Part 2: The Machine Learning-Learning Landscape

5 min readOct 10, 2017

For context, one of the many living + breathing resources focused on being able to break through the noise. See Part 1 with resources for non-machine learning software engineers.

Just as there is more to machine learning than data, there is more to personal learning than a list of urls.

Ultimately, the end goal is knowledge; a firm and consistent ability to use intuition, ask good questions, and reason about a potential decision. Future posts will cover appropriate mental models and strategies to work toward this.

It’s necessary, but a bit overwhelming to move beyond headlines and commercial spin into a nuanced and complicated world. There are far more open questions and concerns than there are established best practices, methods, and models (of the biz kind) to emulate.

The near-term aim for the resources below is threefold:

Begin to effectively sip from the firehose
Find a community of trusted voices to listen to and connect with
Navigate different parts of the vast ML landscape

If you can start to routinely absorb the perspectives and frameworks from some of the individuals and organizations below, you will be in a much stronger position to navigate what lies ahead.

This baseline foundation of being in the flow will help with higher value activities like evaluating a specific implementation for your company, developing an effective bullshit meter, understanding details of new developments, and knowing what you don’t know.

I’ve personally found this small list of folks to be special though you should definitely explore more exhaustive resources if you are feeling adventurous.

“Inside AI”, formerly Technically Sentient:
A newsletter focused on the business side of AI. Each piece covers a Main Idea, Industry Links, Must Reads, and a piece of commentary from the curator Rob May. Commentary ranges from how an angel investor may diligence AI companies to how data partnerships may work between large big co’s.

MIT Technology Review:
Coverage on artificial intelligence and robots are transforming how we work and live. This tends to be a more executive focused and interesting complement for more technical topics.

Architecht:
A new content publication and podcast from Derrick Harris. News, analysis, blog posts, and research in cloud computing, artificial intelligence, and software engineering.

This ~30 min podcast with the founder or investor typically focuses on the business and applications of the technology at a high level. Derrick is often quick to ask how they see the field developing and where the respective product or service fits into that. His non-technical background leads to a lot of sound clarifying questions with a “can you tell us what’s really going on” sentiment.

Conversations that have stood out to me:

- Hilary Mason— the state of big data and AI in the enterprise
- Adam Gibson— selling deep learning to the enterprise
- bradford cross — where’s the money in AI and bot overload
- Scott Clark — tuning AI models and optimization as a service

James Wang:
James primarily covers the developing AI stack where he shares news, commentary, and analysis. It’s not uncommon to see his tweets covering the latest semi-conductor race between the big tech giants or competing strategies in the self-driving car software stack.

on point tweetstorm

Matt Turck:
Matt is a partner at the NYC based venture capital firm, FirstMark. The firm runs Data Driven NYC, a monthly discussion series with a wide variety of talks from ML, data, and infrastructure companies. Several of his recent posts to explore:

Mariya Yao:
Mariya is the CTO of Topbots, an organization that educates business leaders on high-impact applications of modern machine learning and A.I. techniques. She is also currently writing a book on applied ML technologies within the enterprise.

Rob May:
Rob curates and writes Inside AI, the ML and AI newsletter mentioned above. His background as a software engineer, former B2B SaaS CEO, angel investor, and current venture with Talla gives him a special ability to discuss high level trends in the marketplace then ground the conversation into today’s realities.

Tom Simonite:
Tom covers intelligent machines for Wired and does a great job of setting the scene in each one of his pieces.

Bradford Cross:
Bradford is founder of Merlon Intelligence and a former venture capitalist at Data Collective. He has worked on several machine learning companies, invested in dozens more, and is back at it again with Merlon in the financial space. His bold thoughts around where ML companies can and should be built have become well circulated amongst operators and investors.

Dave Gershorn:
Dave is a writer for Quartz covering all things AI. He can be seen frequently interacting with leading researchers in the field discussing nuances around specific news events.

Hilary Mason:
Formerly founder and CEO of Fast Forward Labs, Hilary now works at Cloudera after the recent acquisition. In her own words, Fast Forward was a new mechanism for applied research, helping businesses large and small understand how recently possible machine learning and applied artificial intelligence technologies could be useful for solving real business problems.

As a well respected data scientist and practitioner of ML technologies, she brings clarity about today’s realities with precise technical and business language.

Hilary’s apt and concise description of where we stand

That’s it for now. Open and interested in any recommendations you may have for who else to include. Follow along for future posts in the coming months.

Part 2: The Machine Learning-Learning Landscape

If you can start to routinely absorb the perspectives and frameworks from some of the individuals and organizations below, you will be in a much stronger position to navigate what lies ahead.

Written by Ryan Dawidjan