Shows / Data Skeptic
Data Skeptic
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.
Episodes
Data SkepticPlastic Bag Bans
Becca Taylor joins us to discuss her work studying the impact of plastic bag bans as published in Bag Leakage: The Effect of Disposable Carryout Bag…
Data SkepticSelf Driving Cars and Pedestrians
We are joined by Arash Kalatian to discuss Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep…
Data SkepticComputer Vision is Not Perfect
Computer Vision is not Perfect Julia Evans joins us help answer the question why do neural networks think a panda is a vulture. Kyle talks to Julia about her…
Data SkepticUncertainty Representations
Jessica Hullman joins us to share her expertise on data visualization and communication of data in the media. We discuss Jessica's work on visualizing…
Data SkepticAlphaGo, COVID-19 Contact Tracing and New Data Set
Announcing Journal Club I am pleased to announce Data Skeptic is launching a new spin-off show called "Journal Club" with similar themes but a very different…
Data SkepticInterpretability Tooling
Pramit Choudhary joins us to talk about the methodologies and tools used to assist with model interpretability.
Data SkepticShapley Values
Kyle and Linhda discuss how Shapley Values might be a good tool for determining what makes the cut for a home renovation.
Data SkepticAnchors as Explanations
We welcome back Marco Tulio Ribeiro to discuss research he has done since our original discussion on LIME. In particular, we ask the question Are Red Roses…
Data SkepticAdversarial Explanations
Walt Woods joins us to discuss his paper Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness with…
Data SkepticObjectNet
Andrei Barbu joins us to discuss ObjectNet - a new kind of vision dataset. In contrast to ImageNet, ObjectNet seeks to provide images that are more…
Data SkepticVisualization and Interpretability
Enrico Bertini joins us to discuss how data visualization can be used to help make machine learning more interpretable and explainable. Find out more about…
Data SkepticInterpretable One Shot Learning
We welcome Su Wang back to Data Skeptic to discuss the paper Distributional modeling on a diet: One-shot word learning from text only.
Data SkepticFooling Computer Vision
Wiebe van Ranst joins us to talk about a project in which specially designed printed images can fool a computer vision system, preventing it from identifying a…
Data SkepticAlgorithmic Fairness
This episode includes an interview with Aaron Roth author of The Ethical Algorithm.
Data SkepticInterpretability
Interpretability Machine learning has shown a rapid expansion into every sector and industry. With increasing reliance on models and increasing stakes for the…
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The Limits of NLP
We are joined by Colin Raffel to discuss the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer".
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Jumpstart Your ML Project
Seth Juarez joins us to discuss the toolbox of options available to a data scientist to jumpstart or extend their machine learning efforts.
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Serverless NLP Model Training
Alex Reeves joins us to discuss some of the challenges around building a serverless, scalable, generic machine learning pipeline. The is a technical deep dive…
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Team Data Science Process
Buck Woody joins Kyle to share experiences from the field and the application of the Team Data Science Process - a popular six-phase workflow for doing data…
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Ancient Text Restoration
Thea Sommerschield joins us this week to discuss the development of Pythia - a machine learning model trained to assist in the reconstruction of ancient…
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ML Ops
Kyle met up with Damian Brady at MS Ignite 2019 to discuss machine learning operations.
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Annotator Bias
The modern deep learning approaches to natural language processing are voracious in their demands for large corpora to train on. Folk wisdom estimates used to…
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NLP for Developers
While at MS Build 2019, Kyle sat down with Lance Olson from the Applied AI team about how tools like cognitive services and cognitive search enable non-data…
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Indigenous American Language Research
Manuel Mager joins us to discuss natural language processing for low and under-resourced languages. We discuss current work in this area and the Naki Project…
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Talking to GPT-2
GPT-2 is yet another in a succession of models like ELMo and BERT which adopt a similar deep learning architecture and train an unsupervised model on a massive…
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Reproducing Deep Learning Models
Rajiv Shah attempted to reproduce an earthquake-predicting deep learning model. His results exposed some issues with the model. Kyle and Rajiv discuss the…
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What BERT is Not
Allyson Ettinger joins us to discuss her work in computational linguistics, specifically in exploring some of the ways in which the popular natural language…
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SpanBERT
Omer Levy joins us to discuss "SpanBERT: Improving Pre-training by Representing and Predicting Spans". https://arxiv.org/abs/1907.10529
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BERT is Shallow
Tim Niven joins us this week to discuss his work exploring the limits of what BERT can do on certain natural language tasks such as adversarial attacks…
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Applied Data Science in Industry
Kyle sits down with Jen Stirrup to inquire about her experiences helping companies deploy data science solutions in a variety of different settings.
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Building the howto100m Video Corpus
Video annotation is an expensive and time-consuming process. As a consequence, the available video datasets are useful but small. The availability of machine…
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BERT
Kyle provides a non-technical overview of why Bidirectional Encoder Representations from Transformers (BERT) is a powerful tool for natural language processing…
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Catastrophic Forgetting
Kyle and Linhda discuss some high level theory of mind and overview the concept machine learning concept of catastrophic forgetting.
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Transfer Learning
Sebastian Ruder is a research scientist at DeepMind. In this episode, he joins us to discuss the state of the art in transfer learning and his contributions to…
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Facebook Bargaining Bots Invented a Language
In 2017, Facebook published a paper called Deal or No Deal? End-to-End Learning for Negotiation Dialogues. In this research, the reinforcement learning agents…
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Under Resourced Languages
Priyanka Biswas joins us in this episode to discuss natural language processing for languages that do not have as many resources as those that are more…
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Named Entity Recognition
Kyle and Linh Da discuss the class of approaches called "Named Entity Recognition" or NER. NER algorithms take any string as input and return a list of…
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The Death of a Language
USC students from the CAIS++ student organization have created a variety of novel projects under the mission statement of "artificial intelligence for social…
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Neural Turing Machines
Kyle and Linh Da discuss the concepts behind the neural Turing machine.
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Data Infrastructure in the Cloud
Kyle chats with Rohan Kumar about hyperscale, data at the edge, and a variety of other trends in data engineering in the cloud.
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NCAA Predictions on Spark
In this episode, Kyle interviews Laura Edell at MS Build 2019. The conversation covers a number of topics, notably her NCAA Final 4 prediction model.
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The Transformer
Kyle and Linhda discuss attention and the transformer - an encoder/decoder architecture that extends the basic ideas of vector embeddings like word2vec into a…
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Mapping Dialects with Twitter Data
When users on Twitter post with geographic tags, it creates the opportunity for a variety of interesting questions to be posed having to do with language…
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Sentiment Analysis
This is an interview with Ellen Loeshelle, Director of Product Management at Clarabridge. We primarily discuss sentiment analysis.
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Attention Primer
A gentle introduction to the very high-level idea of "attention" in machine learning, as it will play a major role in some upcoming episodes over the next few…
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Cross-lingual Short-text Matching
Modern messaging technology has facilitated a trend towards highly compact, short messages send by users who can presume a great amount of context held between…
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ELMo
ELMo (Embeddings from Language Models) introduced the idea of deep contextualized word representations. It extends previous ideas like word2vec and GloVe. The…
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BLEU
Bilingual evaluation understudy (or BLEU) is a metric for evaluating the quality of machine translation using human translation as examples of acceptable…
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Simultaneous Translation at Baidu
While at NeurIPS 2018, Kyle chatted with Liang Huang about his work with Baidu research on simultaneous translation, which was demoed at the conference.
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Human vs Machine Transcription
Machine transcription (the process of translating audio recordings of language to text) has come a long way in recent years. But how do the errors made during…
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seq2seq
A sequence to sequence (or seq2seq) model is neural architecture used for translation (and other tasks) which consists of an encoder and a decoder. The…
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Text Mining in R
Kyle interviews Julia Silge about her path into data science, her book Text Mining with R, and some of the ways in which she's used natural language processing…
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Recurrent Relational Networks
One of the most challenging NLP tasks is natural language understanding and reasoning. How can we construct algorithms that are able to achieve human level…
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Text World and Word Embedding Lower Bounds
In the first half of this episode, Kyle speaks with Marc-Alexandre Côté and Wendy Tay about Text World. Text World is an engine that simulates text adventure…
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word2vec
Word2vec is an unsupervised machine learning model which is able to capture semantic information from the text it is trained on. The model is based on neural…