Originally published November 7, 2018 An instruction set defines a low level programming language for moving information throughout a computer. In the early 1970’s, the prevalent instruction set language used a large vocabulary of different instructions. One justification for a large instruction set was that it would give a programmer more freedom to express the logic of their programs. Many of these instructions were rarely used. Think of your favorite programming language (or your favorite human language). What percentage of words in the vocabulary do you need to communicate effectively? We sometimes call these language features “syntactic sugar”. They add expressivity to a language, but may not improve functionality or efficiency. These extra language features can have a cost. Dave Patterson and John Hennessy created the RISC architecture: Reduced Instruction Set Compiler architecture. RISC proposed reducing the size of the instruction set so that the important instructions could be optimized for. Programs would become more efficient, easier to analyze, and easier to debug. Dave Patterson’s first paper on RISC was rejected. He continued to research the architecture and advocate for it. Eventually RISC became widely accepted, and Dave won a Turing Award together with John Hennessy. Dave joins the show to talk about his work on RISC and his continued work in computer science research to the present. He is involved in the Berkeley RISELab and works at Google on the Tensor Processing Unit. Machine learning is an ocean of new scientific breakthroughs and applications that will change our lives. It was inspiring to hear Dave talk about the changing nature of computing, from cloud computing to security to hardware design. The post Computer Architecture with Dave Patterson Holiday Repeat appeared first on Software Engineering Daily.

October 1, 2019 The development of self-driving cars is one of the biggest technological changes that is under way. Across the world, thousands of engineers are working on developing self-driving cars. Although it still seems far away, self-driving cars are starting to feel like an inevitability. This is especially true if you spend much time in downtown San Francisco, where you will see a self-driving car being tested every day. Much of the time, that self-driving car will be operated by Cruise. Cruise is a company that is building a self-driving car service. The company has hundreds of engineers working across the stack, from computer vision algorithms to automotive hardware. Cruise’s engineering requires engineers who can work with cloud tools as well as low-latency devices. It also requires product developers and managers to lead these different teams. The field of self-driving is very new. There is not much literature available on how to build a self-driving car. There is even less literature on how to manage a team of engineers that are building, testing, and deploying software and hardware for real cars that are driving around the streets of San Francisco. Mo Elshenawy is VP of engineering at Cruise, and he joins the show to talk about the engineering that is required to develop fully self-driving car technology, as well as how to structure teams to align the roles of product design, software engineering, testing, machine learning, and hardware.  Full disclosure: Cruise is a sponsor of Software Engineering Daily. The post Cruise: Self-Driving Engineering with Mo Elshenawy Holiday Repeat appeared first on Software Engineering Daily.

Newer machine learning tooling is often focused on streamlining the workflows and developer experience. One such tool is BentoML. BentoML is a workflow that allows data scientists and developers to ship models more effectively. Chaoyu Yang is the creator of BentoML and he joins the show to talk about why he created Bento and the engineering behind the project. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Model Deployment and Serving with Chaoyu Yang appeared first on Software Engineering Daily.

Data labeling is a major bottleneck in training and deploying machine learning and especially NLP. But new tools for training models with humans in the loop can drastically reduce how much data is required. Humanloop is a platform for annotating text and training NLP models with much less labelled data. Raza Habib, founder of Humanloop, joins the show to to talk about NLP workflows and his work on Humanloop. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Humanloop: NLP Model Engineering with Raza Habib appeared first on Software Engineering Daily.

Federated learning is machine learning without a centralized data source. Federated Learning enables mobile phones or edge servers to collaboratively learn a shared prediction model while keeping all the training data on device. Mike Lee Williams is an expert in federated learning, and he joins the show to give an overview of the subject and share his thoughts on its applications. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Federated Learning with Mike Lee Williams appeared first on Software Engineering Daily.

Machine learning models require training data, and training data needs to be labeled. Raw images and text can be labeled using a training data platform like Labelbox. Labelbox is a system of labeling tools that enables a human workforce to create data that is ready to be consumed by machine learning training algorithms. The Labelbox team joins the show today to discuss training data and how to label it. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Labelbox: Data Labeling Platform appeared first on Software Engineering Daily.

Training a computer vision model is not easy. Bottlenecks in the development process make it even harder. Ad hoc code, inconsistent data sets, and other workflow issues hamper the ability to streamline models. Roboflow is a company built to simplify and streamline these model training workflows. Brad Dwyer is a founder of Roboflow and joins the show to talk about model development and his company. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Roboflow: Computer Vision Models with Brad Dwyer appeared first on Software Engineering Daily.

Peter Gao, a member of the Aquarium team, discusses the role of Aquarium in improving dataset quality for machine learning models. The podcast explores the challenges of understanding and improving datasets, addresses bias in machine learning models, and discusses the current state of machine learning and its future. It also touches on the customer onboarding process, the software stack used for data visualization, and the potential of machine learning in practical improvements.

Factories require quality assurance work. That QA work can be accomplished by a robot with a camera together with computer vision. This allows for sophisticated inspection techniques that do not require as much manual effort on the part of a human. Arye Barnehama is a founder of Elementary Robotics, a company that makes these kinds of robots. Arye joins the show to talk through the engineering of Elementary Robotics, and his vision for the future of the factory floor. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Elementary Robotics with Arye Barnehama appeared first on Software Engineering Daily.

Robotic process automation involves the scripting and automation of highly repeatable tasks. RPA tools such as UIPath paved the way for a newer wave of automation, including the Robot Framework, an open source system for RPA. Antti Karjalainen is the CEO of Robocorp, a company that provides an RPA tool suite for developers. Antti joins the show to talk through the definition of RPA, common RPA tasks, and what he is building with Robocorp. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Robotic Process Automation with Antti Karjalainen appeared first on Software Engineering Daily.