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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.
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.
