Data Skeptic

In this week's episode, host Kyle Polich interviews author Lance Fortnow about whether P will ever be equal to NP and solve all of life’s problems. Fortnow begins the discussion with the example question: Are there 100 people on Facebook who are all friends with each other? Even if you were an employee of Facebook and had access to all its data, answering this question naively would require checking more possibilities than any computer, now or in the future, could possibly do. The P/NP question asks whether there exists a more clever and faster algorithm that can answer this problem and others like it.

Algorithms with similar runtimes are said to be in the same complexity class. That runtime is measured in the how many steps an algorithm takes relative to the input size. The class P contains all algorithms which run in polynomial time (basically, a nested for loop iterating over the input).  NP are algorithms which seem to require brute force.  Brute force search cannot be done in polynomial time, so it seems that problems in NP are more difficult than problems in P.  I say it "seems" this way because, while most people believe it to be true, it has not been proven.  This is the famous P vs. NP conjecture.  It will be discussed in more detail in a future episode. Given a solution to a particular problem, if it can be verified/checked in polynomial time, that problem might be in NP.  If someone hands you a completed Sudoku puzzle, it's not difficult to see if they made any mistakes.  The effort of developing the solution to the Sudoku game seems to be intrinsically more difficult.  In fact, as far as anyone

In this episode, Professor Michael Kearns from the University of Pennsylvania joins host Kyle Polich to talk about the computational complexity of machine learning, complexity in game theory, and algorithmic fairness. Michael's doctoral thesis gave an early broad overview of computational learning theory, in which he emphasizes the mathematical study of efficient learning algorithms by machines or computational systems. When we look at machine learning algorithms they are almost like meta-algorithms in some sense. For example, given a machine learning algorithm, it will look at some data and build some model, and it’s going to behave presumably very differently under different inputs. But does that mean we need new analytical tools? Or is a machine learning algorithm just the same thing as any deterministic algorithm, but just a little bit more tricky to figure out anything complexity-wise? In other words, is there some overlap between the good old-fashioned analysis of algorithms with the analysis of machine

TMs are a model of computation at the heart of algorithmic analysis.  A Turing Machine has two components.  An infinitely long piece of tape (memory) with re-writable squares and a read/write head which is programmed to change it's state as it processes the input.  This exceptionally simple mechanical computer can compute anything that is intuitively computable, thus says the Church-Turing Thesis. Attempts to make a "better" Turing Machine by adding things like additional tapes can make the programs easier to describe, but it can't make the "better" machine more capable.  It won't be able to solve any problems the basic Turing Machine can, even if it perhaps solves them faster. An important concept we didn't get to in this episode is that of a Universal Turing Machine.  Without the prefix, a TM is a particular algorithm.  A Universal TM is a machine that takes, as input, a description of a TM and an input to that machine, and subsequently, simulates the inputted machine running on the given input. Turing Mach

Over the past several years, we have seen many success stories in machine learning brought about by deep learning techniques. While the practical success of deep learning has been phenomenal, the formal guarantees have been lacking. Our current theoretical understanding of the many techniques that are central to the current ongoing big-data revolution is far from being sufficient for rigorous analysis, at best. In this episode of Data Skeptic, our host Kyle Polich welcomes guest John Wilmes, a mathematics post-doctoral researcher at Georgia Tech, to discuss the efficiency of neural network learning through complexity theory.

How long an algorithm takes to run depends on many factors including implementation details and hardware.  However, the formal analysis of algorithms focuses on how they will perform in the worst case as the input size grows.  We refer to an algorithm's runtime as it's "O" which is a function of its input size "n".  For example, O(n) represents a linear algorithm - one that takes roughly twice as long to run if you double the input size.  In this episode, we discuss a few everyday examples of algorithmic analysis including sorting, search a shuffled deck of cards, and verifying if a grocery list was successfully completed. Thanks to our sponsor Brilliant.org, who right now is featuring a related problem as their Brilliant Problem of the Week.

In this episode, Microsoft's Corporate Vice President for Cloud Artificial Intelligence, Joseph Sirosh, joins host Kyle Polich to share some of the Microsoft's latest and most exciting innovations in AI development platforms. Last month, Microsoft launched a set of three powerful new capabilities in Azure Machine Learning for advanced developers to exploit big data, GPUs, data wrangling and container-based model deployment. Extended show notes found here. Thanks to our sponsor Springboard.  Check out Springboard's Data Science Career Track Bootcamp.

Last year, the film development and production company End Cue produced a short film, called Sunspring, that was entirely written by an artificial intelligence using neural networks. More specifically, it was authored by a recurrent neural network (RNN) called long short-term memory (LSTM). According to End Cue’s Chief Technical Officer, Deb Ray, the company has come a long way in improving the generative AI aspect of the bot. In this episode, Deb Ray joins host Kyle Polich to discuss how generative AI models are being applied in creative processes, such as screenwriting. Their discussion also explores how data science for analyzing development projects, such as financing and selecting scripts, as well as optimizing the content production process.

One Shot Learning is the class of machine learning procedures that focuses learning something from a small number of examples.  This is in contrast to "traditional" machine learning which typically requires a very large training set to build a reasonable model. In this episode, Kyle presents a coded message to Linhda who is able to recognize that many of these new symbols created are likely to be the same symbol, despite having extremely few examples of each.  Why can the human brain recognize a new symbol with relative ease while most machine learning algorithms require large training data?  We discuss some of the reasons why and approaches to One Shot Learning.

Recommender systems play an important role in providing personalized content to online users. Yet, typical data mining techniques are not well suited for the unique challenges that recommender systems face. In this episode, host Kyle Polich joins Dr. Joseph Konstan from the University of Minnesota at a live recording at FARCON 2017 in Minneapolis to discuss recommender systems and how machine learning can create better user experiences. 

Thanks to our sponsor brilliant.org/dataskeptics A Long Short Term Memory (LSTM) is a neural unit, often used in Recurrent Neural Network (RNN) which attempts to provide the network the capacity to store information for longer periods of time. An LSTM unit remembers values for either long or short time periods. The key to this ability is that it uses no activation function within its recurrent components. Thus, the stored value is not iteratively modified and the gradient does not tend to vanish when trained with backpropagation through time.

Zillow is a leading real estate information and home-related marketplace. We interviewed Andrew Martin, a data science Research Manager at Zillow, to learn more about how Zillow uses data science and big data to make real estate predictions.

Our guest Pranav Rajpurkar and his coauthored recently published Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, a paper in which they demonstrate the use of Convolutional Neural Networks which outperform board certified cardiologists in detecting a wide range of heart arrhythmias from ECG data.

RNNs are a class of deep learning models designed to capture sequential behavior.  An RNN trains a set of weights which depend not just on new input but also on the previous state of the neural network.  This directed cycle allows the training phase to find solutions which rely on the state at a previous time, thus giving the network a form of memory.  RNNs have been used effectively in language analysis, translation, speech recognition, and many other tasks.

Thanks to our sponsor Springboard. In this week's episode, guest Andre Natal from Mozilla joins our host, Kyle Polich, to discuss a couple exciting new developments in open source speech recognition systems, which include Project Common Voice. In June 2017, Mozilla launched a new open source project, Common Voice, a novel complementary project to the TensorFlow-based DeepSpeech implementation. DeepSpeech is a deep learning-based voice recognition system that was designed by Baidu, which they describe in greater detail in their research paper. DeepSpeech is a speech-to-text engine, and Mozilla hopes that, in the future, they can use Common Voice data to train their DeepSpeech engine.

A Bayesian Belief Network is an acyclic directed graph composed of nodes that represent random variables and edges that imply a conditional dependence between them. It's an intuitive way of encoding your statistical knowledge about a system and is efficient to propagate belief updates throughout the network when new information is added.

In this episode, Tony Beltramelli of UIzard Technologies joins our host, Kyle Polich, to talk about the ideas behind his latest app that can transform graphic design into functioning code, as well as his previous work on spying with wearables.

In statistics, two random variables might depend on one another (for example, interest rates and new home purchases). We call this conditional dependence. An important related concept exists called conditional independence. This phrase describes situations in which two variables are independent of one another given some other variable. For example, the probability that a vendor will pay their bill on time could depend on many factors such as the company's market cap. Thus, a statistical analysis would reveal many relationships between observable details about the company and their propensity for paying on time. However, if you know that the company has filed for bankruptcy, then we might assume their chances of paying on time have dropped to near 0, and the result is now independent of all other factors in light of this new information. We discuss a few real world analogies to this idea in the context of some chance meetings on our recent trip to New York City.

Animals can't tell us when they're experiencing pain, so we have to rely on other cues to help treat their discomfort. But it is often difficult to tell how much an animal is suffering. The sheep, for instance, is the most inscrutable of animals. However, scientists have figured out a way to understand sheep facial expressions using artificial intelligence. On this week's episode, Dr. Marwa Mahmoud from the University of Cambridge joins us to discuss her recent study, "Estimating Sheep Pain Level Using Facial Action Unit Detection." Marwa and her colleague's at Cambridge's Computer Laboratory developed an automated system using machine learning algorithms to detect and assess when a sheep is in pain. We discuss some details of her work, how she became interested in studying sheep facial expression to measure pain, and her future goals for this project. If you're able to be in Minneapolis, MN on August 23rd or 24th, consider attending Farcon. Get your tickets today via https://farcon2017.eventbrite.com.

This episode collects interviews from my recent trip to Microsoft Build where I had the opportunity to speak with Dharma Shukla and Syam Nair about the recently announced CosmosDB. CosmosDB is a globally consistent, distributed datastore that supports all the popular persistent storage formats (relational, key/value pair, document database, and graph) under a single streamlined API. The system provides tunable consistency, allowing the user to make choices about how consistency trade-offs are managed under the hood, if a consumer wants to go beyond the selected defaults.