Bringing proprietary AI in-house is not a workable plan that has occasional side effects. It is a bad move with a straight line to failure – and that’s not hyperbole. Gartner reports that 85% of enterprise AI initiatives fail directly, meaning they never advance beyond the prototype (proof-of-concept) stage. Producing a fieldable product is an outright impossibility for most enterprise teams. Those that beat the odds by getting past the prototype stage still only stand a desperate hope for success. Success with proprietary AI requires surviving a gauntlet of execution challenges covering training at scale, generalization, performance, field qualification testing, software engineering, systems integration, human-centered design, and other workstreams. Mix in a highly technical domain that is packed with esoteric engineering challenges and the typical enterprise stands no chance. I’m sorry, but that’s the reality.
Category: Machine Learning
AI had a breakout year in 2022, where artificial neural networks with over a hundred-billion learnable parameters escaped their labs and fell into the hands of ordinary people.
Model drift can cost you. Just ask Zillow. AI decision making may be the future for more effective business decisions. But when AI changes how it makes decisions, without anyone knowing – it can get you into a world of trouble. That is what likely happened with Zillow Offers where an AI algorithm overestimated the