@lufthansa not a single apology or attempt to organize the situation. I will absolutely not be flying Lufthansa in the future and will recommend that all at my workplace avoid it
@lufthansa if you’re going to try to prioritize passengers by flight time, you should make that clear so that an entire jumbo jet’s worth of passengers to SFO aren’t blocking earlier flights...and so the people waiting for SFO aren’t waiting 1.5 hours while others are prioritized
Look who’s on the cover of @IEEESpectrum’s July issue! Read more about supersize AI powered by our record breaking 2.6 transistor Wafer Scale Engine #AICompute#AIResearch#Innovation
https://t.co/vp1MWTVWQf
Mental health, cardiovascular disease, and diabetes are the top-funded clinical indications in digital health, with oncology investments dipping from third to sixth place since 2020. via @Rock_Health
https://t.co/hJdssHEqoz
(Also super cool to be quoted in this)
@ajbosco@ApacheAirflow@DevotedHealth Hey Adam -- would love to learn more about your process for integration testing and data validation. You touched on it, but this is something that could warrant an entire piece(s) in and of itself.
@stuz5000 Intrinsically explainable models are on the surface a nice antidote to fear of black boxiness, but I’m interested in what it is about surrogate models that can feel “second class” in some, often high stakes, applications.
@stuz5000 The most interesting question brought up here, though, is one of what actually allows us to trust decision making? And why do we arrive at that trust standard?
@berkustun@AlexanderSpangh@berkustun this one really got me thinking...do you think actionable recourse is a property that should necessarily exist in all models affecting humans?
@Calsoft_Data Sounds like this champions algorithms that are intrinsically explainable, but what about building explanatory layers on top otherwise black box models? How will those be accepted by explanation-driven fields like healthcare?