Who needs an algorithm to ride a bike? We do

Wednesday, 3 June, 2015

A bicycle that moves left when when you turn the handle bars to the right. That’s certainly not your usual, run-of-the-mill, pedal powered device. So how much difficulty might you have riding a bike that operates thus?

It looks like it might pose quite the challenge, given riding a conventional bicycle is far more demanding than we realise, in fact our brain is required to formulate an algorithm to deal with the task…

The algorithm associated with riding a bike in a person’s brain is extremely complicated. Downwards force on the pedal, leaning your whole body, pulling and pushing the handle-bar, gyroscopic procession in the wheels; every single force is a part of this algorithm. And if you change any one part, it affects the entire control system.

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When movie recommendations become new job recommendations

Friday, 17 April, 2015

Netflix, an online movie download service, recently arrived in Australia. While its impact on cinema attendances, if any, remains to be seen, the workplace may be another matter.

And, no, I’m not talking about people watching movies, or TV shows, from their laptops when they’re supposed to be working, but rather the Netflix algorithm that recommends films for viewers based on, presumably, things like past downloads and search histories.

Seemingly this technology has been adapted, and will somehow bring employees who appear to be considering jumping ship, to the attention of management.

For example, it sifts through years worth of HR data, ranging from time between promotions, time at current job, and number of job functions. It then combines that with job posting data from sites like Indeed.com to gauge the market demand rate for certain employees. Based on that, Workday can come up with the employees at risk of leaving and how much it would take to replace them.

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The bot as an art critic

Tuesday, 3 February, 2015

An algorithm powered bot, Novice Art Blogger, attempts to review artworks. Here is a critique of “Marina”, an installation created by Bryan Kneale in 1967:

A small airplane with a mirror on a person’s tail on it, or possibly a statue of a graffiti painted on a plane. I once saw a silver and red airplane and its door, open.

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Can a low frequency share trader take on high frequency traders?

Wednesday, 9 April, 2014

US financial journalist Micheal Lewis has taken the investment world by storm with the publication of his book, Flash Boys: A Wall Street Revolt, where he claims US stock markets are rigged by high frequency traders.

High frequency trading, or HFT, involves the use of algorithms to determine the best times to buy and sell – usually quite large – holdings of shares in a company. Needless to say these sorts of trades could have a significant impact on the value of the stock in question.

While we are told concerns regarding HFT in Australia are “overstated”, it nonetheless pays to take no chances – after all, how many of us are hoping to fund our retirement through investments we have in shares today – there are a couple of steps investors can take to mitigate some of the risks posed by HFT.

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Algorithms may drive traffic jams off the roads

Thursday, 7 November, 2013

Photo by epSos.de

Traffic jams intrigue me, not the ones with some discernible cause, say road works or an accident, but more the apparently random build-ups of vehicles along a stretch of road, especially freeways, that seem to crop up without rhyme or reason.

These, I learn, are actually referred to as “traffic flow instabilities”, and there may be a way to deal with them through the use of algorithms.

Traffic flow instabilities arise, Horn explains, because variations in velocity are magnified as they pass through a lane of traffic. “Suppose that you introduce a perturbation by just braking really hard for a moment, then that will propagate upstream and increase in amplitude as it goes away from you,” Horn says. “It’s kind of a chaotic system. It has positive feedback, and some little perturbation can get it going.”

(Photo by epSos.de)

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Black book is the new method of manufacturing orange juice

Monday, 11 February, 2013

Erratic weather, ever changing sources of supply, fickle consumer tastes… what’s an orange juice manufacturer to do so as to keep ahead of the game? Simple, devise an algorithm – that takes into account a mere one quintillion possible variables – in the production of their juice:

The Black Book model includes detailed data about the myriad flavors – more than 600 in all – that make up an orange, and consumer preferences. Those data are matched to a profile detailing acidity, sweetness, and other attributes of each batch of raw juice. The algorithm then tells Coke how to blend batches to replicate a certain taste and consistency, right down to pulp content. Another part of Black Book incorporates external factors such as weather patterns, expected crop yields, and cost pressures. This helps Coke plan so that supplies will be on hand as far ahead as 15 months. “If we have a hurricane or a freeze,” Bippert says, “we can quickly replan the business in 5 or 10 minutes just because we’ve mathematically modeled it.”

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Social media algorithms spoil triple j’s Hottest 100

Tuesday, 5 February, 2013

Guessing what will feature in the Hottest 100, and whereabouts, has been open to keen speculation for as long as Australian radio station triple j’s annual countdown of their listeners’ favourite music has been around.

It’s something that a couple of listeners, Nick Drewe and Tom Knox, clearly have an above average interest in however… they trawled through Hottest 100 related Twitter and Facebook posts in the lead up to last Australia Day’s countdown, and were able to, quite accurately, predict the composition of the chart:

Nick Drewe and his mate Tom Knox analysed about 35,000 Hottest 100 votes submitted to Triple J via social media sites in the run-up to Australia Day and published their own chart, the Warmest 100. The site accurately forecast 92 of the 100 songs on the chart. Messrs Drewe and Knox forecast all 10 songs in the Hottest 100 top 10, including five in the correct position.

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Evade big brother by adopting an ever changing schedule

Friday, 17 August, 2012

If the geolocation data provided by our mobile phones isn’t quite enough to help others discern our future movements – assuming our activities adhere to some sort of recurring pattern – then tapping into such data on our friend’s phones may help pin us down in the event we unexpectedly change schedules:

Studies have shown that most people follow fairly consistent patterns over time, but traditional prediction algorithms have no way of accounting for breaks in the routine. The researchers solved that problem by combining tracking data from individual participants’ phones with tracking data from their friends – i.e., other people in their mobile phonebooks. By looking at how an individual’s movements correlate with those of people they know, the team’s algorithm is able to guess when she might be headed, say, downtown for a show on a Sunday afternoon rather than staying uptown for lunch as usual.

Slightly concerning, don’t you think?

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But are algorithmic reporters programmed not to miss deadlines?

Tuesday, 1 May, 2012

Despite being a touch rigid, some of the stories written by algorithmic reporters, or computers programmed to write news reports, are anything but robotic or mechanical.

OK, it’s not Roger Angell. But the grandparents of a Little Leaguer would find this game summary – available on the web even before the two teams finished shaking hands – as welcome as anything on the sports pages. Narrative Science’s algorithms built the article using pitch-by-pitch game data that parents entered into an iPhone app called GameChanger. Last year the software produced nearly 400,000 accounts of Little League games. This year that number is expected to top 1.5 million.

Also of interest is the way these electronic journalists craft their news accounts, as they harvest data submitted by people, via smartphone apps, who are say spectators at a sporting event, and then use algorithms to weave divergent strands of information into a report.

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Devising hard to solve Sudoku puzzles? There’s algorithms for that

Monday, 12 March, 2012

Mathematicians – whose ranks most certainly do not include me – who play Sudoku, will be pleased to know that the popular number placement puzzle can feature sixteen starter clues, maybe even fewer, and still be solvable. This after it was widely believed at least seventeen clues were required to produce a solvable game.

Rather than searching through every sixteen-clue subset of a given Sudoku square, desperately looking for one that is actually a proper puzzle, we need only consider sets of sixteen starting clues containing at least one cell from each known unavoidable set. Finding those particular sets of starting clues is a specific instance of a more general problem, known to mathematicians as the “hitting set problem.” The really clever part of McGuire’s work is the development of algorithms for solving the hitting set problem in a reasonable amount of time. Solving the minimum-clue problem for Sudoku was just an application of this new algorithm.

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