Top A.I. Industry Trends 2019

Top AI Industry Trends 2019

The best thing about predicting trends is watching the year unfold and seeing which ones really take off. While we have no doubt that 2019 has some jaw-dropping tech surprises in store for us, we couldn’t resist getting the team together to compile some of our best guesses.  

There was a lot of buzz around Natural Language Processing and virtual assistants last year. The ability for A.I. to process complex images within the health sector – x-rays, for example – really took off. While these advancements are likely to continue into 2019, we believe this year will be all about the engineer: helping the behind-the-scenes work that goes into creating Artificial Intelligence. This year’s biggest trends will revolve around giving data scientists and machine learning engineers better tools for bringing their work to life.

2019 AI Industry Trends We See In Our Tealeaves:

Automation

2019 will see us developing tools that take Process Automation even further. This essentially boils down to applying A.I. to processes through Intelligent Process Automation – which makes the whole process of storing, processing and delivering data more efficient for the people who deploy machine learning models. 

Our work with MetTel has already shown what Intelligent Process Automation can achieve, which includes cost savings, increased capacity, and improved quality of outputs – to name a few.

Hardware Improvements

With the massive growth of A.I. comes a need for greater, faster automation – but also processing power. Artificial Intelligence requires a tremendous amount of bandwidth between CPU, GPU and memory, which often creates a bottleneck. Manufacturers are aware of this, and know that A.I. companies are concerned with delivering a faster time to market. We believe they’ll spend at least a portion of this year creating new processor models that are specially designed for A.I. projects. We can expect a new Moore’s Law happening on this side in a similar way to the CPU power in the last decades.

Adopting DataOps

Applying DevOps practices to Artificial Intelligence? Yes, please. Many A.I. companies are applying their DevOps mindset to A.I. projects, and Intelygenz are right there with them. We fully embrace DevOps culture, so it’s been a natural shift for us to apply it within the our Intelligent Product offerings. Some are calling it DataOps, others AIOps – either way, we love any automated, process-oriented methodology that boosts the quality of our work.

Working with Non-Annotated Data

No data? No problem. Where much of Artificial Intelligence has relied on data quality and quantity until now, 2019 will be the year we break free from many of those constraints. Learning by reinforcement – or rather, the ability to train models in spite of not having any annotated data – or working with simulated data to bootstrap initial versions of models will both blossom in 2019, and we couldn’t be more excited about the possibilities that advancement will open up.


Download our free Retail guide here to see how you can implement A.I. into your workflow.

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