A basketball game gives off endless amounts of data. Cameras from all angles capture the players making their way around the court, dribbling, passing, and shooting. With computer vision, a computer can build a well-defined understanding for what a sport looks like. With other machine learning techniques, the computer can make predictions by combining historical data with a game that is going on right now. Second Spectrum is a company that builds products for analyzing sports. At major basketball arenas, Second Spectrum cameras sit above the court, recording the game and feeding that information to the cloud. Second Spectrum’s servers crunch on the raw data, processing it through computer vision and putting it into deep learning models. The output can be utilized by teams, coaches, and fans. Yu-Han Chang and Jeff Su are co-founders of Second Spectrum. They join the show to describe the data pipeline of Second Spectrum from the cameras on the basketball court to the entertaining visualizations. After talking to them, I am convinced that machine learning will completely change how sports are played–and will probably open up a platform for new sports to be invented. The iOS app is the first project to come out of the Software Engineering Daily Open Source Project. There are more projects on the way, and we are looking for contributors–if you want to help build a better SE Daily experience, check out github.com/softwareengineeringdaily. We are working on an Android app, the iOS app, a recommendation system, and a web frontend. Help us build a new way to consume software engineering content at github.com/softwareengineeringdaily. The post Sports Deep Learning with Yu-Han Chang and Jeff Su appeared first on Software Engineering Daily.

The applications that demand deep learning range from self-driving cars to healthcare, but the way that models are developed and trained is similar. A model is trained in the cloud and deployed to a device. The device engages with the real world, gathering more data. That data is sent back to the cloud, where it can improve the model. From the processor level to the software frameworks at the top of the stack, the impact of deep learning is so significant that it is driving changes everywhere. At the hardware level, new chips are being designed to perform the matrix calculations at the heart of a neural net. At the software level, programmers are empowered by new frameworks like Neon and TensorFlow. In between the programmer and the hardware, middleware can transform software models into representations that can execute with better performance. Milena Marinova is the senior director of AI solutions at the Intel AI products group, and joins the show today to talk about modern applications of machine learning and how those translate into Intel’s business strategy around hardware, software, and cloud. From September 18-20, Milena is attending the O’Reilly AI Conference, hosted by Intel Nervana and O’Reilly. Full disclosure: Intel is a sponsor of Software Engineering Daily. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie.  Show Notes Data Skeptic podcast: Generative Adversarial Networks The post Deep Learning Systems with Milena Marinova appeared first on Software Engineering Daily.

If I have a picture of a dog, and I want to search the Internet for pictures that look like that dog, how can I do that? I need to make an algorithm to build an index of all the pictures on the Internet. That index can define the different features of my images. I can find mathematical features in each image that describe that image. The mathematical features can be represented by a matrix of numbers. Then I can run the same algorithm on the picture of my dog, which will make another matrix of numbers. I can compare the matrix representing my dog picture to the matrices of all the pictures on the internet. This is what Google and Facebook do–and we covered this topic in our episode about similarity search a few weeks ago. Today, we evaluate a similar problem: searching images within Squarespace. Squarespace is a platform where users can easily build their own website for blogging, e-commerce, or anything else. Neel Vadoothker is a machine learning engineer at Squarespace, and he joins the show to talk about how and why he built a visual similarity search engine. If you like this episode, we have done many other shows about machine learning. You can check out our back catalog by going to softwareengineeringdaily.com or by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that. The post Visual Search with Neel Vadoothker appeared first on Software Engineering Daily.

Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers.  Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a human. In recent shows, we have explored how to train machine learning models to understand images and video. Today, we explore words. You might be thinking–”isn’t a word easy to understand? Can’t you just take the dictionary definition?” A dictionary definition does not capture the richness of a word. Dictionaries do not give you a way to measure similarity between one word and all other words in a given language. Word2vec is a system for defining words in terms of the words that appear close to that word. For example, the sentence “Howard is sitting in a Starbucks cafe drinking a cup of coffee” gives an obvious indication that the words “cafe,” “cup,” and “coffee” are all related. With enough sentences like that, we can start to understand the entire language. Adrian Colyer is a venture capitalist with Accel, and blogs about technical topics such as word2vec. We talked about word2vec specifically, and the deep learning space more generally. We also explored how the rapidly improving tools around deep learning are changing the venture investment landscape. If you like this episode, we have done many other shows about machine learning with guests like Matt Zeiler, the founder of Clarif.ai and Francois Chollet, the creator of Keras. You can check out our back catalog by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie.  The post Word2Vec with Adrian Colyer appeared first on Software Engineering Daily.

Software companies that have been around for a decade have a ton of data. Modern machine learning techniques are able to turn that data into extremely useful models. Salesforce users have been entering petabytes of data into the company’s CRM tool since 1999. With its Einstein suite of products, Salesforce is using that data to build new product features and APIs. Simon Chan is the senior director of product management with Einstein. He oversees the efforts to give longtime Salesforce customers new value, and the efforts to build brand new APIs for image recognition and recommendation systems, which can form the backbone of entirely new businesses. Companies spend billions of dollars on sales and marketing, and I wanted to understand where the best opportunities for Salesforce were. Simon and I spent much of our time exploring higher level applications, but we got to lower level engineering eventually. There are 600 episodes of Software Engineering Daily, and it can be hard to find the shows that will interest you. If you have an iPhone and you listen to a lot of Software Engineering Daily, check out the Software Engineering Daily mobile app in the iOS App Store. Every episode can be accessed through the app, and we give you recommendations based on the ones you have already heard. The post Artificial Intelligence APIs with Simon Chan appeared first on Software Engineering Daily.

Automation will make healthcare more efficient and less prone to error. Today, machine learning is already being used to diagnose diabetic retinopathy and improve radiology accuracy. Someday, an AI assistant will assist a doctor in working through a complicated differential diagnosis. Our hospitals look roughly the same today as they did ten years ago, because getting new technology into the hands of doctors and nurses is a slow process–just ask anyone who has tried to sell software in the healthcare space. But technological advancement in healthcare is inevitable. Cosima Gretton is a medical doctor and a product manager with KariusDX, a company that is building diagnostic tools for infectious diseases. She writes about the future of healthcare, exploring the ways that workflows will change and how human biases could impact the diagnostic process–even in the presence of sophisticated AI. The post Healthcare AI with Cosima Gretton appeared first on Software Engineering Daily.

Querying a search index for objects similar to a given object is a common problem. A user who has just read a great news article might want to read articles similar to it. A user who has just taken a picture of a dog might want to search for dog photos similar to it. In both of these cases, the query object is turned into a vector and compared to the vectors representing the objects in the search index. Facebook contains a lot of news articles and a lot of dog pictures. How do you index and query all that information efficiently? Much of that data is unlabeled. How can you use deep learning to classify entities and add more richness to the vectors? Jeff Johnson is a research engineer at Facebook. He joins the show to discuss how similarity search works at scale, including how to represent that data and the tradeoffs of this kind of search engine across speed, memory usage, and accuracy. Notes: Jeff’s blog post about similarity search The post Similarity Search with Jeff Johnson appeared first on Software Engineering Daily.

Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a computer every single circumstance that a car might find itself in. The computer needs to perceive its surroundings, plan how to take action, execute control over the situation, and respond to changing circumstances inside and outside of the car. Lex Fridman has worked on autonomous vehicles with companies like Google and Tesla. He recently taught a class on deep learning for semi-autonomous vehicles at MIT, which is freely available online. There was so much ground to cover in this conversation. Most of the conversation was higher level. How do you even approach the problem? What is the hardware and software architecture of a car? I enjoyed talking to Lex, and if you want to hear more from him check out his podcast Take It Uneasy, which is about jiu jitsu, judo, wrestling, and learning. The post Self-Driving Deep Learning with Lex Fridman appeared first on Software Engineering Daily.

Instacart is a grocery delivery service. Customers log onto the website or mobile app and pick their groceries. Shoppers at the store get those groceries off the shelves. Drivers pick up the groceries and drive them to the customer. This is an infinitely complex set of logistics problems, paired with a rich data set given by the popularity of Instacart. Jeremy Stanley is the VP of data science for Instacart. In this episode, he explains how Instacart’s 4-sided marketplace business is constructed, and how the different data science teams break down problems like finding the fastest route to groceries within a store, finding the best path to delivering groceries from a store to a user, and personalizing recommendations so people can find new items to try. Are you looking for old episodes of Software Engineering Daily, but don’t know how to find the ones that are interesting to you? Check out our new topic feeds, in iTunes or wherever you find your podcasts. We’ve sorted all 500 of our old episodes into categories like business, blockchain, cloud engineering, JavaScript, machine learning, and greatest hits. Whatever specific area of software you are curious about, we have a feed for you. Check the show notes for more details. The post Instacart Data Science with Jeremy Stanley appeared first on Software Engineering Daily.

Deep learning allows engineers to build models that can make decisions based on training data. These models improve over time using stochastic gradient descent. When a model gets big enough, the training must be broken up across multiple machines. Two strategies for doing this are “model parallelism” which divides the model across machines and “data parallelism” which divides the data across multiple copies of the model. Distributed deep learning brings together two advanced software engineering concepts: distributed systems and deep learning. In this episode, Will Constable, the head of distributed deep learning algorithms at Intel Nervana, joins the show to give us a refresher on deep learning and explain how to parallelize training a model. Full disclosure: Intel is a sponsor of Software Engineering Daily, and if you want to find out more about Intel Nervana including other interviews and job postings, go to softwareengineeringdaily.com/intel. Intel Nervana is looking for great engineers at all levels of the stack, and in this episode we’ll dive into some of the problems the Intel Nervana team is solving. Related episodes about machine learning can be found here. The post Distributed Deep Learning with Will Constable appeared first on Software Engineering Daily.