oops...,

antimedia |  Oxitec first unveiled its large-scale, genetically-modified mosquito farm in Brazil in July 2012, with the goal of reducing “the incidence of dengue fever,” as The Disease Daily reported. Dengue fever is spread by the same Aedes mosquitoes which spread the Zika virus — and though they “cannot fly more than 400 meters,” WHO stated, “it may inadvertently be transported by humans from one place to another.” By July 2015, shortly after the GM mosquitoes were first released into the wild in Juazeiro, Brazil, Oxitec proudly announced they had “successfully controlled the Aedes aegypti mosquito that spreads dengue fever, chikungunya and zika virus, by reducing the target population by more than 90%.”

Though that might sound like an astounding success — and, arguably, it was — there is an alarming possibility to consider.

Nature, as one Redditor keenly pointed out, finds a way — and the effort to control dengue, zika, and other viruses, appears to have backfired dramatically.

The particular strain of Oxitec GM mosquitoes, OX513A, are genetically altered so the vast majority of their offspring will die before they mature — though Dr. Ricarda Steinbrecher published concerns in a report in September 2010 that a known survival rate of 3-4 percent warranted further study before the release of the GM insects. Her concerns, which were echoed by several other scientists both at the time and since, appear to have been ignored — though they should not have been.

Those genetically-modified mosquitoes work to control wild, potentially disease-carrying populations in a very specific manner. Only the male modified Aedes mosquitoes are supposed to be released into the wild — as they will mate with their unaltered female counterparts. Once offspring are produced, the modified, scientific facet is supposed to ‘kick in’ and kill that larvae before it reaches breeding age — if tetracycline is not present during its development. But there is a problem.

Aedes aegypti mosquito. Image credit: Muhammad Mahdi Karim

According to an unclassified document from the Trade and Agriculture Directorate Committee for Agriculture dated February 2015, Brazil is the third largest in “global antimicrobial consumption in food animal production” — meaning, Brazil is third in the world for its use of tetracycline in its food animals. As a study by the American Society of Agronomy, et. al., explained, “It is estimated that approximately 75% of antibiotics are not absorbed by animals and are excreted in waste.” One of the antibiotics (or antimicrobials) specifically named in that report for its environmental persistence is tetracycline.

In fact, as a confidential internal Oxitec document divulged in 2012, that survival rate could be as high as 15% — even with low levels of tetracycline present. “Even small amounts of tetracycline can repress” the engineered lethality. Indeed, that 15% survival rate was described by Oxitec:

“After a lot of testing and comparing experimental design, it was found that [researchers] had used a cat food to feed the [OX513A] larvae and this cat food contained chicken. It is known that tetracycline is routinely used to prevent infections in chickens, especially in the cheap, mass produced, chicken used for animal food. The chicken is heat-treated before being used, but this does not remove all the tetracycline. This meant that a small amount of tetracycline was being added from the food to the larvae and repressing the [designed] lethal system.”

Even absent this tetracycline, as Steinbrecher explained, a “sub-population” of genetically-modified Aedes mosquitoes could theoretically develop and thrive, in theory, “capable of surviving and flourishing despite any further” releases of ‘pure’ GM mosquitoes which still have that gene intact. She added, “the effectiveness of the system also depends on the [genetically-designed] late onset of the lethality. If the time of onset is altered due to environmental conditions … then a 3-4% [survival rate] represents a much bigger problem…”

teensy band-aid proposals can't close the gaping chasm in the american body politic...,

billmoyers |  The answer to the problem of money in politics is political change. We need a Supreme Court that will accept political equality as a compelling interest that justifies reasonable campaign regulations, and to build the jurisprudence necessary for a new progressive Supreme Court. That Court cannot come until after the retirement of four older justices currently sitting on it, which would open up the potential for a new progressive majority.

This book sets out new thinking about how to rescue our politics from plutocracy. We need to move beyond a partisan world in which Chris Christie bows before Sheldon Adelson and in which Democrats propose futile amendments to “overturn Citizens United” while engaging in fundraising practices similar to Republicans. We should think through these issues before American democracy is too far skewed toward the interests of the wealthy, in the hope that some future Supreme Court proves willing to accept reasonable limits on money in politics. This book offers a way to advance the goal of political equality to resolve the inevitable tension between free economic markets and voter equality.  Fist tap Arnach.

why doesn't granny goodness' staggering mendacity matter?

WaPo |  Calling someone dishonest is one of the most serious political insults in the United States. The country has been obsessed with its politicians' honesty at least since President George Washington's first biographer popularized the tale of him hacking at the trunk of a cherry tree. "I cannot tell a lie," a young Washington supposedly said when confronted about the damage.

Now, with less than a week until the Iowa caucuses and with Bernie Sanders advancing in the polls, Hillary Clinton still hasn't been able to clear away the accusations of dishonesty that have clouded her campaign. At the Democratic presidential town hall on Monday, a Sanders supporter noted that it's a reason Clinton has struggled to attract young voters: "I've heard from quite a few people my age that they think you're dishonest," he said.

Here's the thing, though: There was no cherry tree. Washington's biographer apparently fabricated it. "The great founding myth of American political integrity, chopping down the cherry tree, is, in fact, itself a lie," said Martin Jay, a historian at the University of California at Berkeley and author of a book called "The Virtues of Mendacity."

That's the real lesson of the tale of Washington's cherry tree: Americans might just be overly attached to the ideal of a scrupulously honest president. Especially at a time of intense polarization in Congress, recent experience suggests that the direction of public policy will have little to do with whether the Oval Office's next occupant really believes what he or she says on the campaign trail.

"It's necessary, in politics, to have a certain willingness to bend the truth," Jay said. "You're not electing the pope."

how the GOP's dishonesty led to the rise of trump and cruz?

WaPo |  To understand why the current conservative crack-up so confounds the Republican establishment, you have to recognize that the party is facing two separate but simultaneous revolts: one led by Ted Cruz, the other by Donald Trump.

The first is well described by E.J. Dionne Jr. in his important new book, “Why the Right Went Wrong.” For six decades, he explains, conservatives promised their voters that they were going to roll back big government. In the 1950s and early ’60s, they ran against the New Deal (Social Security). Then they railed against the Great Society (Medicare). Today it is Obamacare.

But they never actually did anything. Despite nominating Goldwater and electing Nixon, Reagan and two Bushes, despite a congressional revolution led by Newt Gingrich, these programs endured, and new ones were created.

The simple reason for this is that while Americans might oppose the welfare state in theory, in practice they like it. And the bulk of government spending is on the middle class, not the poor. Social Security and Medicare take up more than twice as much of the federal budget as all non-defense discretionary spending . One middle-class tax exemption — for employer-based health care — costs the federal government more than three times the total for the food stamp program.

Whatever the reality, Republicans kept promising something to their base but never delivered. This has led to what Dionne calls the “great betrayal.” Party activists are enraged, feel hoodwinked and view those in Washington as a bunch of corrupt compromisers. They want someone who will finally deliver on the promise of repeal and rollback.

Enter Cruz. How did a first-term senator, despised within his party both in Washington and Texas, get so far so fast? By promising to take on the party elites and finally throttle big government. Cruz has said that he will repeal Obamacare, abolish the IRS and propose a constitutional amendment to balance the budget — which would mean hundreds of billions of dollars in spending cuts.

Trump’s supporters, on the other hand, are old-fashioned economic liberals. In a powerful analysis, drawing on recent survey data from the Rand Corp., Michael Tesler shows that the Trump voter is very different from the Cruz voter. “Cruz outperforms Trump by about 15 percentage points among the most economically conservative Republicans,” he writes. “But Cruz loses to Trump by over 30 points among the quarter of Republicans who hold progressive positions on health care, taxes, the minimum wage and unions.” Trump is well aware of this fact, which explains why he has said repeatedly he won’t touch Social Security or Medicare, spoke fondly of the Canadian single-payer system, denounces high chief executive salaries, promises to build infrastructure and opposes free-trade deals.

a hole in my bucket and firing line in the coming civil war...,

archdruid | And that, dear reader, is where Donald Trump comes in.

The man is brilliant. I mean that without the smallest trace of mockery. He’s figured out that the most effective way to get the wage class to rally to his banner is to get himself attacked, with the usual sort of shrill mockery, by the salary class. The man’s worth several billion dollars—do you really think he can’t afford to get the kind of hairstyle that the salary class finds acceptable? Of course he can; he’s deliberately chosen otherwise, because he knows that every time some privileged buffoon in the media or on the internet trots out another round of insults directed at his failure to conform to salary class ideas of fashion, another hundred thousand wage class voters recall the endless sneering putdowns they’ve experienced from the salary class and think, “Trump’s one of us.”

The identical logic governs his deliberate flouting of the current rules of acceptable political discourse. Have you noticed that every time Trump says something that sends the pundits into a swivet, and the media starts trying to convince itself and its listeners that this time he’s gone too far and his campaign will surely collapse in humiliation, his poll numbers go up?  What he’s saying is exactly the sort of thing that you’ll hear people say in working class taverns and bowling alleys when subjects such as illegal immigration and Muslim jihadi terrorism come up for discussion. The shrieks of the media simply confirm, in the minds of the wage class voters to whom his appeal is aimed, that he’s one of them, an ordinary Joe with sensible ideas who’s being dissed by the suits.

Notice also how many of Trump’s unacceptable-to-the-pundits comments have focused with laser precision on the issue of immigration. That’s a well-chosen opening wedge, as cutting off illegal immigration is something that the GOP has claimed to support for a while now. As Trump broadens his lead, in turn, he’s started to talk about the other side of the equation—the offshoring of jobs—as his recent jab at Apple’s overseas sweatshops shows. The mainstream media’s response to that jab does a fine job of proving the case argued above: “If smartphones were made in the US, we’d have to pay more for them!” And of course that’s true: the salary class will have to pay more for its toys if the wage class is going to have decent jobs that pay enough to support a family. That this is unthinkable for so many people in the salary class—that they’re perfectly happy allowing their electronics to be made for starvation wages in an assortment of overseas hellholes, so long as this keeps the price down—may help explain the boiling cauldron of resentment into which Trump is so efficiently tapping.

It’s by no means certain that Trump will ride that resentment straight to the White House, though at this moment it does seem like the most likely outcome. Still, I trust none of my readers are naive enough to think that a Trump defeat will mean the end of the phenomenon that’s lifted him to front runner status in the teeth of everything the political establishment can throw at him. I see the Trump candidacy as a major watershed in American political life, the point at which the wage class—the largest class of American voters, please note—has begun to wake up to its potential power and begin pushing back against the ascendancy of the salary class.

a crazy establishment demands sanity...,

consortiumnews |  A “sane” Establishment, one that truly cared about the interests of the American people, would have undertaken a serious self-examination after the Iraq War. Yet, there was none. Rather than cleaning house and banishing the neocons and liberal interventionists to the farthest reaches of national power, the Establishment rewarded these warmongers, ceding to them near-total control of American foreign policy thinking.

If anything, the neocons and liberal hawks consolidated their power after the Iraq War. By contrast, the foreign policy “realists” and anti-war progressives who warned against the invasion were the ones cast out of any positions of influence. How crazy is that!

It was as if supporting the Iraq War was the new initiation rite to join the Establishment’s elite fraternity of worthies, a kind of upside-down application of rewards and punishments that would only make sense at the Mad Hatter’s tea party in Alice’s Wonderland.

In a sane world, the publishers of The New York Times and The Washington Post would have purged their lead editorial writers who had advocated for the catastrophe. Instead, the Post retained its neocon editorial page editor Fred Hiatt – and nearly all of its pro-war columnists – and the Times even promoted liberal interventionist Bill Keller to the top job of executive editorafter it became clear that he had been snookered about Iraq’s WMD.

Similar patterns were followed across the board, from The New Yorker on the Left to The Wall Street Journal on the Right. Pro-Iraq War writers and commentators continued on as if nothing untoward had happened. They remained the media big shots, rewarded with book contracts and TV appearances.

The same held true for the major think tanks. Instead of dumping neocons, the center-left Brookings Institution went off in search of neocon A-listers to sign, like Robert Kagan, a co-founder of the Project for the New American Century. The ultra-Establishment Council on Foreign Relations recruited its own neocon “stars,” Max Boot and Elliott Abrams.

And what did this year’s “sane” presidential candidates do as the deadly and dangerous consequences of neocon thinking spread from the Middle East into Europe? They pledged fealty to more neocon strategies. For instance, Establishment favorite, Sen. Marco Rubio, is advocating more “regime change” tough talk and more expansion of U.S. military power.

putin calls out an obamamandian big lie and nobody reports it...,

ICH |  Russian President Vladimir Putin said that in an interview (part 1 and part 2) with Germany’s Das Bild, published on January 11th, but the press has ignored this very serious charge – an accusation of deception, bad faith, outright lying, on the part of the US President.

Here is the historical record:

On 17 September 2009, the White House issued «Remarks by the President on Strengthening Missile Defense in Europe». He said there: «This new ballistic missile defense program will best address the threat posed by Iran's ongoing ballistic missile defense program… Our clear and consistent focus has been the threat posed by Iran's ballistic missile program, and that continues to be our focus and the basis of the program that we're announcing today».

All the rest of Putin’s account is likewise entirely accurate.

US President Barack Obama lied, about a very important matter. The people calling him out on it should be more than just the Russian President. This is an issue – World War III – that affects the entire world.

There is no issue that’s more serious than this. But, where is the press on it?

Only one English-language site carried Bild’s English-language translation – the rabidly anti-Russian Business Insider, which was founded by the Wall Street fraudster, Henry Blodget. Wikipedia says of him: «In 2002, then New York State Attorney General Eliot Spitzer published Merrill Lynch e-mails in which Blodget gave assessments about stocks which allegedly conflicted with what was publicly published. In 2003, he was charged with civil securities fraud by the US Securities and Exchange Commission. He agreed to a permanent ban from the securities industry and paid a $2 million fine plus a $2 million disgorgement». His site’s headline for this article was «Putin defends Russia's recent aggression, blames US and Europe for rising tensions». The headline for the same article at Bild was: «PUTIN THE INTERVIEW: ‘For me, it is not borders that matter.’»

The Blodget site posted five reader-comments to the interview: all were rabidly anti-Putin, such as, «The man is a Machiavellian sociopath. Just what Russia has always had, and probably needs»; nothing was commented about the lie that Putin alleged to have been asserted by Obama. All of them ignored it.

In other words, there was no press-coverage of the US President’s lie. Obama had asserted a globally mega-important lie, which displays Obama’s building up toward a war between the United States and Russia, and doing it under the false pretext that the US is preparing for a war against Iran (instead of for a nuclear WWIII against what is actually the world’s other major nuclear power, Russia – and while constantly demonizing and lying about Russia’s leader); Putin called the US President on that single mega-lie, and the press ignored this entire mega-important matter, which could end in global annihilation.

What kind of ‘press’ is this? What kind of ‘democracy’ is this?

It’s even worse than that.

we don't need no stinkin aliens....,

pqhr |  Why would someone go to the time and expense of trying to build a Quantum Neural Network (QNN)?  In other words, even if it is possible to do so, what is it useful for?

 1. You can run a quantum Turing machine on a QNN.  A quantum Turing machine is the basis for quantum computing (QC).  One thing we know QC can do is easily and rapidly crack public key cryptography.  Anyone with access to a QC can read other people's (encrypted) mail, which is the primary purpose of several Government agencies.  No other reason is required, from the perspective of e.g. NSA, to spend billions trying to build one, even if the chance of success is quite low.  This works best if the QC remains a secret, because QC can not crack encryption done the old-fashioned way: shared secret keys, transmitted offline, and synchronous encryption.

2.  A whole host of new technologies can be derived from a QNN.  A QNN is, in fact, a new General Purpose Technology, a technology that enables other new technologies.  Some examples of other General Purpose Technologies include: fire, agriculture, combustion engine, electricity, radio, chemistry, mechanization.  This author suspects this factor was not a consideration by the people who may have funded this project, as it is not at all obvious that QNN is a new General Purpose Technology.  It is, though.  Below are some new technologies enabled by practical QNN technology.

3.  A QNN can be the basis of Advanced Artificial Intelligence.  A QNN is a physical-system starting point for artificial brain technology.  This has many valuable and important uses in both military and civilian life.  Advanced AI enables many new technologies.  E.g.  self-driving vehicles, Jeopardy-winning computers, automatic language translation, extreme data compression rates,  improved data-mining, digital personal assistance, et cetera.

4.  A QNN can provide a secret communications system.  The system provides a quantum channel that effectively teleports information from Alice directly to Bob.  Alice and Bob would each need to be near a Node of the QNN, using a conventional computer with an oracular connection to the QNN.  There must be a (steganographic) classical back channel.  This system is probably quite scale-able.  This system could be used to securely distribute cryptographic keys for symmetric cryptography (e.g. AES), which can not be broken by any known algorithm, quantum or classical.

5.  A QNN might behave as a room temperature superconductor, but only for very tiny currents.

6.  A QNN would be a great starting basis for adiabatic (reversible) computing.  This would be one way to overcome an expected impending quantum limit to Moore's Law.

7.  QNN technology provides an excellent basis for developing, implementing, and securing advanced nanotechnology.

8.  QNN technology might be helpful in the field of Energy Resources, in several different ways.

9.    A QNN could be trained to drastically improve the effectiveness and range of quantum teleportation. The current 'official' range limit for quantum teleportation is about 16,000 meters.  This author supposes that QNN-enhanced quantum teleportation has a much greater range, probably enough range to reach satellites in orbit.

no help with my IQ-200+ wild goose chase hair, so I'll wrap it up with this superb survey...,

imperialcollegelondon |  The intention of this review was to present a limited number of quantum neural network models as impartially as possible, with sufficient background material to be accessible to anyone with a good understanding of quantum mechanics. Four models have been examined and their properties discussed, with results of simulations by the authors given with their interpretations. In three models simulations were performed to test their effectiveness at standard neural network problems, and in each case the authors report their models of QNNs performing better in some cases than comparable classical neural networks. 

It was the original intention of this dissertation, which the scope of necessary work prevented, to produce a much more comprehensive review of QNNs and research into them. In particular, several models exist which could not be studied here, including those described in work by Kak [26], Peruˇs [24], Zak and Williams [25], and Gupta and Zia [27], and in further discussions by Ventura et al. EzhovVentura00b, Ventura00b, Ezhov [30], and many others. In those models discussed, the review has aimed chiefly to summarize the authors’ working and to report their claims for the model’s properties, without commenting on further physical considerations which may cause difficulty for the model. A deeper study of neural networks, including the operation of self-organizing neural networks which through unsupervised learning can discover clusters of data independently, is a possible extension in foundational material, as one of the possible applications proposed by authors of QNNs not covered here.

None of the models discussed make extensive use of results in quantum information theory, the most obvious being entanglement. Ventura and Martinez claim [7] to use entanglement to maintain connections in their model, but this is not described or mentioned in their most extensive paper on the subject [23]. It is possible that as many of the authors cited are primarily experts in neural networks, they may have misused results in quantum theory in constructing their models, particularly on the wavefunction collapse, the no-cloning theorem, and the degree to which quantum parallelism may be exploited; the author has chosen to err on the side of caution by not attempting to find such errors, leaving that for an extension of this review after further study. 

As a prognosis for the field of QNNs, this review is perhaps not promising, but strictly incomplete and inconclusive. It is hoped that this review could form a useful base for further study into quantum neural networks by familiarizing the reader with existing models, which they may build upon or use as inspiration for another model. It should be noted that most papers cited above date from 1996 to 2001, after which most authors appeared to discontinue study, or at any rate ceased to publish on the topic. Whether this is because of unpublished negative results, a loss of personal motivation, issues with funding, or any other reason is not known, but it opens a conspicuous area for possible study given an additional ten years of rapid research into quantum information theory.

them deep Q-networks though...,

wikipedia |  Q-learning is a model-free reinforcement learning technique. Specifically, Q-learning can be used to find an optimal action-selection policy for any given (finite) Markov decision process (MDP). It works by learning an action-value function that ultimately gives the expected utility of taking a given action in a given state and following the optimal policy thereafter. A policy is a rule that the agent follows in selecting actions, given the state it is in. When such an action-value function is learned, the optimal policy can be constructed by simply selecting the action with the highest value in each state. One of the strengths of Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment. Additionally, Q-learning can handle problems with stochastic transitions and rewards, without requiring any adaptations. It has been proven that for any finite MDP, Q-learning eventually finds an optimal policy, in the sense that the expected value of the total reward return over all successive steps, starting from the current state, is the maximum achievable.

Delayed Q-learning is an alternative implementation of the online Q-learning algorithm, with Probably approximately correct learning (PAC).^[11]

Because the maximum approximated action value is used in the Q-learning update, in noisy environments Q-learning can sometimes overestimate the actions values, slowing the learning. A recent variant called Double Q-learning was proposed to correct this. ^[12]

Greedy GQ is a variant of Q-learning to use in combination with (linear) function approximation.^[13] The advantage of Greedy GQ is that convergence guarantees can be given even when function approximation is used to estimate the action values.

Q-learning may suffer from slow rate of convergence, especially when the discount factor  is close to one.^[14] Speedy Q-learning, a new variant of Q-learning algorithm, deals with this problem and achieves a provably same rate of convergence as model-based methods such as value iteration.^[15]

A recent application of Q-learning to deep learning, by Google DeepMind, titled "deep reinforcement learning" or "deep Q-networks", has been successful at playing some Atari 2600 games at expert human levels. Preliminary results were presented in 2014, with a paper published in February 2015 in Nature.^[16]

a quantum associative memory based on grover's algorithm?

arvix |  This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce such a quantum associative memory. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. The paper covers necessary high-level quantum mechanical and quantum computational ideas and introduces a quantum associative memory. Theoretical analysis proves the utility of the memory, and it is noted that a small version should be physically realizable in the near future.

searching a database?

wikipedia |  Grover's algorithm is a quantum algorithm that finds with high probability the unique input to a black box function that produces a particular output value, using just O(N^1/2) evaluations of the function, where N is the size of the function's domain.

The analogous problem in classical computation cannot be solved in fewer than O(N) evaluations (because, in the worst case, the Nth member of the domain might be the correct member). At roughly the same time that Grover published his algorithm, Bennett, Bernstein, Brassard, and Vazirani published a proof that no quantum solution to the problem can evaluate the function fewer than O(N^1/2) times, so Grover's algorithm is asymptotically optimal.^[1]

Unlike other quantum algorithms, which may provide exponential speedup over their classical counterparts, Grover's algorithm provides only a quadratic speedup. However, even quadratic speedup is considerable when N is large. Grover's algorithm could brute force a 128-bit symmetric cryptographic key in roughly 2⁶⁴ iterations, or a 256-bit key in roughly 2¹²⁸ iterations. As a result, it is sometimes suggested that symmetric key lengths be doubled to protect against future quantum attacks.

Like many quantum algorithms, Grover's algorithm is probabilistic in the sense that it gives the correct answer with a probability of less than 1. Though there is technically no upper bound on the number of repetitions that might be needed before the correct answer is obtained, the expected number of repetitions is a constant factor that does not grow with N.

Grover's original paper described the algorithm as a database search algorithm, and this description is still common. The database in this analogy is a table of all of the function's outputs, indexed by the corresponding input.

ironically enough...,

NYTimes |  Marvin Lee Minsky was born on Aug. 9, 1927, in New York City. The precocious son of Dr. Henry Minsky, an eye surgeon who was chief of ophthalmology at Mount Sinai Hospital, and Fannie Reiser, a social activist and Zionist.

Fascinated by electronics and science, the young Mr. Minsky attended the Ethical Culture School in Manhattan, a progressive private school from which J. Robert Oppenheimer, who oversaw the creation of the first atomic bomb, had graduated. (Mr. Minsky later attended the affiliated Fieldston School in Riverdale.) He went on to attend the Bronx High School of Science and later Phillips Academy in Andover, Mass.

After a stint in the Navy during World War II, he studied mathematics at Harvard and received a Ph.D. in math from Princeton, where he met John McCarthy, a fellow graduate student.

Intellectually restless throughout his life, Professor Minsky sought to move on from mathematics once he had earned his doctorate. After ruling out genetics as interesting but not profound, and physics as mildly enticing, he chose to focus on intelligence itself.

“The problem of intelligence seemed hopelessly profound,” he told The New Yorker magazine when it profiled him in 1981. “I can’t remember considering anything else worth doing.”

To further those studies he reunited with Professor McCarthy, who had been awarded a fellowship to M.I.T. in 1956. Professor Minsky, who had been at Harvard by then, arrived at M.I.T. in 1958, joining the staff at its Lincoln Laboratory. A year later, he and Professor McCarthy founded M.I.T.’s AI Project, later to be known as the AI Lab. (Professor McCarthy left for Stanford in 1962.)

Professor Minsky’s courses at M.I.T. — he insisted on holding them in the evenings — became a magnet for several generations of graduate students, many of whom went on to become computer science superstars themselves.

Mr. Hillis said he had so been taken by Professor Minsky’s intellect and charisma that he found a way to insinuate himself into the AI Lab and get a job there. He ended up living in the Minsky family basement in Brookline, Mass.

Among them were Ray Kurzweil, the inventor and futurist; Gerald Sussman, a prominent A.I. researcher and professor of electrical engineering at M.I.T.; and Patrick Winston, who went on to run the AI Lab after Professor Minsky stepped aside.

Another of his students, Danny Hillis, an inventor and entrepreneur, co-founded Thinking Machines, a supercomputer maker in the early 1990s.

supertools?- quantum computer science so frosty, they ain't even tryna tell you what he's up to...,

iarpa |  Mathematics (algebra, number theory and algebraic geometry), cryptology, quantum information science (theoretical and experimental), computer science-theory of algorithms, symbolic computation, complexity theory, machine learning, computer-human interfaces, scientometrics, geology

The biggest breakthrough would be if NSA could build a quantum computer that could implement Shor's algorithm using the Quantum Fourier Transform (QFT). This breaks RSA and Diffie-Hellman. The quantum research program at NSA used to be headed up by Mark Heiligman who is now at IARPA:

RAVEN	Microelectronics, nondestructive analysis, nanoscale imaging, hardware assurance	Dr. Carl McCants
SCITE	Engineering enterprises that detect low probability events with low accuracy sensors, innovative research methods to evaluate analytic and forecasting tradecraft, innovative statistical methods to estimate performance of systems addressing complex analysis and forecasting problems, scientific research on organizational lessons-learned methods, evidence-based forecasting methods, inductive logic, probabilistic reasoning and its application to analytic tradecraft	Dr. Paul Lehner
SHARP	Cognition, psychometrics, fluid reasoning and intelligence, neuroscience, human performance	Dr. Alexis Jeannotte
SILMARILS	Chemical detection and identification (including standoff, remote, and ultra-compact/low power approaches), spectroscopy/spectrometry/chromatography, optical sensors, novel laser designs, frequency combs, nonlinear optics, fiber optic sensors/lasers/devices	Dr. Kristy DeWitt
SLiCE	Communication systems, geolocation, electromagnetics, radio frequency	Dr. Chris Reed
SuperTools		Dr. Mark Heiligman
TIC	Cybersecurity and information assurance, hardware assurance, microelectronics	Dr. Carl McCants
TRUST	Interpersonal trust, neurophysiology, behavioral science, advanced data analytics, social science	Dr. Alexis Jeannotte

quantum computation, quantum theory, and AI

sciencedirect |  The main purpose of this paper is to examine some (potential) applications of quantum computation in AI and to review the interplay between quantum theory and AI. For the readers who are not familiar with quantum computation, a brief introduction to it is provided, and a famous but simple quantum algorithm is introduced so that they can appreciate the power of quantum computation. Also, a (quite personal) survey of quantum computation is presented in order to give the readers a (unbalanced) panorama of the field. The author hopes that this paper will be a useful map for AI researchers who are going to explore further and deeper connections between AI and quantum computation as well as quantum theory although some parts of the map are very rough and other parts are empty, and waiting for the readers to fill in.

let's play what if?

What if during the 1990s Stuart Kauffman was involved in classified research for DARPA to develop, via synthetic biology techniques, a physical quantum neural network capable of performing quantum computations (e.g. Shor's Algorithm) suited for cracking public key cryptography?

What if the basic substrate was anyon quasiparticles interacting in a two-dimensional electron gas 2DEG , specifically a 2DEG environment in the common Field Effect Transistor?

What if the result of this project was a highly successful form of advanced nanotechnology best described as a winner-take-all-style of teleportation-based recurrent topological quantum neural network which could then be trained and optimized to solve particular problem sets?

What if this hypothetical new technology (only available to NSA, GCHQ, etc) was the real reason for President Bill Clinton's 1996 Executive Order 13026, legalizing export of the mathematical concepts behind public key cryptography?

What if some of the project scientists realized, to their great surprise and horror, that their new technology posed an existential threat to humanity comparable to the invention of atomic weapons?

What if some of the project scientists therefore formed a secret cabal to manipulate events in a way that *forced* humanity, including all governments, to forever relinquish control of this technology?

What if the story of just how they accomplished this has not yet been told, and will cause great consternation (huge understatement!) when it is finally understood?

What if in order to protect their (probably posthumous) reputations, members of the cabal published an essay explaining why they had done what they did?

Kauffman is into his seventies. While certainly forbidden to speak honestly about this work due to ironclad Non-Disclosure Agreements, what if he tried by sneaking past his censors a paragraph at the end of Chapter 8 of the first hardcover edition of *At Home In the Universe* that explicitly describes a global-scale teleportation/entanglement quantum neural network, and that this paragraph was forcibly redacted in all future published versions of this book?

Other scientists probably involved in this project include Steven Wolfram  (probably project leader), Brosl Hasslacher, possibly the great Murray Gell-Mann at an early stage before his 1991 ailment rendered him unable to participate, and possibly Danny Hillis.

order for free?

edge |  What kinds of complex systems can evolve by accumulation of successive useful variations? Does selection by itself achieve complex systems able to adapt? Are there lawful properties characterizing such complex systems? The overall answer may be that complex systems constructed so that they're on the boundary between order and chaos are those best able to adapt by mutation and selection.

Chaos is a subset of complexity. It's an analysis of the behavior of continuous dynamical systems — like hydrodynamic systems, or the weather — or discrete systems that show recurrences of features and high sensitivity to initial conditions, such that very small changes in the initial conditions can lead a system to behave in very different ways. A good example of this is the so called butterfly effect: the idea is that a butterfly in Rio can change the weather in Chicago. An infinitesimal change in initial conditions leads to divergent pathways in the evolution of the system. Those pathways are called trajectories. The enormous puzzle is the following: in order for life to have evolved, it can't possibly be the case that trajectories are always diverging. Biological systems can't work if divergence is all that's going on. You have to ask what kinds of complex systems can accumulate useful variation.

We've discovered the fact that in the evolution of life very complex systems can have convergent flow and not divergent flow. Divergent flow is sensitivity to initial conditions. Convergent flow means that even different starting places that are far apart come closer together. That's the fundamental principle of homeostasis, or stability to perturbation, and it's a natural feature of many complex systems. We haven't known that until now. That's what I found out twenty-five years ago, looking at what are now called Kauffman models — random networks exhibiting what I call "order for free."

Complex systems have evolved which may have learned to balance divergence and convergence, so that they're poised between chaos and order. Chris Langton has made this point, too. It's precisely those systems that can simultaneously perform the most complex tasks and evolve, in the sense that they can accumulate successive useful variations. The very ability to adapt is itself, I believe, the consequence of evolution. You have to be a certain kind of complex system to adapt, and you have to be a certain kind of complex system to coevolve with other complex systems. We have to understand what it means for complex systems to come to know one another — in the sense that when complex systems coevolve, each sets the conditions of success for the others. I suspect that there are emergent laws about how such complex systems work, so that, in a global, Gaia- like way, complex coevolving systems mutually get themselves to the edge of chaos, where they're poised in a balanced state. It's a very pretty idea. It may be right, too.

My approach to the coevolution of complex systems is my order-for-free theory. If you have a hundred thousand genes and you know that genes turn one another on and off, then there's some kind of circuitry among the hundred thousand genes. Each gene has regulatory inputs from other genes that turn it on and off. This was the puzzle: What kind of a system could have a hundred thousand genes turning one another on and off, yet evolve by creating new genes, new logic, and new connections?

Suppose we don't know much about such circuitry. Suppose all we know are such things as the number of genes, the number of genes that regulate each gene, the connectivity of the system, and something about the kind of rules by which genes turn one another on and off. My question was the following: Can you get something good and biology-like to happen even in randomly built networks with some sort of statistical connectivity properties? It can't be the case that it has to be very precise in order to work — I hoped, I bet, I intuited, I believed, on no good grounds whatsoever — but the research program tried to figure out if that might be true. The impulse was to find order for free. As it happens, I found it. And it's profound.

One reason it's profound is that if the dynamical systems that underlie life were inherently chaotic, then for cells and organisms to work at all there'd have to be an extraordinary amount of selection to get things to behave with reliability and regularity. It's not clear that natural selection could ever have gotten started without some preexisting order. You have to have a certain amount of order to select for improved variants.

self-organization and far from equalibrium dynamics

wikipedia |  Stuart Alan Kauffman (born September 28, 1939) is an American medical doctor, theoretical biologist, and complex systems researcher who studies the origin of life on Earth.

In 1967^[1] and 1969^[2] Kauffman proposed applying models of random boolean networks to simplified genetic circuits. These were very early models of large genetic regulatory networks, proposing that cell types are dynamical attractors of such networks and that cell differentiation steps are transitions between attractors. Recent evidence strongly suggests that cell types in humans and other organisms are indeed attractors.^[3] In 1971 he suggested that the zygote may not access all the cell type attractors in the repertoire of the genetic network's dynamics, hence some of the unused cell types might be cancers.^[4] This suggested the possibility of "cancer differentiation therapy".

In 1971, Kauffman proposed the self-organized emergence of collectively autocatalytic sets of polymers, specifically peptides, for the origin of molecular reproduction.^[5][6]Reproducing peptide, DNA, and RNA collectively autocatalytic sets have now been made experimentally.^[7][8] He is best known for arguing that the complexity of biological systems and organisms might result as much from self-organization and far-from-equilibrium dynamics as from Darwinian natural selection as discussed in his book Origins of Order (1993). His hypotheses stating that cell types are attractors of such networks, and that genetic regulatory networks are "critical", have found experimental support.^[3][9] It now appears that the brain is also dynamically critical.^[10]