Data Skeptic15 Tammi 2016

Detecting Pseudo-profound BS

A recent paper in the journal of Judgment and Decision Making titled On the reception and detection of pseudo-profound bullshit explores empirical questions around a reader's ability to detect statements which may sound profound but are actually a collection of buzzwords that fail to contain adequate meaning or truth. These statements are definitively different from lies and nonesense, as we discuss in the episode.

This paper proposes the Bullshit Receptivity scale (BSR) and empirically demonstrates that it correlates with existing metrics like the Cognitive Reflection Test, building confidence that this can be a useful, repeatable, empirical measure of a person's ability to detect pseudo-profound statements as being different from genuinely profound statements. Additionally, the correlative results provide some insight into possible root causes for why individuals might find great profundity in these statements based on other beliefs or cognitive measures.

The paper's lead author Gordon Pennycook joins me to discuss this study's results.

If you'd like some examples of pseudo-profound bullshit, you can randomly generate some based on Deepak Chopra's twitter feed.

To read other work from Gordon, check out his Google Scholar page and find him on twitter via @GordonPennycook.

And just for fun, if you think you've dreamed up a Data Skeptic related pseudo-profound bullshit statement, tweet it with hashtag #pseudoprofound. If I see an especially clever or humorous one, I might want to send you a free Data Skeptic sticker.

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(601)

LLMs in Music Composition

In this episode, we are joined by Carlos Hernández Oliván, a Ph.D. student at the University of Zaragoza. Carlos's interest focuses on building new models for symbolic music generation. Carlos shared ...

28 Elo 202333min

Cuttlefish Model Tuning

Hongyi Wang, a Senior Researcher at the Machine Learning Department at Carnegie Mellon University, joins us. His research is in the intersection of systems and machine learning. He discussed his resea...

21 Elo 202327min

Which Professions Are Threatened by LLMs

On today's episode, we have Daniel Rock, an Assistant Professor of Operations Information and Decisions at the Wharton School of the University of Pennsylvania. Daniel's research focuses on the econom...

15 Elo 202338min

Why Prompting is Hard

We are excited to be joined by J.D. Zamfirescu-Pereira, a Ph.D. student at UC Berkeley. He focuses on the intersection of human-computer interaction (HCI) and artificial intelligence (AI). He joins us...

8 Elo 202348min

Automated Peer Review

In this episode, we are joined by Ryan Liu, a Computer Science graduate of Carnegie Mellon University. Ryan will begin his Ph.D. program at Princeton University this fall. His Ph.D. will focus on the ...

31 Heinä 202336min

Prompt Refusal

The creators of large language models impose restrictions on some of the types of requests one might make of them. LLMs commonly refuse to give advice on committing crimes, producting adult content, ...

24 Heinä 202344min

A Long Way Till AGI

Our guest today is Maciej Świechowski. Maciej is affiliated with QED Software and QED Games. He has a Ph.D. in Systems Research from the Polish Academy of Sciences. Maciej joins us to discuss findings...

18 Heinä 202337min

Brain Inspired AI

Today on the show, we are joined by Lin Zhao and Lu Zhang. Lin is a Senior Research Scientist at United Imaging Intelligence, while Lu is a Ph.D. candidate at the Department of Computer Science and En...

11 Heinä 202336min