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Published on 2nd February 2017 at 14:35 by John Louis Swaine
OpenAsset is an indispensable marketing and organizational tool that is used and loved by over 600 companies. Within our team, we have a pretty good understanding of why that’s the case: it’s because the more you use OpenAsset, the more useful it becomes.
As a result we spend a lot of our time trying to make it easier to get started with OpenAsset, so our users can start reaping the rewards of a DAM that truly understands the needs of the AEC and property sectors.
One area of development which has the potential to greatly improve both the power of OpenAsset, and the ease of getting up and running with it, is Artificial Intelligence. In this blog post I’d like to share with you an insight into our AI R&D work and our roadmap for what AI can and will bring to OpenAsset in the next 2 years.
AI assistance for OpenAsset tasks such as Keyword Tagging has been on our radar for a long time now, but it’s only in the last year that it’s really started to become feasible for us to properly engage with developments in the field. That’s because more and more clients have been migrating to OpenAsset Cloud, meaning that we can harness the massive computing power of Amazon’s cloud platform.
Image recognition is still in its early stages of development but there are now offerings in the machine learning marketplace capable of reliably delivering simple results. The service we’ve been most excited about integrating with is Amazon’s Rekognition API, which has already impressed us with the breadth and accuracy of its results.
However, like every service of its kind, the Rekognition API is still far from infallible. It’s a bit like listening to commentary of a sporting event from someone who has never heard of the game they’re describing - you get a lot of information and very little understanding.
That’s where OpenAsset’s 15 years of expertise comes in. We have incredible domain knowledge that’s come from building and improving a powerful tool that truly understands the AEC and property sectors.
Using machine learning, trained on datasets we’ve collected over our history, we can take a raw stream of information and apply meaning to it that’s unique to the needs of our clients. It’s something only OpenAsset can do and it’s another reason why, even after 15 years, our team is still excited to be innovating in the DAM space.
In 2017 our first goal will be to provide AI Assistance for common OpenAsset tasks such as applying keywords to files and projects. That means we’ll be making it easier than ever to perform one of the most important tasks in any DAM. By processing ever-improving Rekognition API data using our own machine learning platform and then combining that information with forthcoming User Interface improvements, our goal is to make this vital workflow as frictionless as possible for users.
Eventually, as our machine learning platform improves, we’ll start taking the information we’ve distilled and begin integrating it automatically into OpenAsset - improving our powerful Search tools as we head into 2018. The aim will be to move from a model of AI Assistance to one of Automation. It’s an extremely ambitious goal but we’ve been encouraged by the results of our R&D so far.
We have a great many other ideas that we’d like to explore - from categorising similar images to automatically recognising and cropping faces for employee photos, however I’m glad to be able to share this early R&D roadmap with you and our engineering team hope you’re as excited about what OpenAsset will be able to do for you and your company in the coming years, as we are.