Christian Theil Have retweeted
The take-home message from the debate on Bing AI is not how easily AI now passes the Turing Test.
It is how easily *we* fail it: Falsely perceiving AI as human conscious intelligence.
The reason is a well-established psychological effect: Hyperactive agency detection.
🧵 1/5
Christian Theil Have retweeted
Mike Wooldridge @wooldridgemike puts his finger squarely on the big reason why #AI researchers should take "informing public about #AI" seriously. #AAAI2021
(Certainly resonates with my own reasons for my modest involvement.. bit.ly/39SkWnS bit.ly/2Sy2GnE )
Christian Theil Have retweeted
My impression is that some folks use machine learning to try to "solve" problems of artificial scarcity. Eg: we won't give everyone the healthcare they need, so let's use ML to decide who to deny.
Question: What have you read about this? What examples have you seen?
Christian Theil Have retweeted
Statistical Sin? RIGHT to jail 🔐
#statsTikTok #parksAndRec
Not so optimistic and has really good point about bias amplification which is all too real..
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Christian Theil Have retweeted
Except we accept that people might be wrong but with algorithms you just get:
I see this as progress and a cause for optimism: By scrutinizing algorithms (which is easier than scrutinizing people processes), we get a grip on uncovering and handling these biases.
People are biased.
Data is biased, in part because people are biased.
Algorithms trained on biased data are biased.
But learning algorithms themselves are not biased.
Bias in data can be fixed.
Bias in people is harder to fix. nytimes.com/2019/12/06/busin…
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Christian Theil Have retweeted
“Biased algorithms are easier to fix than biased people”.
But, in some cases, I don't see these as entirely different problems. Sometimes fixing a biased algorithm first requires fixing biased people.
nytimes.com/2019/12/06/busin…
Christian Theil Have retweeted
With the last week's launch of Google Cloud’s Explainable AI, the conversation around #ExplainableAI has accelerated.
But it begs the questions - Should Google be explaining their own AI algorithms? Who should be doing the explaining? /thread
Christian Theil Have retweeted
"While the first wave of algorithmic accountability focuses on improving existing systems, a second wave of research has asked whether they should be used at all—and, if so, who gets to govern them." @FrankPasquale lpeblog.org/2019/11/25/the-s…
Heard! Focusing narrowly on human-imitative AI prevents an appropriately wide range of voices from being heard: Michael Jordan's is voicing his concern about the ongoing "AI revolution" in this excellent article.
[D] Artificial Intelligence—The Revolution Hasn’t Happened Yet (Michael Jordan opinion piece) : Ma... bit.ly/2RPDt9L #ai #ml #dl
Christian Theil Have retweeted
I’m available and actively looking for an engineering or research position solving enticing problems using logic programming and symbolic AI. Any leads that you can share? For a sample of my work, please visit logtalk.org Retweets most appreciated.
Christian Theil Have retweeted
I’m happy to announce my lab is working on AI+Climate Change. Climate Change is one of humanity’s most pressing problems & the Tech community must help. I’m still exploring more projects. If you want to help, let me know here: deeplearning.ai/climate #EarthDay – at Stanford, CA
Nice blogpost about ML systems as (coincidental) policies that can be learned and gamed changes incentive structures & behavior. We should focus on building understandable systems incentivising good behavior.
approximatelycorrect.com/201…
Christian Theil Have retweeted
Julia + TPUs = fast and easily expressible ML computations!
Our new paper today: arxiv.org/abs/1810.09868. Compile your #julialang code straight to @Google's #CloudTPU. Must go faster! We'll have an (alpha quality) repo up soon for people to start playing with this.