Category Machine Learning

Machine Learning Deployments with Kinnary Jangla

http://traffic.libsyn.com/sedaily/2018_02_14_ProductionMLSystems.mp3Podcast: Play in new window | Download Pinterest is a visual feed of ideas, products, clothing, and recipes. Millions of users browse Pinterest to find images and text that are tailored to their interests. Like most companies, Pinterest started with a large monolithic application that served all requests. As Pinterest’s engineering resources expanded, some of the architecture was broken up into microservices and Dockerized, which make the system easier to

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Deep Learning Hardware with Xin Wang

http://traffic.libsyn.com/sedaily/2018_01_29_DeepLearningHardware.mp3Podcast: Play in new window | Download Training a deep learning model involves operations over tensors. A tensor is a multi-dimensional array of numbers. For several years, GPUs were used for these linear algebra calculations. That’s because graphics chips are built to efficiently process matrix operations. Tensor processing consists of linear algebra operations that are similar in some ways to graphics processing–but not identical. Deep learning workloads do not run

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Edge Deep Learning with Aran Khanna

http://traffic.libsyn.com/sedaily/2018_01_26_EdgeDeepLearning.mp3Podcast: Play in new window | Download A modern farm has hundreds of sensors to monitor the soil health, and robotic machinery to reap the vegetables. A modern shipping yard has hundreds of computers working together to orchestrate and analyze the freight that is coming in from overseas. A modern factory has temperature gauges and smart security cameras to ensure workplace safety. All of these devices could be considered “edge”

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Machine Learning and Technical Debt with D. Sculley Holiday Repeat

http://traffic.libsyn.com/sedaily/ml_techdebt_ad_free.mp3Podcast: Play in new window | DownloadOriginally published November 17, 2015 “Changing anything changes everything.” Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems. D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High Interest Credit Card of Technical Debt. Questions How do

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Training the Machines with Russell Smith

http://traffic.libsyn.com/sedaily/RainforestQA.mp3Podcast: Play in new window | Download Automation is changing the labor market. To automate a task, someone needs to put in the work to describe the task correctly to a computer. For some tasks, the reward for automating a task is tremendous–for example, putting together mobile phones. In China, companies like FOXCONN are investing time and money into programming the instructions for how to assemble your phone. Robots execute

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Model Training with Yufeng Guo

http://traffic.libsyn.com/sedaily/ModelTraining.mp3Podcast: Play in new window | Download Machine learning models can be built by plotting points in space and optimizing a function based off of those points. For example, I can plot every person in the United States in a 3 dimensional space: age, geographic location, and yearly salary. Then I can draw a function that minimizes the distance between my function and each of those data points. Once I

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Sports Deep Learning with Yu-Han Chang and Jeff Su

http://traffic.libsyn.com/sedaily/SportsAnalytics.mp3Podcast: Play in new window | Download 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

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Deep Learning Systems with Milena Marinova

http://traffic.libsyn.com/sedaily/DeepLearningSystems.mp3Podcast: Play in new window | Download 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

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Visual Search with Neel Vadoothker

http://traffic.libsyn.com/sedaily/Visual_Search.mp3Podcast: Play in new window | Download 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

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Word2Vec with Adrian Colyer

http://traffic.libsyn.com/sedaily/Word2vecAdrianColyer.mp3Podcast: Play in new window | Download 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

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Artificial Intelligence APIs with Simon Chan

http://traffic.libsyn.com/sedaily/SalesforceEinstein.mp3Podcast: Play in new window | Download 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

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Healthcare AI with Cosima Gretton

http://traffic.libsyn.com/sedaily/HealthwithCosima.mp3Podcast: Play in new window | Download 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

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Similarity Search with Jeff Johnson

http://traffic.libsyn.com/sedaily/SimilaritySearch.mp3Podcast: Play in new window | Download 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

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Self-Driving Deep Learning with Lex Fridman

http://traffic.libsyn.com/sedaily/SelfDrivingDeepLearning.mp3Podcast: Play in new window | Download 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

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Instacart Data Science with Jeremy Stanley

http://traffic.libsyn.com/sedaily/InstacartDataScience.mp3Podcast: Play in new window | Download 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

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Distributed Deep Learning with Will Constable

http://traffic.libsyn.com/sedaily/Distributeddeeplearning.mp3Podcast: Play in new window | Download 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

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Video Object Segmentation with the DAVIS Challenge Team

http://traffic.libsyn.com/sedaily/objectsegmentation.mp3Podcast: Play in new window | Download Video object segmentation allows computer vision to identify objects as they move through space in a video. The DAVIS challenge is a contest among machine learning researchers working off of a shared dataset of annotated videos. The organizers of the DAVIS challenge join the show today to explain how video object segmentation models are trained and how different competitors take part in the

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Poker Artificial Intelligence with Noam Brown

http://traffic.libsyn.com/sedaily/Libratus.mp3Podcast: Play in new window | Download Humans have now been defeated by computers at heads up no-limit holdem poker. Some people thought this wouldn’t be possible. Sure, we can teach a computer to beat a human at Go or Chess. Those games have a smaller decision space. There is no hidden information. There is no bluffing. Poker must be different! It is too human to be automated. The game

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Convolutional Neural Networks with Matt Zeiler

http://traffic.libsyn.com/sedaily/ClarifaiCNNs.mp3Podcast: Play in new window | Download Convolutional neural networks are a machine learning tool that uses layers of convolution and pooling to process and classify inputs. CNNs are useful for identifying objects in images and video. In this episode, we focus on the application of convolutional neural networks to image and video recognition and classification. Matt Zeiler is the CEO of Clarifai, an API for image and video recognition.

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Google Brain Music Generation with Doug Eck

http://traffic.libsyn.com/sedaily/GoogleBrain.mp3Podcast: Play in new window | Download Most popular music today uses a computer as the central instrument. A single musician is often selecting the instruments, programming the drum loops, composing the melodies, and mixing the track to get the right overall atmosphere. With so much work to do on each song, popular musicians need to simplify–the result is that pop music today consists of simple melodies without much chord

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