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Joe Faith proslijedio/la je Tweet
The biggest difference between statistics and machine learning may be in language! So a few months ago I created this (inspired by
@DanielOberski) but haven't made much progress since. Welcoming suggestions for improvementspic.twitter.com/p6YUx4qbmW
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Lesson 3/3: ML is opaque. Regulators, including
@federalreserve and EBA, mandate effective oversight of#ML models. Banks are accountable for their models, so must be able to justify their decisions. This means all decisions must be auditable, traceable, and#XAI explainable.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Lesson 2/3: ML is brittle. Aggregate performance metrics can hide issues on high risk minority cases (eg premium customers or risky locales). Evaluating risk properly requires schema completion and stress testing to understand operational safe limits.https://lukeoakdenrayner.wordpress.com/2019/10/14/improving-medical-ai-safety-by-addressing-hidden-stratification/ …
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Lesson 1/3: Data is a bottleneck. We don't have enough data on verified cases, esp. new threats like
#humantrafficking, so pure-data approaches miss significant risks. The solution is to encode the knowledge of domain experts -- don't wait until the threats appear in the data.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
$4tn of criminal proceeds flow through banks every year. One third of banks use
#ML to detect it, and a third more are running pilots. So what can#regtech learn from#rebootingAI about building#AI we can trust to protect us? https://www.caspian.co.uk/resources/the-rebooting-ai-debate-what-can-financial-crime-compliance-teams-learn/ … Three lessons...Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Joe Faith proslijedio/la je Tweet
ML papers that report model performance really need to start including uncertainty. An easy way to do this is once you're ready to publish, train the model ~10 times with different random seeds. Evaluate each model and report the mean and variance of the results.
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Joe Faith proslijedio/la je Tweet
"A developed country is not a place where the poor have cars. It's where the rich use public transportation." —
@EnriquePenalosa (Photo:#Zurich)pic.twitter.com/bNeS3MPwt5
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Joe Faith proslijedio/la je Tweet
Good science ends when you become more attached to your solution than to the problem.
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Joe Faith proslijedio/la je Tweet
Super excited to do a short Hunting for Monsters in Open Data workshop - building on work with
@alffayard into Monsters, Metaphors and ML to be presented at#CHI2020https://twitter.com/noneck/status/1223776999702958080 …
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Joe Faith proslijedio/la je Tweet
good tech can’t fix bad policy
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Joe Faith proslijedio/la je Tweet
Become a better communicator overnight: - Replace every “but” you can with an “and” - Never start a sentence with “no” or “disagree” - Only give feedback if there’s a chance it will be implemented - Tell people you will be late before you are late - Bias to overcommunicating
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The point of a PhD is not to gain knowledge, it's learning how to do rigourous, original, reproducible research. The content of most PhDs are forgotten as soon as you submit them. But that's ok. The content is just the proof. The value is in the skills you carry for a lifetime.https://twitter.com/jm_alexia/status/1224300493884874753 …
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The grizzled veteran cuts them dead. 'They're nothing til I see 'em'
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The experienced ref says 'I call them as I see them' 3/4
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Daniel Dennett used to sum up various epistemological stances by reference to three baseball umpires. 1/4https://twitter.com/daisychristo/status/1223648167385796611 …
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Joe Faith proslijedio/la je Tweet
If you want a problem solved make it someone’s project. If you want it managed make it someone’s job.
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Joe Faith proslijedio/la je Tweet
We’re categorically ignorant of anything beyond our consciousness. That explains the epistemic gap between experiences and their neural mechanisms.https://twitter.com/minds_making/status/1223411456906948610 …
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Guide to doing effective
#ML research. Key takeaway: focus on goal-driven research, rather than ideas-driven, but look for generalisable solutions. Necessity is the mother of invention. Concrete problems often demand novel solutions. http://joschu.net/blog/opinionated-guide-ml-research.html …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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