The Growing Importance of “Non-Sexy” AI “Generally I see that up to now AI has been inspiring in showing what’s possible.
There are numerous examples spanning sectors of how AI can be truly transformative.
Now there are the continuing business realities and internal scaling and process challenges.
So I see the need for a lot of innovations around the less sexy stuff: Cost optimization tools, automated accounting, and administration of big data/analytics platforms.
” Stephen Galsworthy, Head of Data Science at Quby More Focus on Streaming “Streaming in and of itself is not really brand new.
But Rue Gilt Groupe is planning to leverage it on the Databricks Unified Analytics Platform for significant innovations in 2019, like real-time recommendations based on up-to-the-minute data from our order management, click tracking, and other systems.
This is especially important for us because we’re a flash sale retail site, with products and online browsing and purchase behaviors changing by the minute.
This will be fully compatible and complementary to current data science projects like Associations Rules and other personalized recommendations.
” Stephen Harrison, Data Science Architect at Rue Gilt Groupe Machine Learning for All “I see ML becoming far focused on rapid development frameworks or products that make it faster to build and deliver ML solutions with more products that are based 100% off ML behind the scenes.
” Bradley Kent, AVP of Program Analytics at LoyaltyOne Better Data = Better AI “We are already seeing significant progress in the tech and science of Machine Learning and AI.
However, ML/AI technology is as good (or not) as the data behind it.
The next wave of innovation will be driven by better data that can activate the full potential of AI.
” Mainak Mazumdar, Chief Research Officer at Nielsen Deep Learning Will Make AI Better “Building off of 2018, deep learning will continue to improve core AI and ML algorithms.
There will also be a lot of focus on computer vision using deep learning to make more accurate ML models.
From a product standpoint, I think pageless content will only continue to become more relevant.
At Overstock, we have the benefit of having nearly two decades of data that help us create personalized content in real-time, but not all organizations have that same benefit.
As important as pageless content will become, the speed at which pageless happens will have to also need to accelerate in order for customers to have the best experience.
” Kamelia Aryafar, Chief Algorithm Officer at Overstock The promise of what AI can deliver is now coming to fruition.
Companies are realizing that to overcome the barriers of entry, a new approach is needed.
In fact, 79% of organizations cite that they highly value the notion of Unified Analytics as the answer to achieving AI success.
With more companies moving towards this unified approach, we fully expect this year to be a significant year in terms of AI being a foundational asset to help scale and drive the business.
But we’re not done tapping into the minds of today’s leading technologists to understand what they think is the next big innovation in big data, machine learning, and AI.
In the 3rd installment of this blog series, we will dig into the challenges most companies still face when trying to harness the power of AI and putting it into production, and how those challenges will change in 2019.
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