Machine Learning Archives - Software Engineering Daily

Originally posted on 28 July 2017. Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at The post Self-Driving Deep Learning with Lex...

Originally posted on May 12, 2015. Humans have now been defeated by computers at heads up no-limit holdem poker. Some people thought this wouldn’t be possible. Sure, we can teach a computer to beat a human at Go or Chess. Those games have a smaller decision space. There is no hidden information. There is no The post Poker Artificial Intelligence with Noam Brown Holiday Repeat appeared first on Sof...

The volume of data in the world is always increasing. The costs of storing that data is always decreasing. And the means for processing that data is always evolving. Sensors, cameras, and other small computers gather large quantities of data from the physical world around us. User analytics tools gather information about how we are The post Reflow: Distributed Incremental Processing with Marius Er...

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 The post Computer Architecture with Dave Pa...

Google Search allows humans to find and access information across the web. A human enters an unstructured query into the search box, the search engine provides several links as a result, and the human clicks on one of those links. That link brings up a web page, which is a set of unstructured data. Humans The post Diffbot: Knowledge Graph API with Mike Tung appeared first on Software Engineering D...

David Cancel has started five companies, most recently Drift. Drift is a conversational marketing and sales platform. David has a depth of engineering skills and a breadth of business experience that make him an amazing source of knowledge. In today’s episode, David discusses topics ranging from the technical details of making a machine learning-driven sales The post Drift: Sales Bot Engineering w...

Google Brain is an engineering team focused on deep learning research and applications. One growing area of interest within Google Brain is that of generative models. A generative model uses neural networks and a large data set to create new data similar to the ones that the network has seen before. One approach to making The post Generative Models with Doug Eck appeared first on Software Engineer...

Stock traders have access to high volumes of information to help them make decisions on whether to buy an asset. A trader who is considering buying a share of Google stock can find charts, reports, and statistical tools to help with their decision. There are a variety of machine learning products to help a technical The post Real Estate Machine Learning with Or Hiltch appeared first on Software En...

Self-driving transportation will be widely deployed at some point in the future. How far off is that future? There are widely varying estimations: maybe you will summon a self-driving Uber in a New York within 5 years, or maybe it will take 20 years to work out all of the challenges in legal and engineering. The post RideOS: Fleet Management with Rohan Paranjpe appeared first on Software Engineeri...

Stitch Fix is a company that recommends packages of clothing based on a set of preferences that the user defines and updates over time. Stitch Fix’s software platform includes the website, data engineering infrastructure, and warehouse software. Stitch Fix has over 5000 employees, including a large team of engineers. Cathy Polinsky is the CTO of The post Stitch Fix Engineering with Cathy Polinsky...

DoorDash is a last mile logistics company that connects customers with their favorite national and local businesses. When a customer orders from a restaurant, DoorDash needs to identify the ideal driver for picking up the order from the restaurant and dropping it off with the customer. This process of matching an order to a driver The post DoorDash Engineering with Raghav Ramesh appeared first on...

In the smartphone market there are two dominant operating systems: one closed source (iPhone) and one open source (Android). The market for self-driving cars could play out the same way, with a company like Tesla becoming the closed source iPhone of cars, and a company like Comma.ai developing the open source Android of self-driving cars. The post Self-Driving Engineering with George Hotz appeared...

“Bots” are becoming increasingly relevant to our everyday interactions with technology. A bot sometimes mediates the interactions of two people. Examples of bots include automated reply systems, intelligent chat bots, classification systems, and prediction machines. These systems are often powered by machine learning systems that are black boxes to the user. Today’s guest Rob May The post Botchain...

Machine learning models allow our applications to perform highly accurate inferences. A model can be used to classify a picture as a cat, or to predict what movie I might want to watch. But before a machine learning model can be used to make these inferences, the model must be trained and deployed. In the The post Machine Learning Deployments with Diego Oppenheimer appeared first on Software Engin...

When a patient comes into the hospital with stroke symptoms, the hospital will give that patient a CAT scan, a 3-dimensional imaging of the patient’s brain. The CAT scan needs to be examined by a radiologist, and the radiologist will decide whether to refer the patient to an interventionist–a surgeon who can perform an operation The post Machine Learning Stroke Identification with David Gola...

Evolutionary algorithms can generate surprising, effective solutions to our problems. Evolutionary algorithms are often let loose within a simulated environment. The algorithm is given a function to optimize for, and the engineers expect that algorithm to evolve a solution that optimizes for the objective function given the constraints of the simulated environment. But sometimes these The post Dig...

Moore’s Law states that the number of transistors in a dense integrated circuit double about every two years. Moore’s Law is less like a “law” and more like an observation or a prediction. Moore’s Law is ending. We can no longer fit an increasing amount of transistors in the same amount of space with a The post Future of Computing with John Hennessy appeared first on Software Engineering Daily .

Applications of artificial intelligence are permeating our everyday lives. We notice it in small ways–improvements to speech recognition; better quality products being recommended to us; cheaper goods and services that have dropped in price because of more intelligent production. But what can we quantitatively say about the rate at which artificial intelligence is improving? How The post Ope...

A sample of the human voice is a rich piece of unstructured data. Voice recordings can be turned into visualizations called spectrograms. Machine learning models can be trained to identify features of these spectrograms. Using this kind of analytic strategy, breakthroughs in voice analysis are happening at an amazing pace. Rita Singh researches voice at The post Voice with Rita Singh appeared firs...

Data Skeptic is a podcast about machine learning, data science, and how software affects our lives. The first guest on today’s episode is Kyle Polich, the host of Data Skeptic. Kyle is one of the best explainers of machine learning concepts I have met, and for this episode, he presented some material that is perfect The post Machine Learning with Data Skeptic and Second Spectrum at Telesign appear...

Algorithms for building neural networks have existed for decades. For a long time, neural networks were not widely used. Recent changes to the cost of compute and the size of our data have made neural networks extremely useful. Our smartphones generate terabytes of useful data. Lower storage costs make it economical to keep that data. The post Deep Learning Topologies with Yinyin Liu appeared firs...

Keybase is a platform for managing public key infrastructure. Keybase’s products simplify the complicated process of associating your identity with a public key. Keybase is the subject of the first half of today’s show. Michael Maxim, an engineer from Keybase gives an overview of how the technology works and what kinds of applications Keybase unlocks. The post Keybase Architecture / Clarifai Infra...

Rajat Monga is a director of engineering at Google where he works on TensorFlow. TensorFlow is a framework for numerical computation developed at Google. The majority of TensorFlow users are building machine learning applications such as image recognition, recommendation systems, and natural language processing–but TensorFlow is actually applicable to a broader range of scientific computatio...

The easiest way to train a computer to recognize a picture of a cat is to show the computer a million labeled images of cats. The easiest way to train a computer to recognize a stop sign is to show the computer a million labeled stop signs. Supervised machine learning systems require labeled data. Today, The post Scale Self-Driving with Alexandr Wang appeared first on Software Engineering Daily .

Pinterest is a visual feed of ideas, products, clothing, and recipes. Millions of users browse Pinterest to find images and text that are tailored to their interests. Like most companies, Pinterest started with a large monolithic application that served all requests. As Pinterest’s engineering resources expanded, some of the architecture was broken up into microservices The post Machine Learning D...