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http://traffic.libsyn.com/sedaily/AI_Research_Edited_2.mp3Podcast: Play in new window | Download Research in artificial intelligence takes place mostly at universities and large corporations, but both of these types of institutions have constraints that cause the research to proceed a certain way. In a university, basic research might be hindered by lack of funding. At a big corporation, the researcher might be encouraged to study a domain that is not squarely in the interest of
http://traffic.libsyn.com/sedaily/TensorFlow_with_Rajat__Edited.mp3Podcast: Play in new window | Download TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the process of deploying a machine learning model from
http://traffic.libsyn.com/sedaily/datavalidation_edited_2.mp3Podcast: Play in new window | Download Data Validation is the process of ensuring that data is accurate. In many software domains, an application is pulling in large quantities of data from external sources. That data will eventually be exposed to users, and it needs to be correct. Radius Intelligence is a company that aggregates data on small businesses. In order to ensure that business addresses and phone numbers are
http://traffic.libsyn.com/sedaily/Xeneta.mp3Podcast: Play in new window | Download Machine learning has become simplified. Similar to how Ruby on Rails made web development approachable, scikit-learn takes away much of the frustrating aspects of machine learning, and lets the developer focus on building functionality with high-level APIs. Per Harald Borgen is a developer at Xeneta. He started programming fairly recently, but has already built a machine learning application that cuts down on
http://traffic.libsyn.com/sedaily/Truecaller_Edited.mp3Podcast: Play in new window | Download The war against spam has been going on for decades. Email spam blockers and ad blockers help protect us from unwanted messages in our communication and browsing experience. These spam prevention tools are powered by machine learning, which catches most of the emails and ads that we don’t want to see. TrueCaller is a company that is bringing this quality of spam detection
http://traffic.libsyn.com/sedaily/healthcareML_Edited.mp3Podcast: Play in new window | Download “Building a model to predict disease and deploying that in the wild – the bar for success is much higher there than, say, deciding what ad to show you.” Diagnosing illness today requires the trained eye of a doctor. With machine learning, we might someday be able to diagnose illness using only a data set. Today on Software Engineering Daily, we are joined
http://traffic.libsyn.com/sedaily/Monsanto_Edited_FInal.mp3Podcast: Play in new window | Download “Nothing’s cool unless you call it ‘as a service.’ ” Monsanto is a company that is known for its chemical and biological engineering. It is less well known for its data science and software engineering teams. Tim Williamson is a data scientist at Monsanto, and on today’s show he talked about how he and a small group of engineers at Monsanto dramatically shifted
“I definitely think we can try to abstract away the first principles of intelligence and then try to go from these principles to an intelligent machine that might look nothing like the brain.”
“You’ve got software engineers who are interested in machine learning, and think what they need to do is just bring in another module and then that will solve their problem. It’s particularly important for those people to understand that this is a different type of beast.”
“You don’t mind if failures slow things down, but its very important that failures do not stop forward progress.”
“I normally try to sit together or very close to a product team or engineering team. And by doing so, I get very close to the source of all kinds of challenging problems.”
“When I was a graduate student, I was sitting in the office of my advisor in electrical engineering and he said, ‘Look out that window – you see a Volkswagon, I see a realization of a random variable.’ ”
“Changing anything changes everything.”
Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems.
Current infrastructure makes it difficult for data scientists to share analytical models with the software engineers who need to integrate them. Yhat is an enterprise software company tackling the challenge of how data science gets done. Their products enable companies and users to easily deploy data science environments and translate analytical models into production code.
Data science competitions are an effective way to crowdsource the best solutions for challenging datasets. Kaggle is a platform for data scientists to collaborate and compete on machine learning problems with the opportunity to win money from the competitions’ sponsors.
There is a need for more data scientists to make sense of the vast amounts of data we produce and store. Dataquest is an in-browser platform for learning data science that is tackling this problem.
Vik Paruchuri is the founder of Dataquest. He was previously a machine learning engineer at EdX and before that a U.S. diplomat.