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Machine learning 'causing science crisis' - BBC News ...”Machine-learning techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong.” Replication in new data essential https://t.co/BvEV2R8lEJ
— Richard Riley (@Richard_D_Riley) February 17, 2019
A leading US scientist says artificial intelligence is leading to inaccurate findings in biomedical research https://t.co/bqOMGT7rjb pic.twitter.com/4duXLpdaP5
— Financial Times (@FinancialTimes) February 15, 2019
“A lot of these techniques are designed to always make a prediction. They never come back with 'I don't know' or 'I didn't discover anything' because they aren’t made to.” https://t.co/oK7aQj8Emq (cc @v_schulercosta)
— Justin Pickard (@justinpickard) February 16, 2019
A leading US scientist says artificial intelligence is leading to inaccurate findings in biomedical research https://t.co/bqOMGT7rjb pic.twitter.com/4duXLpdaP5
— Financial Times (@FinancialTimes) February 15, 2019
“A lot of these techniques are designed to always make a prediction. They never come back with 'I don't know' or 'I didn't discover anything' because they aren’t made to.” https://t.co/oK7aQj8Emq (cc @v_schulercosta)
— Justin Pickard (@justinpickard) February 16, 2019
risks of trusting ML too much in biomedicine https://t.co/JIFKcHwuwU
— Gary Marcus (@GaryMarcus) February 16, 2019
The problem with humans warning about discoveries made with AI is that we fail to understand that #AI will be better than us in this, because it doesn't have any limits. AI can make ALL the discoveries. We just need to improve the validation process.
— Mikko Alasaarela | AI ❤ Blockchain (@alasaarela) February 17, 2019
https://t.co/tguXkJsIt3
Scientist warns against discoveries made with AI https://t.co/Ed8wpHvi0B
— Financial Times (@FT) February 15, 2019
We can't trust findings from machine learning applied to large biomedical data sets. They are often not accurate or reproducible, says @Genevera_Allenhttps://t.co/zJtJhABqdh via @financialtimes
— Clive Cookson (@clivecookson) February 15, 2019
“A lot of these techniques are designed to always make a prediction. They never come back with 'I don't know' or 'I didn't discover anything' because they aren’t made to.” #AI #MachineLearning
— Tom Hillenbrand (@tomhillenbrand) February 16, 2019
https://t.co/Yb2OUPLjGn
Scientist warns against discoveries made with AI https://t.co/60qCuFn9d7 via @financialtimes @JohnAFlood @PabloRedux @David_Gunkel @EmergTechEthics @sd_marlow @DorotheaBaur @bronwynwilliams
— Paresh Kathrani (@PKathrani) February 15, 2019
Scientist warns against discoveries made with AI. #ArtificialIntelligence #dataresponsible #robotics https://t.co/IkcCjDwn4X
— Nordic AI Artificial Intelligence Institute (@nordicinst) February 15, 2019
Statistician raises red flag about reliability of machine learning techniques: Machine learning is everywhere in science and technology. But how reliable are these techniques really? A statistician argues that questions of accuracy and reproducibility of… https://t.co/CUNc0ZkRZe pic.twitter.com/Eetj9L6xsm
— Abinmbr (@abinmbr) February 17, 2019
Machine learning is contributing to a “reproducibility crisis” within science...which is essentially what happens when anything becomes popular mainstream #AI #MachineLearning #ML #datascience https://t.co/fLP95izRbw
— Colin McGuire (@realColinMac) February 18, 2019
Why #MachineLearning is contributing
— Spiros Margaris (@SpirosMargaris) February 18, 2019
to a “reproducibility #crisis” within #science https://t.co/dGX5RA7CMm #fintech #insurtech #AI #ArtificialIntelligence #DeepLeaning #robotics @techreview @RiceUniversity @RiceStatistics pic.twitter.com/lvYDCnXUDP
Statistician raises red flag about reliability of #machinelearning techniques https://t.co/6n3tkzgppU#innovation #AI #in
— Alex Jiménez (@RAlexJimenez) February 18, 2019
Why #Statistician Raises #RedFlag
— Spiros Margaris (@SpirosMargaris) February 18, 2019
about Reliability of #MachineLearning https://t.co/8q67AdsByD #fintech #insurtech @GeorginaTorbet @DigitalTrends #AI #ArtificialIntelligence #DeepLearning #robotics @DimDrandakis @pierrepinna @Ronald_vanLoon @jblefevre60 @Paula_Piccard pic.twitter.com/n0IgRjyzp5
Not only do machines have opinions, they don’t know how to say ‘I don’t know.’ https://t.co/Ne7qRQ7pRT
— mr. mild irony (@JohnSumser) February 18, 2019
Statistician Raises Red Flag about Reliability of Machine Learning #machinelearning @digitaltrends #science https://t.co/WsrYAJiu2q
— Evan Kirstel in #SanFrancisco @samsung #unpacked (@evankirstel) February 18, 2019
The problem is that machine learning techniques do not have a way to say “I don’t know” or “It’s not clear.” The techniques will generally always produce an answer — but this answer may not be as certain or accurate as it is believed to be. #AI #ML https://t.co/2JoweUtywM
— Phil & Pam Lawson (@SocializingAI) February 18, 2019
Statistician raises red flag about reliability of machine learning techniques https://t.co/9VT6n4kmfh
— Thierry Lorho (@globexpert) February 18, 2019
'I would not trust a very large fraction of the discoveries that are currently being made using machine-learning techniques applied to large data sets'https://t.co/MtiQa8mng9
— FT Health (@fthealth) February 18, 2019
Beware the AI on the march https://t.co/EeIEpaNXBO @FT #blockchain #ai #payments #quantum @dinisguarda @ztudium @thomaspower @Ericvanderkleij @ronaldvanloon @evankirstel @michaeldacosta @davidwhite_ai @TheActiveledger @JulesRatcliffe @walkermartyn @francescoswiss @jenniferchung_
— Keybox - The Distributed Vault (@TheKeybox) February 17, 2019
“There is always a story that you can construct to show why a particular group of {insert anything} is grouped together,” Something to think deeply about today. https://t.co/bev8Oqy89h #DataScience #webmarketing #dataintegration #webdesign
— new target, inc. (@NewTargetInc) February 18, 2019
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