Face Recognition Audit, Gamers Cheat with AI, Who Rules the Smart City?, Language Learning Generalizes to Other Domains
In earlier letters, I discussed some differences between developing traditional software and AI products, including the challenges of unclear technical feasibility, complex product specification, and need for data to start development.
Amazon's Algorithmic Mismanagement, Brainwaves to Text, OpenAI Drops Robotics, Multi-Scene Synthesis
In a recent letter, I mentioned some challenges to building AI products. These problems are distinct from the issues that arise in building traditional software. They include unclear technical feasibility and complex product specification.
Walking the Robot Dog, Mistaking German for English, Making Art With an Image Classifier, Zero-Shot Object Detection
I’ve been following with excitement the recent progress in space launches. Earlier this week, Richard Branson and his Virgin Galactic team flew a rocket plane 53 miles up, earning him astronaut wings.
Zillow's New Neural Net, Optimizing Traffic City-Wide, Classifying Creepy Crawlies, Behavioral Cloning
In a recent letter, I noted that one difference between building traditional software and AI products is the problem of complex product specification. With traditional software, product managers can specify a product in ways
Amazon's Grab-And-Go Grocery, The Trouble With Ethical AI, Airlines Optimized, Few-Shot Learning
Last week, I mentioned that one difference between traditional software and AI products is the problem of unclear technical feasibility. In short, it can be hard to tell whether it’s practical to build a particular AI system. That’s why it’s
Wildfire Alert Network, AI Invades Campuses, Synthetic Videos, Reviving Lost Traditions
With the rise of software engineering over several decades, many principles of how to build traditional software products and businesses are clear. But the principles of how to build AI products and businesses are still developing.
Computers Spawn Computers, Self-Riding Bike, AI-Against-Covid Progress Report, Handwriting Deciphered
I’m thrilled to announce the first data-centric AI competition! I invite you to participate.For decades, model-centric AI competitions, in which the dataset is held fixed while you iterate on the code, have driven our field forward. But deep learning
Face Recognition at the Border, Robot Manicurists, Irresponsible AI, Synthesizing Real-World Scenes
Around the world, students are graduating. If you’re one of them, or if someone close to you is graduating, congratulations!!! My family swapped pictures on WhatsApp recently and came across this one, which was taken when I graduated from Carnegie Mellon (I’m standing in the middle).
Autonomous Weapons Used in Combat, Tesla Doubles Down on Computer Vision, Transformers Decipher Proteins
In school, most questions have only one right answer. But elsewhere, decisions often come down to a difficult choice among imperfect options. I’d like to share with you some approaches that have helped me make such decisions.
Face Recognition for the Masses, Labeling Libel, Documenting Datasets, What Machines Want to See
Benchmarks have been a significant driver of research progress in machine learning. But they've driven progress in model architecture, not approaches to building datasets, which can have a large impact on performance in practical applications.
Surgical Robots Go Autonomous, Virtual Reality On Speed, AI Crossword Champ, Algorithms For Orcas
I decided last weekend not to use a learning algorithm. Sometimes, a non-machine learning method works best.Now that my daughter is a little over two years old and highly mobile, I want to make sure the baby gate that keeps her away
The Batch Special Issue! Machine Learning in Production: MLOps at scale with Amazon, Google, Microsoft
So you’ve trained an accurate neural network model in a Jupyter notebook. You should celebrate! But . . . now what? Machine learning engineering in production is an emerging discipline that helps individual engineers and teams put models into the hands of users.
Rise of the Robocoders, Banks Embrace Face Recognition, Toward Greener AI, Cloning 3D Objects
It can take 6 to 24 months to bring a machine learning project from concept to deployment, but a specialized development platform can make things go much fasterMy team at Landing AI has been working on a platform called LandingLens
Europe's AI Backlash, Robot Debater, Car Wreck Recognition, Funding For Biomedicine
How much data do you need to collect for a new machine learning project? If you’re working in a domain you’re familiar with, you may have a sense based on experience or from the literature. But when you’re working on a novel application, it’s hard to tell. In this circumstance,
Top AI Startups, Muting Griefers, Re-Creating Lost Masterpieces, Better Video Search
Last Sunday was my birthday. That got me thinking about the days leading to this one and those that may lie ahead.As a reader of The Batch, you’re probably pretty good at math. But let me ask you a question, and please answer from your gut, without calculating.
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