Predicting the future - one day at a time
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
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
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.)
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?
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.
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
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
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.
There are growing discussions about the viability of Universal Basic Income as a way to combat poverty and to stimulate new types of economic growth (i.e. startups and small business). But in any of these conversations, we quickly get to the question of how to pay for UBI. The default answer (and a non-starter for most fiscal conservatives) is that it requires some sort of wealth redistribution mechanism such as increased taxes. Which is why this treatment of the topic by Dylan Matthews, writing for Vox, is so interesting.
If you’re not willing to entertain big tax increases, then you should be thinking about a negative income tax. That’s the term used for a universal basic income that tapers off with income. So a negative income tax of $10,000 for adults and $5,000 for children, and a 50 percent phaseout rate, for example, would offer a family of four with $0 in earnings benefits worth $20,000; if they started earning $10,000 in wages, the benefits would fall to $15,000, for a total income of $25,000; by the time they earned $40,000 in wages, they’d be getting no basic income payment at all. A negative income tax is just a UBI financed in part by a somewhat regressive tax on the first chunk of earnings people make, and because of that tax, its net price tag is much lower.
A negative income tax sidesteps the issue with UBI of paying people a basic income who don't need it - like paying Warren Buffet a basic income on top of his massive wealth.
Wiederspan, Rhodes, Shaefer, 2015 In an absolute must-read paper for anyone interested in the basic income debate, the University of Michigan’s Jessica Wiederspan, Elizabeth Rhodes, and Luke Shaefer estimated the cost of the US adopting a negative income tax large enough to wipe out poverty. To be conservative and get a high-end cost estimate, they assume that such a program would discourage work substantially.
Okay, so the cost of people not working is factored into the program. Check.
Despite that, they find that a household-based negative income tax, set at the US poverty line and with a 50 percent phaseout rate, would cost $219 billion a year. That’s almost exactly the same as the combined cost of the earned income tax credit (which supports the working poor), Supplemental Security Income (itself basically a negative income tax but only for the elderly and disabled), food stamps, cash welfare, school meal programs, and housing subsidies. You could swap those programs out, put a guaranteed income in their place, and wipe out poverty entirely.
And there we have it. This analysis shows that if we use the money we're already paying on programs for the poor, we could eradicate poverty in our country. Seems like a good thing to do.
