Archive of posts with tag 'machine learning'

Weekend Reading: Intellectual Humility, Scoping, and Gboard

August 31, 2019 • #

🛤 Missing the Light at the End of the Tunnel

Honest postmortems are insightful to get the inside backstory on what happened behind the scenes with a company. In this one, Jason Crawford goes into what went wrong with Fieldbook before they shut it down and were acquired by Flexport a couple years ago:

Now, with a year to digest, I think this is true and was a core mistake. I vastly underestimated the resources it was going to take—in time, effort and money—to build a launchable product...

Weekend Reading: Universal Laws, Tandem, and Computers That Can See

August 3, 2019 • #

📚 Universal Laws of the World

A list of broad laws that apply to all fields. Thoughtful stuff as always from Morgan Housel:

6. Parkinson’s Law: Work expands to fill the time available for its completion.

In 1955 historian Cyril Parkinson wrote in The Economist:

IT is a commonplace observation that work expands so as to fill the time available for its completion. Thus, an elderly lady of leisure can spend the entire day in writing and despatching a postcard to her niece at Bognor Regis. An hour will be spent...

Weekend Reading: Data Moats, China, and Distributed Work

May 25, 2019 • #

🏰 The Empty Promise of Data Moats

In the era of every company trying to play in machine learning and AI technology, I thought this was a refreshing perspective on data as a defensible element of a competitive moat. There’s some good stuff here in clarifying the distinction between network effects and scale effects:

But for enterprise startups — which is where we focus — we now wonder if there’s practical evidence of data network effects at all. Moreover, we suspect that even the more straightforward data scale effect has limited...

Weekend Reading: LiDAR, Auto Generated Textbooks, and Paleo Plate Tectonics

February 9, 2019 • #

🛣 Creating Low-Cost LiDAR

This is a great breakdown of the different elements of LiDAR technology, looking at three broad areas: beam direction, distance measurement, and frequencies. They compare the tech of 10 different companies in the space to see how each is approaching the problem.

📚 An Algorithm to Auto-Generate Textbooks

Taking off of the Wikibooks project, this team is aiming to generate books from Wikipedia content using ML techniques.

Given the advances in artificial intelligence in recent years, is there a...

The Incredible Inventions of Intuitive AI

January 2, 2019 • #

This talk on “generative AI” was interesting. One bit stuck out to me as really thought-provoking:

Dutch designers have created a system to 3D print functional things in-place, like this bridge concept. Imagine that you can place a machine, give it a feed of raw material input and cut it loose to generate something in physical space. As the presenter mentions at the end of the talk, moving from things that are “constructed” to ones that are “grown”.

Weekly Links: Tensor Processing, Amazon, and Preventing Traffic Jams

April 13, 2017 • #

Google’s “Tensor Processing Unit” 💻

Google has built their own custom silicon dedicated to AI processing. The power efficiency gains with these dedicated chips is estimated to have saved them from building a dozen new datacenters.

But about six years ago, as the company embraced a new form of voice recognition on Android phones, its engineers worried that this network wasn’t nearly big enough. If each of the world’s Android phones used the new Google voice search for just three minutes a day, these engineers realized, the company would need twice as many data centers.