10.16.16 Recap

Annnd, we're back. Continuing to map various readings, notes, and musings over the weeks.

Differentiable neural computers by Google DeepMind
Blog post on DeepMind's recent work on adding memory to neural networks (see below for paper).

Hybrid computing using a neural network with dynamic external memory by Google DeepMind
The associated paper in Nature from DeepMind mentioned above ☝️.

Graph Powered Machine Learning at Google by Sujith Ravi
Highlights Google's work with graph-based, semi-supervised learning. Pretty awesome work where they are taking multi-graph representations of data and are pairing them with nodes of concepts and edges connected by similarities.

Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation by Sujith Ravi and Qiming Diao
A paper from the Sujith Ravi (author of the Google post above ☝️) that covers some of this work.

The Hive is the New Network by Arjun Sethi
Networks are so 2015, or so they say. Arjun Sethi from Social Capital outlines the notion of a 'hive' and its implications for products and companies.

We don't sell saddles here by Stuart Butterfield
Found this old memo from Stuart Butterfield to the team as they built Slack. Great principles for early stage companies to embrace and hits on core topics from building great product, to selling, to driving change, to execution. TL;DR - Build something people want. Market from both ends. Sell the innovation, not the product (to which he noted, we're not selling "a group chat system...what we're selling is organizational transformation). Who do we want our customers to become? How do we do it? ("We do it really fucking good.") Why? ("Because why the fuck else would you even want to be alive but to do things as well as you can? Now: let’s do this.")...

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09.04.16 Recap

How Tech Giants are Devising Real Ethics for Artificial Intelligence by John Markoff
Alphabet (Google), Amazon, Facebook, IBM, and Microsoft are coming together to form an industry group focused on the creation of a standard of ethics around artificial intelligence. The group's aims will be to be to ensure that AI research is focused on benefiting people, not hurting them.

One Hundred Year Study on Artificial Intelligence (AI100) by Stanford + Co. (thx Eric Horvitz)
A consortium of researchers released a report out of Stanford University that details the impact of artificial intelligence on society. The brainchild of Eric Horvitz, a computer scientist at Microsoft Research and Stanford alum, the group includes a host of leading academicians and talented contributors. Certainly an interesting read and, incidentally, the inspiration for the industry group noted earlier ☝️ .

The Next Big Shift in SaaS by Tomasz Tunguz
In addition to sharing his thoughts on the workflow of the future, which I shared a week or so ago, Tomasz discusses how SaaS applications can move from being displacers to disruptors. I had a recent conversation that arrived at a similar conclusion. Doing some reading after the fact led me to this post from July. A timely read, despite the delay.

First Round Capital's FAQs from First Round Capital
First Round Capital opens up their playbook to provide more insight into what they're looking for in new investments, their process, and how they make decisions. Great to see this type of increased transparency in the venture space. ICYMI - Bloomberg Beta recently open sourced their manual, too.

Facebook AI Research open sourcing tools
FAIR is releasing some of Facebook's tools including fastText for text classification and DeepMask, SharpMask, and MultiPathNet which are used to understand objects in photos...

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08.21.16 Recap

Machine Intelligence 2.0 in charts and graphs by Shivon Zilis and VentureBeat
Nice general overview of the ML/AI landscape aptly dubbed here as Machine Intelligence 2.0.

Event Driven SaaS - The Workflow Of The Future by Tomasz Tunguz
Outlines how a new wave of SaaS products, if properly designed, can mitigate / eliminate the agency problem of classic software by benefiting users directly. I am personally a big fan of this play. By offering value to the people actually using the product from day to day -- not just the executives -- products can generate buy-in through accruing benefit. This, in turn, creates both stickiness for the product and the ability to generate even more value through increased data capture and engagement.

What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? by Michael Copeland and NVIDIA
In case you were wondering, NVIDIA spells it out for you in a thoughtful post (and Part One of their series on the fundamentals of deep learning).

Human collective intelligence as distributed Bayesian inference by Peter Krafft, et al
Awesome new study finding that a cognitive decision mechanism can treat a variable as a prior distribution upon which to base decisions...

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Introducing the Recap* (*until I find a better name for it)

If not evident from the lack of recent content, I've found less time to write. Or, more precisely, to write here. While building product and growing a company keep me more than fulfilled creatively (more on those in due time), I recognize that reflection remains important. So instead of writing longer form posts, until further notice, I've decided to at least provide a weekly(ish) recap of some things that I've found interesting from the days prior -- mostly articles, blog posts, podcasts, and others bits primarily focused on tech. Perhaps, but not always, they might be accompanied by the occasional commentary from yours truly. For readers, I hope it will be an interesting curation of content. For me, it will help keep track of some of the interesting things I've read from week to week.
 

08.14.16 Recap

Innovating at Scale by Arun Sethi
A good commentary on (and reminder of) the Innovator's Dilemma. Basically, companies have to disrupt themselves through innovation or they will be victims of their own success. Old news, perhaps, but companies still continuously fall victim to the plight. Worth a read to refresh with some survival tips for the operators...

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