Jun Wu, Forbes – March 31, 2020 In the last two years, large enterprise organizations have been scaling up their artificial intelligence and machine learning efforts. To apply models to hundreds of use-cases, organizations need to operationalize their machine learning...
Enterprise AI, ground truth, and the ‘corona effect’
Nothing in our lifetimes has prepared us for what’s happening in our world today. We’ve certainly had our share of major catastrophes in the past 100 years — both natural and man made — but nothing matches the impact of the COVID-19 pandemic. We are living in a time when fundamental assumptions about how our societies function are being thrown out and re-written with blinding speed.
The degree of global disruption is unprecedented in scope and scale, and we’re still in the early phases. Given the confluence of medical, social, political, and economic factors, we have not yet reached the peak of the impact, and the world we’ll inherit as the storm tide recedes will be significantly changed, and changeable. This is not to suggest that “the end is nigh” or that all changes wrought by the pandemic will be bad. But the undeniable truth is that we are experiencing an unexpected and extreme test of our AI technologies and their ability to automate and improve our ability to make good decisions quickly in increasingly complex situations. With respect to AI, we are entering an especially critical phase.
The “truth” I’m focusing on here is what is known to data scientists as “ground truth”, who’s dictionary definition is “factual data as ascertainable through direct observation rather than through inference from remote sensing.” In data science circles, the term generally refers to the reality that underlies the data being fed into AI models in production, and the concern is around any differences between the current ground truth and that reflected in the data with which machine learning models are trained.
Most enterprises have not yet organized themselves around the principles of Enterprise AI in which the traditional business, actuarial, optimization models, etc are modernized to be driven by ML/AI algorithms and operationalized, automated, and governed at enterprise scale. The notion of Enterprise AI highlights a certain “ground truth”: Models are very different from conventional software, and companies need to adjust accordingly if they’re going to be able to use AI effectively in a fast-changing world.
A new discipline is emerging in the large enterprise called ModelOps that, in ways analogous to (but different from) DevOps, combines process, technology and organizational alignment to enable models to move quickly from data science into production — without compromising visibility, operational control or governance. When implemented as an enterprise-wide capability accountable to the CIO, ModelOps enables organizations to ensure that they can get new and updated models into production as fast as the ground truth is changing — which as we now know can be much faster than we’d previously imagined. The alternative is to see AI investments squandered, or worse, to drive business decisions based on models that no longer reflect the world we live in. This is the “Corona Effect”, and those of us in the business of developing and using AI in the real world need to take heed.
For the moment, consider where the ground truth in your business has shifted (and will continue to shift) as the pandemic peaks, ebbs and returns us to a “new normal.” The ability to respond to these unanticipated and potentially dramatic shifts in your business’s operating conditions as they occur is the ultimate goal of Enterprise AI.