(re)Introducing Merrio

Merrio is back - and starting with a simple concept:

1. We'll send your team a reminder to share what they've worked on (they can share as often as they like).
2. Using artificial intelligence, updates are categorized by emotion and automatically tagged with relevant hashtags.
3. Your team's notes are presented in an easy-to-read format (see below) designed to make it fast to see who's doing what.

In the near future, we'll be adding additional ways to gauge the health and engagement of your team. The overall focus is building the tools for your team(s) to be successful.

If you'd like to be an Alpha Tester, drop me a note (via email or on the About page on this site.)

Highlights trending topics, contributors, and the team's sentiment based on their updates..

Uber now run by a 14-person committee→

With Uber CEO Travis Kalanick officially taking a leave of absence, and his effective right-hand Emil Michael gone from the company, the day-to-day duties of running Uber now fall to a committee consisting of Kalanick’s direct reports.

What could go wrong?

Via: CB Insights

Apple uses AI in its products, we just don't know it

Fascinating interview with Tim Cook. Says Apple uses AI in its products, we just don't know it. https://t.co/5uK5HTjESN pic.twitter.com/q9VqZZIxqv

— Christopher Mims (@mims) June 9, 2017

Now we know the pager is truly dead→

According to a report from the EFF, ”No California county has applied for a wiretap for a pager under state law since 2011."

(A good friend of mine shared this with me and it was too good not to share with you.)

It’s a bland, tepid, barely competent knock-off of Shark Tank→

Maureen Ryan, Variety:

Apple’s first offering, “Planet of the Apps,” feels like something that was developed at a cocktail party, and not given much more rigorous thought or attention after the pitcher of mojitos was drained.

Why is Apple doing this?

Machine learning on iOS→

From Apple's teaser page on the new Core ML feature announced as part of iOS 11:

Core ML lets you integrate a broad variety of machine learning model types into your app. [...] Core ML seamlessly takes advantage of the CPU and GPU to provide maximum performance and efficiency. You can run machine learning models on the device so data doesn't need to leave the device to be analyzed.

A big part of the keynote was privacy, and this is another way to help protect privacy by ensuring data doesn't have to leave the device. But it also seems to be a terrific technical accomplishment - like where Google is heading with Tensorflow on mobile, but here and now with models at your ready.

With Core ML, Apple is adding a ton of capability in image recognition and language processing.

Vision - You can easily build computer vision machine learning features into your app. Supported features include face tracking, face detection, landmarks, text detection, rectangle detection, barcode detection, object tracking, and image registration.

Natural Language Processing - The natural language processing APIs in Foundation use machine learning to deeply understand text using features such as language identification, tokenization, lemmatization, part of speech, and named entity recognition.

These of course join Speech Recognition introduced at last year's WWDC.

Speakers should be for playing music, not shopping→

What Hi-Fi reviews Apple's HomePod:

As Sia’s The Greatest played out, the HomePod sounded impressive: strong bass rang out – which was perhaps the overriding audio takeaway for the speaker – but the vocals still seemed sharp and crisp.

In comparison, the Sonos Play:3 appeared uncharacteristically flat, while the Amazon Echo felt almost pedestrian.

I've had a bluetooth speaker for a few years now that's just clunky to get working with my iPhone. I'm really excited about a terrific-sounding speaker that just works. Oh, and having Siri will be pretty neat too.

Via: MacRumors

Why we make plans instead of goals→

This is insightful:

You may notice that there are fewer goals at our company than most. Instead of setting goals, we prefer to make plans. Here’s how that works, and how the two relate.

We do start with a goal - a change we’d like to see. A goal is an outcome - it could be a cutting down the time it takes to add content to the app, doubling traffic to our site, or hitting a certain revenue. It’s important to know where you want to get so that you can choose the most important actions every day!

The big problem with goals is that they don’t tell you what to do every day, especially the bigger more all-encompassing goals. If you look at a goal like “get to 20,000 users” - what exactly are you supposed to do next to go get to that goal? Unless your job is sales, you don’t directly affect that goal.

Instead your actions indirectly affect it in various ways. Even if a goal relates to you directly - like increasing traffic to our site if you are in charge of the blog - there are many steps to get there. Just looking at the goal doesn’t help you figure out what to do next, or what to prioritize.

That’s why we don’t make goals, we make plans!

Plans tell us what to do every day. Plans have a clear yes or no whether or not we enacted them. With goals we might fall a bit short, hit them exactly or exceed them - and what does each of those outcomes mean? Which was a success and which was a failure? We don’t agree that we have failed if we did our very best work but the outcome wasn’t what we anticipated. Yes you can say that in the end only the outcome matters - but we believe this is the most effective path to get to that successful outcome.

Via: Software Lead Weekly

Effective tensorFlow for non-experts (Google I/O '17)→

Today, unless you're using a higher-level API such as image or speech recognition, you need to have a fair amount of expertise in how machine learning works. It's similar to having to know how a database works vs knowing how to use a database - deep understanding and manipulation vs. functional usage.