What Did the Ancient Messages Say?

technologyreview |  In 1886, the British archaeologist Arthur Evans came across an ancient stone bearing a curious set of inscriptions in an unknown language. The stone came from the Mediterranean island of Crete, and Evans immediately traveled there to hunt for more evidence. He quickly found numerous stones and tablets bearing similar scripts and dated them from around 1400 BCE.

That made the inscription one of the earliest forms of writing ever discovered. Evans argued that its linear form was clearly derived from rudely scratched line pictures belonging to the infancy of art, thereby establishing its importance in the history of linguistics.

He and others later determined that the stones and tablets were written in two different scripts. The oldest, called Linear A, dates from between 1800 and 1400 BCE, when the island was dominated by the Bronze Age Minoan civilization.

 The other script, Linear B, is more recent, appearing only after 1400 BCE, when the island was conquered by Mycenaeans from the Greek mainland.

Evans and others tried for many years to decipher the ancient scripts, but the lost languages resisted all attempts. The problem remained unsolved until 1953, when an amateur linguist named Michael Ventris cracked the code for Linear B.

His solution was built on two decisive breakthroughs. First, Ventris conjectured that many of the repeated words in the Linear B vocabulary were names of places on the island of Crete. That turned out to be correct.

His second breakthrough was to assume that the writing recorded an early form of ancient Greek. That insight immediately allowed him to decipher the rest of the language. In the process, Ventris showed that ancient Greek first appeared in written form many centuries earlier than previously thought.

Ventris’s work was a huge achievement. But the more ancient script, Linear A, has remained one of the great outstanding problems in linguistics to this day.

It’s not hard to imagine that recent advances in machine translation might help. In just a few years, the study of linguistics has been revolutionized by the availability of huge annotated databases, and techniques for getting machines to learn from them. Consequently, machine translation from one language to another has become routine. And although it isn’t perfect, these methods have provided an entirely new way to think about language.

Enter Jiaming Luo and Regina Barzilay from MIT and Yuan Cao from Google’s AI lab in Mountain View, California. This team has developed a machine-learning system capable of deciphering lost languages, and they’ve demonstrated it by having it decipher Linear B—the first time this has been done automatically. The approach they used was very different from the standard machine translation techniques.

First some background. The big idea behind machine translation is the understanding that words are related to each other in similar ways, regardless of the language involved.

Weaponized Autism: Fin d'Siecle Programmer's Stone

melmagazine |  We know that people on the spectrum can exhibit remarkable mental gifts in addition to their difficulties; Asperger syndrome has been associated with superior IQs that reach up to the “genius” threshold (4chan trolls use “aspie” and “autist” interchangeably). In practice, weaponized autism is best understood as a perversion of these hidden advantages. Think, for example, of the keen pattern recognition that underlies musical talent repurposed for doxxing efforts: Among the more “successful” deployments of weaponized autism, in the alt-right’s view, was a collective attempt to identify an antifa demonstrator who assaulted several of their own with a bike lock at a Berkeley rally this past April.

As Berkeleyside reported, “the amateur detectives” of 4chan’s /pol/ board went about “matching up his perceived height and hairline with photos of people at a previous rally and on social media,” ultimately claiming that Eric Clanton, a former professor at Diablo Valley College, was the assailant in question. Arrested and charged in May, Clanton faces a preliminary hearing this week, and has condemned the Berkeley PD for relying on the conjecture of random assholes. “My case threatens to set a new standard in which rightwing extremists can select targets for repression and have police enthusiastically and forcefully pursue them,” he wrote in a statement.

The denizens of /pol/, meanwhile, are terribly proud of their work, and fellow Trump boosters have used their platforms to applaud it. Conspiracy theorist Jack Posobiec called it a new form of “facial recognition,” as if it were in any way forensic, and lent credence to another dubious victory for the forces of weaponized autism: supposed coordination with the Russian government to take out ISIS camps in Syria. 4chan users are now routinely deconstructing raw videos of terrorist training sites and the like to make estimations about where they are, then sending those findings to the Russian Ministry of Defense’s Twitter account. There is zero reason to believe, as Posobiec and others contend, that 4chan has ever “called in an airstrike,” nor that Russia even bothered to look at the meager “intel” offered, yet the aggrandizing myth persists.

Since “autistic” has become a catchall idiom on 4chan, the self-defined mentality of anyone willing to spend time reading and contributing to the site, it’s impossible to know how many users are diagnosed with the condition, or could be, or earnestly believe that it correlates to their own experience, regardless of professional medical opinion. They tend to assume, at any rate, that autistic personalities are readily drawn to the board as introverted, societal misfits in search of connection. The badge of “autist” conveys the dueling attitudes of pride and loathing at work in troll communities: They may be considered and sometimes feel like failures offline — stereotyped as sexless, jobless and immature — but this is because they are different, transgressive, in a sense better, elevated from the realm of polite, neurotypical normies. Their handicap is a virtue.

Robert Mercer: Brainbug and Moneybag Behind the Trump Presidency

NewYorker |  In 1993, when Nick Patterson mailed Robert Mercer a job offer from Renaissance, Mercer threw it in the trash: he’d never heard of the hedge fund. At the time, Mercer was part of a team pioneering the use of computers to translate languages. I.B.M. considered the project a bit of a luxury, and didn’t see its potential, though the work laid the foundation for Google Translate and Apple’s Siri. But Mercer and his main partner, Peter Brown, found the project exciting, and had the satisfaction of showing up experts in the field, who had dismissed their statistical approach to translating languages as impractical. Instead of trying to teach a computer linguistic rules, Mercer and Brown downloaded enormous quantities of dual-language documents—including Canadian parliamentary records—and created code that analyzed the data and detected patterns, enabling predictions of probable translations. According to a former I.B.M. colleague, Mercer was obsessive, and at one point took six months off to type into a computer every entry in a Spanish-English dictionary. Sebastian Mallaby, in his 2010 book on the hedge-fund industry, “More Money Than God,” reports that Mercer’s boss at I.B.M. once jokingly called him an “automaton.”

In 2014, Mercer accepted a lifetime-achievement award from the Association for Computational Linguistics. In a speech at the ceremony, Mercer, who grew up in New Mexico, said that he had a “jaundiced view” of government. While in college, he had worked on a military base in Albuquerque, and he had showed his superiors how to run certain computer programs a hundred times faster; instead of saving time and money, the bureaucrats ran a hundred times more equations. He concluded that the goal of government officials was “not so much to get answers as to consume the computer budget.” Mercer’s colleagues say that he views the government as arrogant and inefficient, and believes that individuals need to be self-sufficient, and should not receive aid from the state. Yet, when I.B.M. failed to offer adequate support for Mercer and Brown’s translation project, they secured additional funding from DARPA, the secretive Pentagon program. Despite Mercer’s disdain for “big government,” this funding was essential to his early success.

Meanwhile, Patterson kept asking Mercer and Brown to join Renaissance. He thought that their technique of extracting patterns from huge amounts of data could be applied to the pile of numbers generated daily by the global trade in stocks, bonds, commodities, and currencies. The patterns could generate predictive financial models that would give traders a decisive edge.

the conscious mind and the photon interaction...,

Journey through the layers of the mind from Memo Akten on Vimeo.

guardian |  What do machines dream of? New images released by Google give us one potential answer: hypnotic landscapes of buildings, fountains and bridges merging into one.

The pictures, which veer from beautiful to terrifying, were created by the company’s image recognition neural network, which has been “taught” to identify features such as buildings, animals and objects in photographs.

They were created by feeding a picture into the network, asking it to recognise a feature of it, and modify the picture to emphasise the feature it recognises. That modified picture is then fed back into the network, which is again tasked to recognise features and emphasise them, and so on. Eventually, the feedback loop modifies the picture beyond all recognition.

At a low level, the neural network might be tasked merely to detect the edges on an image. In that case, the picture becomes painterly, an effect that will be instantly familiar to anyone who has experience playing about with photoshop filters:

 The pictures are stunning, but they’re more than just for show. Neural networks are a common feature of machine learning: rather than explicitly programme a computer so that it knows how to recognise an image, the company feeds it images and lets it piece together the key features itself.

But that can result in software that is rather opaque. It’s difficult to know what features the software is examining, and which it has overlooked. Fist tap John Kurman.

Deep Dream - a code example for visualizing neural networks

Inceptionism Going Deeper into Neural Networks

The Limits of Western Mentality REDUX [originally posted 10/27/07]

The Conscious Mind is Fitted to the Photon Interaction

What is normally referred to as the "conscious, thinking mind" is simply a functioning temporal (rigorously, chronotopological) mechanism that is painfully built up in the individual's awareness (his mind in the greater sense of both thought and awareness, whether monocular or multiocular) by training, conditioning and experience. Its functioning is largely conditioned by one's 90% or so attention to visual stimuli (to the partial reality remaining after photon interaction has been invoked, and to the memory-collated ordering of vast numbers of such photon interactions) and by one's cultural conditioning - which itself has been almost exclusively conditioned and shaped by the monocular photon interaction at base root.

Thus, since the beginning of man, (Bearden radically overstates the case here. It would be more accurate to say that since a time definite in the western epoch) his conscious, rational mind has been trained and constructed to function almost exclusively in basic correspondence with the photon interaction, and his experiential reality consists of the partial reality stripped from fundamental reality by photon interaction.

All "perceived differences," e.g., are created by this deep mind-set. As has been previously pointed out, ⁶ the solitary human problem responsible for all man's inhumanity to his fellow man is directly dependent upon man's almost exclusive detection, observation, perception, and conception of "difference" between humans, these "differences" being due exclusively and totally to the fitting of men's conscious minds to the photon interaction's monocular separation of spatial reality from nonspatial reality, i.e., to

∂/∂T (L³T) => L³

Such well-nigh total devotion to, and enslavement by, photon interaction also is responsible for the scientist's well-nigh total devotion to, and enslavement by, the present imperfect and incomplete three laws of logic, as presented by Aristotle. The depth of that devotion and enslavement is evidenced by the fact that the resolution of such paradoxes as Heraclitus's problem of change have eluded the best minds of humanity for several thousands of years. Indeed, these paradoxes cannot be resolved by the conscious, rational mind in its present state, for it has been most firmly constructed and fitted to function in accordance with the photon interaction.⁷ One cannot hope to resolve any logical paradox by using only those same logical methods that found the situation to be paradoxical in the first place!