Data Skeptic

In this episode, Kozzy discusses his endeavors to compare the cognitive abilities of humans, animals, and AI programs. Specifically, we discussed object permanence, the ability to understand an object still exists in space even when you can’t see it. Our conversation traverses both philosophical and practical questions surrounding AI evaluation. We also learned about Animal AI 3, a gaming environment developed in Unity where AI programs and humans can go head-to-head to solve different problems in a gaming environment.

Théo Michelot has made a career out of tackling tough ecological questions using time-series data. How do scientists turn a series of GPS location observations over time into useful behavioral data? GPS tech has improved to the point that modern data sets are large and complex. In this episode, Théo takes us through his research and the application of Hidden Markov Models to complex time series data. If you have ever wondered what biologists do with data from those GPS collars you have seen on TV, this is the episode for you! 

Brian Taylor shares his research on magnetoreception. Animals like birds and sea turtles use magnetoreception to use the Earth’s magnetic field for navigation, but it’s not a sense that’s well understood. Brian uses animal magnetoreception to engineer new ways to navigate the globe. Even cooler, he also takes hypotheses for how magnetoreception works in animals and uses computational simulations to digitally test them. Check out this episode to hear more about Brian’s research and learn more about this little known sensory ability. 

Modeling evolutionary processes goes way beyond the Hardy-Weinberg Equilibrium we all learned in biology class. Natural selection comes from many sources like resources availability, mate preferences, competition. Modeling entire populations of organisms of different species is the holy grail of digital evolution. Join our discussion with evolutionary biologist and software engineer Ben Haller to learn about his work on SLiM and how it helps other biologists model population genetics over time. 

It’s almost impossible to think about animal behavior without thinking of dogs! Our canine friends are a subspecies of wolf that has been co-evolving with us for tens of thousands of years. The transition from wolf to pet has required intense natural and artificial selection for behaviors that allow dogs to live alongside humans, but behavior is not so simple. Join us for a discussion with Dr. Jessica Hekman and learn about dog welfare, behavioral genetics, and the quest to understand the dogs in our lives. 

In this episode, we are joined by Barbara Webb and Anna Hadjitofi. Barbara runs the Insect Robotics lab at the University of Edinburgh, and Anna is a PhD student at the School of Informatics at the university. She is interested in studying and understanding the neural mechanism of the honeybee waggle dance. They join us to discuss the paper: Dynamic antennal positioning allows honeybee followers to decode the dance.

Many researchers and students have painstakingly labeled precise details about the body positions of the creatures they study. Can AI be used for this labeling? Of course it can! Today's episode discusses Social LEAP Estimates Animal Poses (SLEAP), a software solution to train AI to perform this tedious but important labeling work.

Our guest in this episode is Sebastien Motsch, an assistant professor at Arizona State University, working in the School of Mathematical and Statistical Science. He works on modeling self-organized biological systems to understand how complex patterns emerge.

Our guest in this episode is Ryan Hanscom. Ryan is a Ph.D. candidate in a joint doctoral evolution program at San Diego State University and the University of California, Riverside. He is a terrestrial ecologist with a focus on herpetology and mammalogy.  Ryan discussed how the behavior of rattlesnakes is studied in the natural world, particularly with an increase in temperature.

We are joined by Hank Schlinger, a professor of psychology at California State University, Los Angeles. His research revolves around theoretical issues in psychology and behavioral analysis.  Hank establishes that words have references and questions the reference for intelligence. He discussed how intelligence can be observed in animals. He also discussed how intelligence is measured in a given context.

On today’s episode, we are joined by Aimee Dunlap. Aimee is an assistant professor at the University of Missouri–St. Louis and the interim director at the Whitney R. Harris World Ecology Center. Aimee discussed how animals perceive information and what they use it for. She discussed the connection between their environment and learning for decision-making. She also discussed the costs required for learning and factors that affect animal learning.

We are joined by Tamar Gutnick, a visiting professor at the University of Naples Federico II, Napoli, Italy. She studies the octopus nervous system and their behavior, focusing on cognition and learning behaviors. Tamar gave a background to the kind of research she does — lab research. She discussed some challenges with observing octopuses in the lab. She discussed some patterns observed by the octopus lifestyle in a controlled setting. Tamar discussed what they know about octopus intelligence. She discussed the octopus nervous system and why they are unique compared to other animals. She discussed how they measure the behavior of octopuses using a video recording and a logger to track brain activity.

Claire Hemmingway, an assistant professor in the Department of Psychology and Ecology and Evolutionary Biology at the University of Tennessee in Knoxville, is our guest today. Her research is on decision-making in animal cognition, focusing on neotropical bats and bumblebees. Claire discussed how bumblebees make foraging decisions and how they communicate when foraging. She discussed how they set up experiments in the lab to address questions about bumblebees foraging. She also discussed some nuances between bees in the lab and those in the wild. Claire discussed factors that drive an animal's foraging decisions. She explained the foraging theory and how a colony works together to optimize its foraging. She also touched on some irrational foraging behaviors she observed in her study. Claire discussed some techniques bees use to learn from past behaviors. She discussed the effect of climate change on foraging bees' learning behavior. Claire discussed how bats respond to calling frogs when foraging. She also sp

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 ## Resources 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 stude

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.

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.

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.

We celebrate episode 1000000000 with some Q&A from host Kyle Polich.  We boil this episode down to four key questions: 1) How do you find guests 2) What is Data Skeptic all about? 3) What is Kyle all about? 4) What are Kyle's thoughts on AGI?   Thanks to our sponsorsdataannotation.tech/programmers https://www.webai.com/dataskeptic