Human

New Zealand’s Quixotic (Perhaps) Mission to Kill Every One of Its Rats

A rat in a cage in New Zealand.
A rat in a cage in New Zealand.

Thousands of years ago—when humans weren’t a threat to the Earth and invasive species couldn’t stow away on ships, enter foreign lands, and destroy many of the things that made those lands unique and beautiful—all sorts of distinct native birds flourished on the islands that now constitute New Zealand.

Today, more than 40 of those unique species are extinct, thanks to humans. We hunted them, destroyed their habitats, and, maybe most importantly, introduced rats and opossums and stoats—a type of weasel—which slaughtered the birds, many of which, the Associated Press says, “gave up flight altogether to strut about the forest floor.” The 40-odd surviving native bird species struggle on.

Now, the government and activists have come up with a solution: kill all the rats and opossums and stoats, every last one of…

How Long Would It Take to Count to a Billion and What’s the Highest Anyone Has Counted?

Joseph B. asks: How long would it take to count to a million? What about a billion? What’s the highest anyone has ever counted?

counting-boy

Counting is one of life’s most basic skills and something most humans are quite adept at. Despite most of us being experts on the subject, theoretically capable of counting infinitely high with the ceiling bounded only by available time and how good we are at staving off psychosis, few can accurately guess how long it would take to count to a million, let alone a billion. This is largely owing to the fact that our brains have an amazing amount of difficulty conceptualizing such large numbers. This all brings us to the question of the hour- just how long would it take to count to a million or a billion?

Let’s start with a million. The most commonly put forward time it would take to count from one to a million out loud is about 23 days. This time frame is cited in a number of textbooks we consulted and seems to have originated, as far as we can tell, in a children’s book suitably called, How Much is a Million by David Schwartz, which uses various examples to put into perspective how amazingly big numbers like a million really are.

Given the figure being cited in many a textbook and first appearing in a book literally titled How Much is a Million, you might assume it’s reasonably close to correct. This is not the case, however; this number significantly underestimates the actual time needed.

You see, Mr. Schwartz wasn’t trying to come up with a real world figure here, just a simple exercise to blow kid’s minds without bogging them down in the details. As such, there are a couple assumptions being made in the “23 days” figure that turn out to make it completely useless as a real world estimate of how long it would take to count to a million. The first assumption is that the person counting would be able to do so 24 hours a day non-stop.

The second assumption is not quite so absurd on the surface: assuming that it would take only about 2 seconds on average to say each number. However, while certainly some of the lowest numbers can easily be said much faster than that and with little time needed for thinking, the majority of the numbers for an average speed speaker would take slightly longer. For example, just consider how long it would take you to say out loud “one hundred ninety-five thousand five hundred sixty-five”.

Granted, when speed speaking, one can easily hit the 2 second mark, or even less. But when actually counting aloud for many hours on end, there is a certain level of physical and mental fatigue that goes along with it that makes it so speed speaking isn’t really viable long-term. This is a marathon, not a sprint.

So just how long would it actually take someone to count to a million? Thanks to the efforts of one Jeremy Harper, we know the real world answer is somewhere in the vicinity of 89 days.

How did Mr. Harper figure this out? Well, he did it from June 18 to September 14, 2007 and live-streamed the entire thing online for everyone to watch. Harper, a software engineer whose boss gave him time off to do this, neither left his apartment nor shaved during the event; he recited numbers aloud (read off a computer monitor) for about an average of 16 hours every 24 hour period for 89 straight days. This is about 3.9 times longer than the oft’ quoted 23 day estimate.

To be clear, we’re not saying this all couldn’t be realistically done faster. Harper did take the occasional mini-break to break out in dance and things of this nature- he wasn’t trying to set a speed record to count to a million. However, given his overall pacing when counting was reasonably quick, he dedicated the vast majority of his waking hours to the task at hand, and the large sample size of counting time we’re dealing with here, this seems a pretty good ballpark figure to go with on the “How long does it take to…

Thanks, Robot! Humans are Showing Kindness with Their AI Helpers.

Article Image

Siri, sorry for being rude earlier. My bad.

We spend a lot of time in 2017 talking and writing to non-humans. Whether it’s from interacting with a chatbot or asking Alexa for help, much of our communication is moving away from human-to-human and its attached norms of etiquette. Which begs the question: how should we be treating our AI helpers? With kindness or indifference?

“People tend to be very kind in the initial handover,” says Dennis Mortensen, founder and CEO of x.ai. The NYC-based company created two virtual assistants, Amy and Andrew Ingram, that assist with scheduling meetings. Users of the service simply copy Amy or Andrew on an email and the virtual assistant, handing over the often time-consuming back-and-forth when scheduling meetings.

Dennis Mortensen, founder of x.ai (Courtesy Photo)
Dennis Mortensen, founder of x.ai (Courtesy Photo)

According to Mortensen, 11% of the communication with “Amy” and “Andrew” is to show gratitude for their work. Even though the virtual assistants don’t have feelings, people feel an urge to express their thanks along with adding social niceties. Hey Amy, would you be so kind…

People are clearly treating “Amy” or “Andrew” as if they exist as warm-blooded humans. And while some people may wrongly assume that Amy Ingram is a human assistant, x.ai has no intention of blurring the line (hence the AI initials). “We don’t want to fool anyone into thinking it is a human,” states Mortensen.

My favorite go to person now is Amy Ingram @xdotai

— David Chou (@dchou1107) May 9, 2017

If we imagine AI agents as being more akin to employees or team-mates than apps, our relationship with them will need to be similarly dynamic and two sided…You can think of it as granting these autonomous agents a sort of rudimentary self-consciousness; this should, in turn, enable us humans to extend…

How generative artificial networks are accelerating AI learning

Image Credit: Shutterstock.com / Mopic

One of the biggest limiting factors of artificial intelligence (AI) systems is that they can’t think or conceptualize the world the way humans can.

Rather than intuitively discerning patterns in chaos, like how you can identify a cat in a photograph instantly, traditional AI models require in-depth descriptions of what constitutes a “cat” object and how to identify one by evaluating individual groups of pixels within the image.

Deep learning systems are starting to bypass the necessity for brute force computations, as evidenced by the landmark victory of AI program AlphaGo against an international champion of Go, a game once thought to be too intuitive and conceptual for AI to master. But a new, yet intuitively simple, leap forward in AI learning may be able to accelerate the pace of AI development even further.

Google’s GANs

Google researcher and AI expert Ian Goodfellow is working on AI that belongs to a group of “generative models,” which are designed to create images and sounds comparable to those you’d find in the real world. This is a deceptively difficult task, as AI programs must first conceptually understand what it is they’re trying to replicate, a leap forward in intuitive thinking that has historically been reserved for human beings.

Goodfellow is attempting to accomplish this using something called generative artificial networks, or GANs, which are sets of two dueling, semi-competing AI algorithms designed to continuously one-up each other. For example, one AI may be programmed to generate imagery that looks realistic, while the other AI will be programmed to distinguish real images from machine-generated ones. Over time, the image generator will get better at generating realistic images, and the “judge” will get better at discerning them.

Both AI programs utilize artificial neural networks, which are designed to mimic the process human brains use to store and recall information. Rather than strict inputs and outputs, both machines will be establishing…

This Is How Mark Cuban Thinks Humans Could Trump The Rise Of Automation

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More and more people’s jobs are coming under attack from machine-learning, artificially-intelligent robots. That much is clear, at least based on a growing number of business leaders, labor experts, and tech industry insiders ringing bells to let workers and companies know they had better be prepared for a more-automated world of work. Mark Cuban is among them—and he has an idea about how to get ahead of what is coming.

The billionaire owner of the NBA Dallas Mavericks and investor on the ABC reality television series Shark Tank has recently been talking up what he sees in store for the human workforce as the automated workforce takes on an increasing number of tasks. Cuban figures that complicated-but-routine jobs are as much at risk as low-skills jobs. That includes jobs in now-popular fields, such as analytics, in which “you just take the data, have it spit out whatever you need.”

The way Cuban sees it, it is only a matter of time before this transformation completely takes place. One of his concerns is that not enough people perceive the extent and speed that this technological change is having on the ways we work. He includes President Donald Trump in that group, along with executives at major companies who influence job creation and manufacturing policy. The bottom line, according to Cuban, is that companies will be building more facilities and getting more work done with automation, yet may end up employing fewer people.

Dallas Mavericks team owner Mark Cuban reacts from his seat during the second half of an NBA basketball game. (Photo credit: Carlos Osorio/AP)

Whether this shift ultimately amounts more to machines displacing humans than replacing them, the reality is that staying in the workforce will mean finding ways to adapt to a world increasingly made up of new and different man-machine combinations. But getting people already in the workforce to adapt to…

Peace and quiet is becoming more elusive in U.S. wild areas

Alcatraz Island
NOISE POLLUTION Alcatraz Island is a former prison now managed by the federal government as a protected natural area and historical site. Long-term audio recordings taken in places like this one are helping scientists understand just how much human noise affects natural places.

Even in the wilderness, humans are making a ruckus.

In 63 percent of America’s protected places — including parks, monuments and designated wilderness areas — sounds made by human activity are doubling the volume of background noise. And in 21 percent of protected places, this racket can make things 10 times noisier.

Enough clatter from cars, planes and suburban sprawl is seeping into wild places to diminish animals’ ability to hear mating calls and approaching predators, a team of researchers based in Colorado reports in the May 5 Science. Human noise doesn’t always have to be loud to override natural sounds, though. Some places are so quiet to begin with that even the smallest amount of human noise can dominate, the researchers found.

“The world is changing, and protected areas are getting louder — the last strongholds of diversity,” says Jesse Barber, an ecologist at Boise State University in Idaho. Studies like this one that show the impact of human-related noise across the entire country instead of in a single park are important, he says, because “this is the scale at which conservation occurs.”

Researchers measured the reach of human noise by tapping into a National Park Service dataset containing long-term audio recordings from 492 sites across the United States. At each site, the scientists linked the sound volume in decibels (averaged over weeks of recording and adjusted to prioritize the frequencies that human ears are most sensitive to) to the presence or absence of dozens of possible features. Such factors include whether the terrain was mountainous or flat, if there was a river nearby, and how close the site was to a highway or a farm.

Machine learning algorithms then predicted the volume in areas without audio monitors, based on the features of that place — and figured out how much of the noise in any given location came from human sources compared with…

Emerson on Individual Integrity and Resisting the Tyranny of the Masses

“When you adopt the standards and the values of someone else,” Eleanor Roosevelt wrote in her timeless meditation on happiness and conformity, “you surrender your own integrity [and] become, to the extent of your surrender, less of a human being.” And yet we exist within a society, as individual particles that coagulate into the so-called masses, awash in societal standards that often permeate our consciousness without our conscious consent or even awareness. How, then, do we mediate between the inescapable social dimension of our lives and the unassailable integrity of individual personhood?

Wedged in time between Søren Kierkegaard’s keen insight into the psychology of conformity and Nobel laureate Elias Canetti’s incisive treatise on crowds and power was another intellectual titan of the human spirit, Ralph Waldo Emerson (May 25, 1803–April 27, 1882), who addressed this question in an essay titled “Considerations by the Way,” found in his indispensable Essays and Lectures (public library | free download).

Ralph Waldo Emerson

With spirited disdain for conformity, Emerson writes:

Leave this hypocritical prating about the masses. Masses are rude, lame, unmade, pernicious in their demands and influence, and need not to be flattered but to be schooled. I wish not to concede anything to them, but to tame, drill, divide, and break them up, and draw individuals out of them… Masses! the calamity is the masses. I do not wish any mass at all, but honest men only, lovely, sweet, accomplished women only, and no shovel-handed, narrow-brained, gin-drinking million stockingers or lazzaroni at all. If government knew how, I should like to see it check, not multiply the population. When it reaches its true law of action, every man that is born will be hailed as essential. Away with this hurrah of masses, and let us have the considerate vote of single men spoken on their honor and their…

Stephen Hawking Warns We Must Colonize Another Planet Soon – Here’s Why He’s Wrong

Why You Should Open A Roth IRA Today

In this picture taken on March 24, 2017, renowned physicist Stephen Hawking, 75, speaks to an audience by hologram (L) in Hong Kong (Credit: ANTHONY WALLACE/AFP/Getty Images)

I trust Stephen Hawking’s word when it comes to black holes and quantum mechanics, but I’m more dubious when the famous cosmologist says, as he does in an upcoming BBC special, that it is urgent for humans to colonize another planet in the face of catastrophes like climate change, asteroid strikes, epidemics and overpopulation.

The Beeb is resurrecting its long-running science program, “Tomorrow’s World” with a documentary called “Expedition New Earth,” in which Hawking says the human species must set up shop beyond Earth within the next hundred years in order to ensure the survival of our species, according to the Telegraph.

I really hope someone on the show’s production team, or the BBC’s promotions people, or maybe the outlets picking up the story are misrepresenting Hawking’s level of hysteria and urgency here, because it’s sorely lacking in the logic department.

The most likely worlds for colonization are our moon or Mars (which is also Elon Musk’s target of choice for a colony in the next century), and in case you hadn’t heard, neither of these places are habitable. Even if Earth were to suffer the catastrophes that Hawking worries about — and they all worry me, too — it would still be more habitable than the moon or Mars.

This is to say nothing of the trip to each place, which is highly risky, expensive and involves exposure to an awful lot of cosmic radiation and weightlessness that can do very real damage to the human body, especially on the long trip to Mars.

I feel like I should be able to drop the mic here, but openly disagreeing with one of the smartest men on Earth demands a little extra diligence.

So to be clear, I think pursuing a small human colony is a great idea. The scientific discoveries and technological innovations that will come out of such a venture are well worth the effort.

But…

AI tool generates ‘DeepBeats’ to challenge human rappers

Rapping is no easy feat. In-demand artists like Sean “Diddy” Combs and Jay Z earned $735 million and $550 million respectively for their stylish flow. That kind of dough can buy you ~3,600,000 bottles at any hot club. Or ~816 million hours of GPU server time from AWS. Depends on your priorities.

But Dr. Dre better watch out, because a bunch of Finnish nerds just developed a “novel deep neural network architecture” to give him a run for his money ($700 million, to be precise). The technique is aptly named DopeLearning and powers an online tool called DeepBeat. When a homogenous bunch of European, presumably white dudes build an AI rapper, does this count as a new digital form of cultural appropriation? Debatable, but we can save that argument for another day.

Aside from broaching important cultural subjects, rap consists of intricate structures and complex rhyme patterns that require sophisticated language and lyrical skills to generate. Most of us couldn’t freestyle rap to save our lives. DeepBeat tackles the challenge by first looking at rap lyrics and predicting the next line. These predictions are then combined to merge lines from existing songs into new rhymes with meaning.

The team started with half a million lines of rap songs from over 100 artists. Lyrics creation was then modeled as an “information retrieval” problem, where the query is the first x number of lines of a song, and the answer is the most relevant follow-up lyrics. This approach simplifies the challenge of measuring performance, because accuracy can be assessed against actual songs. A generative model that constructs new lyrics word by word would yield more creative output, but also drive the complexity up significantly. Perhaps an aspiring academic should hustle on this front and produce a Dope Learning 2 paper.

After mapping lines to high-dimensional vector space, DeepBeat leverages a Ranking SVM to pick the most relevant next lyric. Document…

Photographer To Capture Every Skin Tone In The World For A Human Pantone Project

Race, ethnicity, and skin colour have been dividing factors among humankind for centuries, but Brazilian photographer Angélica Dass is seeking to break down the barriers with her latest project, Humanae. She’s on a mission to capture examples of every skin colour in the world, to prove that diversity goes beyond the standard confines of white, black, red, and yellow.

Humanae quickly gained momentum shortly after its inception in early 2016, and thanks to an extensive social media campaign, Dass was able to capture over 200 portraits while travelling through 19 different international cities. She followed a ritual of first photographing the subjects against a white background, then selecting an 11-pixel square from each of their noses and matching the colour…