On the Future of Machine Intelligence

This is a very thought-provoking read on the future of machine intelligence and how we will cope with its advancement. The author, Douglas Coupland, begins the narrative with some hypothetical apps that track data (geolocation, etc.) and then paints a dystopian view:

To summarise. Everyone, basically, wants access to and control over what you will become, both as a physical and metadata entity. We are also on our way to a world of concrete walls surrounding any number of niche beliefs. On our journey, we get to watch machine intelligence become profoundly more intelligent while, as a society, we get to watch one labour category after another be systematically burped out of the labour pool. (Doug’s Law: An app is only successful if it puts a lot of people out of work.)

The darkest thought of all may be this: no matter how much politics is applied to the internet and its attendant technologies, it may simply be far too late in the game to change the future. The internet is going to do to us whatever it is going to do, and the same end state will be achieved regardless of human will. Gulp.

Do we at least want to have free access to anything on the internet? Well yes, of course. But it’s important to remember that once a freedom is removed from your internet menu, it will never come back. The political system only deletes online options — it does not add them. The amount of internet freedom we have right now is the most we’re ever going to get.

I found the notion of Artificial Intuition (as opposed to Artificial Intelligence) worth highlighting:

Artificial Intuition happens when a computer and its software look at data and analyse it using computation that mimics human intuition at the deepest levels: language, hierarchical thinking — even spiritual and religious thinking. The machines doing the thinking are deliberately designed to replicate human neural networks, and connected together form even larger artificial neural networks. It sounds scary . . . and maybe it is (or maybe it isn’t). But it’s happening now. In fact, it is accelerating at an astonishing clip, and it’s the true and definite and undeniable human future.

Worth reading in its entirety.


Note: I usually don’t link to The Financial Times (because of its stringent paywall), but at the time of this post, the article is free to access.


On Learning Data Science

I’ve been learning more about data science in the last couple of months and recently stumbled upon a very good blog post from Dataquest on how to learn data science.

First, it’s important that there is some inherent motivation to learn data science:

Nobody ever talks about motivation in learning. Data science is a broad and fuzzy field, which makes it hard to learn. Really hard. Without motivation, you’ll end up stopping halfway through and believing you can’t do it, when the fault isn’t with you – it’s with the teaching.

You need something that will motivate you to keep learning, even when it’s midnight, formulas are starting to look blurry, and you’re wondering if this will be the night that neural networks finally make sense.

You need something that will make you find the linkages between statistics, linear algebra, and neural networks. Something that will prevent you from struggling with the “what do I learn next?” question.

My entry point to data science was predicting the stock market, although I didn’t know it at the time. Some of the first programs I coded to predict the stock market involved almost no statistics. But I knew they weren’t performing well, so I worked day and night to make them better.

There are good links throughout, including 100 data sets for statistics.

I like the suggestions on communicating your findings and/or your learning process:

Part of communicating insights is understanding the topic and theory well. Another part is understanding how to clearly organize your results. The final piece is being able to explain your analysis clearly.

It’s hard to get good at communicating complex concepts effectively, but here are some things you should try:

Start a blog. Post the results of your data analysis.

Try to teach your less tech-savvy friends and family about data science concepts. It’s amazing how much teaching can help you understand concepts…

More resources and links here.

The Cascadia Earthquake

A thought-provoking, and frightening, piece by Kathryn Schulz in The New Yorker on the very big earthquake that is likely to strike the Pacific Northwest sometime in the future:

The first sign that the Cascadia earthquake has begun will be a compressional wave, radiating outward from the fault line. Compressional waves are fast-moving, high-frequency waves, audible to dogs and certain other animals but experienced by humans only as a sudden jolt. They are not very harmful, but they are potentially very useful, since they travel fast enough to be detected by sensors thirty to ninety seconds ahead of other seismic waves. That is enough time for earthquake early-warning systems, such as those in use throughout Japan, to automatically perform a variety of lifesaving functions: shutting down railways and power plants, opening elevators and firehouse doors, alerting hospitals to halt surgeries, and triggering alarms so that the general public can take cover. The Pacific Northwest has no early-warning system. When the Cascadia earthquake begins, there will be, instead, a cacophony of barking dogs and a long, suspended, what-was-that moment before the surface waves arrive. Surface waves are slower, lower-frequency waves that move the ground both up and down and side to side: the shaking, starting in earnest.

Soon after that shaking begins, the electrical grid will fail, likely everywhere west of the Cascades and possibly well beyond. If it happens at night, the ensuing catastrophe will unfold in darkness. In theory, those who are at home when it hits should be safest; it is easy and relatively inexpensive to seismically safeguard a private dwelling. But, lulled into nonchalance by their seemingly benign environment, most people in the Pacific Northwest have not done so. That nonchalance will shatter instantly. So will everything made of glass. Anything indoors and unsecured will lurch across the floor or come crashing down: bookshelves, lamps, computers, cannisters of flour in the pantry. Refrigerators will walk out of kitchens, unplugging themselves and toppling over. Water heaters will fall and smash interior gas lines. Houses that are not bolted to their foundations will slide off—or, rather, they will stay put, obeying inertia, while the foundations, together with the rest of the Northwest, jolt westward. Unmoored on the undulating ground, the homes will begin to collapse.

Worth reading in entirety.