Software Engineering Daily

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 public good–such as targeted advertising.

Oren Etzioni is the CEO of the Allen Institute for Artificial Intelligence, and in this episode we discuss AI research–from the doomful premonitions of Nick Bostrom to the unbridled optimism of Ray Kurzweil, as well as the realities of how AI research actually proceeds. Projects at the Allen Institute are defined and structured to solve problems in an intelligent, scalable fashion, so that engineering can proceed steadily from the local maxima of a problem domain to the global maxima. The Allen Institute seeks to bridge the gap by providing ample funding for open source AI research for the common good.

Oren is also speaking at the O’Reilly Artificial Intelligence Conference in New York, September 26-27.

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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.

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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.

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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 the time his sales team has to spend qualifying leads. What I found most interesting about this episode was that machine learning gets used by a single developer to solve a simple business problem and deliver solid value. This is in contrast to how many of us think about machine learning–as an intimidating domain that requires a large team to build anything meaningful.

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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 to our phone call systems.

Umut Alp is the CTO of TrueCaller, and he joins the show today to break down the engineering problems of preventing telephone call spam. Users of TrueCaller install it on their phones, and the software allows users to report when they have received a spam call. Using this reporting mechanism, and other learning algorithms, TrueCaller is able to learn what types of calls it should block from being accepted by your phone. Today on Software Engineering Daily, we discuss cell phone spam prevention.

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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 by David Kale, a researcher at the intersection of machine learning and clinical data. We discuss the machine learning and research techniques he is using to diagnose illnesses using neural networks, and we also talk about the challenges of performing data science in hospitals, where the data is mostly confidential. David will also be presenting at Strata + Hadoop World in San Jose. We’re partnering with O’Reilly to support this conference – if you want to go to Strata, you can save 20% off a ticket with our code PCSED.

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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 the culture around data science-driven genetic engineering.

In this episode, Tim explains how useful graph databases are for modeling the genetic lineages, and talks about how Monsanto manages simulations and experiments on their genomics software pipeline. Tim also talks about how just a few engineers can create a cultural shift within a large company like Monsanto using the leverage allowed by software.

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Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. In this episode, François discusses the state of deep learning, and explains why the field is experiencing a cambrian explosion that eventually may taper off. He explains the need for Keras and why its simplicity and ease makes it a useful deep learning library for developers to experiment and build with.

François Chollet is the author of Keras and the founder of Wysp, learning platform for artists. He currently works for Google as a deep learning engineer and researcher.

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Machine learning is something that many business are starting to tack onto their existing processes. Yet, to add machine learning capabilities after the fact is often a fool’s errand. Joshua argues that machine learning cannot be an afterthought, but rather must be custom developed to suit the specific problem or question that each company is trying to answer. His company, Wise.io, tackles this challenge of helping business build ground up machine learning applications that generate accurate predictions for use in an array of business processes.

Joshua Bloom is the cofounder and CTO of Wise.io. He is also an astrophysicist, and a professor of astronomy at UC Berkeley.

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TensorFlow is an open source machine learning library intended to bring large-scale, distributed machine learning and deep learning to everyone. Google recently released the framework to the public as a second-generation API, having learned from the successes and failures of DistBelief.

Greg Corrado is a senior research scientist and tech lead at Google, where he focuses on the research areas of machine intelligence, machine perception and natural language processing.

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