Cooking for Steve Jobs and 4 Other Stories

Welcome to this week’s Via Waverly, where I expose diverse and unexpected finds that were served to me by Waverly.

Cooking for Steve Jobs

This week featured article is an excellent interview of MIT professor Sherry Turkle.

She talks about her quest to find her father — and her discovery that he had been experimenting on her as a child, ouch! She talks about how she once cooked a vegetarian dinner for Steve Jobs that he rejected because it was not his kind of vegetarian. Most importantly, though, she talks about how she pioneered the study of the social impacts of technology in days where engineers only saw computers as tools.

Here’s a child who says, when she programs a computer, ‘It’s like putting a little piece of my mind in the computer, and I come to see myself differently.’ And I would argue that programming this machine was changing her way of thinking about herself, giving her more feelings of control over her life. Now, no one said that I was wrong, but they would say that it was irrelevant. Nobody ever said my transcripts weren’t real or I hadn’t done the work. They said, “That’s not what the computer is about.”

Sherry Turkle

There’s a really interesting bit about her research on phones and how they act as empathy-draining devices:

If you’re at a table, and your phone is turned off and put face down on the table, and you’re in a conversation with another person, you will feel less of an empathic connection with that person. Even a phone turned off and face down—even one taken off the table, but still in your peripheral vision—undermines our capacity for empathy. The reason is that the presence of the phone reminds you of all the “elsewheres” you can be. A person in conversation just cannot compete with that.

Sherry Turkle

This really made me want to read the whole book.

Wave: 🥰 Empathy in AI (Contributed by Kristine Gloria)

Why your Consciousness Depends on the Low-Entropy Early Universe

That title grasped me. Consciousness? Entropy? Early Universe? Count me in!

The article by Jonathan Simon does a great job of introducing the second law of thermodynamics. I wish I had encountered earlier the thought experiment of a “time-reverse twin” earlier, it’s sticky.

Wave: 🧮 Math Geekiness

How to Poison the Data that Big Tech is Using to Surveil You

This article, by Karen Hao, came just before her famous piece on Facebook’s director of AI and I had missed it.

She cites research from Northwestern that explains how we could use our collective data as a bargaining chip. I’ve been researching that topic when I studied data trusts and I do believe federating our data is a great way to bring balance back. If I go dark on Google Map, the Mountain View giant wont shed a tear, but if all Montreal users go dark at once, they will lose their ability to forecast local traffic — amongst many other things.

In the absence of data trusts, though, what could we do? Here’s what the Northwestern students suggest:

Data strikes, inspired by the idea of labor strikes, which involve withholding or deleting your data so a tech firm cannot use it —leaving a platform or installing privacy tools, for instance.

Data poisoning, which involves contributing meaningless or harmful data. AdNauseam, for example, is a browser extension that clicks on every single ad served to you, thus confusing Google’s ad-targeting algorithms.

Conscious data contribution, which involves giving meaningful data to the competitor of a platform you want to protest, such as by uploading your Facebook photos to Tumblr instead.

Wave: 📖 Open Everything

Quantum Computing Explained… Well!

A lot of popular explanations of quantum computing consistently get a lot of details wrong. How often have I heard Quantum Computers are going to make everything exponentially faster! Ouch.

This 10 minutes explainer video, which I found via Scott Aaronson’s article about it, might just be the best short explanation of Quantum Computing I’ve seen.

Wave: ⚛️ A Quantum of Quantum

A Map of Perception Sensors

We talk a lot about AI, but when you look at the most important progress deep learning brought us it tends to be in a few fields: vision, speech processing, natural language understanding…

Our ability to do well hinges on being able to perceive things better. Tesla seems to believe cameras are all you need, but I happen to think that progress in the space of sensors is going to play a key role.

That’s why I was pleasantly surprised to find this map of companies powering vision-enabled platform.

Wave: ⚙️ Exploded View