  The use of machine learning in Healthcare has the potential to improve
patient outcomes as well as broaden the reach and affordability of Healthcare.
The history of other application areas indicates that strong benchmarks are
essential for the development of intelligent systems. We present Personal
Health Interfaces Leveraging HUman-MAchine Natural interactions (PhilHumans), a
holistic suite of benchmarks for machine learning across different Healthcare
settings - talk therapy, diet coaching, emergency care, intensive care,
obstetric sonography - as well as different learning settings, such as action
anticipation, timeseries modeling, insight mining, language modeling, computer
vision, reinforcement learning and program synthesis
