cited “data” and “lack of people” as the primary challenges they face as they attempt to adopt more AI technologies.

When it comes to data, there are new tools designed to help companies overcome lack of data, including tools for generating synthetic data, simulation environments, and automation technologies designed to supercharge human labelers (Amazon Ground Truth is the most recent example).

Leading companies will present some of their internal tools and platforms at the Artificial Intelligence Conference in April: In parallel with the progress in AI research and the progress in tools for building AI applications, there has been strong awareness around issues that go beyond simply optimizing business or quantitative metrics.

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