2021 Crystal Ball: What’s in Store for AI, Machine Learning, and Data

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Rachel Roumeliotis.

Artificial intelligence (AI) is no longer a “nice-to-have.” From business processes and smart home technology to healthcare and life sciences, AI continues to evolve and grow as it plays an increasing role in many aspects of our work, home lives, and beyond. As we bid 2020 a very welcome goodbye and head into a new year, below are the top five trends across AI, machine learning (ML), and data that we can expect to accelerate in 2021.


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We Have Work to Do When It Comes to MLOps 

MLOps will attempt to bridge the gap between ML applications and the continuous integration and continuous delivery (CI/CD) pipelines that have become a standard practice. Historically, ML presents a problem for CI/CD for several reasons: The data that powers ML applications is as important as code, making version control difficult; outputs are probabilistic rather than deterministic, making testing difficult; and training a model is processor-intensive and time-consuming, making rapid build/deploy cycles difficult. While none of these problems are unsolvable, developing solutions will require substantial effort over the coming years. 

The Time to Adopt Responsible Machine Learning Is Now

The era in which tech companies had a regulatory “free ride” has come to an end. Data use is no longer a practice in which anything goes, and there are legal and reputational consequences for using data improperly. Responsible ML is a movement to make AI systems accountable for the results they produce. This includes explainable AI (e.g., systems that can explain why a decision was made), human-centered ML, regulatory compliance, ethics, interpretability, fairness, and building secure AI. Until now, corporate adoption of responsible ML has been lukewarm and reactive at best. In the next year, increased regulation (such as the GDPR and CCPA), antitrust, and other legal forces will compel companies to adopt responsible ML practices. 

Cloud Data Lakes and Data Lakehouses Will Gain Traction 

Data lakes have experienced a fairly robust resurgence over the last few years, specifically cloud data lakes. With more businesses migrating their data infrastructure to the cloud, as well as the increase of open-source projects driving innovation in cloud data lakes, these will remain on the radar in 2021. Similarly, the data lakehouse, an architecture that features attributes of both the data lake and the data warehouse, gained traction in 2020 and will continue to grow in prominence in 2021. Cloud data warehouse engineering will develop as a particular focus as database solutions progressively move  to the cloud.

We’ll See a Wave of Cloud-Native, Distributed Data Frameworks

Data Science grew up with Hadoop and its vast ecosystem. Hadoop could now be considered a legacy system as momentum has shifted to Spark, which currently dominates the way Hadoop used to. But there are newcomer challengers out there. Distributed computing frameworks like Ray and Dask are more flexible and are cloud-native, meaning they make it very simple to move workloads to the cloud. With both seeing strong growth, time will tell what the next platform on the horizon will be.

Natural Language Processing (NLP) Will Advance Significantly

Last year, the most exciting development in AI was GPT-3 and its ability to generate almost human-sounding prose. What will that lead to in 2021? There are many possibilities, ranging from interactive assistants and automated customer service to automated fake news. Looking at GPT-3 more closely, there are some big questions we should be considering as we kick off the new year. With GPT-3 being delivered via an API (and not by incorporating the model directly into applications), is “Language-as-a-service” the future? While GPT-3 is great at creating English text but has no concept of common sense or facts, how can more sophisticated language models overcome those limitations? For example, GPT-3 has recommended suicide as a cure for depression — misinterpretations like this can cause big, unintended challenges. Lastly, how can biases built into languages be overcome, and who does that responsibility fall on? 

While AI and ML have been transforming our world for decades, the last year has placed a bigger spotlight on these technologies more than ever before. As the world continues to adopt new techniques and practices like MLOps, responsible ML, and NLP, it’s an exciting time to see how the future of AI will unfold.

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