Tag Machine Learning

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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Tinder Engineering Management with Bryan Li

http://traffic.libsyn.com/sedaily/TinderManagement.mp3Podcast: Play in new window | Download Tinder is a rapidly growing social network for meeting people and dating. In the past few years, Tinder’s userbase has grown rapidly, and the engineering team has scaled to meet the demands of increased popularity. On Tinder, you are presented with a queue of suggested people that you might match with, and you swipe left or right to indicate that you like or

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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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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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Lending Machine Learning with Ofer Mendelevitch

http://traffic.libsyn.com/sedaily/Lendup.mp3Podcast: Play in new window | Download Loans give people more financial security. If people know that they can receive a loan, they will be more willing to take intelligent risks. A loan can allow for a short-term investment that pays off enough to justify the interest rate on that loan. For the lender, a loan can be a fantastic return on capital–as long as the lendee does not default.

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Reinforcement Learning with Michal Kempa

http://traffic.libsyn.com/sedaily/ReinforcementLearning.mp3Podcast: Play in new window | Download Reinforcement learning is a type of machine learning where a program learns how to take actions in an environment based on how that program has been rewarded for actions it took in the past. When program takes an action, and it receives a reward for that action, it is likely to take that action again in the future because it was positively reinforced.

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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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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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Hedge Fund Artificial Intelligence with Xander Dunn

http://traffic.libsyn.com/sedaily/numerai_edited.mp3Podcast: Play in new window | Download A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their bets are less correlated, and they can be successful in a variety of future scenarios. Engineering-focused hedge funds have used what might be called “machine learning” for a

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Stripe Machine Learning with Michael Manapat

http://traffic.libsyn.com/sedaily/stripeantifraud_edited.mp3Podcast: Play in new window | Download Every company that deals with payments deals with fraud. The question is not whether fraud will occur on your system, but rather how much of it you can detect and prevent. If a payments company flags too many transactions as fraudulent, then real transactions might accidentally get flagged as well. But if you don’t reject enough of the fraudulent transactions, you might not

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Where Machines Go to Learn with Auren Hoffman

http://traffic.libsyn.com/sedaily/MLwithAuren.mp3Podcast: Play in new window | Download If you wanted to build a machine learning model to understand human health, where would you get the data? A hospital database would be useful, but privacy laws make it difficult to disclose that patient data to the public. In order to publicize the data safely, you would have to anonymize it, so that a patient’s identity could not be derived from data

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Machine Learning is Hard with Zayd Enam

http://traffic.libsyn.com/sedaily/WhyMLisHard.mp3Podcast: Play in new window | Download Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process. A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable results even in the presence of a mistaken

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Giphy Engineering with Anthony Johnson

http://traffic.libsyn.com/sedaily/giphy_edited.mp3Podcast: Play in new window | Download Giphy is a search engine for gifs, the short animated graphics that we see around the Internet. Giphy is also a creative platform where people create new gifs. Every search engine requires the construction of a search index, which is a data structure that responds to search queries efficiently. Since Giphy is a search engine for graphics, there is almost no text inherently

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Robots for the Elderly with Itai Mendelsohn

http://traffic.libsyn.com/sedaily/robotics_edited.mp3Podcast: Play in new window | Download Many elderly people live with unhealthy levels of isolation. Social isolation is a problem for anybody, but younger people can use technology to alleviate their isolation with tools like Skype and Facebook. How can we bridge the generational gap and give elderly people access to the same technological tools that younger people find easy to use? Voice interfaces are an important new medium

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Translation with Vasco Pedro

http://traffic.libsyn.com/sedaily/Unbabel_edited.mp3Podcast: Play in new window | Download Translation is a classic problem in computer science. How do you translate a sentence from one human language into another? This seems like a problem that computers are well-suited to solve. Languages follow well-defined rules, we have lots of sample data to train our machine learning models. And yet, the problem has not been solved–largely because languages don’t always follow rules. We have

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Medical Machine Learning with Razik Yousfi and Leo Grady

http://traffic.libsyn.com/sedaily/heartflow_edited_fixed.mp3Podcast: Play in new window | Download Medical imaging is used to understand what is going on inside the human body and prescribe treatment. With new image processing and machine learning techniques, the traditional medical imaging techniques such as CT scans can be enriched to get a more sophisticated diagnosis. HeartFlow uses data from a standard CT scan to model a human heart and understand blockages of blood flow using

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Antifraud Architecture with Josh Yudaken

http://traffic.libsyn.com/sedaily/antifraud_architecture_edited.mp3Podcast: Play in new window | Download Online marketplaces and social networks often have a trust and safety team. The trust and safety team helps protect the platform from scams, fraud, and malicious actors. To detect these bad actors at scale requires building a system that classifies every transaction on the platform as safe or potentially malicious. Since every social platform has to build something like this, Smyte decided to

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Scale API with Lucy Guo and Alexandr Wang

http://traffic.libsyn.com/sedaily/scaleapi_edited1.mp3Podcast: Play in new window | Download Some tasks are simple, but cannot be performed by a computer. Audio transcription, image recognition, survey completion–these are simple procedures that almost any human could execute, but the machine learning models have not gotten consistent enough to do them accurately. Scale is an API for human labor, created by Lucy Guo and Alexandr Wang. Similar to Amazon Mechanical Turk, Scale sends small, simple

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Bot Memorial with Eugenia Kuyda

http://traffic.libsyn.com/sedaily/botmemorial_edited.mp3Podcast: Play in new window | Download When a human passes away, we create a tombstone as a memorial. Friends and family visit a grave to remember the times they had with that person while they were still alive. Memorial bots are another way to celebrate the life of someone who has passed away. A memorial bot is created by taking the messages sent by a deceased person and passing

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