Personal messages to the AI community
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.
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.
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. That’s why it’s
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.
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. But when you’re working on a novel application,
Data-Centric AI Development, Part 2: A Critical Shift in Perspective
Earlier today, I spoke at a DeepLearning.AI event about MLOps, a field that aims to make building and deploying machine learning models more systematic. AI system development will move faster if we can shift from being model-centric to being data-centric.
Five Steps to Scoping AI Projects
One of the most important skills of an AI architect is the ability to identify ideas that are worth working on. Over the years, I’ve had fun applying machine learning to manufacturing, healthcare, climate change, agriculture, ecommerce, advertising, and other industries.
AI Versus Human-Level Performance, Part 2
Last week, I wrote about the limitation of using human-level performance (HLP) as a metric to beat in machine learning applications for manufacturing and other fields. In this letter, I would like to show why beating HLP isn’t always the best way to improve performance.
Why AI Projects Fail, Part 5: Change Management
My last two letters explored robustness and small data as common reasons why AI projects fail. In the final letter of this three-part series, I’d like to discuss change management. Change management isn’t an issue specific to AI, but given the technology’s
Why AI Projects Fail, Part 4: Small Data
In this series exploring why machine learning projects fail, let’s examine the challenge of “small data.” Given 1 million labeled images, many teams can build a good classifier using open source. But say you are building a visual inspection system for a factory to detect
Why AI Projects Fail, Part 3: Robustness
Building AI systems is hard. Despite all the hype, AI engineers struggle with difficult problems every day. For the next few weeks, I’ll explore some of the major challenges. Today’s topic: The challenge of building AI systems that are robust to real-world conditions.
Can Tech Regain the Public’s Trust?
Each year, the public relations agency Edelman produces a report on the online public’s trust in social institutions like government, media, and business. The latest Edelman Trust Barometer contains a worrisome finding: While technology was ranked the most trusted industry
How Should We Manage AI threats?
Last week, I saw a lot of social media discussion about a paper using deep learning to generate artificial comments on news articles. I’m not sure why anyone thinks this is a good idea. At best, it adds noise to the media environment. At worst, it’s a tool for con artists and propagandists.
How to Think About Probabilities
As I write this letter, the vote count is underway in yesterday’s U.S. presidential election. The race has turned out to be tight. In their final forecast last night, the political analysts at fivethirtyeight.com suggested an 89 percent chance that Joe Biden would win.
Introducing the Machine Learning Engineering for Production (MLOps) Specialization
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.
Introducing the Generative Adversarial Networks Specialization
This special issue of The Batch celebrates the launch of our new Generative Adversarial Networks Specialization! GANs are among the most exciting technologies to emerge from deep learning. These networks learn in a very different way than typical supervised methods
The Power of Sleep
I received a copy of Why We Sleep: Unlocking the Power of Sleep and Dreams as a Christmas gift — back in the pre-Covid era — and finished it last weekend. This book by Matthew Walker, director of UC Berkeley’s sleep and neuroimaging lab, is a useful reminder of the importance of sleep
AI Topics for Dinner-Table Discussion Building Trustworthy AI
I’ll be spending Thanksgiving with Nova and watching her taste turkey for the first time. To those of you who celebrate Thanksgiving, I hope you spend time with loved ones, reflect on what you are thankful for, and discuss some very important
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