Bilingual evaluation understudy (or BLEU) is a metric for evaluating the quality of machine translation using human translation as examples of acceptable quality results. This metric has become a widely used standard in the research literature. But is it the perfect measure of quality of machine translation?
While at NeurIPS 2018, Kyle chatted with Liang Huang about his work with Baidu research on simultaneous translation, which was demoed at the conference.
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 machine transcription compare to the errors made by a human transcriber? Find out in this episode!
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 encoder/decoder architecture has obvious promise for machine translation, and has been successfully applied this way. Encoding an input to a small number of hidden nodes which can effectively be decoded to a matching string requires machine learning to learn an efficient representation of the essence of the strings. In addition to translation, se...
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 in projects both personal and professional. Related Links https://stack-survey-2018.glitch.me/ https://stackoverflow.blog/2017/03/28/realistic-developer-fiction/...
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 understanding of text and be able to answer general questions about it? This is truly an open problem, and one with the bAbI dataset has been constructed to facilitate. bAbI presents a variety of different language understanding and reasoning tasks and exists as benchmark for comparing approaches. In this episode, Kyle talks to Rasmus Berg Pa...
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 games. Developers are encouraged to try out their reinforcement learning skills building agents that can programmatically interact with the generated text adventure games. In the second half of this episode, Kyle interviews Kevin Patel about his paper Towards Lower Bounds on Number of Dimensions for Word Embeddings. In this research, the ex...
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 networks. Several large organizations like Google and Facebook have trained word embeddings (the result of word2vec) on large corpora and shared them for others to use. The key algorithmic ideas involved in word2vec is the continuous bag of words model (CBOW). In this episode, Kyle uses excerpts from the 1983 cinematic masterpiece War Games...
In a recent paper, Leveraging Discourse Information Effectively for Authorship Attribution, authors Su Wang, Elisa Ferracane, and Raymond J. Mooney describe a deep learning methodology for predict which of a collection of authors was the author of a given document.
The earliest efforts to apply machine learning to natural language tended to convert every token (every word, more or less) into a unique feature. While techniques like stemming may have cut the number of unique tokens down, researchers always had to face a problem that was highly dimensional. Naive Bayes algorithm was celebrated in NLP applications because of its ability to efficiently process highly dimensional data. Of course, other algorithms were applied to natural language tasks as well. W...
Github is many things besides source control. It's a social network, even though not everyone realizes it. It's a vast repository of code. It's a ticketing and project management system. And of course, it has search as well. In this episode, Kyle interviews Hamel Husain about his research into semantic code search.
This episode reboots our podcast with the theme of Natural Language Processing for the next few months. We begin with introductions of Yoshi and Linh Da and then get into a broad discussion about natural language processing: what it is, what some of the classic problems are, and just a bit on approaches. Finishing out the show is an interview with Lucy Park about her work on the KoNLPy library for Korean NLP in Python. If you want to share your NLP project, please join our Slack channel. We're e...
Kyle shares a few thoughts on mistakes observed by job applicants and also shares a few procedural insights listeners at early stages in their careers might find value in.
Epicac by Kurt Vonnegut.
In today's episode, Kyle chats with Alexander Zhebrak, CTO of Insilico Medicine, Inc. Insilico self describes as artificial intelligence for drug discovery, biomarker development, and aging research. The conversation in this episode explores the ways in which machine learning, in particular, deep learning, is contributing to the advancement of drug discovery. This happens not just through research but also through software development. Insilico works on data pipelines and tools like MOSES , a be...
At the NeurIPS 2018 conference, Stradigi AI premiered a training game which helps players learn American Sign Language . This episode brings the first of many interviews conducted at NeurIPS 2018. In this episode, Kyle interviews Chief Data Scientist Carolina Bessega about the deep learning architecture used in this project. The Stradigi AI team was exhibiting a project called the American Sign Language (ASL) Alphabet Game at the recent NeurIPS 2018 conference. They also published a detailed blo...
This week, Kyle interviews Scott Nestler on the topic of Data Ethics. Today, no ubiquitous, formal ethical protocol exists for data science, although some have been proposed. One example is the INFORMS Ethics Guidelines . Guidelines like this are rather informal compared to other professions, like the Hippocratic Oath . Yet not every profession requires such a formal commitment. In this episode, Scott shares his perspective on a variety of ethical questions specific to data and analytics....
Kyle interviews Mick West, author of Escaping the Rabbit Hole: How to Debunk Conspiracy Theories Using Facts, Logic, and Respect about the nature of conspiracy theories, the people that believe them, and how to help people escape the belief in false information. Mick is also the creator of metabunk.org . The discussion explores conspiracies like chemtrails, 9/11 conspiracy theories, JFK assassination theories, and the flat Earth theory. We live in a complex world in which no person can have a su...
Fake news attempts to lead readers/listeners/viewers to conclusions that are not descriptions of reality. They do this most often by presenting false premises, but sometimes by presenting flawed logic. An argument is only sound and valid if the conclusions are drawn directly from all the state premises, and if there exists a path of logical reasoning leading from those premises to the conclusion. While creating a theorem does feel to most mathematicians as a creative act of discovery, some theor...
Fake news can be responded to with fact-checking. However, it's easier to create fake news than the fact check it. Full Fact is the UK's independent fact-checking organization. In this episode, Kyle interviews Mevan Babakar , head of automated fact-checking at Full Fact. Our discussion talks about the process and challenges in doing fact-checking. Full Fact has been exploring ways in which machine learning can assist in automating parts of the fact-checking process. Progress in areas like this a...
In mathematics, truth is universal. In data, truth lies in the where clause of the query. As large organizations have grown to rely on their data more significantly for decision making, a common problem is not being able to agree on what the data is. As the volume and velocity of data grow, challenges emerge in answering questions with precision. A simple question like "what was the revenue yesterday" could become mired in details. Did your query account for transactions that haven't been finali...
Fast radio bursts are an astrophysical phenomenon first observed in 2007. While many observations have been made, science has yet to explain the mechanism for these events. This has led some to ask: could it be a form of extra-terrestrial communication? Probably not. Kyle asks Gerry Zhang who works at the Berkeley SETI Research Center about this possibility and more importantly, about his applications of deep learning to detect fast radio bursts. Radio astronomy captures observations from space ...
This episode explores the root concept of what it is to be Bayesian: describing knowledge of a system probabilistically, having an appropriate prior probability, know how to weigh new evidence, and following Bayes's rule to compute the revised distribution. We present this concept in a few different contexts but primarily focus on how our bird Yoshi sends signals about her food preferences. Like many animals, Yoshi is a complex creature whose preferences cannot easily be summarized by a straight...
This is our interview with Dorje Brody about his recent paper with David Meier, How to model fake news. This paper uses the tools of communication theory and a sub-topic called filtering theory to describe the mathematical basis for an information channel which can contain fake news. Thanks to our sponsor Gartner .
Without getting into definitions, we have an intuitive sense of what a "community" is. The Louvain Method for Community Detection is one of the best known mathematical techniques designed to detect communities. This method requires typical graph data in which people are nodes and edges are their connections. It's easy to imagine this data in the context of Facebook or LinkedIn but the technique applies just as well to any other dataset like cellular phone calling records or pen-pals. The Louvain...
In this episode, our guest is Dan Kahan about his research into how people consume and interpret science news. In an era of fake news, motivated reasoning, and alternative facts, important questions need to be asked about how people understand new information. Dan is a member of the Cultural Cognition Project at Yale University, a group of scholars interested in studying how cultural values shape public risk perceptions and related policy beliefs. In a paper titled Cultural cognition of scientif...
A false discovery rate (FDR) is a methodology that can be useful when struggling with the problem of multiple comparisons. In any experiment, if the experimenter checks more than one dependent variable, then they are making multiple comparisons. Naturally, if you make enough comparisons, you will eventually find some correlation. Classically, people applied the Bonferroni Correction . In essence, this procedure dictates that you should lower your p-value (raise your standard of evidence) by a sp...
Digital videos can be described as sequences of still images and associated audio. Audio is easy to fake. What about video? A video can easily be broken down into a sequence of still images replayed rapidly in sequence. In this context, videos are simply very high dimensional sequences of observations, ripe for input into a machine learning algorithm. The availability of commodity hardware, clever algorithms, and well-designed software to implement those algorithms at scale make it possible to d...
In this episode, Kyle reviews what we've learned so far in our series on Fake News and talks briefly about where we're going next.
Two weeks ago we discussed click through rates or CTRs and their usefulness and limits as a metric. Today, we discuss a related metric known as quality score. While that phrase has probably been used to mean dozens of different things in different contexts, our discussion focuses around the idea of quality score encountered in Search Engine Marketing (SEM). SEM is the practice of purchasing keyword targeted ads shown to customers using a search engine. Most SEM is managed via an auction mechanis...
