Presenting a Technical Concept? Use a Jupyter Notebook
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
Matt Zeiler: Advance AI for Good
Technical Insights

Matt Zeiler: Advance AI for Good

There’s a reason why artificial intelligence is sometimes referred to as “software 2.0”: It represents the most significant technological advance in decades. Like any groundbreaking invention, it raises concerns about the future

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
Yoav Shoham: Language Models That Reason
Technical Insights

Yoav Shoham: Language Models That Reason

I believe that natural language processing in 2022 will re-embrace symbolic reasoning, harmonizing it with the statistical operation of modern neural networks. Let me explain what I mean by this.

2 min read
Chip Huyen: AI That Adapts to Changing Conditions
Technical Insights

Chip Huyen: AI That Adapts to Changing Conditions

Until recently, big data processing has been dominated by batch systems like MapReduce and Spark, which allow us to periodically process a large amount of data very efficiently. As a result, most of today’s

2 min read
Alexei Efros: Learning From the Ground Up
Technical Insights

Alexei Efros: Learning From the Ground Up

Things are really starting to get going in the field of AI. After many years (decades?!) of focusing on algorithms, the AI community is finally ready to accept the central role of data and the high-capacity models that are

2 min read
Wolfram Burgard: Train Robots in the Real World
Technical Insights

Wolfram Burgard: Train Robots in the Real World

Robots are tremendously useful machines, and I would like to see them applied to every task where they can do some good. Yet we don’t have enough programmers for all this hardware and all these tasks. To be useful, robots ne

3 min read
Abeba Birhane: Clean Up Web Datasets
Technical Insights

Abeba Birhane: Clean Up Web Datasets

From language to vision models, deep neural networks are marked by improved performance, higher efficiency, and better generalizations. Yet, these systems are also marked by perpetuation of bias and injustice, inaccurat

3 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.

3 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

Subscribe to The Batch

Stay updated with weekly AI News and Insights delivered to your inbox