Eight AI trends we’re watching in 2020 Posted on : Dec 25 - 2019

New developments in automation, hardware, model development, and more that will shape AI in 2020.

Roger Magoulas, VP of Radar at O’Reilly takes a look at the new developments in automation, hardware, tools, model development, and more that will shape (or accelerate) AI in 2020.

1. Signs point toward an acceleration of AI adoption

We see the AI space poised for an acceleration in adoption, driven by more sophisticated AI models being put in production, specialised hardware that increases AI’s capacity to provide quicker results based on larger datasets, simplified tools that democratise access to the entire AI stack, small tools that enables AI on nearly any device, and cloud access to AI tools that allow access to AI resources from anywhere.

Integrating data from many sources, complex business and logic challenges, and competitive incentives to make data more useful all combine to elevate AI and automation technologies from optional to required. And AI processes have unique capabilities that can address an increasingly diverse array of automation tasks—tasks that defy what traditional procedural logic and programming can handle, for example, image recognition, summarisation, labeling, complex monitoring, and response.

In fact, in our 2019 surveys over half of the respondents say AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning.

2. The line between data and AI is blurring

Access to the amount of data necessary for AI, proven use cases for both consumer and enterprise AI, and more-accessible tools for building applications have grown dramatically, spurring new AI projects and pilots.

To stay competitive, data scientists need to at least dabble in machine and deep learning. At the same time, current AI systems rely on data-hungry models, so AI experts will require high-quality data and a secure and efficient data pipeline. As these disciplines merge, data professionals will need a basic understanding of AI, and AI experts will need a foundation in solid data practices, and, likely, a more formal commitment to data governance.

3. New (and simpler) tools, infrastructures, and hardware are being developed

We’re in a highly empirical era for machine learning. Tools for machine learning development need to account for the growing importance of data, experimentation, model search, model deployment, and monitoring. At the same time, managing the various stages of AI development is getting easier with the growing ecosystem of open source frameworks and libraries, cloud platforms, proprietary software tools, and SaaS. View More