Today, we sat down with Dr. Ignacio Escalante Meza to learn about opiliones and treehoppers. Opiliones, known as “daddy long legs” in the US, are understudied arachnids known for their tenacious locomotor behavior, sociality, and chemical communication. Treehoppers communicate through the stems of plants using vibrations. They can signal danger, attract mates, and communicate with their offspring. Join us to learn how researchers turn their vibrations into sound waves and study what they have to say.

Weird Communication

Human shipping operations have increased significantly in the past few decades.  While that means international trade and cheap goods for humans, it also means the ocean has experienced an increase in noise pollution.  This has a measurable negative impact on marine mammals and other aquatic life.  Could mathematics be the solution?  This interview explores how optimization techniques can guide voyage optimization in a way that handles multiple optimization objectives including fuel cost and sound reduction.

Reducing the Impact of Ship Noise on Marine Mammals

Robbie Moon from the Georgia Tech Scheller College of Business joins us to discuss the analysis of unstructured data and the application of NLP methodologies towards financial data.

Analysis of Unstructured Data

Have you ever participated in citizen science? Do you want to? One of the most popular platforms for crowdsourcing biodiversity data is iNaturalist. In addition to being a great science tool, the iNaturalist app can help you identify the organisms you encounter every day. We talked to Executive Director Scott Laurie about how scientists use iNaturalist. We also got to discuss what makes iNaturalist’s AI species recognition so good, and how citizen scientists are constantly providing high-quality training data. Listen in and learn how this fun-to-use tool works, where it's headed, and how you can get involved.

iNaturalist

Do you code or are you interested in learning to code? Join us today and hear from three individuals that are at very different stages of their coding journeys. Becky Hansis-O’Neill (also our co-host this season) shares her experiences as a newbie who wants to learn more. Dr. Malia Gehan, a self-taught developer interested in studying plant phenotypes, explains why and how she and her colleagues learned to code and developed PlantCV. Finally, Dr. John Wilmes discusses his work as a professional mathematician and Machine Learning Research Engineer. Whether you are thinking about learning to code or an expert, we’re sure you will see a bit of yourself in this episode.

Learn to Code

You’ve heard of Human Computer Interaction (HCI), now get ready for Animal Computer Interaction (ACI). Ilyena has made a career developing computer interfaces for non-human animals. She has worked with dogs, parrots, primates, and even giraffes. This is challenging because animals have a wide range of abilities and preferences. Parrots, for example, use their tongues to make selections on touchscreens. Listen in on our conversation and learn about interface development and testing with animals and how technology may improve animal welfare.

Animal Computer Interaction

Cat observes great apes in the wild and in the lab to crack the code of their gestural communication. We discussed the challenges and benefits of studying apes in the wild vs in the lab. Cat also shared how her lab identifies and studies ape gestures. It turns out that humans are pretty good at guessing what apes are trying to communicate with one another. Join us in this episode to learn more about the evolution of communication in great apes, and what we can learn from our closest relatives.

Ape Gestures

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.

Evaluating AI Abilities

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!

HMMs for Behavior

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.

Bioinspired Engineering

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.

Modelling Evolution

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.

Behavioral Genetics

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.

Signal in the Noise

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.

Pose Tracking

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.

Modeling Group Behavior

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.

Advances in Data Loggers

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.

What You Know About Intelligence is Wrong (fixed)

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.

Animal Decision Making

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.

Octopus Cognition

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 spoke about choice overload in that they make detrimental decisions when loaded with too many options.

Optimal Foraging

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 <a href= "https://www.sciencedirect.com/science/article/abs/pii/0010028573900042"> Perception in chess</a> ## Resources <a href= "https://dataskeptic.com/blog/episodes/2015/detecting-cheating-in-chess"> Detecting Cheating in Chess with Ken Regan</a>

Memory in Chess

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.

OpenWorm

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.

What the Antlion Knows

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, or respond with any details about a variety of sensitive subjects.  As with any content filtering system, you have false positives and false negatives. Today's interview with Max Reuter and William Schulze discusses their paper "I'm Afraid I Can't Do That: Predicting Prompt Refusal in Black-Box Generative Language Models".  In this work, they explore what types of prompts get refused and build a machine learning classifier adept at predicting if a particular prompt will be refused or not.

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, or respond with any details about a variety of...

Prompt Refusal

Generative AI can struggle to create realistic animals and 2D representations often have mistakes like extra limbs and tails. If 2D wasn’t hard enough, there are researchers working on generative 3D models. 3D models present an extra challenge because there is paucity of training datasets.In this episode, PhD students Sandeep and Oindrila walked us through their work on creating 3D animals using 2D data. Join us to learn about their pipelines, quality control, tie in with iNaturalist, and how this tech could streamline FX pipelines.

Generative AI can struggle to create realistic animals and 2D representations often have mistakes like extra limbs and tails. If 2D wasn’t hard enough, there are researchers working on generative 3D models. 3D models present an extra challenge...

Generating 3D Animals with YouDream

Prompt Refusal