Reinforcement learning is a paradigm in machine learning that uses incentives- or “reinforcement”- to drive learning. The learner is conceptualized as an intelligent agent working within a system of rewards and penalties in order to solve a novel problem. The agent is designed to maximize rewards while pursuing a solution by trial-and-error.  Programming a system to respond to the complex and unpredictable “real world” is one of the principal challenges in robotics engineering. One field which is finding new applications for reinforcement learning is the study of MEMS devices- robots or other electronic devices built at the micrometer scale. The use of reinforcement learning in microscopic devices poses a challenging engineering problem, due to constraints with power usage and computational power. Nathan Lambert is a PhD student at Berkeley who works with the Berkeley Autonomous Microsystems Lab. He has also worked at Facebook AI Research and Tesla. He joins the show today to talk about the application of reinforcement learning to robotics and how deep learning is changing the MEMS device landscape. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Reinforcement Learning and Robotics with Nathan Lambert appeared first on Software Engineering Daily.

Companies can have a negative impact on the environment by outputting excess carbon. Many companies want to reduce their net carbon impact to zero, which can be done by investing in forests. Pachama is a marketplace for forest investments. Pachama uses satellites, imaging, machine learning, and other techniques to determine how much carbon is being absorbed by different forests. Diego Saez-Gil is a founder of Pachama, and joins the show to talk through how Pachama works and the long-term goals of the company. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Machine Learning Carbon Capture with Diego Saez-Gil appeared first on Software Engineering Daily.

TensorFlow Lite is an open source deep learning framework for on-device inference. TensorFlow Lite was designed to improve the viability of machine learning applications on phones, sensors, and other IoT devices. Pete Warden works on TensorFlow Lite at Google and joins the show to talk about the world of machine learning applications and the necessary frameworks and devices necessary to build them. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post TensorFlow Lite with Pete Warden appeared first on Software Engineering Daily.

Originally published July 30, 2019 “Internet of Things” is a term used to describe the increasing connectivity and intelligence of physical objects within our lives.  IoT has manifested within enterprises under the term “Industrial IoT,” as wireless connectivity and machine learning have started to improve devices such as centrifuges, conveyor belts, and factory robotics. In the consumer space, IoT has moved slower than many people expected, and it remains to be seen when we will have widespread computation within consumer devices such as microwaves, washing machines, and lightswitches. IoT computers have different constraints than general purpose computers. Security, reliability, battery life, power consumption, and cost structures are very different in IoT devices than in your laptop or smartphone. One technology that could solve some of the problems within IoT is WebAssembly, a newer binary instruction format for executable programs. Jonathan Beri is a software engineer and the organizer of the San Francisco WebAssembly Meetup. He has significant experience in the IoT industry, and joins the show to discuss the state of WebAssembly, the surrounding technologies, and their impact on IoT. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post WebAssembly on IoT with Jonathan Beri (Repeat) appeared first on Software Engineering Daily.

Originally published April 17, 2019 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. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Drishti: Deep Learning for Manufacturing with Krish Chaudhury (Repeat) appeared first on Software Engineering Daily.

Originally published June 21, 2019 Niantic is the company behind Pokemon Go, an augmented reality game where users walk around in the real world and catch Pokemon which appear on their screen. The idea for augmented reality has existed for a long time. But the technology to bring augmented reality to the mass market has appeared only recently. Improved mobile technology makes it possible for a smartphone to display rendered 3-D images over a video stream without running out of battery. Ingress was the first game to come out of Niantic, followed by Pokemon Go, but there are other games on the way. Niantic is also working on the Niantic Real World platform, a “planet-scale” AR platform that will allow independent developers to build multiplayer augmented reality experiences that are as dynamic and entertaining as Pokemon Go. Paul Franceus is an engineer at Niantic, and he joins the show to describe his experience building and launching Pokemon Go, as well as abstracting the technology from Pokemon Go and opening up the Niantic Real World platform to developers. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Niantic Real World with Paul Franceus (Repeat) appeared first on Software Engineering Daily.

Originally published December 9, 2019 Machine learning algorithms have existed for decades. But in the last ten years, several advancements in software and hardware have caused dramatic growth in the viability of applications based on machine learning. Smartphones generate large quantities of data about how humans move through the world. Software-as-a-service companies generate data about how these humans interact with businesses. Cheap cloud infrastructure allows for the storage of these high volumes of data. Machine learning frameworks such as Apache Spark, TensorFlow, and PyTorch allow developers to easily train statistical models. These models are deployed back to the smartphones and the software-as-a-service companies, which improves the ability for humans to move through the world and gain utility from their business transactions. And as the humans interact more with their computers, it generates more data, which is used to create better models, and higher consumer utility. The combination of smartphones, cloud computing, machine learning algorithms, and distributed computing frameworks is often referred to as “artificial intelligence.” Chris Benson is the host of the podcast Practical AI, and he joins the show to talk about the modern applications of artificial intelligence, and the stories he is covering on Practical AI. On his podcast, Chris talks about everything within the umbrella of AI, from high level stories to low level implementation details. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Practical AI with Chris Benson (Repeat) appeared first on Software Engineering Daily.

Originally published January 25, 2019 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. The post Kubeflow: TensorFlow on Kubernetes with David Aronchick (Repeat) appeared first on Software Engineering Daily.

Originally published April 3, 2017 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 long time to predict what will happen in the future. Numerai is a hedge fund that crowdsources its investment strategies by allowing anyone to train models against Numerai’s data. A model that succeeds in a simulated environment will be adopted by Numerai and used within its real money portfolio. The engineers who create the models are rewarded in proportion to how well the models perform. Xander Dunn is a software engineer at Numerai and in this episode he explains what a hedge fund is, why the traditional strategies are not optimal, and how Numerai creates the right incentive structure to crowdsource market intelligence. This interview was fun and thought provoking–Numerai is one of those companies that makes me very excited about the future. The post Hedge Fund Artificial Intelligence with Xander Dunn (Repeat) appeared first on Software Engineering Daily.

For several years, we have had the ability to create artificially generated text articles. More recently, audio and video synthesis have been feasible for artificial intelligence. Rosebud is a company that creates animated virtual characters that can speak. Users can generate real or fictional presenters easily with Rosebud. Dzmitry Pletnikau is an engineer with Rosebud and joins the show to talk about the technology and engineering behind the company. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Rosebud: Artificially Generated Media with Dzmitry Pletnikau appeared first on Software Engineering Daily.