AI Versus Human-Level Performance, Part 2
Technical Insights

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.
2 min read
AI Versus Human-Level Performance
Technical Insights

AI Versus Human-Level Performance

Beating human-level performance (HLP) has been a goal of academic research in machine learning from speech recognition to X-ray diagnosis. When your model outperforms humans, you can argue that you’ve reached a significant milestone and publish a paper!
2 min read
How to Learn Coding
Technical Insights

How to Learn Coding

I’d like to share a programming tip that I’ve used for years. A large part of programming involves googling for code snippets you need on Stack Overflow and other websites. (Shh. Don’t tell the nondevelopers. ????)
1 min read
How to Build Deep Expertise
Technical Insights

How to Build Deep Expertise

Did you ever spend days obsessing over a technical problem? If so, I applaud you. Determined pursuit of solutions to hard problems is an important step toward building deep expertise.
1 min read
From Proof of Concept to Production
Technical Insights

From Proof of Concept to Production

There has been a lot of excitement about the idea of using deep learning to diagnose diabetic retinopathy: That is by taking a photo of the retina and using AI to detect signs of disease. I was fascinated by a new paper by Emma Beede and
2 min read
Tips for Building Practical Machine learning systems
Technical Insights

Tips for Building Practical Machine learning systems

In an earlier letter, I wrote about the challenge of robustness: A learning algorithm that performs well on test data often doesn’t work well in a practical production environment because the real world turns out to be different than the test set.
1 min read
Deep Learning Against Covid
Technical Insights

Deep Learning Against Covid

Last week, I asked readers to tell me what they’re doing to address the Covid-19 pandemic. Many of you wrote to say you’re taking actions such as shopping for neighbors, making masks, and creating posters that promote Covid-safe practices
1 min read
Unsupervised Learning Ascendent
Technical Insights

Unsupervised Learning Ascendent

Nearly a decade ago, I got excited by self-taught learning and unsupervised feature learning — ways to learn features from unlabeled data that afterward can be used in a supervised task. These ideas contributed only marginally to practical
1 min read
Can AI Fall in Love?
Technical Insights

Can AI Fall in Love?

A student once asked me, “Can an AI ever love?”Since the early days of AI, people have wondered whether AI can ever be conscious or feel emotions. Even though an artificial general intelligence may be centuries away, these are important questions.
1 min read
Why AI Projects Fail, Part 5: Change Management
Technical Insights

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
2 min read
Why AI Projects Fail, Part 4: Small Data
Technical Insights

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
2 min read
Why AI Projects Fail, Part 3: Robustness
Technical Insights

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.
2 min read
Why AI Projects Fail, Part 2: Common Pitfalls
Technical Insights

Why AI Projects Fail, Part 2: Common Pitfalls

Welcome to the Halloween edition of The Batch! I promised last week to share some common reasons for AI project failures. But first, let’s start with some of the least common reasons.
1 min read
Why AI Projects Fail
Technical Insights

Why AI Projects Fail

I’ve heard this conversation in multiple companies: Machine learning engineer: Look how well I did on the test set! Business owner: But your ML system doesn’t work. This sucks! Machine learning engineer: But look how well I did on the test set!
1 min read
Reducing Essential Complexity
Technical Insights

Reducing Essential Complexity

Thinking about the future of machine learning programming frameworks, I recently reread computer scientist Fred Brooks’ classic essay, “No Silver Bullet: Essence and Accidents of Software Engineering.” Three decades after its initial
2 min read

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