Where AI has made real progress


We’ve been overselling current capabilities of AI for years, but that doesn’t mean it doesn’t have a bright future. That’s perhaps why Stanford University researchers conceived of a “One Hundred Year Study on Artificial Intelligence” (100 years!) back in 2016, with plans to update the report every five years through 2116, charting the progress of AI along the way. Five years after the inaugural report, the study authors recently released the second report.

The TL;DR? We’ve made “remarkable progress” in just five years, on the back of ever-improving data infrastructure, yet we still fall “far short of the field’s founding aspiration of recreating full human-like intelligence in machines.” What we are discovering, however, is the importance of meshing human and machine to achieve better outcomes. Is it “true” AI? Not as originally envisioned. But arguably it’s better.

Big data? Try ‘easy data’

One of the primary inhibitors to data science (and resultant AI) becoming real has little to do with science and everything to do with data. As FirstMark investor Matt Turck recently called out in “The 2021 Machine Learning, AI, and Data (MAD) Landscape,” only recently have data warehouses evolved “to store massive amounts of data in a way that’s useful, not completely cost-prohibitive, and doesn’t require an army of very technical people to maintain.” Yes, we’ve had data warehouses for decades, but they’ve been complicated and costly. More recently we’ve dabbled in Apache Hadoop, which made things cheaper but still overly complex.

Only in the past few years has the industry focused on maturing our data infrastructure such that it has become dramatically more approachable for mere mortals (who may or may not have a PhD). By making it “finally possible to store and process big data” in a cost-effective manner, Turck argues, it “has proven to be a major unlock for the rest of the data/AI space” in three primary ways:

  • The rise of data warehouses considerably increases market size not just for its category, but for the entire data and AI ecosystem.
  • Data warehouses have unlocked an entire ecosystem of tools and companies that revolve around them, such as extract, load, transform (ELT).
  • Data warehouses liberate companies to start focusing on high-value projects that appear higher in the hierarchy of data needs.

Although Turck chooses to focus on the positive impact of modern data warehouses, the industry has also benefited from other advances in databases (distributed databases, NoSQL, etc.) and the cloud, which has made it easier to iterate on data. Through these and other forces, it has become easier to store and work with data which, in turn, has enabled organizations to do more with that data.

Which brings us back to Stanford’s AI100.

Copyright © 2021 IDG Communications, Inc.



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