Artificial-general-intelligence meme
Technical Insights

Artificial General Intelligence: Hope or Hype?

I’ve always thought that building artificial general intelligence — a system that can learn to perform any mental task that a typical human can — is one of the grandest challenges of our time. In fact, nearly 17 years ago, I co-organized a NeurIPS workshop on building human-level AI.
Principles of data
Technical Insights

Toward Systematic Data Engineering

I’ve seen many new technologies go through a predictable process on their journey from idea to large scale adoption. First, a handful of experts apply their ideas intuitively.
Different types of semaphores
Technical Insights

Making Software For a Heterogeneous World

The physical world is full of unique details that differ from place to place, person to person, and item to item. In contrast, the world of software is built on abstractions that make for relatively uniform coding environments and user...
Left: flawless gear | Right: gear with a defect
Technical Insights

Imaging Systems for Data-Centric AI Development

The image below shows two photos of the same gear taken under different conditions. From the point of view of a computer-vision algorithm — as well as the human eye — the imaging setup that produced the picture on the right makes...
Cartoon about data
Technical Insights

Developing AI Products Part 5: Data Drift, Concept Drift, and Other Maintenance Issues

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.
Series of spreadsheets with different data
Technical Insights

Developing AI Products Part 4: Getting Data To Start Development

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.
Cartoon about traditional software and AI
Technical Insights

Developing AI Products Part 3: Coping With Product Specification

In a recent letter, I noted that one difference between building traditional software and AI products is the problem of complex product specification.
Magnifying glass over the words Unclear, Technical and Feasibility
Technical Insights

Developing AI Products Part 2: How To Assess Technical Feasibility

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.
Slide with information about challenges to building AI products and businesses
Technical Insights

Developing AI Products Part 1: How AI Product Development is Different From Traditional Software

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.
Data-Centric AI Competition slide
Technical Insights

Ready, Set, Improve the Data!

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.
Conventional and data-centric benchmarks
Technical Insights

Data-Centric AI Development: A New Kind of Benchmark

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.
Alert door widget
Technical Insights

When Not to Use Machine Learning

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 from the stairs is always shut.
Machine Learning project lifecycle
Technical Insights

Data-Centric-AI Development: The Platform Approach

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 faster.My team at Landing AI has been working on a platform called LandingLens for efficiently building computer vision models.
Iteration workflow
Technical Insights

Data-Centric AI Development, Part 3: Limit Data Collection Time

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.
Lifecycle of an Machine Learning project
Technical Insights

Iteration in AI Development

Machine learning development is highly iterative. Rather than designing a grand system, spending months to build it, and then launching it and hoping for the best, it’s usually better to build a quick-and-dirty system, get feedback...

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