Machine Learning – Software Engineering Daily

ChatGPT is an artificial intelligence language model developed by OpenAI. It is part of the GPT (Generative Pre-trained Transformer) family of models, which are designed to generate human-like text based on input prompts. ChatGPT is specifically trained to carry out conversational tasks, such as answering questions, completing sentences, and engaging in dialogue. It has been The post Surviving ChatGPT with Christian Hubicki appeared first on Software Engineering Daily.

Today, we spoke with Daniel Situnayake of Edge Impulse. We discussed AI, machine learning, edge devices, TinyML and AI tool chain.             Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Edge Impulse with Daniel Situnayake appeared first on Software Engineering Daily.

The default configuration in most databases is meant for broad compatibility rather than performance. Database tuning is a process in which the configurations of a database are modified to achieve optimal performance. Databases have hundreds of configuration knobs that control various factors, such as the amount of memory to use for caches or how often The post Automatic Database Tuning with Andy Pavlo appeared first on Software Engineering Daily.

Originally published on January 1, 2022. Charlie Gerard is an incredibly productive developer.  In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art.  In this episode we touch on The post Practical Machine Learning in JavaScript with Charlie Gerard appeared first on Software Engineering Daily.

At Lyft, Ketan Umare worked on Flyte, an orchestration system for machine learning. Flyte provides reliability and APIs for machine learning workflows, and is used at companies outside of Lyft such as Spotify. Since leaving Lyft, Ketan founded Union.ai, a company focused on productionizing Flyte as a service. He joins the show to talk about The post Union.ai with Ketan Umare appeared first on Software Engineering Daily.

Historically, search engines made money by showing sponsored ads alongside organic results. As the idiom goes, if you're not paying for something, you are the product. Neeva is a new take on search engines. When you search at neeva.com, you get the type of result you'd expect from a search engine minus any advertising. In The post Ad-free Search on Neeva with Darin Fisher appeared first on Software Engineering Daily.

Charlie Gerard is an incredibly productive developer.  In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art.  In this episode we touch on some of these projects and broadly The post Practical Machine Learning in JavaScript with Charlie Gerard appeared first on Software Engineering Daily.

Once a machine learning model is trained and validated, it often feels like a major milestone has been achieved.  In reality, it's more like the first lap in a relay race.  Deploying ML to production bears many similarities to a typical software release process, but brings several novel challenges like failing to generalize as expected The post Responsibly Deploy AI in Production with Anupam Datta appeared first on Software Engineering Daily.

Software Engineering Daily invites Owen Frank Davis, Paul Davis, Kyle Davis, and Robbie Davis for a joint interview on the subject of reproduction and teething, as well as Lisch fascitis. Aledade and Kubernetes are both inconsiderate ideas for navigation. They need improvements in infrastructure. I prefer Dominaria to New Phyrexia (though I can get by The post #FREEZUCK | ?masked…?… !(DOCTORS)!! appeared first on Software Engineering Daily.

Machine learning models must first be trained.  That training results in a model which must be serialized or packaged up in some way as a deployment artifact.  A popular deployment path is using Tensorflow.js to take advantage of the portability of JavaScript, allowing your model to be run on a web server or client. Gant The post Learning Tensorflow.js with Gant Laborde appeared first on Software Engineering Daily.

Imagine a world where you own some sort of building whether that's a grocery store, a restaurant, a factory… and you want to know how many people reside in each section of the store, or maybe how long did the average person wait to be seated or how long did it take the average factory The post No Code AI for Video Analytics with Alex Thiele appeared first on Software Engineering Daily.

The dream of machines with artificial general intelligence is entirely plausible in the future, yet well beyond the reach of today's cutting edge technology.  However, a virtual agent need not win in Alan Turing's Imitation Game to be useful.  Modern technology can deliver on some of the promises of narrow intelligence for accomplishing specific tasks. The post Virtual Agents for IT and HR with Dan Turchin appeared first on Software Engineering Daily.

Interest in autonomous vehicles dates back to the 1920s. It wasn't until the 1980s that the first truly autonomous vehicle prototypes began to appear. The first DARPA Grand Challenge took place in 2004 offering competitors $1 million dollars to complete a 150-mile course through the Mojave desert. The prize was not claimed. Since then, rapid The post Autonomous Driving Infrastructure with Vinoj Kumar appeared first on Software Engineering Daily.

Governments, consumers, and companies across the world are becoming more aware and attentive to the risks and causes of climate change. From recycling to using solar power, people are looking for ways to reduce their carbon footprint. Markets like the financial sector, governments, and consulting are looking for ways to understand climate data to make The post Sust Global: Taking Action Against Climate Change with Josh Gilbert appeared first on Software Engineering Daily.

Mark Saroufim is the author of an article entitled “Machine Learning: The Great Stagnation”. Mark is a PyTorch Partner Engineer with Facebook AI. He has spent his entire career developing machine learning and artificial intelligence products. Before joining Facebook to do PyTorch engineering with external partners, Mark was a Machine Learning Engineer at Graphcore. Before The post Machine Learning: The Great Stagnation with Mark Saroufim appeared first on Software Engineering Daily.

Arun Kumar is an Assistant Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics.  Systems and ideas based on his research have been released as part The post Data Management Systems and Artificial Intelligence with Arun Kumar appeared first on Software Engineering Daily.

Application Programming Interfaces (APIs) are interfaces that enable multiple software applications to send and retrieve data from one another. They are commonly used for retrieving, saving, editing, or deleting data from databases, transmitting data between apps, and embedding third-party services into apps. The company BaseTen helps companies build and deploy machine learning APIs and applications. The post BaseTen: Creating Machine Learning APIs with Tuhin Srivastava and Amir Haghighat appeared first on Software Engineering Daily.

Natural Language Processing (NLP) is a branch of artificial intelligence concerned with giving computers the ability to understand text and spoken words. “Understanding” includes intent, sentiment, and what’s important in the message. NLP powers things like voice-operated software, digital assistants, customer service chat bots, and many other academic, consumer and enterprise tools. The company Botpress The post Botpress: Natural Language Processing with Sylvain Perron appeared first on Software Engineering Daily.

Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it The post MindsDB: Automated Machine Learning with Jorge Torres appeared first on Software Engineering Daily.

Creation Labs is helping bring Europe 1 step closer to fully autonomous long haul trucking. They have developed an AI Driver Assistance System (AIDAS) that retrofits to any commercial vehicle, starting with VW Crafters and MAN TGE trucks. Their system uses camera hardware mounted to the vehicle to capture video data that is processed with The post Creation Labs: Self Driving Trucks with Jakub Langr appeared first on Software Engineering Daily.

Vectors are the foundational mathematical building blocks of Machine Learning. Machine Learning models must transform input data into vectors to perform their operations, creating what is known as a vector embedding. Since data is not stored in vector form, an ML application must perform significant work to transform data in different formats into a form The post Pinecone: Vector Database with Edo Liberty appeared first on Software Engineering Daily.

The incredible advances in machine learning research in recent years often take time to propagate out into usage in the field. One reason for this is that such “state-of-the-art” results for machine learning performance rely on the use of handwritten, idiosyncratic optimizations for specific hardware models or operating contexts. When developers are building ML-powered systems The post OctoML: Automated Deep Learning Engineering with Jason Knight and Luis Ceze appeared first on Software Engineering Daily.

Embedded Software Engineering is the practice of building software that controls embedded systems- that is, machines or devices other than standard computers. Embedded systems appear in a variety of applications, from small microcontrollers, to consumer electronics, to large-scale machines such as cars, airplanes, and machine tools.  iRobot is a consumer robotics company that applies embedded The post iRobot with Chris Svec appeared first on Software Engineering Daily.

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 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 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 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 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 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 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 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 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. 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 The post Rosebud: Artificially Generated Media with Dzmitry Pletnikau appeared first on Software Engineering Daily.

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 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 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 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, 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 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 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 post Roboflow: Computer Vision Models with Brad Dwyer appeared first on Software Engineering Daily.

Machine learning models are only as good as the datasets they’re trained on. Aquarium is a system that helps machine learning teams make better models by improving their dataset quality. Model improvement is often made by curating high quality datasets, and Aquarium helps make that a reality. Peter Gao works on Aquarium, and he joins The post Aquarium: Dataset Quality Improvement with Peter Gao appeared first on Software Engineering Daily.

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 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 post Robotic Process Automation with Antti Karjalainen appeared first on Software Engineering Daily.

Hyperparameters define the strategy for exploring a space in which a machine learning model is being developed. Whereas the parameters of a machine learning model are the actual data coming into a system, the hyperparameters define how those data points are fed into the training process for building a model to be used by an The post Hyperparameter Tuning with Richard Liaw appeared first on Software Engineering Daily.

CrowdFlower was a company started in 2007 by Lukas Biewald, an entrepreneur and computer scientist. CrowdFlower solved some of the data labeling problems that were not being solved by Amazon Mechanical Turk. A decade after starting CrowdFlower, the company was sold for several hundred million dollars. Today, data labeling has only grown in volume and The post Machine Learning Labeling and Tooling with Lukas Biewald appeared first on Software Engineering Daily.

Chatbots are useful for developing well-defined applications such as first-contact customer support, sales, and troubleshooting. But the potential for chatbots is so much greater. Over the last five years, there have been numerous platforms that have arisen to allow for better, more streamlined chatbot creation. Dialogue software enables the creation of sophisticated chatbots. ParlAI is The post ParlAI: Facebook Dialogue Platform with Stephen Roller appeared first on Software Engineering Daily.

Image annotation is necessary for building supervised learning models for computer vision. An image annotation platform streamlines the annotation of these images. Well-known annotation platforms include Scale AI, Amazon Mechanical Turk, and Crowdflower. There are also large consulting-like companies that will annotate images in bulk for you. If you have an application that requires lots The post SuperAnnotate: Image Annotation Platform with Vahan and Tigran Petrosyan appeared first on Software Engineering Daily.

Drug trials can lead to new therapeutics and preventative medications being discovered and placed on the market. Unfortunately, these drug trials typically require animal testing. This means animals are killed or harmed as a result of needing to verify that a drug will not kill humans. Animal testing is unavoidable, but the extent to which The post Drug Simulations with Bryan Vicknair and Jason Walsh appeared first on Software Engineering Daily.

Netflix runs all of its infrastructure on Amazon Web Services. This includes business logic, data infrastructure, and machine learning. By tightly coupling itself to AWS, Netflix has been able to move faster and have strong defaults about engineering decisions. And today, AWS has such an expanse of services that it can be used as a The post Metaflow: Netflix Machine Learning Platform with Savin Goyal appeared first on Software Engineering Daily.

Developing machine learning models is not easy. From the perspective of the machine learning researcher, there is the iterative process of tuning hyperparameters and selecting relevant features. From the perspective of the operations engineer, there is a handoff from development to production, and the management of GPU clusters to parallelize model training. In the last The post Determined AI: Machine Learning Ops with Neil Conway appeared first on Software Engineering Daily.

Deepgram is an end-to-end deep learning platform for speech recognition. Unlike the general purpose APIs from Google or Amazon, Deepgram models are custom-trained for each customer. Whether the customer is a call center, a podcasting company, or a sales department, Deepgram can work with them to build something specific to their use case. Sound data The post Deepgram: End-to-End Speech Recognition with Scott Stephenson appeared first on Software Engineering Daily.