Via GeekOSystem

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A new installation at the Amsterdam Foam gallery by Erik Kessels takes a literal look at the digital deluge of photos online by printing out 24 hours worth of uploads to Flickr. The result is rooms filled with over 1,000,000 printed photos, piled up against the walls.

There’s a sense of waste and a maddening disorganization to it all, both of which are apparently intentional. According to Creative Review, Kessels said of his own project:

“We’re exposed to an overload of images nowadays,” says Kessels. “This glut is in large part the result of image-sharing sites like Flickr, networking sites like Facebook, and picture-based search engines. Their content mingles public and private, with the very personal being openly and un-selfconsciously displayed. By printing all the images uploaded in a 24-hour period, I visualise the feeling of drowning in representations of other peoples’ experiences.”

Humbling, and certainly thought provoking, Kessel’s work challenges the notion that everything can and should be shared, which has become fundamental to the modern web. Then again, perhaps it’s only wasteful and overwhelming when you print all the pictures and divorce them from their original context.

Via Recursivity.blog()

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At the beginning of last week, I launched GreedAndFearIndex - a SaaS platform that automatically reads thousands of financial news articles daily to deduce what companies are in the news and whether financial sentiment is positive or negative.

It’s an app built largely on Scala, with MongoDB and Akka playing prominent roles to be able to deal with the massive amounts of data on a relatively small and cheap amount of hardware.

The app itself took about 4-5 weeks to build, although the underlying technology in terms of web crawling, data cleansing/normalization, text mining, sentiment analysis, name recognition, language grammar comprehension such as subject-action-object resolution and the underlying “God”-algorithm that underpins it all took considerably longer to get right.

Doing it all was not only lots of late nights of coding, but also reading more academic papers than I ever did at university, not only on machine learning but also on neuroscience and research on the human neocortex.

What I am getting at is that financial news and sentiment analysis might be a good showcase and the beginning, but it is only part of a bigger picture and problem to solve.

Unlocking True Machine Intelligence & Predictive Power
The human brain is an amazing pattern matching & prediction machine - in terms of being able to pull together, associate, correlate and understand causation between disparate, seemingly unrelated strands of information it is unsurpassed in nature and also makes much of what has passed for “Artificial Intelligence” look like a joke.

However, the human brain is also severely limited: it is slow, it’s immediate memory is small, we can famously only keep track of 7 (+-) things at any one time unless we put considerable effort into it. We are awash in amounts of data, information and noise that our brain is evolutionary not yet adapted to deal with.

So the bigger picture of what I’m working on is not a SaaS sentiment analysis tool, it is the first step of a bigger picture (which admittedly, I may not solve, or not solve in my lifetime):

What if we could make machines match our own ability to find patterns based on seemingly unrelated data, but far quicker and with far more than 5-9 pieces of information at a time?

What if we could accurately predict the movements of financial markets, the best price point for a product, the likelihood of natural disasters, the spreading patterns of infectious diseases or even unlock the secrets of solving disease and aging themselves?

The Enablers
I see a number of enablers that are making this future a real possibility within my lifetime:

• Advances in neuroscience: our understanding of the human brain is getting better year by year, the fact that we can now look inside the brain on a very small scale and that we are starting to build a basic understanding of the neocortex will be the key to the future of machine learning. Computer Science and Neuroscience must intermingle to a higher degree to further both fields.
• Cloud Computing, parallelism & increased computing power: Computing power is cheaper than ever with the cloud, the software to take advantage of multi-core computers is finally starting to arrive and Moore’s law is still advancing at ever (the latest generation of MacBook Pro’s have roughly 2.5 times the performance of my barely 2 year old MBP).
• “Big Data”:  we have the data needed to both train and apply the next generation of machine learning algorithms on abundantly available to us. It is no longer locked away in the silos of corporations or the pages of paper archives, it’s available and accessible to anyone online.
• Crowdsourcing: There are two things that are very time intensive when working with machine learning - training the algorithms, and once in production, providing them with feedback (“on the job training”) to continually improve and correct. The internet and crowdsourcing lowers the barriers immensely. Digg, Reddit, Tweetmeme, DZone are all early examples of simplistic crowdsourcing with little learning, but where participants have a personal interest in participating in the crowdsourcing. Combine that with machine learning and you have a very powerful tool at your disposal.

Babysteps & The Perfect Storms
All things considered, I think we are getting closer to the perfect storm of taking machine intelligence out of the dark ages where they have lingered far too long and quite literally into a brave new world where one day we may struggle to distinguish machine from man and artificial intelligence from biological intelligence.

It will be a road fraught with setbacks, trial and error where the errors will seem insurmountable, but we’ll eventually get there one babystep at a time.
I’m betting on it and the first natural step is predictive analytics & adaptive systems able to automatically detect and solve problems within well-defined domains.

Via POPSCI

By Rebecca Boyle

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"Spatial humanities," the future of history

Even using the most detailed sources, studying history often requires a great imagination, so historians can visualize what the past looked and felt like. Now, new computer-assisted data analysis can help them really see it.

Geographic Information Systems, which can analyze information related to a physical location, are helping historians and geographers study past landscapes like Gettysburg, reconstructing what Robert E. Lee would have seen from Seminary Ridge. Researchers are studying the parched farmlands of the 1930s Dust Bowl, and even reconstructing scenes from Shakespeare’s 17th-century London.

But far from simply adding layers of complexity to historical study, GIS-enhanced landscape analysis is leading to new findings, the New York Times reports. Historians studying the Battle of Gettysburg have shed light on the tactical decisions that led to the turning point in the Civil War. And others examining records from the Dust Bowl era have found that extensive and irresponsible land use was not necessarily to blame for the disaster.

GIS has long been used by city planners who want to record changes to the landscape over time. And interactive map technology like Google Maps has led to several new discoveries. But by analyzing data that describes the physical attributes of a place, historians are finding answers to new questions.

Anne Kelly Knowles and colleagues at Middlebury College in Vermont culled information from historical maps, military documents explaining troop positions, and even paintings to reconstruct the Gettysburg battlefield. The researchers were able to explain what Robert E. Lee could and could not see from his vantage points at the Lutheran seminary and on Seminary Hill. He probably could not see the Union forces amassing on the eastern side of the battlefield, which helps explain some of his tactical decisions, Knowles said.

Geoff Cunfer at the University of Saskatchewan studied a trove of data from all 208 affected counties in Texas, New Mexico, Colorado, Oklahoma and Kansas — annual precipitation reports, wind direction, agricultural censuses and other data that would have been impossible to sift through without the help of a computer. He learned dust storms were common throughout the 19th century, and that areas that saw nary a tiller blade suffered just as much.

The new data-mapping phenomenon is known as spatial humanities, the Times reports. Check out their story to find out how advanced technology is the future of history.

[New York Times]

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