Text-to-Image Generation and the Path to Truly Open AI
Technical Insights

Text-to-Image Generation and the Path to Truly Open AI

Stable Diffusion, an image generation model that takes a text prompt and produces an image, was released a few weeks ago in a landmark event for AI. While similar programs can be used via API calls or a web user interface, Stable Diffusion can be freely downloaded and run on the user’s hardware.
2 min read
A statue of Lady Justice holds a set of scales in each hand, signifying inconsistent decision making.
Technical Insights

Toward More Consistent Decision-Making

Andrew Ng considers how inconsistent human decisions are, and how AI can reduce that inconsistency.
2 min read
vehicle avoiding traffic cones
Technical Insights

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. They’re very sensitive to hyperparameter
2 min read
microscope versus statue
Technical Insights

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. Still, I worry that
2 min read
Balloon on nails
Technical Insights

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. This creates a unique
2 min read
Jupyter Notebooks
Technical Insights

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:
2 min read
How AI Can Help Achieve Humanity's Grand Challenges
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
2 min read
Artificial General Intelligence: Hope or Hype?
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.
2 min read
Yale Song: Foundation Models for Vision
Technical Insights

Yale Song: Foundation Models for Vision

Large models pretrained on immense quantities of text have been proven to provide strong foundations for solving specialized language tasks. My biggest hope for AI in 2022 is to see the same thing happen in computer visio
2 min read
Toward Systematic Data Engineering
Technical Insights

Toward Systematic Data Engineering

Dear friends, 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. For example, 15 years ago, a handful of individuals were building neural networks from scratch in
2 min read
Making Software For a Heterogeneous World
Technical Insights

Making Software For a Heterogeneous World

Dear friends, 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
2 min read
Imaging Systems for Data-Centric AI Development
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
2 min read
Developing AI Products Part 5: Data Drift, Concept Drift, and Other Maintenance Issues
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.
2 min read
Developing AI Products Part 4: Getting Data To Start Development
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.
2 min read
Developing AI Products Part 3: Coping With Product Specification
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. With traditional software, product managers can specify a product in ways that
2 min read

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