Jeremy Howard

[Airicist]

Founder and researcher at fast.ai

Co-founder and R&D of Enlitic

Personal website - howard.fastmail.fm

youtube.com/howardjeremyp

twitter.com/jeremyphoward

linkedin.com/in/howardjeremy

Jeremy Howard on Wikipedia

[Airicist]

Jeremy Howard: The wonderful and terrifying implications of computers that can learn

Published on Dec 16, 2014

What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. (One deep learning tool, after watching hours of YouTube, taught itself the concept of “cats.”) Get caught up on a field that will change the way the computers around you behave … sooner than you probably think.

[Airicist]

Meet the computer diagnosing cancer

Published on Mar 12, 2015

The 'robot radiologist' is here. San Francisco based startup Enlitic is using artificial intelligence to detect and prevent cancer, pushing medicine firmly into the 21st century. Their deep-learning algorithms can be better at diagnosing tumors than doctors.

[Airicist]

A conversation with Jeremy Howard

Published on Oct 26, 2015

Peter interviews Jeremy Howard, CEO of Enlitic and former president of Kaggle. The discussion includes how to use data competitions to save millions of dollars (and pay only for results). Originally recorded August 21, 2012.

[Airicist]

data.bythebay.io: Jeremy Howard, Why you can't afford to ignore deep learning

Published on Aug 2, 2016

Few involved in life sciences have a background in the neural network fundamentals that underpin deep learning, and that can make it quite overwhelming when trying to learn about this topic for the first time. And it doesn't help that there's so much breathless hype around, which makes it hard to know what opportunities are real, and what is just marketing.

I came to deep learning applications in the life sciences from the other direction - although I've been working with neural networks for around 25 years, I only started looking at life sciences applications in the last couple of years. The data in life sciences tends to be challeging to work with - generally unstructured (genome sequences, natural language text, imaging, sound, etc) and often data sets are quite large. This kind of data turns out to be where deep learning really shines. In fact, many classic approaches to data analysis in the life sciences are either in the process of, or are about to be, totally transformed by deep learning.

In this talk, I'll describe what deep learning can do, and give some examples of how it can be applied, with a particular focus on medical applications. I'll also provide some suggested places to learn more, so if I'm successful in my goal to convince you that you can't afford to ignore deep learning, you'll know where to look next.

[Airicist2]

Jeremy Howard: AnswerAI, FastAI, Fine-tuning & AI recruiting | Around the Prompt #1

Mar 27, 2024

Join Logan Kilpatrick and Nolan Fortman as we dive into a conversation with Jeremy Howard on:

- Why Jeremy created a new startup AswerAI
- Why fine-tuning is still underrated
- How attracting the world's best AI talent requires global and remote hiring
- What the future for FastAI will look like
- Building AI community

And so much more. This was an immensely inspiring and thoughtful conversation that Nolan and I are excited to bring to the world. We will be sharing many more of these conversations under the "Around the Prompt" umbrella, stay tuned!

00:00 Introduction and Background
01:11 Transition from Fast AI to Answer AI
03:35 Exploring the Potential of AI
04:28 Different Approaches to Applied Research
05:27 Creating Answer AI as a Separate Entity
06:30 Future of Fast AI
07:34 Motivation and Persistence in Learning AI
08:29 Takeaways from Fast AI Courses
09:25 Challenges in AI Education
10:44 Building Startups with AI Knowledge
11:44 The Impact of Fast AI Courses
12:44 The Gap in AI Applications
13:37 Continuous Pre-training and Fine-tuning
15:24 Entrepreneurship and Innovation in AI
17:46 Hiring and R&D at Answer AI
21:32 Access to AI Resources Worldwide
23:55 Creating Opportunities for Brilliant Minds
30:59 Continuous Pre-training and Extracting Value
38:54 The Future of Fine-tuning
40:15 Openness and Collaboration in AI
40:58 AI for All and Avoiding Inequality