History of air conditioning, invented by Willis Carrier – cool reading for a hot day

Willis Carrier submitted drawings of the first modern air conditioning system on July 17, 1902.

Carrier was working to solve a problem that effected the quality of printing…

He came up with the brilliant idea to circulate cold water rather than steam through heating coils in a machine he used to test heaters.

Carrier’s design was credited as the first to address four basic functions necessary for air conditioning. An air conditioner must: 1. control temperature, 2. control humidity, 3. control air circulation, and 4. cleanse the air.

After the first appearance of Carrier’s air conditioner drawings in 1902, the air conditioner has revolutionized the comfort of people in many different activities.

This timeline from Carrier highlights some of the major impacts of air conditioning on society.

 

1902– First application of modern mechanical air conditioning, Sackett-Wilhelms

printing plant, Brooklyn, N.Y.

1914– First application of air conditioning in a residence – Charles Gates mansion, Minneapolis, Minn.

1924– First department store air conditioned, J.L. Hudson’s, Detroit, Mich.

1925– Movie theaters cooled: Grauman’s Theater, Los Angeles, Calif., Rivoli Theater, N.Y.

1928-29– Chambers of the U.S. House of Representatives and Senate air conditioned

 

See the rest of the timeline and read the full articleThe Journey of Air Conditioning: 1902-Today

 

 

Continue reading History of air conditioning, invented by Willis Carrier – cool reading for a hot day

America regains the title of ‘fastest supercomputer on the planet’

Every six months, Earth’s biggest supercomputers have a giant race to see which can lay claim to being the world’s fastest high-performance computing cluster.

In the latest Top 500 Supercomputer Sites list unveiled Monday morning, a newly assembled cluster built with IBM hardware at the Department of Energy’s Lawrence Livermore National Laboratory (LLNL) takes the top prize. Its speed? A whopping 16.32 petaflops, or 16 thousand trillion calculations per second. With 96 racks, 98,304 compute nodes, 1.6 million cores, and 1.6 petabytes of memory across 4,500 square feet, the IBM Blue Gene/Q system installed at LLNL overtakes the 10-petaflop, 705,000-core “K computer” in Japan’s RIKEN Advanced Institute for Computational Science.

The Japanese computer had been world’s fastest twice in a row. Before that, the top spot was held by a Chinese system. The DOE computer, named “Sequoia,” was delivered to LLNL between January and April. It’s the first US system to be ranked #1 since November 2009.

To get to 16 petaflops, Sequoia ran the Linpack benchmark for 23 hours without a single core failing, LLNL division leader Kim Cupps told Ars Friday in advance of the list’s release. The system is capable of hitting more than 20 petaflops—during the tests it ran at 81 percent efficiency.
Learn moreWith 16 petaflops and 1.6M cores, DOE supercomputer is world’s fastest

Continue reading America regains the title of ‘fastest supercomputer on the planet’

Most of the translation on the planet is now done by Google Translate

“In a given day we translate roughly as much text as you’d find in 1 million books. To put it another way: what all the professional human translators in the world produce in a year, our system translates in roughly a single day. By this estimate, most of the translation on the planet is now done by Google Translate.”

Pulled from Breaking Down the Language Barrier via the Google Translate Blog:

The rise of the web has brought the world’s collective knowledge to the fingertips of more than two billion people. But what happens if it’s in Hindi or Afrikaans or Icelandic, and you speak only English—or vice versa?

In 2001, Google started providing a service that could translate eight languages to and from English. It used what was then state-of-the-art commercial machine translation (MT), but the translation quality wasn’t very good, and it didn’t improve much in those first few years. In 2003, a few Google engineers decided to ramp up the translation quality and tackle more languages. That’s when I got involved. I was working as a researcher on DARPA projects looking at a new approach to machine translation—learning from data—which held the promise of much better translation quality. I got a phone call from those Googlers who convinced me (I was skeptical!) that this data-driven approach might work.

I joined Google, and we started to retool our translation system toward competing in the NIST Machine Translation Evaluation, a “bake-off” among research institutions and companies to build better machine translation. Google’s massive computing infrastructure and ability to crunch vast sets of web data gave us strong results. This was a major turning point: it underscored how effective the data-driven approach could be.

But at that time our system was too slow to run as a practical service—it took us 40 hours and 1,000 machines to translate 1,000 sentences. So we focused on speed, and a year later our system could translate a sentence in under a second, and with better quality. In early 2006, we rolled out our first languages: Chinese, then Arabic.

We announced our statistical MT approach on April 28, 2006, and in the six years since then we’ve focused primarily on core translation quality and language coverage. We can now translate among any of 64 different languages, including many with a small web presence, such as Bengali, Basque, Swahili, Yiddish, even Esperanto.

Today we have more than 200 million monthly active users on translate.google.com (and even more in other places where you can use Translate, such as Chrome, mobile apps, YouTube, etc.). People also seem eager to access Google Translate on the go (the language barrier is never more acute than when you’re traveling)—we’ve seen our mobile traffic more than quadruple year over year. And our users are truly global: more than 92 percent of our traffic comes from outside the United States.

 

by Franz Och

Distinguished Research Scientist, Google

 

// Thx to – The Next Web

Enroll in free online in courses from top institutions – Princeton, Stanford, Michigan

Online educational marketplaces are on the rise, with tools like Udemy and Khan Academy allowing people of all ages to become an expert in any topic.

New company Coursera is targeting higher education by offering university-level courses from top institutions to students all over the world, all for free.

The company launched with $16 million in Series A funding and is announcing partnerships with four schools:

  • Princeton University
  • Stanford University
  • University of Pennsylvania
  • University of Michigan.

Coursera will offer over 30 courses from its partner schools across a variety of disciplines, including computer science, sociology, medicine, and math.

 

A selection of the classes:

 

Classes typically last for five to ten weeks, and during that time students commit to watching the lectures, and completing interactive quizzes and assignments, which are auto-graded or graded by peers. Upon completion, the student receives a statement of accomplishment, a letter from the professor, and a score, but the course doesn’t count for any actual credit with that specific institution. The site also features a Q&A forum where students can ask questions about the course material and get answers from fellow students.

via Betakit

 

Screenshot of Coursera offerings

Gesture-based vending machine – recognizes hugs and dispenses soda

The Coca-Cola Hug Machine. You hug it, it returns the favour with a Coke. Because vending machines have feelings too. #hugmecoke

A new campaign from Ogilvy & Mather features the Coca-Cola hug me machine. Look for more of these gesture-based machines to pop up across Asia in the upcoming months.

They’re Made Out of Meat – what aliens think of us – hilarious short film

“They’re made out of meat.”

“Meat?”

“Meat. They’re made out of meat.”

“Meat?”

“There’s no doubt about it. We picked several from different parts of the planet, took them aboard our recon vessels, probed them all the way through. They’re completely meat.”

“That’s impossible. What about the radio signals? The messages to the stars.”

“They use the radio waves to talk, but the signals don’t come from them. The signals come from machines.”

“So who made the machines? That’s who we want to contact.”

“They made the machines. That’s what I’m trying to tell you. Meat made the machines.”

Keep reading – “They’re Made Out of Meat

They're Made Out of Meat – what aliens think of us – hilarious short film

“They’re made out of meat.”

“Meat?”

“Meat. They’re made out of meat.”

“Meat?”

“There’s no doubt about it. We picked several from different parts of the planet, took them aboard our recon vessels, probed them all the way through. They’re completely meat.”

“That’s impossible. What about the radio signals? The messages to the stars.”

“They use the radio waves to talk, but the signals don’t come from them. The signals come from machines.”

“So who made the machines? That’s who we want to contact.”

“They made the machines. That’s what I’m trying to tell you. Meat made the machines.”

Keep reading – “They’re Made Out of Meat