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 Fridman Holiday Repeat appeared first on Software Engineering Daily.

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 Software Engineering Daily.

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 Eriksen appeared first on Software Engineering Daily.

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 Patterson appeared first on Software Engineering Daily.

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 Daily.

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 with David Cancel appeared first on Software Engineering Daily.

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 Engineering Daily.

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 Engineering Daily.

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 Engineering Daily.

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 appeared first on Software Engineering Daily.

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 Software Engineering Daily.

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 first on Software Engineering Daily.

“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 with Rob May appeared first on Software Engineering Daily.

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 Engineering Daily.

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 Golan appeared first on Software Engineering Daily.

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 Digital Evolution with Joel Lehman, Dusan Misevic, and Jeff Clune appeared first on Software Engineering Daily.

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 OpenAI: Compute and Safety with Dario Amodei appeared first on Software Engineering Daily.

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 first on Software Engineering Daily.

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 appeared first on Software Engineering Daily.

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 first on Software Engineering Daily.

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 Infrastructure Meetup Talks appeared first on Software Engineering Daily.

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 computation The post TensorFlow Applications with Rajat Monga appeared first on Software Engineering Daily.

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 Deployments with Kinnary Jangla appeared first on Software Engineering Daily.

Training a deep learning model involves operations over tensors. A tensor is a multi-dimensional array of numbers. For several years, GPUs were used for these linear algebra calculations. That’s because graphics chips are built to efficiently process matrix operations. Tensor processing consists of linear algebra operations that are similar in some ways to graphics processing–but The post Deep Learning Hardware with Xin Wang appeared first on Software Engineering Daily.

A modern farm has hundreds of sensors to monitor the soil health, and robotic machinery to reap the vegetables. A modern shipping yard has hundreds of computers working together to orchestrate and analyze the freight that is coming in from overseas. A modern factory has temperature gauges and smart security cameras to ensure workplace safety. The post Edge Deep Learning with Aran Khanna appeared first on Software Engineering Daily.

Originally published November 17, 2015 “Changing anything changes everything.” Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems. D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High The post Machine Learning and Technical Debt with D. Sculley Holiday Repeat appeared first on Software Engineering Daily.

Automation is changing the labor market. To automate a task, someone needs to put in the work to describe the task correctly to a computer. For some tasks, the reward for automating a task is tremendous–for example, putting together mobile phones. In China, companies like FOXCONN are investing time and money into programming the instructions The post Training the Machines with Russell Smith appeared first on Software Engineering Daily.

Machine learning models can be built by plotting points in space and optimizing a function based off of those points. For example, I can plot every person in the United States in a 3 dimensional space: age, geographic location, and yearly salary. Then I can draw a function that minimizes the distance between my function The post Model Training with Yufeng Guo appeared first on Software Engineering Daily.

A basketball game gives off endless amounts of data. Cameras from all angles capture the players making their way around the court, dribbling, passing, and shooting. With computer vision, a computer can build a well-defined understanding for what a sport looks like. With other machine learning techniques, the computer can make predictions by combining historical The post Sports Deep Learning with Yu-Han Chang and Jeff Su appeared first on Software Engineering Daily.

The applications that demand deep learning range from self-driving cars to healthcare, but the way that models are developed and trained is similar. A model is trained in the cloud and deployed to a device. The device engages with the real world, gathering more data. That data is sent back to the cloud, where it The post Deep Learning Systems with Milena Marinova appeared first on Software Engineering Daily.

If I have a picture of a dog, and I want to search the Internet for pictures that look like that dog, how can I do that? I need to make an algorithm to build an index of all the pictures on the Internet. That index can define the different features of my images. I The post Visual Search with Neel Vadoothker appeared first on Software Engineering Daily.

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 The post Word2Vec with Adrian Colyer appeared first on Software Engineering Daily.

Software companies that have been around for a decade have a ton of data. Modern machine learning techniques are able to turn that data into extremely useful models. Salesforce users have been entering petabytes of data into the company’s CRM tool since 1999. With its Einstein suite of products, Salesforce is using that data to The post Artificial Intelligence APIs with Simon Chan appeared first on Software Engineering Daily.

Automation will make healthcare more efficient and less prone to error. Today, machine learning is already being used to diagnose diabetic retinopathy and improve radiology accuracy. Someday, an AI assistant will assist a doctor in working through a complicated differential diagnosis. Our hospitals look roughly the same today as they did ten years ago, because The post Healthcare AI with Cosima Gretton appeared first on Software Engineering Daily.

Querying a search index for objects similar to a given object is a common problem. A user who has just read a great news article might want to read articles similar to it. A user who has just taken a picture of a dog might want to search for dog photos similar to it. In The post Similarity Search with Jeff Johnson appeared first on Software Engineering Daily.

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 reasoning. We cannot enumerate to a The post Self-Driving Deep Learning with Lex Fridman appeared first on Software Engineering Daily.

Instacart is a grocery delivery service. Customers log onto the website or mobile app and pick their groceries. Shoppers at the store get those groceries off the shelves. Drivers pick up the groceries and drive them to the customer. This is an infinitely complex set of logistics problems, paired with a rich data set given The post Instacart Data Science with Jeremy Stanley appeared first on Software Engineering Daily.

Deep learning allows engineers to build models that can make decisions based on training data. These models improve over time using stochastic gradient descent. When a model gets big enough, the training must be broken up across multiple machines. Two strategies for doing this are “model parallelism” which divides the model across machines and “data The post Distributed Deep Learning with Will Constable appeared first on Software Engineering Daily.

Video object segmentation allows computer vision to identify objects as they move through space in a video. The DAVIS challenge is a contest among machine learning researchers working off of a shared dataset of annotated videos. The organizers of the DAVIS challenge join the show today to explain how video object segmentation models are trained The post Video Object Segmentation with the DAVIS Challenge Team appeared first on Software Engineering Daily.

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 bluffing. Poker must be different! It The post Poker Artificial Intelligence with Noam Brown appeared first on Software Engineering Daily.

Convolutional neural networks are a machine learning tool that uses layers of convolution and pooling to process and classify inputs. CNNs are useful for identifying objects in images and video. In this episode, we focus on the application of convolutional neural networks to image and video recognition and classification. Matt Zeiler is the CEO of The post Convolutional Neural Networks with Matt Zeiler appeared first on Software Engineering Daily.

Most popular music today uses a computer as the central instrument. A single musician is often selecting the instruments, programming the drum loops, composing the melodies, and mixing the track to get the right overall atmosphere. With so much work to do on each song, popular musicians need to simplify–the result is that pop music The post Google Brain Music Generation with Doug Eck appeared first on Software Engineering Daily.

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 The post Hedge Fund Artificial Intelligence with Xander Dunn appeared first on Software Engineering Daily.

Multiagent systems involve the interaction of autonomous agents that may be acting independently or in collaboration with each other. Examples of these systems include financial markets, robot soccer matches, and automated warehouses. Today’s guest Peter Stone is a professor of computer science who specializies in multiagent systems and robotics. In this episode, we discuss some The post Multiagent Systems with Peter Stone appeared first on Software Engineering Daily.

Biology research is complex. The sample size of a biological data set is often too small to make confident judgments about the biological system being studied. During Jason Knight’s PhD research, the RNA sequence data that he was studying was not significant enough to make strong conclusions about the gene regulatory networks he was trying The post Biological Machine Learning with Jason Knight appeared first on Software Engineering Daily.

Every company that deals with payments deals with fraud. The question is not whether fraud will occur on your system, but rather how much of it you can detect and prevent. If a payments company flags too many transactions as fraudulent, then real transactions might accidentally get flagged as well. But if you don’t reject The post Stripe Machine Learning with Michael Manapat appeared first on Software Engineering Daily.

Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process. A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable The post Machine Learning is Hard with Zayd Enam appeared first on Software Engineering Daily.

Deep learning uses neural networks to identify patterns. Neural networks allow us to sequence “layers” of computing, with each layer using learning algorithms such as unsupervised learning, supervised learning, and reinforcement learning. Deep learning has taken off in the last few years, but it has been around for much longer. Adam Gibson founded Skymind, the The post Deep Learning with Adam Gibson appeared first on Software Engineering Daily.