In the past 20 years, machine learning (ML) has progressively moved from an academic endeavor to a pervasive technology adopted in almost every aspect of computing. ML-powered products are now embedded in every aspect of our digital lives: from recommendations of what to watch, to divining our search intent, to powering virtual assistants in consumer and enterprise settings. Moreover, recent successes in applying ML in natural sciences have revealed that ML can be used to tackle some of the hardest real-world problems that humanity faces today.19
For these reasons, ML has become central to the strategy of tech companies and has gathered even more attention from academia than ever before. The journey that led to the current ML-centric computing world was hastened by several factors, including hardware improvements that enabled massively parallel processing, data infrastructure improvements that resulted in the storage and consumption of the massive datasets needed to train most ML models, and algorithmic improvements that allowed for better performance and scaling.
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