Paramotoring for the Paranoid: Google’s AI and Relationship Mining

My son approached me the other day with his best 17-year-old sales pitch: “Dad, I need a bucket of cash!” Given that I was elbow deep in suds doing the dishes he neglected to do the night before, I mentioned that it was a singularly bad time for him to ask for anything.

Never one to be dissuaded, he plunged ahead with the reason for the funding request. He had stumbled upon a series of YouTube videos about paramotoring, and it was love at first sight for him. He waxed eloquent about how cool it would be to strap a big fan to his back and soar with the birds on a nylon parasail wing. It was actually a pretty good pitch, complete with an exposition on the father-son bonding opportunities paramotoring presented. He kind of reminded me of the twelve-year-old version of myself trying to convince my dad to spend $600 on something called a “TRS-80” that I’d surely perish if I didn’t get.

Needless to say, the $2500 he needed for the opportunity to break his neck was not forthcoming. But what happened the next day kind of blew my mind. As I was reviewing my YouTube feed, there among the [Abom79] and [AvE] videos I normally find in my “Recommended” queue was a video about – paramotoring. Now how did that get there?

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The AI is Always Watching

My phone can now understand me but it’s still an idiot when it comes to understanding what I want. We have both the hardware capacity and the software capacity to solve this right now. What we lack is the social capacity.

We are currently in a dumb state of personal automation. I have Google Now enabled on my phone. Every single month Google Now reminds me of bills coming due that I have already paid. It doesn’t see me pay them, it just sees the email I received and the due date. A creature of habit, I pay my bills on the last day of the month even though that may be weeks early. This is the easiest thing in the world for a computer to learn. But it’s an open loop system and so no learning can happen.

Earlier this month [Cameron Coward] wrote an outstanding pair or articles on AI research that helped shed some light on this problem. The correct term for this level of personal automation is “weak AI”. What I want is Artificial General Intelligence (AGI) on a personal level. But that’s not going to happen, and I am the problem. Here’s why.

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The Future of Artificial Intelligence

Last week we covered the past and current state of artificial intelligence — what modern AI looks like, the differences between weak and strong AI, AGI, and some of the philosophical ideas about what constitutes consciousness. Weak AI is already all around us, in the form of software dedicated to performing specific tasks intelligently. Strong AI is the ultimate goal, and a true strong AI would resemble what most of us have grown familiar with through popular fiction.

Artificial General Intelligence (AGI) is a modern goal many AI researchers are currently devoting their careers to in an effort to bridge that gap. While AGI wouldn’t necessarily possess any kind of consciousness, it would be able to handle any data-related task put before it. Of course, as humans, it’s in our nature to try to forecast the future, and that’s what we’ll be talking about in this article. What are some of our best guesses about what we can expect from AI in the future (near and far)? What possible ethical and practical concerns are there if a conscious AI were to be created? In this speculative future, should an AI have rights, or should it be feared?

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AI Beats Poker Pros: Skynet Looms

There have been a few “firsts” in AI-versus-human gaming lately, and the computers are now beating us at trivia, chess and Go. But in some sense, none of these are really interesting; they’re all games of fact. Poker is different. Aside from computing the odds of holding the winning hand, where a computer would obviously have an advantage, the key to winning in poker is bluffing, and figuring out when your opponent is bluffing. Until recently, this has helped man beat the machine. Those days are over.

Chess and Go are what a game theorist would call games of perfect information: everyone knows everything about the state of the game just from looking at the board, and this means that there is, in principle, a best strategy (series of moves) for every possible position. Granted, it’s hard to figure these out because it’s a big brute-force problem, but it’s still a brute-force problem where computers have an innate advantage. Chess and Go are games where the machines should be winning.  Continue reading “AI Beats Poker Pros: Skynet Looms”

AI and the Ghost in the Machine

The concept of artificial intelligence dates back far before the advent of modern computers — even as far back as Greek mythology. Hephaestus, the Greek god of craftsmen and blacksmiths, was believed to have created automatons to work for him. Another mythological figure, Pygmalion, carved a statue of a beautiful woman from ivory, who he proceeded to fall in love with. Aphrodite then imbued the statue with life as a gift to Pygmalion, who then married the now living woman.

Pygmalion by Jean-Baptiste Regnault, 1786, Musée National du Château et des Trianons

Throughout history, myths and legends of artificial beings that were given intelligence were common. These varied from having simple supernatural origins (such as the Greek myths), to more scientifically-reasoned methods as the idea of alchemy increased in popularity. In fiction, particularly science fiction, artificial intelligence became more and more common beginning in the 19th century.

But, it wasn’t until mathematics, philosophy, and the scientific method advanced enough in the 19th and 20th centuries that artificial intelligence was taken seriously as an actual possibility. It was during this time that mathematicians such as George Boole, Bertrand Russel, and Alfred North Whitehead began presenting theories formalizing logical reasoning. With the development of digital computers in the second half of the 20th century, these concepts were put into practice, and AI research began in earnest.

Over the last 50 years, interest in AI development has waxed and waned with public interest and the successes and failures of the industry. Predictions made by researchers in the field, and by science fiction visionaries, have often fallen short of reality. Generally, this can be chalked up to computing limitations. But, a deeper problem of the understanding of what intelligence actually is has been a source a tremendous debate.

Despite these setbacks, AI research and development has continued. Currently, this research is being conducted by technology corporations who see the economic potential in such advancements, and by academics working at universities around the world. Where does that research currently stand, and what might we expect to see in the future? To answer that, we’ll first need to attempt to define what exactly constitutes artificial intelligence.

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Use Machine Learning To Identify Superheroes and Other Miscellany

[Massimiliano Patacchiola] writes this handy guide on using a histogram intersection algorithm to identify different objects. In this case, lego superheroes. All you need to follow along are eyes, Python, a computer, and a bit of machine learning magic.

He gives a good introduction to the idea. You take a histogram of the colors in a properly cropped and filtered photo of the object you want to identify. You then feed that into a neural network and train it to identify the different superheroes by color. When you feed it a new image later, it will compare the new image’s histogram to its model and output confidences as to which set it belongs.

This is a useful thing to know. While a lot of vision algorithms try to make geometric assertions about the things they see, adding color to the mix can certainly help your friendly robot project recognize friend from foe.


Train Your Robot To Walk with a Neural Network

[Basti] was playing around with Artificial Neural Networks (ANNs), and decided that a lot of the “hello world” type programs just weren’t zingy enough to instill his love for the networks in others. So he juiced it up a little bit by applying a reasonably simple ANN to teach a four-legged robot to walk (in German, translated here).

While we think it’s awesome that postal systems the world over have been machine sorting mail based on similar algorithms for years now, watching a squirming quartet of servos come to forward-moving consensus is more viscerally inspiring. Job well done! Check out the video embedded below.

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