The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
Memory in Chess
On today’s show, we are joined by our co-host, Becky Hansis-O’Neil. Becky is a Ph.D. student at the University of Missouri, St Louis, where she studies bumblebees and tarantulas to understand their learning and cognitive work.
She joins us to discuss the paper: Perception in Chess. The paper aimed to understand how chess players perceive the positions of chess pieces on a chess board. She discussed the findings paper. She spoke about situations where grandmasters had better recall of chess positions than beginners and situations where they did not.
Becky and Kyle discussed the use of chess engines for cheating. They also discussed how chess players use chunking. Becky discussed some approaches to studying chess cognition, including eye tracking, EEG, and MRI.
## Paper in Focus
Perception in chess
Detecting Cheating in Chess with Ken Regan
On this episode, we are joined by Stephen Larson, the CEO of MetaCell and an affiliate of the OpenWorm foundation. Stephen discussed what the Openworm project is about. They hope to use a digital C. elegans nematode (C. elegans for short) to study the basics of life.
Stephen discussed why C. elegans is an ideal organism for studying life in the lab. He also discussed the steps involved in simulating a digital organism. He mentioned the constraints on the cellular scale that informed their development of a digital C. elegans.
Stephen discussed the validation process of the simulation. He discussed how they discovered the best parameters to capture the behavior of natural C. elegans. He also discussed how biologists embraced the project.
Stephen discussed the computational requirements for improving the simulation parameters of the model and the kind of data they require to scale up. Stephen discussed some findings that the machine-learning communities can take away from the project. He also mentioned how students can get involved in the Openworm project. Rounding up, he shared future plans for the project.
What the Antlion Knows
Our guest is Becky Hansis-O’Neil, a Ph.D. student at the University of Missouri, St Louis, and our co-host for the new "Animal Intelligence" season. Becky shares her background on how she got into the field of behavioral intelligence and biology.
Kyle is joined by friends and former guests Pramit Choudhary and Frank Bell to have an open discussion of the impacts LLMs and machine learning have had in the past year on industry, and where things may go in the current year.
Uncontrollable AI Risks
We are joined by Darren McKee, a Policy Advisor and the host of Reality Check — a critical thinking podcast. Darren gave a background about himself and how he got into the AI space.
Darren shared his thoughts on AGI's achievements in the coming years. He defined AGI and discussed how to differentiate an AGI system. He also shared whether AI needs consciousness to be AGI.
Darren discussed his worry about AI surpassing human understanding of the universe and potentially causing harm to humanity. He also shared examples of how AI is already used for nefarious purposes. He explored whether AI possesses inherently evil intentions and gave his thoughts on regulating AI.
I LLM and You Can Too
It took a massive financial investment for the first large language models (LLMs) to be created. Did their corporate backers lock these tools away for all but the richest? No. They provided comodity priced API options for using them. Anyone can talk to Chat GPT or Bing. What if you want to go a step beyond that and do something programatic? Kyle explores your options in this episode.
I found the show as a result of a google search, I started listening as a way of building on my developing interest in data science and my hope to learn more about deep learning.
Generally I find that podcasts assume the listener has been there from the beginning and so to make sure I didn’t miss out on any important topics, I found the first episode and started listening.
At the point of writing this review, I’m still working my way through the back catalogue, I’m somewhere in 2015, in fact my review title is based on the crypto zoology episode which was pure entertainment.
Kyle, Lin Da and Yoshi are fantastic hosts. I’ve definitely learned a few things so far, I especially like the bigger sized mini episodes that explain really cool things like k-means clustering in really simple ways, just enough to give you an understanding and help you go off and do a bit more work on your own.
One thing to be aware of, with both Kyle and several of his guests, you will find many references to really great books, I’m spending a small fortune on reading material as a result 😁
Really enjoy the show, the format and the hosts. Highly recommended!
What's the sentiment ? I got a pointer for this podcast
from a data scientist in London. The only Podcast I rate
anywhere near this level is TWIST. I can't belive it's free !
Love this show
I am getting into data science and I really like the way this show tackles topics in ML and stats. I absolutely loved the Library Problem show because it was so cool to see a real life example worked all the way though and hear about how a data scientist might decide what to use (all these methods and ideas we'd heard about in pervious shows). If it's not giving away too many of your interview secrets, I'd love to hear more like the Library Problem.
This is an excellent show for anyone wanting to learn more about data science.