mobrec

So
Constraints can be a good creative tension to help temper and refine innovation. Just don't have 'too many' constraints.

So
This is the stuff of sci-fi starting to come to life. It will be interesting to see how this develops over time.

https://www.apnews.com/Business%20Wire/856d3482387741d5937af2fcec9d2314

So
> Science has found that reading is essential for a healthy brain. We already know reading is good for children’s developing noggins: A study of twins at the University of California at Berkeley found that kids who started reading at an earlier age went on to perform better on certain intelligence tests, such as analyses of their vocabulary size. > >
> Other studies show that reading continues to develop the brains of adults. One 2012 Stanford University study, where people read passages of Jane Austen while inside an MRI, indicates that different types of reading exercise different parts of your brain. As you get older, another study suggests, reading might help slow down or even halt cognitive decline.Science has found that reading is essential for a healthy brain. We already know reading is good for children’s developing noggins: A study of twins at the University of California at Berkeley found that kids who started reading at an earlier age went on to perform better on certain intelligence tests, such as analyses of their vocabulary size. > >
> Other studies show that reading continues to develop the brains of adults. One 2012 Stanford University study, where people read passages of Jane Austen while inside an MRI, indicates that different types of reading exercise different parts of your brain. As you get older, another study suggests, reading might help slow down or even halt cognitive decline. > > https://www.popsci.com/read-more-books

 

And it doesn't seem to matter if it is a physical book, an e-reader or an audio book (although the audio book has a slightly different impact on the brain).

 

As for audiobooks, the research so far has found that they stimulate the brain just as deeply as black-and-white pages, although they affect your gray matter somewhat differently. Because you’re listening to a story, you’re using different methods to decode and comprehend it. With print books, you need to provide the voice, called the prosody—you’re imagining the “tune and rhythm of speech,” the intonation, the stress on certain syllables, and so. With audio, the voice actor provides that information for you, so your brain isn’t generating the prosody itself, but rather working to understand the prosody in your ears.

So
These types of articles seem to come down to the insatiable need for writers to sensationalize things that they don't necessarily understand.

For example, in the scenario outlined in the article, it is unlikely that the 'AI' (aka computer algorithm) was self aware and said to itself “hey, I have a comprehensive understanding of humans and their capabilities, so I will modify myself to 'cheat' at this task in a way that a human would find difficult to detect”.

More likely is that the algorithm was poorly defined and the brute force computational model (aka 'AI') found a way to 'solve' the problem in a way that wasn't contemplated by the software developer.

https://techcrunch.com/2018/12/31/this-clever-ai-hid-data-from-its-creators-to-cheat-at-its-appointed-task/

Feed Shark

So

I knew that flickr has been on the decline for a while.  IMHO, Yahoo's acquisition was the beginning of the end.  SmugMug's heavy handed idiocy of late was the last straw for me.

After a few arrogant email demands from SmarmMug, I had had enough so I requested all of my data from flickr and it only took them a week and a half to provide the requested files.  I happily downloaded my content and deleted my account after 13 years of use.

So
Autumn has always been my favorite time of year – especially when you get late afternoons like this.

So
Dramatic Halloween sky at a park near the office.

So
There is a well worn axiom in business that 'data should be treated as a corporate asset'.  This is, of course, very true and the advances in data science and 'big data' are giving the potential for that data to become even more valuable.

This got me thinking about how personal data should be thought about in the same way.  Think about all the data generated from what you watch, what you listen to, where you visit, what you review, data from wearables, etc.  All of this data is consumed and analyzed by 3rd parties currently, but what if individuals were able to take control of, what is, after all, their data.

Would this give rise to data science companies marketing algorithms directly to consumers (much like pharmaceutical companies market drugs directly)?  Could it also give rise to the equivalent 'data quackery' similar to the natural supplements and homeopathic industry?  That is, junk algorithms that, at their most benign, do no harm and at their worst incent you to dangerous courses of action?

Would there also be a new industry for 'personal data scientists' (like financial councilors or tax advisers) that would help you assess all of the data assets you have and how to best combine or leverage them with third parties to your best benefit (and not just the benefit of 3rd parties)?  Wouldn't it be great to have some control over the hundreds of arbitrage-like transactions that go on behind the scenes when you are waiting for a page to load on a commercial web site via browser setting that allow you to control what information about you gets shared (and with companies).

So
For me, RSS never really went away, as as my Feedly app convincingly proves.

https://ar.al/2018/06/29/reclaiming-rss/

So
Interesting post that suggests that in deep learning algorithms, questioning things may lead to higher quality conclusions.

Researchers at Uber and Google are working on modifications to the two most popular deep-learning frameworks that will enable them to handle probability. This will provide a way for the smartest AI programs to measure their confidence in a prediction or a decision—essentially, to know when they should doubt themselves.

Deep learning, which involves feeding example data to a large and powerful neural network, has been an enormous success over the past few years, enabling machines to recognize objects in images or transcribe speech almost perfectly. But it requires lots of training data and computing power, and it can be surprisingly brittle.

Somewhat counterintuitively, this self-doubt offers one fix. The new approach could be useful in critical scenarios involving self-driving cars and other autonomous machines.

“You would like a system that gives you a measure of how certain it is,” says Dustin Tran, who is working on this problem at Google. “If a self-driving car doesn’t know its level of uncertainty, it can make a fatal error, and that can be catastrophic.”