We are joined by Abhishek Paudel, a PhD Student at George Mason University with a research focus on robotics, machine learning, and planning under uncertainty, using graph-based methods to enhance robot behavior. He explains how graph-based approaches can model environments, capture spatial relationships, and provide a framework for integrating multiple levels of planning and decision-making.

We are joined by Maciej Besta, a senior researcher of sparse graph computations and large language models at the Scalable Parallel Computing Lab (SPCL). In this episode, we explore the intersection of graph theory and high-performance computing (HPC), Graph Neural Networks (GNNs) and LLMs.

In this episode, we sit down with Yuanyuan Tian, a principal scientist manager at Microsoft Gray Systems Lab, to discuss the evolving role of graph databases in various industries such as fraud detection in finance and insurance, security, healthcare, and supply chain optimization. 

Our new season "Graphs and Networks" begins here!  We are joined by new co-host Asaf Shapira, a network analysis consultant and the podcaster of NETfrix ? the network science podcast. Kyle and Asaf discuss ideas to cover in the season and explore Asaf's work in the field.

Join us for our capstone episode on the Animal Intelligence season. We recap what we loved, what we learned, and things we wish we had gotten to spend more time on. This is a great episode to see how the podcast is produced. Now that the season is ending, our current co-host, Becky, is moving to emeritus status. In this last installment we got to spend a little more time getting to know Becky and where her work will take her after this. Did Data Skeptic inspire her to learn more about machine learning? Tune in and find out. 

David Obembe, a recent University of Tartu graduate, discussed his Masters thesis on integrating LLMs with process mining tools. He explained how process mining uses event logs to create maps that identify inefficiencies in business processes. David shared his research on LLMs' potential to enhance process mining, including experiments evaluating their performance and future improvements using Retrieval Augmented Generation (RAG).

Our guest today is Risa Shinoda, a PhD student at Kyoto University Agricultural Systems Engineering Lab, where she applies computer vision techniques.

She talked about the OpenAnimalTracks dataset and what it was used for. The dataset helps researchers predict animal footprint. She also discussed how she built a model for predicting tracks of animals. She shared the algorithms used and the accuracy they achieved. She also discussed further improvement opportunities for the model.

This episode features an interview with Mélisande Teng, a PhD candidate at Université de Montréal. Her research lies in the intersection of remote sensing and computer vision for biodiversity monitoring.

In this interview with author Deborah Gordon, Kyle asks questions about the mechanisms at work in an ant colony and what ants might teach us about how to build artificial intelligence. Ants are surprisingly adaptive creatures whose behavior emerges from their complex interactions. Aspects of network theory and the statistical nature of ant behavior are just some of the interesting details you'll get in this episode.

This season it?s become clear that computing skills are vital for working in the natural sciences. In this episode, we were fortunate to speak with Madlen Wilmes, co-author of the book "Computing Skills for Biologists: A Toolbox". We discussed the book and why it?s a great resource for students and teachers. In addition to the book, Madlen shared her experience and advice on transitioning from academia to an industry career and how data analytic skills transfer to jobs that your professionals might not always consider. Join us and learn more about the book and careers using transferable skills.

In this episode, we talked shop with Hager Radi about her biodiversity monitoring work. While biodiversity modeling may sound simple, count organisms and mark their location, there is a lot more to it than that! Incomplete and biased data can make estimations hard. There are also many species with very few observations in the wild. Using machine learning and remote sensing data, scientists can build models that predict species distributions with limited data. Listen in and hear about Hager?s work tackling these challenges and the tools she has built.

Today, Ashay Aswale and Tony Lopez shared their work on swarm robotics and what they have learned from ants. Robotic swarms must solve the same problems that eusocial insects do. What if your pheromone trail goes cold? What if you?re getting bad information from a bad-actor within the swarm? Answering these questions can help tackle serious robotic challenges. For example, a swarm of robots can lose a few members to accidents and malfunctions, but a large robot cannot. Additionally, a swarm could be host to many castes like an ant colony. Specialization with redundancy built in seems like a win-win! Tune in and hear more about this fascinating topic.

During this season we have talked with researchers working to utilize machine learning for behavioral observations. In previous episodes, you have heard about the software people like Richard use, but you haven?t heard much from scientists modifying and using these tools for specific research cases. PhD student, Richard Vogg, is working with multi-camera set-ups to track lemurs and macaques solving puzzle boxes in the wild. His work is part of a larger movement to automate behavioral analyses of video data. Listen in and learn why this tech is useful and why multi-camera setups are a good idea for more reliably identifying poses and individual animals.

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.

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.

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.

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.

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.

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. 

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. 

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. 

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

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 students can get involved in the Openworm project. Rounding up, he shared future plans for the project.

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 sponsors
dataannotation.tech/programmers
https://www.webai.com/dataskeptic

In this episode, we are joined by Amir Netz, a Technical Fellow at Microsoft and the CTO of Microsoft Fabric. He discusses how companies can use Microsoft's latest tools for business intelligence.

Amir started by discussing how business intelligence has progressed in relevance over the years. Amir gave a brief introduction into what Power BI and Fabric are. He also discussed how Fabric distinguishes from other BI tools by building an end-to-end tool for the data journey.

Amir spoke about the process of building and deploying machine learning models with Microsoft Fabric. He shared the difference between Software as a Service (SaaS) and Platform as a Service (PaaS).

Amir discussed the benefits of Fabric's auto-integration and auto-optimization abilities. He also discussed the capabilities of Copilot in Fabric. He also discussed exciting future developments planned for Fabric. Amir shared techniques for limiting Copilot hallucination.

Our guest today is Eric Boyd, the Corporate Vice President of AI at Microsoft. Eric joins us to share how organizations can leverage AI for faster development.

Eric shared the benefits of using natural language to build products. He discussed the future of version control and the level of AI background required to get started with Azure AI. He mentioned some foundational models in Azure AI and their capabilities. Follow Eric on LinkedIn to learn more about his work.

Visit today's sponsor at https://webai.com/dataskeptic

We are excited to be joined by Aaron Reich and Priyanka Shah. Aaron is the CTO at Avanade, while Priyanka leads their AI/IoT offering for the SEA Region. Priyanka is also the MVP for Microsoft AI. They join us to discuss how LLMs are deployed in organizations.

In this episode, we are joined by Jenny Liang, a PhD student at Carnegie Mellon University, where she studies the usability of code generation tools. She discusses her recent survey on the usability of AI programming assistants.

Jenny discussed the method she used to gather people to complete her survey. She also shared some questions in her survey alongside vital takeaways. She shared the major reasons for developers not wanting to us code-generation tools. She stressed that the code-generation tools might access the software developers' in-house code, which is intellectual property.

Learn more about Jenny Liang via https://jennyliang.me/

We are joined by Aman Madaan and Shuyan Zhou. They are both PhD students at the Language Technology Institute at Carnegie Mellon University. They join us to discuss their latest published paper, PAL: Program-aided Language Models.

Aman and Shuyan started by sharing how the application of LLMs has evolved. They talked about the performance of LLMs on arithmetic tasks in contrast to coding tasks. Aman introduced their PAL model and how it helps LLMs improve at arithmetic tasks. He shared examples of the tasks PAL was tested on. Shuyan discussed how PAL?s performance was evaluated using Big Bench hard tasks.

They discussed the kind of mistakes LLMs tend to make and how the PAL?s model circumvents these limitations. They also discussed how these developments in LLMS can improve kids learning.

Rounding up, Aman discussed the CoCoGen project, a project that enables NLP tasks to be converted to graphs. Shuyan and Aman shared their next research steps.

Follow Shuyan on Twitter @shuyanzhxyc. Follow Aman on @aman_madaan.

In this episode, we have Alessio Buscemi, a software engineer at Lifeware SA. Alessio was a post-doctoral researcher at the University of Luxembourg. He joins us to discuss his paper, A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages.  Alessio shared his thoughts on whether ChatGPT is a threat to software engineers. He discussed how LLMs can help software engineers become more efficient.

On the show today, we are joined by Jianan Zhao, a Computer Science student at Mila and the University of Montreal. His research focus is on graph databases and natural language processing. He joins us to discuss how to use graphs with LLMs efficiently.

Today, we are joined by Rajiv Movva, a PhD student in Computer Science at Cornell Tech University. His research interest lies in the intersection of responsible AI and computational social science. He joins to discuss the findings of this work that analyzed LLM publication patterns.

He shared the dataset he used for the survey. He also discussed the conditions for determining the papers to analyze. Rajiv shared some of the trends he observed from his analysis. For one, he observed there has been an increase in LLMs research. He also shared the proportions of papers published by universities, organizations, and industry leaders in LLMs such as OpenAI and Google. He mentioned the majority of the papers are centered on the social impact of LLMs. He also discussed other exciting application of LLMs such as in education.

We are excited to be joined by Josh Albrecht, the CTO of Imbue. Imbue is a research company whose mission is to create AI agents that are more robust, safer, and easier to use. He joins us to share findings of his work; Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety.