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Augmented reality applications can be used on smartphones and dedicated AR headsets. On smartphones, ARCore (Google) and ARKit (Apple) allow developers to build for the camera on a user’s smartphone. AR headsets such as Microsoft HoloLens and Magic Leap allow for a futuristic augmented reality headset experience.

The most prominent use of augmented reality today is gaming, with a notable example being Niantic’s Pokemon Go. Tony Godar is a software engineer who works on augmented and virtual reality applications. He joins the show to talk about his day job working on virtual reality experiences, and an AR game he built called ARhythm.

Tony was the winner of the FindCollabs Hackathon and we also discussed his experience working on the project through FindCollabs.

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Drishti is a company focused on improving manufacturing workflows using computer vision.

A manufacturing environment consists of assembly lines. A line is composed of sequential stations along that manufacturing line. At each station on the assembly line, a worker performs an operation on the item that is being manufactured. This type of workflow is used for the manufacturing of cars, laptops, stereo equipment, and many other technology products.

With Drishti, the manufacturing process is augmented by adding a camera at each station. Camera footage is used to train a machine learning model for each station on the assembly line. That machine learning model is used to ensure the accuracy and performance of each task that is being conducted on the assembly line.

Krish Chaudhury is the CTO at Drishti. From 2005 to 2015 he led image processing and computer vision projects at Google before joining Flipkart, where he worked on image science and deep learning for another four years. Krish had spent more than twenty years working on image and vision related problems when he co-founded Drishti.

In today’s episode, we discuss the science and application of computer vision, as well as the future of manufacturing technology and the business strategy of Drishti.

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Until Google DeepMind came into the field, protein structure prediction was dominated by academics.

Protein structure prediction is the process of predicting how a protein will fold by looking at genetic code. Protein structure prediction is a perfect field to approach through the application of deep learning, because the inputs are highly dimensional and there is a plentiful array of different sets of labeled data. Protein structure deep learning is a field in which many different approaches are taken, often involving supervised learning and reinforcement learning.

Mohammed Al Quraishi is a systems biologist at Harvard. His background spans computer engineering, statistics, and genetics. In his work, Mohammed explores the interplay between biology and computer systems.

One area of Mohammed’s focus is protein structure prediction. In a blog post last year, Mohammed gave a brief history of protein structure prediction and described the significance of DeepMind entering the field. DeepMind’s AlphaFold technology surpassed all other competitors in the most recent CASP protein structure competition.

Mohammed joins the show to discuss biology, academia, deep learning, and DeepMind.

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Data sets can be modeled in a row-wise, relational format. When two data sets share a common field, those data sets can be combined in a procedure called a join. A join combines the data of two data sets into one data set that is often bigger than the initial two data sets independently occupied. In fact, this new data set is often so much bigger that it creates problems for the machine learning engineers.

Arun Kumar is an assistant professor at UC San Diego. He joins the show to discuss the modern lifecycle of machine learning models, and the gaps in the tooling.

Arun’s research into improving processing of joined data sets has been adopted by companies such as Google. Some of that research has been adapted into open source machine learning tools that improve the performance of machine learning jobs with minimal code required.

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The demand for electricity is based on the consumption of the electrical grid at a given time. The supply of electricity is based on how much energy is being produced or stored on the grid at a given time. Because these sources of supply and demand fluctuate rapidly but predictably, energy markets present profit opportunities for traders.

Minh Dang and Corey Noone are engineers with Advanced Microgrid Solutions, a company that builds software to help traders capture better opportunities in the energy markets. Minh and Corey join the show to talk about how their company builds and deploys machine learning models for market prediction.

We discussed data infrastructure, machine learning model deployments, and the dynamics of the energy markets.

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Zoox is a full-stack self-driving car company. Zoox engineers work on everything a self-driving car company needs, from the physical car itself to the algorithms running on the car to the ride hailing system which the company plans to use to drive around riders. Since starting in 2014, Zoox has grown to over 500 employees.

Ethan Dreyfuss is a software infrastructure engineer at Zoox. He joins the show to discuss scaling an engineering team for self-driving. Machine learning was a big part of our conversation, because there are so many different approaches that an engineering team can take when it comes to machine learning for cars.

Can you take computer vision algorithms from academic papers and apply them to cars? Can you use the computer vision APIs from the cloud providers for anything useful? What about physical world mapping companies like Mapillary? How do you do data labeling, and data management? And how do you manage the interactions across the stack, from mechanical engineering to user interface design?

We touched on some of these areas, but barely scratched the surface of the self-driving car domain.

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Artificial intelligence is reshaping every aspect of our lives, from transportation to agriculture to dating. Someday, we may even create a superintelligence–a computer system that is demonstrably smarter than humans. But there is widespread disagreement on how soon we could build a superintelligence. There is not even a broad consensus on how we can define the term “intelligence”.

Information technology is improving so rapidly we are losing the ability to forecast the near future. Even the most well-informed politicians and business people are constantly surprised by technological changes, and the downstream impact on society. Today, the most accurate guidance on the pace of technology comes from the scientists and the engineers who are building the tools of our future.

Martin Ford is a computer engineer and the author of Architects of Intelligence, a new book of interviews with the top researchers in artificial intelligence. His interviewees include Jeff Dean, Andrew Ng, Demis Hassabis, Ian Goodfellow, and Ray Kurzweil.

Architects of Intelligence is a privileged look at how AI is developing. Martin Ford surveys these different AI experts with similar questions. How will China’s adoption of AI differ from that of the US? What is the difference between the human brain and that of a computer? What are the low-hanging fruit applications of AI that we have yet to build?

Martin joins the show to talk about his new book. In our conversation, Martin synthesizes ideas from these different researchers, and describes the key areas of disagreement from across the field.

To find all 900 of our old episodes, including past episodes with authors and artificial intelligence researchers, check out the Software Engineering Daily app in the iOS and Android app stores. Whether or not you are a software engineer, we have lots of content about technology, business, and culture. In our app, you can also become a paid subscriber and get ad-free episodes–and you can have conversations with other members of the Software Engineering Daily community.

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When TensorFlow came out of Google, the machine learning community converged around it. TensorFlow is a framework for building machine learning models, but the lifecycle of a machine learning model has a scope that is bigger than just creating a model. Machine learning developers also need to have a testing and deployment process for continuous delivery of models.

The continuous delivery process for machine learning models is like the continuous delivery process for microservices, but can be more complicated. A developer testing a model on their local machine is working with a smaller data set than what they will have access to when it is deployed. A machine learning engineer needs to be conscious of versioning and auditability.

Kubeflow is a machine learning toolkit for Kubernetes based on Google’s internal machine learning pipelines. Google open sourced Kubernetes and TensorFlow, and the projects have users AWS and Microsoft. David Aronchick is the head of open source machine learning strategy at Microsoft, and he joins the show to talk about the problems that Kubeflow solves for developers, and the evolving strategies for cloud providers.

David was previously on the show when he worked at Google, and in this episode he provides some useful discussion about how open source software presents a great opportunity for the cloud providers to collaborate with each other in a positive sum relationship.

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Robots are making their way into every area of our lives. Security robots roll around industrial parks at night, monitoring the area for intruders. Amazon robots tirelessly move packages around in warehouses, reducing the time and cost of logistics. Self-driving cars have become a ubiquitous presence in cities like San Francisco.

For a hacker in a dorm room, or a researcher in a small lab, how do you get started with robotics? There are drones and other small options like AWS DeepRacer–but what is the equivalent of the Raspberry Pi for large, human-sized robots?

Zach Allen is the founder of Slate Robotics, a company that makes large, human-sized robots that are at a low enough cost to be accessible to tinkerers, researchers, and prototype builders. Zach joins the show to talk about the state of robotics and why he started a robot company.

What Zach is doing is quite hard–he is a solo founder who has bootstrapped a robotics company from scratch. He is set up in a strip mall in Missouri, where he has set up a row of 3-D printers to create the parts for his robots. He programs and assembles these robots himself.

Whether you are interested in robots are thinking about starting a hardware company, this episode could be useful to you.

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Originally posted on 13 September 2017.

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.

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