Letters
Toward More Consistent Decision-Making
Andrew Ng considers how inconsistent human decisions are, and how AI can reduce that inconsistency.
Technical Insights
Letters
The Trouble With Reinforcement Learning
While working on Course 3 of the Machine Learning Specialization, which covers reinforcement learning, I was reflecting on how reinforcement learning algorithms are still quite finicky.
Letters
Can an AI System Be Sentient? Ask a Philosopher
A Google Engineer recently announced he believes that a language model is sentient. I’m highly skeptical that any of today’s AI models are sentient. Some reporters, to their credit, also expressed skepticism.
Letters
How to Build AI Startups Part 3: Set Customer Expectations!
One of the challenges of building an AI startup is setting customer expectations. Machine learning is a highly experiment-driven field. Until you’ve built something, it’s hard to predict how well it will work.
Letters
The Joy of Conversation (About AI and Other Things)
With the pandemic easing in the United States and Canada, I’ve been traveling more in the last two weeks. I spoke at TED 2022 in Vancouver and ScaleUp:AI in New York and attended a manufacturing conference in California.
Letters
Presenting a Technical Concept? Use a Jupyter Notebook
Machine learning engineers routinely use Jupyter Notebooks for developing and experimenting with code. They’re a regular feature in DeepLearning.AI’s courses. But there’s another use of Jupyter Notebooks that I think is under-appreciated.
Technical Insights
How AI Can Help Achieve Humanity's Grand Challenges
Last week, I wrote about the grand challenge of artificial general intelligence. Other scientific and engineering grand challenges inspire me as well. For example, fusion energy, extended lifespans, and space colonization have massive...
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.
Letters
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.
Letters
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...
Letters
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...
Letters
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.