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please enjoy this tech zen koan, unearthed from a 3-month old interview transcriptpic.twitter.com/HIXr9NOQP4
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#TIL 4 steps for politely, but firmly, ending a disagreement:
Acknowledge how the other person feels
Express regret without apologizing
Stand your ground; no explanations, no excuses
Offer a suitable amount of control back
https://www.reddit.com/r/AmItheAsshole/comments/dp3kbl/comment/f5sc5ck … h/t u/tundarpic.twitter.com/CVVUkAiYed
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Originally from 30 Rock. Bonus points for it sounding like an expletivepic.twitter.com/MNjgVH6xOa
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A moment of personal celebration
In Sept 2017, I booked a meeting room at my old job to livestream the #PAIRSymposium and invited coworkers to join. I remember thinking then, dang, I'd love to be working on something like that. Never would've thought... 2 years later
pic.twitter.com/PWO5dhW9Zj
Prikaži ovu nit -
Josh's thoughtful thinking face
@jesssconpic.twitter.com/8tPocmtjOd
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@wattenberg@DrSimoneStumpf: "Explanations [in ML] goes two ways—in terms of what the machine conveys to the user to be intelligible, and how we meaningfully convey user feedback back into the model."#PAIRSymposium2019#participatoryMLpic.twitter.com/Ueb3KaWMwh
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@genmon@jessscon on the value of speculative design for helping us understand systemic implications over time#PAIRSymposium2019#participatoryMLpic.twitter.com/vAhlWPprHj
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@CosDorobantu highlights potential use cases for AI in government:
Predict energy consumption over time
Forecast which regions may suffer from fuel poverty
Simulate policy impacts in controlled environments
#PAIRSymposium2019#participatoryMLpic.twitter.com/FGQztFc50n
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@dweinberger on fairness: "It seems simple and binary, but as soon as we try to apply it, we realize how difficult 'fairness' is. It requires tradeoffs, but which differences are relevant? ML forces us to be specific about what is an ethical and fair outcome."#PAIRSymposium2019pic.twitter.com/cy9hGk56Nr
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Loved
@tafsiri's point that the accessibility and low barrier to entry of JavaScript is essential to Tensorflow.js supporting true#participatoryML.#PAIRSymposium2019pic.twitter.com/LjBaiLU0Xk
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#TIL transfer learning refers to retraining ML models for new use cases@tafsiri#PAIRSymposium2019#participatoryMLpic.twitter.com/G7ofT09LZ7
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@Raghu__ of@WadhwaniAI overviews 7 key questions their teams consider before moving forward with an AI solution for scaled social impact. Not surprisingly, the criticality of stakeholder acceptance in the larger context/system in which you're working.#PAIRSymposium2019pic.twitter.com/VkynJq4pDm
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Fascinating talk by
@chrisnoessel, mapping common UI elements to ML system terms For instance, this example from Spotify: - When a user
a song, that's a true-positive reinforcement
- When a user
a song, that's signaling a false-positive
#PAIRsymposium#participatoryMLpic.twitter.com/yXjha4esJu
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"Technology decisions may pretend to be value-neutral but will inevitably play out in a value-laden context." —
@Nigel_Shadbolt in conversation with@viegasf at the#PAIRsymposium#participatoryMLpic.twitter.com/cH4DFOWzht
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We're live, y'all!
Tune in now into the #PAIRsymposium.@viegasf kicking things off with the significance of#participatoryML. Agenda here
https://pair.withgoogle.com/events/symposium/ …
and live stream link below
https://twitter.com/googleeurope/status/1193906772492333057 …pic.twitter.com/ch56Zo6iag
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