Modelling Evolution
Data Skeptic
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.
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Behavioral Genetics
Data Skeptic
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.
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Signal in the Noise
Data Skeptic
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.
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Pose Tracking
Data Skeptic
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.
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Modeling Group Behavior
Data Skeptic
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.
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Advances in Data Loggers
Data Skeptic
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.
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What You Know About Intelligence is Wrong (fixed)
Data Skeptic
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.
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Animal Decision Making
Data Skeptic
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.
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Octopus Cognition
Data Skeptic
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...
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Optimal Foraging
Data Skeptic
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...
info_outlineWe revisit the 2018 Microsoft Build in this episode, focusing on the latest ideas in DevOps. Kyle interviews Cloud Developer Advocates Damien Brady, Paige Bailey, and Donovan Brown to talk about DevOps and data science and databases.
For a data scientist, what does it even mean to “build”? Packaging and deployment are things that a data scientist doesn't normally have to consider in their day-to-day work. The process of making an AI app is usually divided into two streams of work: data scientists building machine learning models and app developers building the application for end users to consume.
DevOps includes all the parties involved in getting the application deployed and maintained and thinking about all the phases that follow and precede their part of the end solution. So what does DevOps mean for data science? Why should you adopt DevOps best practices?
In the first half, Paige and Damian share their views on what DevOps for data science would look like and how it can be introduced to provide continuous integration, delivery, and deployment of data science models. In the second half, Donovan and Damian talk about the DevOps life cycle of putting a database under version control and carrying out deployments through a release pipeline.