Translate into a different language

Sunday, October 21, 2018

Liz Phair’s 10 Favorite Books | Books - Vulture

J.D. Salinger, Toni Morrison, and more.
 
Photo: Getty Images

Bookseller One Grand Books has asked celebrities to name the ten titles they’d take to a desert island, and they’ve shared the results with Vulture. 

Anointed as “Rock’s voice of third-wave feminism,” by The Guardian, American singer and songwriter Liz Phair was adopted and raised outside of Chicago. Her 1993 debut, Exile in Guyville, an 18-song double album of jangling indie-rock, is widely considered a seminal record of the era,...

Below is musician Liz Phair’s list.

Source: Vulture


If you enjoyed this post, make sure you subscribe to my Email Updates!

Smartphones are endangering Iceland’s love of books | Quartz

Photo: Alison Griswold
Every holiday season, publishers in Iceland prepare for jólabókaflóðið, the traditional Christmas book flood, inform Alison Griswold, reporter at Quartz.
A book shop window in Reykjavik.

The jólabókaflóðið, in which Icelandic publishers release most of their new books in the months before Christmas, traces back to 1944, when Iceland gained independence from Denmark. Paper was one of the few goods not rationed on the island, making books a popular gift.

But that tradition is struggling to hang on in the increasingly digitized world. Icelanders aren’t buying nearly as many books as they used to.

From 2010 to 2017, book sales in Iceland dropped 43%. In August, Iceland Monitor reported that book sales had fallen another 5% from the same period in 2017.

“The alarming profile we published a year ago has gotten quite a bit worse,” Egill Örn Jóhannsson, CEO of publishing company Forlagið, told a local news outlet, according to Iceland Monitor...

Last year, Iceland’s minister of education, science, and culture appointed a committee to study the state of book publishing in the country.

Source: Quartz


If you enjoyed this post, make sure you subscribe to my Email Updates!

University researchers push for better research methods | Editor's Picks - Minnesota Daily

New workshops this semester are an effort to make social science studies more replicable, inform Theresa Mueller - Minnesota Daily.


Faculty members and graduate students at the University of Minnesota have formed a workshop to hold discussions about reproducibility in research studies

The discussions come during a national movement to replicate research in social science fields, such as psychology. The movement has shown that many previous studies are not reliable.  After discussions last spring regarding ways the University can address these research practices, the Minnesota Center for Philosophy of Science designed workshops for faculty and students to discuss ways to develop replicable research methods. 

“Any scientific discipline will depend upon reproducible findings, that’s how you build a science,” said Matt McGue, a professor in the Department of Psychology.

The Reproducibility Working Group meets biweekly this semester to discuss the issue of reproducibility in psychological research and focus on topics such as measurement. 

Alan Love, director of the Minnesota Center for Philosophy of Science, said the purpose of holding these conversations across campus is for researchers across all disciplines to be actively thinking about the sort of complex issues within their own methods...

Faculty members and graduate students from the philosophy, psychology and statistics departments have been attending the workshops. McGue said all members offer a unique perspective to the discussion of reproducibility as there are intersections across all three areas. 
Read more... 

Source: Minnesota Daily


If you enjoyed this post, make sure you subscribe to my Email Updates!

Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2018 | insideBIGDATA

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month, as insideBIGDATA reports.


Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. arXiv contains a veritable treasure trove of learning methods you may use one day in the solution of data science problems. 
We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a “thumbs up” icon. Consider that these are academic research papers, typically geared toward graduate students, post docs, and seasoned professionals. They generally contain a high degree of mathematics so be prepared. 
Enjoy!
Read more...

Source: insideBIGDATA  


If you enjoyed this post, make sure you subscribe to my Email Updates!

Saturday, October 20, 2018

How to use Machine Learning for IoT analysis | JAXenter

Daniel Bishop, started off as a content consultant for small SEO and web design companies notes, Many of the most exciting high-tech projects nowadays include bringing together knowledge from two or more well-established and fast-growing fields. 

Photo: Shutterstock / Venomous Vector
One of the prominent examples is applying machine learning in order to filter and analyze the huge amount of data we obtain from the Internet of Things (IoT). But first, let’s see why IoT needs help from artificial intelligence in order to reach its full potential.

In essence, the IoT universe includes all sorts of sensors and smart devices that are plugged into the internet and capable of exchanging data with each other. This industry is growing at an enormous rate. It is expected that until 2022 we’ll have around 50 billion devices connected to the network, which is a 140 percent increase compared to 2018. And in 2035, this number could reach 1 trillion devices.

This massive upsurge also means a rise in the amount of exchanged data that will make this data impossible to analyze by using traditional methods. With 90 percent of online data being generated only in the last two years, this problem has already emerged, especially having in mind the reported shortage of data analysts worldwide. So how can machine learning help with sorting and analyzing this data?
Read more...

Source: JAXenter


If you enjoyed this post, make sure you subscribe to my Email Updates!

Machine learning now the top skill sought by developers | Developer - ZDNet

SlashData's latest survey of 20,000 developers identifies machine learning and data science are the skills to know for 2019, according to Joe McKendrick, author and independent analyst. 

Photo: Joe McKendrick
Developers want to learn the data sciences. They see machine learning and data science as the most important skill they need to learn in the year ahead. Accordingly, Python is becoming the language of choice for developers getting into the data science space.

Those are some of the takeaways from a recent survey of more than 20,500 developers conducted by SlashData. The survey shows data science and machine learning to be the top skill to learn in 2019. These will be the most highly sought after skills in the next year, with 45% of developers seeking to gain expertise in these fields...

Data science and related machine learning activities require a wide range of skills, the report's authors, led by Stijn Schuermans, state. "Data scientists live in the intersection of coding, mathematics and business and, therefore, they need to possess a mixture of technical and soft skills as well as have domain-specific knowledge," they relate. "Analyzing large volumes of data with advanced statistical and visualization techniques often requires good mathematical background, especially in probability and statistics. It also requires ability to write code in at least one programming language like Python, which is currently by far the most popular language among data scientists."
Read more...  

Source: ZDNet 


If you enjoyed this post, make sure you subscribe to my Email Updates!

Artificial intelligence better than physicists at designing quantum science experiments | Science - ABC News

Belinda Smith, online science reporter in the ABC RN science unit explains, The quantum world defies logic: wrap your brain around instantaneous messaging between distant particles, or cats that are alive and dead at the same time.


Perhaps physicists should leave human intuition at the laboratory door when designing quantum experiments too.

An Australian crew enlisted the help of a neural network — a type of artificial intelligence — to optimise the way they capture super-cold atoms.

Usually, physicists smoothly tune lasers and magnetic fields to gradually coax atoms into a cloud, according to study co-author Ben Buchler from the Australian National University...

This more vigorous approach, published in Nature Communications, trapped twice as many atoms in half the time when compared to traditional methods devised by humans.

Why trap cold atoms?
In the past few decades, cold clouds of atoms have formed the foundations for advances in precision measurement, optical atomic clocks and quantum processing.

And in many cases, the colder the cloud, the better. That's because a warm atom is a jiggly atom, and this poses a problem for physicists.

When atoms interact with one another, they create noise in the system. 

Read more...

Source: ABC News 


If you enjoyed this post, make sure you subscribe to my Email Updates!

The case against national strategies on artificial intelligence | Technology - Asia Times

While the share of commodities in global trade has fallen, the share of digital services has risen; digitization now underwrites more than 60% of all trade, summarizes Mark Esposito, co-founder of Nexus FrontierTech, professor of business and economics with appointments at Harvard University and Hult International Business School, Terence Tse, co-founder of Nexus FrontierTech, professor at ESCP Europe Business School in London and serves as an adviser to the European Commission and Joshua Entsminger, researcher at Nexus FrontierTech and senior fellow at École des Ponts Center for Policy and Competitiveness.

Artificial intelligence will behave differently in different cultures.
Photo: Wikimedia Commons

Efforts to develop artificial intelligence (AI) are increasingly being framed as a global race, or even a new Great Game. In addition to the race between countries to build national competencies and establish a competitive advantage, firms are also in a contest to acquire AI talent, leverage data advantages, and offer unique services.
In both cases, success will depend on whether AI solutions can be democratized and distributed across sectors.

The global AI race is unlike any other global competition, because the extent to which innovation is being driven by the state, the corporate sector, or academia differs substantially from country to country. On average, though, the majority of innovations so far have emerged from academia, with governments contributing through procurement, rather than internal research and development...

Moreover, in the current environment, national AI programs are competing for a limited talent pool. And though that pool will expand over time, the competencies needed for increasingly AI-driven economies will change. For example, there will be a greater demand for cybersecurity expertise.
So far, AI developers working out of key research centers and universities have found a reliable exit strategy, and a large market of eager buyers. With corporations driving up the price for researchers, there is now a widening global talent gap between the top firms and everyone else. And because the major technology companies have access to massive, rich data stores that are unavailable to newcomers and smaller players, the market is already heavily concentrated.
Read more... 

Source: Asia Times


If you enjoyed this post, make sure you subscribe to my Email Updates!

‘AI Camera’ Combines Machine Vision, Deep Learning | Manufacturing - EnterpriseTech

Is it any wonder there are cameras everywhere imaging nearly everything?, as EnterpriseTech reports.
  

Photo: Intel
One reason is chip scaling, with key components from semiconductor makers allowing equipment manufacturers to fabricate quarter-size cameras that are becoming ubiquitous in agricultural, automotive and industrial markets.

Now, inspection camera manufacturers are adding smarts in the form of image processing that according to one vendor brings “AI to the edge” in the form of a machine vision camera with deep learning inference capabilities.

The marketing pitch for this approach centers on the proposition that placing deep neural network acceleration directly on a camera allows inference to be performed at the network edge, eliminating the need to transmit a raw video stream elsewhere for processing.
Read more...

Source: EnterpriseTech


If you enjoyed this post, make sure you subscribe to my Email Updates!

A pioneering scientist explains ‘deep learning’ | Science - The Verge

Angela Chen, science reporter at The Verge observes, Artificial intelligence meets human intelligence.

Photo: James Bareham / The Verge
Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies.

Photo: Terrence Sejnowski

Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution (out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence.

The Verge spoke to Sejnkowski about how “deep learning” suddenly became everywhere, what it can and cannot do, and the problem of hype.

Source: The Verge


If you enjoyed this post, make sure you subscribe to my Email Updates!