Band-Aids don’t fix bullet holes: Repairing the broken promises of ubiquitous machine learning

Scale
06/17/2019 - 11:00 to 11:40
Kesselhaus
long talk (40 min)
Beginner

Session abstract: 

Buoyed by expensive industrial research efforts, amazing engineering breakthroughs, and an ever-increasing volume of training data, machine learning techniques have recently seen successes on problems that seemed largely intractable twenty years ago.  However, beneath awe-inspiring demos and impressive real-world results, there are cracks in the foundation: ordinary organizations struggle to get real insight or value out of their data and wonder how they’ve missed out on the promised democratization of AI and machine learning.  

This talk will diagnose how we got to this point.  You’ll see how the incentives and rhetoric of software and infrastructure vendors have led to inflated expectations.  We’ll show how internal political pressures can encourage teams to aim for moonshots instead of realistic and meaningful goals.  You’ll learn why contemporary frameworks that have enjoyed prominent successes on perception problems are almost certainly not the best fit for gleaning insights from structured business data.  Finally, you’ll see why many of the solutions the industry has offered to real-world machine learning woes are essentially “bandages” that cover deep problems without addressing their causes.

This talk won’t merely offer a diagnosis without a prescription; we’ll conclude by showing that the way to avoid disappointing machine learning initiatives in the future isn’t a patchwork of superficial fixes to help us ignore that we’re solving the wrong problems.  Instead, we need to radically simplify the way we approach learning from data by embracing broader definitions of “AI” and “machine learning.” Organizations should prioritize results over emulating research labs and practitioners should focus first on fundamental techniques including summaries, sketches, and straightforward models.  These techniques are unlikely to attract acclaim on social media or in the technology press, but they are broadly applicable, allow practitioners to realize business value quickly, produce interpretable results, and truly democratize machine intelligence.