If the freedom of speech is taken away then dumb and silent we may be led, like sheep to the slaughter.
- George Washington
- George Washington
Monday, July 31, 2017
Sunday, July 30, 2017
That Idiot's State of Mind & Ours
For some mental-health practitioners, the ethical debate is a distraction from a larger point. In a forthcoming book called “Twilight of American Sanity,” Allen Frances, a professor emeritus at Duke University Medical College, argues that the more urgent concern is unravelling the national psyche that brought our politics to this moment. Frances told me, “We need to be looking in the mirror to see what’s wrong with us that would allow someone who is so unsuitable for the Presidency to rise to the highest and most dangerous office in the world. Trump’s psychology is far too obvious to be interesting. You don’t have to be a psychoanalyst to understand Trump. He’s the most transparent human being who ever lived. Giving it a name doesn’t explain it or change it.”
As Trump rages in the White House, the country has settled into a summer numbness. The protests have grown less frequent; the country has escaped to the beach; the latest cell-phone push notifications from news apps no longer produce a skip of the heart. Diagnosing that sense of permission and paralysis is an urgent problem, Frances said. “The instruments for dealing with Trump are political,” he added. “Psychological name-calling is an impotent avoidance of our responsibility as citizens, and it represents a failure to try to get insight into us, which is much more important.”
- More Here
As Trump rages in the White House, the country has settled into a summer numbness. The protests have grown less frequent; the country has escaped to the beach; the latest cell-phone push notifications from news apps no longer produce a skip of the heart. Diagnosing that sense of permission and paralysis is an urgent problem, Frances said. “The instruments for dealing with Trump are political,” he added. “Psychological name-calling is an impotent avoidance of our responsibility as citizens, and it represents a failure to try to get insight into us, which is much more important.”
- More Here
Quote of the Day
Just remember that the things you put into your head are there forever, he said. You might want to think about that.
You forget some things, dont you?
Yes. You forget what you want to remember and you remember what you want to forget.
- Cormac McCarthy, The Road
You forget some things, dont you?
Yes. You forget what you want to remember and you remember what you want to forget.
- Cormac McCarthy, The Road
Saturday, July 29, 2017
Wisdom Of The Week
It was early June when I walked through those same doors, to see what was going on. The D.O.E. makes its home in a long rectangular cinder-block-like building propped up on concrete stilts, just off the National Mall. It’s a jarring sight—as if someone had punched out a skyscraper and it never got back on its feet. It’s relentlessly ugly in the way the swamps around Newark Airport are ugly—so ugly that its ugliness bends back around into a sneaky kind of beauty: it will make an excellent ruin. Inside, the place feels like a lab experiment to determine just how little aesthetic stimulation human beings can endure. The endless hallways are floored with white linoleum and almost insistently devoid of personality. “Like a hospital, without the stretchers,” as one employee put it. But this place is at once desolate and urgent. People still work here, doing stuff that, if left undone, might result in unimaginable death and destruction.
By the time I arrived the first eighth of Trump’s first term was nearly complete, and his administration was still, largely, missing. He hadn’t nominated anyone to serve as head of the Patent Office, for instance, or to run FEMA. There was no Trump candidate to head the T.S.A., or anyone to run the Centers for Disease Control. The 2020 national census will be a massive undertaking for which there is not a moment to lose and yet there’s no Trump appointee in place to run it. “The actual government has not really taken over,” says Max Stier. “It’s kindergarten soccer. Everyone is on the ball. No one is at their positions. But I doubt Trump sees the reality. Everywhere he goes everything is going to be hunky-dory and nice. No one gives him the bad news.”
At this point in their administrations Obama and Bush had nominated their top 10 people at the D.O.E. and installed most of them in their offices. Trump had nominated three people and installed just one, former Texas governor Rick Perry. Perry is of course responsible for one of the D.O.E.’s most famous moments—when in a 2011 presidential debate he said he intended to eliminate three entire departments of the federal government. Asked to list them he named Commerce, Education, and … then hit a wall. “The third agency of government I would do away with ... Education ... the … ahhhh … ahhh … Commerce, and let’s see.” As his eyes bored a hole in his lectern, his mind drew a blank. “I can’t, the third one. I can’t. Sorry. Oops.” The third department Perry wanted to get rid of, he later recalled, was the Department of Energy. In his confirmation hearings to run the department Perry confessed that when he called for its elimination he hadn’t actually known what the Department of Energy did—and he now regretted having said that it didn’t do anything worth doing.
The question on the minds of the people who currently work at the department: Does he know what it does now? D.O.E. press secretary Shaylyn Hynes assures us that “Secretary Perry is dedicated to the missions of the Department of Energy.” And in his hearings, Perry made a show of having educated himself. He said how useful it was to be briefed by former secretary Ernest Moniz. But when I asked someone familiar with those briefings how many hours Perry had spent with Moniz, he laughed and said, “That’s the wrong unit of account.” With the nuclear physicist who understood the D.O.E. perhaps better than anyone else on earth, according to one person familiar with the meeting, Perry had spent minutes, not hours. “He has no personal interest in understanding what we do and effecting change,” a D.O.E. staffer told me in June. “He’s never been briefed on a program—not a single one, which to me is shocking.”
Since Perry was confirmed, his role has been ceremonial and bizarre. He pops up in distant lands and tweets in praise of this or that D.O.E. program while his masters inside the White House create budgets to eliminate those very programs. His sporadic public communications have had in them something of the shell-shocked grandmother trying to preside over a pleasant family Thanksgiving dinner while pretending that her blind-drunk husband isn’t standing naked on the dining-room table waving the carving knife over his head.
[---]
What’s the second risk on your list?,” I ask.
“North Korea would be up there,” says MacWilliams.
Why do I, as an incoming official at the D.O.E., need to be worried about North Korea?
MacWilliams explains, patiently, that there lately have been signs that the risk of some kind of attack by North Korea is increasing. The missiles the North Koreans have been firing into the sea are not the absurd acts of a lunatic mind but experiments. Obviously, the D.O.E. is not the only agency inside the U.S. government trying to make sense of these experiments, but the people inside the national labs are the world’s most qualified to determine just what North Korea’s missiles can do. “For a variety of reasons the risk curve has changed,” says MacWilliams guardedly. “The risks of mistakes being made and lots of people being killed is increasing dramatically. It wouldn’t necessarily be a nuclear weapon they might deliver. It could be sarin gas.”
As he doesn’t want to go into further detail and maybe divulge information I am not cleared to hear, I press him to move on. “O.K., give me the third risk on your list.”
“This is in no particular order,” he says with remarkable patience. “But Iran is somewhere in the top five.” He’d watched Secretary Moniz help negotiate the deal that removed from Iran the capacity to acquire a nuclear weapon. There were only three paths to a nuclear weapon. The Iranians might produce enriched uranium—but that required using centrifuges. They might produce plutonium—but that required a reactor that the deal had dismantled and removed. Or they might simply go out and buy a weapon on the open market. The national labs played a big role in policing all three paths. “These labs are incredible national resources, and they are directly responsible for keeping us safe,” said MacWilliams. “It’s because of them that we can say with absolute certainty that Iran cannot surprise us with a nuclear weapon.” After the deal was done, U.S. Army officers had approached D.O.E. officials to thank them for saving American lives. The deal, they felt sure, had greatly lessened the chance of yet another war in the Middle East that the United States would inevitably be dragged into.
At any rate, the serious risk in Iran wasn’t that the Iranians would secretly acquire a weapon. It was that the president of the United States would not understand his nuclear scientists’ reasoning about the unlikelihood of the Iranians’ obtaining a weapon, and that he would have the United States back away foolishly from the deal. Released from the complicated set of restrictions on its nuclear-power program, Iran would then build its bomb. It wasn’t enough to have the world’s finest forensic nuclear physicists. Our political leaders needed to be predisposed to listen to them and equipped to understand what they say.
Yeah, well, never mind science—we’ll deal with Iran, I could hear some Trump person thinking to himself.
- More Here from Mike Lewis
By the time I arrived the first eighth of Trump’s first term was nearly complete, and his administration was still, largely, missing. He hadn’t nominated anyone to serve as head of the Patent Office, for instance, or to run FEMA. There was no Trump candidate to head the T.S.A., or anyone to run the Centers for Disease Control. The 2020 national census will be a massive undertaking for which there is not a moment to lose and yet there’s no Trump appointee in place to run it. “The actual government has not really taken over,” says Max Stier. “It’s kindergarten soccer. Everyone is on the ball. No one is at their positions. But I doubt Trump sees the reality. Everywhere he goes everything is going to be hunky-dory and nice. No one gives him the bad news.”
At this point in their administrations Obama and Bush had nominated their top 10 people at the D.O.E. and installed most of them in their offices. Trump had nominated three people and installed just one, former Texas governor Rick Perry. Perry is of course responsible for one of the D.O.E.’s most famous moments—when in a 2011 presidential debate he said he intended to eliminate three entire departments of the federal government. Asked to list them he named Commerce, Education, and … then hit a wall. “The third agency of government I would do away with ... Education ... the … ahhhh … ahhh … Commerce, and let’s see.” As his eyes bored a hole in his lectern, his mind drew a blank. “I can’t, the third one. I can’t. Sorry. Oops.” The third department Perry wanted to get rid of, he later recalled, was the Department of Energy. In his confirmation hearings to run the department Perry confessed that when he called for its elimination he hadn’t actually known what the Department of Energy did—and he now regretted having said that it didn’t do anything worth doing.
The question on the minds of the people who currently work at the department: Does he know what it does now? D.O.E. press secretary Shaylyn Hynes assures us that “Secretary Perry is dedicated to the missions of the Department of Energy.” And in his hearings, Perry made a show of having educated himself. He said how useful it was to be briefed by former secretary Ernest Moniz. But when I asked someone familiar with those briefings how many hours Perry had spent with Moniz, he laughed and said, “That’s the wrong unit of account.” With the nuclear physicist who understood the D.O.E. perhaps better than anyone else on earth, according to one person familiar with the meeting, Perry had spent minutes, not hours. “He has no personal interest in understanding what we do and effecting change,” a D.O.E. staffer told me in June. “He’s never been briefed on a program—not a single one, which to me is shocking.”
Since Perry was confirmed, his role has been ceremonial and bizarre. He pops up in distant lands and tweets in praise of this or that D.O.E. program while his masters inside the White House create budgets to eliminate those very programs. His sporadic public communications have had in them something of the shell-shocked grandmother trying to preside over a pleasant family Thanksgiving dinner while pretending that her blind-drunk husband isn’t standing naked on the dining-room table waving the carving knife over his head.
[---]
What’s the second risk on your list?,” I ask.
“North Korea would be up there,” says MacWilliams.
Why do I, as an incoming official at the D.O.E., need to be worried about North Korea?
MacWilliams explains, patiently, that there lately have been signs that the risk of some kind of attack by North Korea is increasing. The missiles the North Koreans have been firing into the sea are not the absurd acts of a lunatic mind but experiments. Obviously, the D.O.E. is not the only agency inside the U.S. government trying to make sense of these experiments, but the people inside the national labs are the world’s most qualified to determine just what North Korea’s missiles can do. “For a variety of reasons the risk curve has changed,” says MacWilliams guardedly. “The risks of mistakes being made and lots of people being killed is increasing dramatically. It wouldn’t necessarily be a nuclear weapon they might deliver. It could be sarin gas.”
As he doesn’t want to go into further detail and maybe divulge information I am not cleared to hear, I press him to move on. “O.K., give me the third risk on your list.”
“This is in no particular order,” he says with remarkable patience. “But Iran is somewhere in the top five.” He’d watched Secretary Moniz help negotiate the deal that removed from Iran the capacity to acquire a nuclear weapon. There were only three paths to a nuclear weapon. The Iranians might produce enriched uranium—but that required using centrifuges. They might produce plutonium—but that required a reactor that the deal had dismantled and removed. Or they might simply go out and buy a weapon on the open market. The national labs played a big role in policing all three paths. “These labs are incredible national resources, and they are directly responsible for keeping us safe,” said MacWilliams. “It’s because of them that we can say with absolute certainty that Iran cannot surprise us with a nuclear weapon.” After the deal was done, U.S. Army officers had approached D.O.E. officials to thank them for saving American lives. The deal, they felt sure, had greatly lessened the chance of yet another war in the Middle East that the United States would inevitably be dragged into.
At any rate, the serious risk in Iran wasn’t that the Iranians would secretly acquire a weapon. It was that the president of the United States would not understand his nuclear scientists’ reasoning about the unlikelihood of the Iranians’ obtaining a weapon, and that he would have the United States back away foolishly from the deal. Released from the complicated set of restrictions on its nuclear-power program, Iran would then build its bomb. It wasn’t enough to have the world’s finest forensic nuclear physicists. Our political leaders needed to be predisposed to listen to them and equipped to understand what they say.
Yeah, well, never mind science—we’ll deal with Iran, I could hear some Trump person thinking to himself.
- More Here from Mike Lewis
Friday, July 28, 2017
The Case for Cursing
You know when you stub your toe and involuntarily utter an expletive? You probably didn’t give it much thought, but you might have been on to something.
As children we’re taught that cursing, even when we’re in pain, is inappropriate, betrays a limited vocabulary or is somehow low class in that ambiguous way many cultural lessons suggest. But profanity serves a physiological, emotional and social purpose — and it’s effective only because it’s inappropriate.
“The paradox is that it’s that very act of suppression of the language that creates those same taboos for the next generation,” said Benjamin K. Bergen, author of “What the F: What Swearing Reveals About Our Language, Our Brains and Ourselves.” He calls this the “profanity paradox.”
“The reason that a child thinks the F-word is a bad word is that, growing up, he or she was told that it was a bad word, so profanity is a cultural construct that perpetuates itself through time,” said Dr. Bergen, a professor of cognitive science at the University of California, San Diego. “It’s an affliction of its own creation.”
Swearing and cursing are often used interchangeably, but there’s a subtle difference in their origins. A curse implies damning or punishing someone, while a swear word suggests blasphemy — invoking a deity to empower your words. For the sake of modern discussion, both words are defined as profanity: vulgar, socially unacceptable language you don’t use in polite conversation.
The paradox is that profane words are powerful only because we make them powerful. Without their being censored, all of the words we designate by a first letter and “-word” would just be average terms.
In “The Stuff of Thought,” Steven Pinker, a cognitive scientist and a professor at Harvard, listed a few functions of swearing. There’s emphatic swearing, for instance, which is meant to highlight a point, and dysphemistic swearing, which is meant to make a point provocatively.
But swearing is beneficial beyond making your language more colorful. It can also offer catharsis. A study co-authored by Richard Stephens, a senior lecturer in psychology at Keele University, found that swearing can increase your ability to withstand pain. So when you stub your toe and howl an expletive, it might help you tolerate the pain better.
- More Here
As children we’re taught that cursing, even when we’re in pain, is inappropriate, betrays a limited vocabulary or is somehow low class in that ambiguous way many cultural lessons suggest. But profanity serves a physiological, emotional and social purpose — and it’s effective only because it’s inappropriate.
“The paradox is that it’s that very act of suppression of the language that creates those same taboos for the next generation,” said Benjamin K. Bergen, author of “What the F: What Swearing Reveals About Our Language, Our Brains and Ourselves.” He calls this the “profanity paradox.”
“The reason that a child thinks the F-word is a bad word is that, growing up, he or she was told that it was a bad word, so profanity is a cultural construct that perpetuates itself through time,” said Dr. Bergen, a professor of cognitive science at the University of California, San Diego. “It’s an affliction of its own creation.”
Swearing and cursing are often used interchangeably, but there’s a subtle difference in their origins. A curse implies damning or punishing someone, while a swear word suggests blasphemy — invoking a deity to empower your words. For the sake of modern discussion, both words are defined as profanity: vulgar, socially unacceptable language you don’t use in polite conversation.
The paradox is that profane words are powerful only because we make them powerful. Without their being censored, all of the words we designate by a first letter and “-word” would just be average terms.
In “The Stuff of Thought,” Steven Pinker, a cognitive scientist and a professor at Harvard, listed a few functions of swearing. There’s emphatic swearing, for instance, which is meant to highlight a point, and dysphemistic swearing, which is meant to make a point provocatively.
But swearing is beneficial beyond making your language more colorful. It can also offer catharsis. A study co-authored by Richard Stephens, a senior lecturer in psychology at Keele University, found that swearing can increase your ability to withstand pain. So when you stub your toe and howl an expletive, it might help you tolerate the pain better.
- More Here
Paving the Future of Blockchain Technology
The advent of these massive mining pools and the consequent power bestowed on the organizations behind them really begin to challenge the idea of Bitcoin as a decentralized cryptocurrency while increasing the possibility of 51% attacks, which we’ll discuss later. In short, if the trend continues, we may reach a state where new bitcoin discovery is almost completely dominated by the largest miners.
Micali instead proposes Algorand, an incentive-less public blockchain that attacks the Byzantine Generals’ Problem by swapping out the generals in each round through a randomization process. While I won’t discuss the mechanics in detail here, this approach avoids the amount of computation resources needed for proof-of-work and yields faster transactions as a result.
The differing approaches revolve around the interesting philosophical question about whether humans are dominated by their altruistic or selfish urges as a whole. Proponents of Micali point to chronic, altruistic seeders on Bittorrent and distributed computing projects like Genome@Home as evidence that we do not always need incentives to promote altruistic behavior. Meanwhile, Vitalik Buterin and Vlad Zamfir of the Ethereum Foundation are firmly in the opposite camp, believing that without incentives and penalties, people can be at best apathetic (why even log on?) and at worst malicious.
While the bulk of the blockchain movement embraces the idea of incentives and cryptoeconomics, it is definitely possible that Micali’s system and variants of it may take root in parallel.
It is an open question of whether you need incentives or not, and I don’t think it can be determined in an academic model. It is actually going to be determined by evidence. You launch something and you see what happens. — Charles Hoskinson, Previous Ethereum CEO
While Bitcoin’s PoW system is not perfect, the fact remains that the paradigm-shifting, cryptoeconomic principles it was built on (cryptography to secure the past, economics to ensure the future) have led to its survival and adoption for almost a decade.
[---]
To assess the design of protocol capability to mitigate these existing and theoretical flaws in these security models, developers utilize two concepts:
The first is the cryptoeconomic security margin, which measures the consequences (in dollars lost) of those violating a protocol guarantee. Theoretically, since the attacker can execute the P + epsilon attack at zero cost provided he or she has the budget, Bitcoin’s PoW system can be said to have a cryptoeconomic security margin of zero!
Cryptoeconomic proof is somewhat similar; it is an assurance or message from a participant in the network that something is true. In the event that it turns out not to be true, that participant will lose a certain amount of money.
So let us examine the most ambitious project on blockchain tech today — the coming Casper update to Ethereum that attempts to drill to the heart of these problems by switching the platform proof-of-work to proof-of-stake. While a discussion about the intricacies of Casper’s Proof-of-Stake (PoS) system is beyond the scope of this article, in short PoS seeks to provide a very large cryptoeconomic security margin by enforcing large security deposits of Ethereum in lieu of computing power in order to serve as a validator. This security deposit, or cryptoeconomic proof, acts as a potent deterrent. The message is clear — cause trouble and lose everything!
Casper forces participants to enter a SchellingCoin Game (as outlined by our iron-styrofoam throne example) where they are forced to bet their security deposits on what the majority will be. Using the same recursive logic we discussed in the iron throne game, the majority of participants will accurately vote on which transactions are valid because each participant expects everyone else to reach the same conclusion. As such, PoS is resistant to the P + epsilon attack because the attacker will have to credibly show an enormous budget to subsidize the participants’ security deposits in the event that they end up voting in the minority.
In the context of the security models, we can see Casper’s resilience in the uncoordinated choice model and from bribing attackers. Casper is also theoretically susceptible to the 51% attack stemming from the coordinated choice model. However, like Bitcoin, as Ethereum grows the costs of doing such an attack are so prohibitive as to almost completely discourage it. In Casper’s case, the threat of losing the stakes of all involved is an even stronger deterrent.
- More Here
Micali instead proposes Algorand, an incentive-less public blockchain that attacks the Byzantine Generals’ Problem by swapping out the generals in each round through a randomization process. While I won’t discuss the mechanics in detail here, this approach avoids the amount of computation resources needed for proof-of-work and yields faster transactions as a result.
The differing approaches revolve around the interesting philosophical question about whether humans are dominated by their altruistic or selfish urges as a whole. Proponents of Micali point to chronic, altruistic seeders on Bittorrent and distributed computing projects like Genome@Home as evidence that we do not always need incentives to promote altruistic behavior. Meanwhile, Vitalik Buterin and Vlad Zamfir of the Ethereum Foundation are firmly in the opposite camp, believing that without incentives and penalties, people can be at best apathetic (why even log on?) and at worst malicious.
While the bulk of the blockchain movement embraces the idea of incentives and cryptoeconomics, it is definitely possible that Micali’s system and variants of it may take root in parallel.
It is an open question of whether you need incentives or not, and I don’t think it can be determined in an academic model. It is actually going to be determined by evidence. You launch something and you see what happens. — Charles Hoskinson, Previous Ethereum CEO
While Bitcoin’s PoW system is not perfect, the fact remains that the paradigm-shifting, cryptoeconomic principles it was built on (cryptography to secure the past, economics to ensure the future) have led to its survival and adoption for almost a decade.
[---]
To assess the design of protocol capability to mitigate these existing and theoretical flaws in these security models, developers utilize two concepts:
The first is the cryptoeconomic security margin, which measures the consequences (in dollars lost) of those violating a protocol guarantee. Theoretically, since the attacker can execute the P + epsilon attack at zero cost provided he or she has the budget, Bitcoin’s PoW system can be said to have a cryptoeconomic security margin of zero!
Cryptoeconomic proof is somewhat similar; it is an assurance or message from a participant in the network that something is true. In the event that it turns out not to be true, that participant will lose a certain amount of money.
So let us examine the most ambitious project on blockchain tech today — the coming Casper update to Ethereum that attempts to drill to the heart of these problems by switching the platform proof-of-work to proof-of-stake. While a discussion about the intricacies of Casper’s Proof-of-Stake (PoS) system is beyond the scope of this article, in short PoS seeks to provide a very large cryptoeconomic security margin by enforcing large security deposits of Ethereum in lieu of computing power in order to serve as a validator. This security deposit, or cryptoeconomic proof, acts as a potent deterrent. The message is clear — cause trouble and lose everything!
Casper forces participants to enter a SchellingCoin Game (as outlined by our iron-styrofoam throne example) where they are forced to bet their security deposits on what the majority will be. Using the same recursive logic we discussed in the iron throne game, the majority of participants will accurately vote on which transactions are valid because each participant expects everyone else to reach the same conclusion. As such, PoS is resistant to the P + epsilon attack because the attacker will have to credibly show an enormous budget to subsidize the participants’ security deposits in the event that they end up voting in the minority.
In the context of the security models, we can see Casper’s resilience in the uncoordinated choice model and from bribing attackers. Casper is also theoretically susceptible to the 51% attack stemming from the coordinated choice model. However, like Bitcoin, as Ethereum grows the costs of doing such an attack are so prohibitive as to almost completely discourage it. In Casper’s case, the threat of losing the stakes of all involved is an even stronger deterrent.
- More Here
Thursday, July 27, 2017
Deep Learning for NLP Best Practices
Word embeddings
Word embeddings are arguably the most widely known best practice in the recent history of NLP. It is well-known that using pre-trained embeddings helps (Kim, 2014) [12]. The optimal dimensionality of word embeddings is mostly task-dependent: a smaller dimensionality works better for more syntactic tasks such as named entity recognition (Melamud et al., 2016) [44] or part-of-speech (POS) tagging (Plank et al., 2016) [32], while a larger dimensionality is more useful for more semantic tasks such as sentiment analysis (Ruder et al., 2016) [45].
Depth
While we will not reach the depths of computer vision for a while, neural networks in NLP have become progressively deeper. State-of-the-art approaches now regularly use deep Bi-LSTMs, typically consisting of 3-4 layers, e.g. for POS tagging (Plank et al., 2016) and semantic role labelling (He et al., 2017) [33]. Models for some tasks can be even deeper, cf. Google's NMT model with 8 encoder and 8 decoder layers (Wu et al., 2016) [20]. In most cases, however, performance improvements of making the model deeper than 2 layers are minimal (Reimers & Gurevych, 2017) [46].
These observations hold for most sequence tagging and structured prediction problems. For classification, deep or very deep models perform well only with character-level input and shallow word-level models are still the state-of-the-art (Zhang et al., 2015; Conneau et al., 2016; Le et al., 2017) [28, 29, 30].
- More Here
Word embeddings are arguably the most widely known best practice in the recent history of NLP. It is well-known that using pre-trained embeddings helps (Kim, 2014) [12]. The optimal dimensionality of word embeddings is mostly task-dependent: a smaller dimensionality works better for more syntactic tasks such as named entity recognition (Melamud et al., 2016) [44] or part-of-speech (POS) tagging (Plank et al., 2016) [32], while a larger dimensionality is more useful for more semantic tasks such as sentiment analysis (Ruder et al., 2016) [45].
Depth
While we will not reach the depths of computer vision for a while, neural networks in NLP have become progressively deeper. State-of-the-art approaches now regularly use deep Bi-LSTMs, typically consisting of 3-4 layers, e.g. for POS tagging (Plank et al., 2016) and semantic role labelling (He et al., 2017) [33]. Models for some tasks can be even deeper, cf. Google's NMT model with 8 encoder and 8 decoder layers (Wu et al., 2016) [20]. In most cases, however, performance improvements of making the model deeper than 2 layers are minimal (Reimers & Gurevych, 2017) [46].
These observations hold for most sequence tagging and structured prediction problems. For classification, deep or very deep models perform well only with character-level input and shallow word-level models are still the state-of-the-art (Zhang et al., 2015; Conneau et al., 2016; Le et al., 2017) [28, 29, 30].
- More Here
Quote of the Day
Leaders should never be satisfied. They must always strive to improve, and they must build that mind-set into the team. They must face the facts through a realistic, brutally honest assessment of themselves and their team’s performance. Identifying weaknesses, good leaders seek to strengthen them and come up with a plan to overcome challenges. The best teams anywhere, like the SEAL Teams, are constantly looking to improve, add capability, and push the standards higher. It starts with the individual and spreads to each of the team members until this becomes the culture, the new standard. The recognition that there are no bad teams, only bad leaders facilitates Extreme Ownership and enables leaders to build high-performance teams that dominate on any battlefield, literal or figurative.
- Jocko Willink, Extreme Ownership: How U.S. Navy SEALs Lead and Win
- Jocko Willink, Extreme Ownership: How U.S. Navy SEALs Lead and Win
Wednesday, July 26, 2017
Tuesday, July 25, 2017
Quote of the Day
Becoming a modern society is about industrialization, urbanization, and rising levels of literacy, education, and wealth. The qualities that make a society Western, in contrast, are special: the classical legacy, Christianity, the separation of church and state, the rule of law, civil society.
- Samuel P. Huntington
- Samuel P. Huntington
Monday, July 24, 2017
Samuel Huntington Anticipated the Current Political & Intellectual Battles
To understand our current turmoil, the most relevant of Huntington’s books is not “The Clash of Civilizations and the Remaking of World Order” (1996) or even “Who Are We? The Challenges to America’s National Identity” (2004), whose fans reportedly include self-proclaimed white nationalist Richard Spencer. It is the lesser-known and remarkably prescient “American Politics: The Promise of Disharmony,” published 36 years ago.
In that work, Huntington points to the gap between the values of the American creed — liberty, equality, individualism, democracy, constitutionalism — and the government’s efforts to live up to those values as the central tension of American life. “At times, this dissonance is latent; at other times, when creedal passion runs high, it is brutally manifest, and at such times, the promise of American politics becomes its central agony.”
Whether debating health care, taxes, immigration or war, Americans invariably invoke the founding values to challenge perceived injustices. Reforms cannot merely be necessary or sensible; they must be articulated and defended in terms of the creed. This is why Trump’s opponents attack his policies by declaring not only that they are wrong but that “that’s not who we are.” As Huntington puts it, “Americans divide most sharply over what brings them together.”
The book looks back to the Revolutionary War, the Jacksonian age, the Progressive era and the 1960s as moments of high creedal passions, and Huntington’s descriptions capture America today. In such moments, he writes, discontent is widespread, and authority and expertise are questioned; traditional values of liberty, individualism, equality and popular control of government dominate public debates; politics is characterized by high polarization and constant protest; hostility toward power, wealth and inequality grows intense; social movements focused on causes such as women’s rights and criminal justice flourish; and new forms of media emerge devoted to advocacy and adversarial journalism.
Huntington even predicts the timing of America’s next fight: “If the periodicity of the past prevails,” he writes, “a major sustained creedal passion period will occur in the second and third decades of the twenty-first century.”
We’re right on schedule.
- More Here
In that work, Huntington points to the gap between the values of the American creed — liberty, equality, individualism, democracy, constitutionalism — and the government’s efforts to live up to those values as the central tension of American life. “At times, this dissonance is latent; at other times, when creedal passion runs high, it is brutally manifest, and at such times, the promise of American politics becomes its central agony.”
Whether debating health care, taxes, immigration or war, Americans invariably invoke the founding values to challenge perceived injustices. Reforms cannot merely be necessary or sensible; they must be articulated and defended in terms of the creed. This is why Trump’s opponents attack his policies by declaring not only that they are wrong but that “that’s not who we are.” As Huntington puts it, “Americans divide most sharply over what brings them together.”
The book looks back to the Revolutionary War, the Jacksonian age, the Progressive era and the 1960s as moments of high creedal passions, and Huntington’s descriptions capture America today. In such moments, he writes, discontent is widespread, and authority and expertise are questioned; traditional values of liberty, individualism, equality and popular control of government dominate public debates; politics is characterized by high polarization and constant protest; hostility toward power, wealth and inequality grows intense; social movements focused on causes such as women’s rights and criminal justice flourish; and new forms of media emerge devoted to advocacy and adversarial journalism.
Huntington even predicts the timing of America’s next fight: “If the periodicity of the past prevails,” he writes, “a major sustained creedal passion period will occur in the second and third decades of the twenty-first century.”
We’re right on schedule.
- More Here
Future of Deep Learning
- Models will be more like programs, and will have capabilities that go far beyond the continuous geometric transformations of the input data that we currently work with. These programs will arguably be much closer to the abstract mental models that humans maintain about their surroundings and themselves, and they will be capable of stronger generalization due to their rich algorithmic nature.
- In particular, models will blend algorithmic modules providing formal reasoning, search, and abstraction capabilities, with geometric modules providing informal intuition and pattern recognition capabilities. AlphaGo (a system that required a lot of manual software engineering and human-made design decisions) provides an early example of what such a blend between symbolic and geometric AI could look like.
- They will be grown automatically rather than handcrafted by human engineers, using modular parts stored in a global library of reusable subroutines—a library evolved by learning high-performing models on thousands of previous tasks and datasets. As common problem-solving patterns are identified by the meta-learning system, they would be turned into a reusable subroutine—much like functions and classes in contemporary software engineering—and added to the global library. This achieves the capability for abstraction.
- This global library and associated model-growing system will be able to achieve some form of human-like "extreme generalization": given a new task, a new situation, the system would be able to assemble a new working model appropriate for the task using very little data, thanks to 1) rich program-like primitives that generalize well and 2) extensive experience with similar tasks. In the same way that humans can learn to play a complex new video game using very little play time because they have experience with many previous games, and because the models derived from this previous experience are abstract and program-like, rather than a basic mapping between stimuli and action.
- As such, this perpetually-learning model-growing system could be interpreted as an AGI—an Artificial General Intelligence. But don't expect any singularitarian robot apocalypse to ensue: that's a pure fantasy, coming from a long series of profound misunderstandings of both intelligence and technology. This critique, however, does not belong here.
- More Here
Sunday, July 23, 2017
Quote of the Day
A cat has absolute emotional honesty: human beings, for one reason or another, may hide their feelings, but a cat does not.
- Ernest Hemingway
- Ernest Hemingway
Saturday, July 22, 2017
Wisdom Of The Week
The present tense of climate change — the destruction we’ve already baked into our future — is horrifying enough. Most people talk as if Miami and Bangladesh still have a chance of surviving; most of the scientists I spoke with assume we’ll lose them within the century, even if we stop burning fossil fuel in the next decade. Two degrees of warming used to be considered the threshold of catastrophe: tens of millions of climate refugees unleashed upon an unprepared world. Now two degrees is our goal, per the Paris climate accords, and experts give us only slim odds of hitting it. The U.N. Intergovernmental Panel on Climate Change issues serial reports, often called the “gold standard” of climate research; the most recent one projects us to hit four degrees of warming by the beginning of the next century, should we stay the present course. But that’s just a median projection. The upper end of the probability curve runs as high as eight degrees — and the authors still haven’t figured out how to deal with that permafrost melt. The IPCC reports also don’t fully account for the albedo effect (less ice means less reflected and more absorbed sunlight, hence more warming); more cloud cover (which traps heat); or the dieback of forests and other flora (which extract carbon from the atmosphere). Each of these promises to accelerate warming, and the history of the planet shows that temperature can shift as much as five degrees Celsius within thirteen years. The last time the planet was even four degrees warmer, Peter Brannen points out in The Ends of the World, his new history of the planet’s major extinction events, the oceans were hundreds of feet higher.
The Earth has experienced five mass extinctions before the one we are living through now, each so complete a slate-wiping of the evolutionary record it functioned as a resetting of the planetary clock, and many climate scientists will tell you they are the best analog for the ecological future we are diving headlong into. Unless you are a teenager, you probably read in your high-school textbooks that these extinctions were the result of asteroids. In fact, all but the one that killed the dinosaurs were caused by climate change produced by greenhouse gas. The most notorious was 252 million years ago; it began when carbon warmed the planet by five degrees, accelerated when that warming triggered the release of methane in the Arctic, and ended with 97 percent of all life on Earth dead. We are currently adding carbon to the atmosphere at a considerably faster rate; by most estimates, at least ten times faster. The rate is accelerating. This is what Stephen Hawking had in mind when he said, this spring, that the species needs to colonize other planets in the next century to survive, and what drove Elon Musk, last month, to unveil his plans to build a Mars habitat in 40 to 100 years. These are nonspecialists, of course, and probably as inclined to irrational panic as you or I. But the many sober-minded scientists I interviewed over the past several months — the most credentialed and tenured in the field, few of them inclined to alarmism and many advisers to the IPCC who nevertheless criticize its conservatism — have quietly reached an apocalyptic conclusion, too: No plausible program of emissions reductions alone can prevent climate disaster.
Over the past few decades, the term “Anthropocene” has climbed out of academic discourse and into the popular imagination — a name given to the geologic era we live in now, and a way to signal that it is a new era, defined on the wall chart of deep history by human intervention. One problem with the term is that it implies a conquest of nature (and even echoes the biblical “dominion”). And however sanguine you might be about the proposition that we have already ravaged the natural world, which we surely have, it is another thing entirely to consider the possibility that we have only provoked it, engineering first in ignorance and then in denial a climate system that will now go to war with us for many centuries, perhaps until it destroys us. That is what Wallace Smith Broecker, the avuncular oceanographer who coined the term “global warming,” means when he calls the planet an “angry beast.” You could also go with “war machine.” Each day we arm it more.
- The Uninhabitable Earth by David Wallace-Wells
The Earth has experienced five mass extinctions before the one we are living through now, each so complete a slate-wiping of the evolutionary record it functioned as a resetting of the planetary clock, and many climate scientists will tell you they are the best analog for the ecological future we are diving headlong into. Unless you are a teenager, you probably read in your high-school textbooks that these extinctions were the result of asteroids. In fact, all but the one that killed the dinosaurs were caused by climate change produced by greenhouse gas. The most notorious was 252 million years ago; it began when carbon warmed the planet by five degrees, accelerated when that warming triggered the release of methane in the Arctic, and ended with 97 percent of all life on Earth dead. We are currently adding carbon to the atmosphere at a considerably faster rate; by most estimates, at least ten times faster. The rate is accelerating. This is what Stephen Hawking had in mind when he said, this spring, that the species needs to colonize other planets in the next century to survive, and what drove Elon Musk, last month, to unveil his plans to build a Mars habitat in 40 to 100 years. These are nonspecialists, of course, and probably as inclined to irrational panic as you or I. But the many sober-minded scientists I interviewed over the past several months — the most credentialed and tenured in the field, few of them inclined to alarmism and many advisers to the IPCC who nevertheless criticize its conservatism — have quietly reached an apocalyptic conclusion, too: No plausible program of emissions reductions alone can prevent climate disaster.
Over the past few decades, the term “Anthropocene” has climbed out of academic discourse and into the popular imagination — a name given to the geologic era we live in now, and a way to signal that it is a new era, defined on the wall chart of deep history by human intervention. One problem with the term is that it implies a conquest of nature (and even echoes the biblical “dominion”). And however sanguine you might be about the proposition that we have already ravaged the natural world, which we surely have, it is another thing entirely to consider the possibility that we have only provoked it, engineering first in ignorance and then in denial a climate system that will now go to war with us for many centuries, perhaps until it destroys us. That is what Wallace Smith Broecker, the avuncular oceanographer who coined the term “global warming,” means when he calls the planet an “angry beast.” You could also go with “war machine.” Each day we arm it more.
- The Uninhabitable Earth by David Wallace-Wells
Quote of the Day
All good books are alike in that they are truer than if they had really happened and after you are finished reading one you will feel that all that happened to you and afterwards it all belongs to you: the good and the bad, the ecstasy, the remorse and sorrow, the people and the places and how the weather was. If you can get so that you can give that to people, then you are a writer.
- Ernest Hemingway
- Ernest Hemingway
Friday, July 21, 2017
Quote of the Day
We always hope for the easy fix: the one simple change that will erase a problem in a stroke. But few things in life work this way. Instead, success requires making a hundred small steps go right - one after the other, no slipups, no goofs, everyone pitching in.
- Atul Gawande, Better: A Surgeon's Notes on Performance
- Atul Gawande, Better: A Surgeon's Notes on Performance
Thursday, July 20, 2017
Quote of the Day
Just because someone screwed up your past, it doesn’t mean you should give them permission to screw up your future.
- Zig Ziglar
- Zig Ziglar
Wednesday, July 19, 2017
Quote of the Day
The more precise you are, in general the more likely you are to be wrong.
- J.L. Austin
- J.L. Austin
Tuesday, July 18, 2017
Quote of the Day
What man actually needs is not a tensionless state but rather the striving and struggling for some goal worthy of him.
- Viktor Frankl
- Viktor Frankl
Monday, July 17, 2017
Quote of the Day
I repeat this history because I don’t think moral obliviousness is built in a day. It takes generations to hammer ethical considerations out of a person’s mind and to replace them entirely with the ruthless logic of winning and losing; to take the normal human yearning to be good and replace it with a single-minded desire for material conquest; to take the normal human instinct for kindness and replace it with a law-of-the-jungle mentality.
It took a few generations of the House of Trump, in other words, to produce Donald Jr.
- David Brooks
It took a few generations of the House of Trump, in other words, to produce Donald Jr.
- David Brooks
Sunday, July 16, 2017
Quote of the Day
If you seek tranquility, do less. Or (more accurately) do what’s essential. Do less, better. Because most of what we do or say is not essential. If you can eliminate it, you’ll have more tranquility. But to eliminate the necessary actions, we need to eliminate unnecessary assumptions as well.
- Marcus Aurelius, Meditations
- Marcus Aurelius, Meditations
Saturday, July 15, 2017
Wisdom Of The Week
Lesson 1 - Scope Matters
In discussing large-scale models, it’s difficult to avoid mentioning Jorge Luis Borges’s thought experiment about a 1:1 scale map from “On Exactitude in Science”:
The point we take from Borges (and from Cheramie) is that no model can be a complete recapitulation of the real world. Instead, we bracket off parts of the world, model those parts, and use the insights it gives us to make interventions in the world. The Army Corps couldn’t model the entire Missippi Basin drainage system either. They could only follow tributaries so far upstream before having to make generalized assumptions about the inputs to the system they modeled. They also couldn’t model all the outputs - their model doesn’t extend past Baton Rouge, let alone out into the Gulf of Mexico.
Similarly, the inputs for computer models are the outputs of other processes not captured by the model itself, and so the outputs of a model are only as valid as the understanding of the conditions that feed into it. If a minor creek jumps its bank upstream from region modeled by the Mississippi Basin Model, it could have downstream effects that the model could never capture. If the conditions that produce the data points we use to feed our model change, so too can the validity of our model change. The success of projects like AlphaGo rely on modeling closed systems, e.g. the game of Go, which is why AI for games are (relatively) easy and applied, real-world AI is much harder. Machine learning is great at predicting the future when the future resembles the past, but it takes a lot more to predict the lay of the land when the ground shifts under our feet.
Lesson 2 - Materials Matter
In building their Mississippi Basin Model, the Army Corps had to approximate the “real world” with the materials they had at their disposal. The engineers shaped and textured concrete, installed brass plugs, and accordion-folded sheet metal to approximate the incredibly complex effects of trees, sand, clay, roads, and crops on the speed, direction, and volume of water passing over the landscape in high-water conditions. They had to develop a measure of “frictional resistance” to translate between the real world of rocks and trees and the model world of concrete and metal. In computer modeling, the proxies we choose to represent the real world are just as important. We don’t know where people are, necessarily, but we do have a great degree of confidence about where their GPS-enabled phones are. Similarly, another example of this comes from the world of computer vision, where attempts to produce soccer highlights from video struggled with following the ball (exciting moments are more likely the closer the ball is to the goal). Eventually, one team discovered that players tend to follow the ball, and players are easier to track, so the players became a useful proxy for addressing a harder question.
It is from these approximations of reality that we’re able to train the coefficients of our models, and so, importantly, the proxies we choose are the materials that shape how inputs relate to outputs. The models themselves have a material affect on outputs, too. If we assume that inputs are linear, and put them in to a linear model, they will produce a linear output. If the relationship between inputs and outputs is not actually linear, then the model will not fit, in every sense of the word. The Mississippi Basin Model had to pick and choose what it could approximate, and reduce everything else to coefficients. Wetlands disappeared form the model, as did evaporation and siltation. The lesson Cheramie draws from this is that “it doesn’t matter how much territory the model covers if it relies on the amputation of inconvenient complexities to be manageable. The simulation becomes thin.” Computer models can manage a great deal more complexity than physical models, but the crucial complexity that data scientists should pay careful attention to is the material relationship between the reality we hope to model and the proxies we choose to represent that reality. Neural networks with external memory, that learn to remember and recollect, are attempts to build “context awareness” and long-term memory into neural networks. This can be understood as an attempt to model a larger chunk of the world, to bring in more materials without having to explicitly declare every variable worth considering.
- Learning from Real-World Models: The Mississippi Basin Model and Machine Learning
In discussing large-scale models, it’s difficult to avoid mentioning Jorge Luis Borges’s thought experiment about a 1:1 scale map from “On Exactitude in Science”:
“In time, . . . the cartographers guilds struck a map of the empire whose size was that of the empire, and which coincided point for point with it. The following generations, who were not so fond of the study of cartography as their forebears had been, saw that that vast map was useless, and not without some pitilessness was it, that they delivered it up to the inclemencies of sun and winters. In the deserts of the west, there are tattered ruins of that map, inhabited by animals and beggars, in all the land there is no other relic of the disciplines of geography.”
The point we take from Borges (and from Cheramie) is that no model can be a complete recapitulation of the real world. Instead, we bracket off parts of the world, model those parts, and use the insights it gives us to make interventions in the world. The Army Corps couldn’t model the entire Missippi Basin drainage system either. They could only follow tributaries so far upstream before having to make generalized assumptions about the inputs to the system they modeled. They also couldn’t model all the outputs - their model doesn’t extend past Baton Rouge, let alone out into the Gulf of Mexico.
Similarly, the inputs for computer models are the outputs of other processes not captured by the model itself, and so the outputs of a model are only as valid as the understanding of the conditions that feed into it. If a minor creek jumps its bank upstream from region modeled by the Mississippi Basin Model, it could have downstream effects that the model could never capture. If the conditions that produce the data points we use to feed our model change, so too can the validity of our model change. The success of projects like AlphaGo rely on modeling closed systems, e.g. the game of Go, which is why AI for games are (relatively) easy and applied, real-world AI is much harder. Machine learning is great at predicting the future when the future resembles the past, but it takes a lot more to predict the lay of the land when the ground shifts under our feet.
Lesson 2 - Materials Matter
In building their Mississippi Basin Model, the Army Corps had to approximate the “real world” with the materials they had at their disposal. The engineers shaped and textured concrete, installed brass plugs, and accordion-folded sheet metal to approximate the incredibly complex effects of trees, sand, clay, roads, and crops on the speed, direction, and volume of water passing over the landscape in high-water conditions. They had to develop a measure of “frictional resistance” to translate between the real world of rocks and trees and the model world of concrete and metal. In computer modeling, the proxies we choose to represent the real world are just as important. We don’t know where people are, necessarily, but we do have a great degree of confidence about where their GPS-enabled phones are. Similarly, another example of this comes from the world of computer vision, where attempts to produce soccer highlights from video struggled with following the ball (exciting moments are more likely the closer the ball is to the goal). Eventually, one team discovered that players tend to follow the ball, and players are easier to track, so the players became a useful proxy for addressing a harder question.
It is from these approximations of reality that we’re able to train the coefficients of our models, and so, importantly, the proxies we choose are the materials that shape how inputs relate to outputs. The models themselves have a material affect on outputs, too. If we assume that inputs are linear, and put them in to a linear model, they will produce a linear output. If the relationship between inputs and outputs is not actually linear, then the model will not fit, in every sense of the word. The Mississippi Basin Model had to pick and choose what it could approximate, and reduce everything else to coefficients. Wetlands disappeared form the model, as did evaporation and siltation. The lesson Cheramie draws from this is that “it doesn’t matter how much territory the model covers if it relies on the amputation of inconvenient complexities to be manageable. The simulation becomes thin.” Computer models can manage a great deal more complexity than physical models, but the crucial complexity that data scientists should pay careful attention to is the material relationship between the reality we hope to model and the proxies we choose to represent that reality. Neural networks with external memory, that learn to remember and recollect, are attempts to build “context awareness” and long-term memory into neural networks. This can be understood as an attempt to model a larger chunk of the world, to bring in more materials without having to explicitly declare every variable worth considering.
- Learning from Real-World Models: The Mississippi Basin Model and Machine Learning
Quote of the Day
The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ but ‘That’s funny…’
- Isaac Asimov
- Isaac Asimov
Friday, July 14, 2017
Thursday, July 13, 2017
Quote of the Day
I have seen many storms in my life. Most storms have caught me by surprise, so I had to learn very quickly to look further and understand that I am not capable of controlling the weather, to exercise the art of patience and to respect the fury of nature.
- Paulo Coelho
- Paulo Coelho
Wednesday, July 12, 2017
Quote of the Day
Medicine is a science of uncertainty and an art of probability.
- Sir William Osler
- Sir William Osler
Tuesday, July 11, 2017
Quote of the Day
Of all the plants that cover the earth and lie like a fringe of hair upon the body of our grandmother, try to obtain knowledge that you may be strengthened in life.
- Winnebago (Native American) (on nature)
- Winnebago (Native American) (on nature)
Monday, July 10, 2017
Quote of the Day
The contemporary proliferation of bullshit also has deeper sources, in various forms of skepticism which deny that we can have any reliable access to an objective reality and which therefore reject the possibility of knowing how things truly are. These "anti-realist" doctrines undermine confidence in the value of disinterested efforts to determine what is true and what is false, and even in the intelligibility of the notion of objective inquiry. One response to this loss of confidence has been a retreat from the discipline required by dedication to the ideal of correctness to a quite different sort of discipline, which is imposed by pursuit of an alternative ideal of sincerity. Rather than seeking primarily to arrive at accurate representations of a common world, the individual turns toward trying to provide honest representations of himself. Convinced that reality has no inherent nature, which he might hope to identify as the truth about things, he devotes himself to being true to his own nature. It is as though he decides that since it makes no sense to try to be true to the facts, he must therefore try instead to be true to himself.
But it is preposterous to imagine that we ourselves are determinate, and hence susceptible both to correct and to incorrect descriptions, while supposing that the ascription of determinacy to anything else has been exposed as a mistake. As conscious beings, we exist only in response to other things, and we cannot know ourselves at all without knowing them. Moreover, there is nothing in theory, and certainly nothing in experience, to support the extraordinary judgment that it is the truth about himself that is the easiest for a person to know. Facts about ourselves are not peculiarly solid and resistant to skeptical dissolution. Our natures are, indeed, elusively insubstantial -- notoriously less stable and less inherent than the natures of other things. And insofar as this is the case, sincerity itself is bullshit.
- Harry G. Frankfurt, On Bullshit
But it is preposterous to imagine that we ourselves are determinate, and hence susceptible both to correct and to incorrect descriptions, while supposing that the ascription of determinacy to anything else has been exposed as a mistake. As conscious beings, we exist only in response to other things, and we cannot know ourselves at all without knowing them. Moreover, there is nothing in theory, and certainly nothing in experience, to support the extraordinary judgment that it is the truth about himself that is the easiest for a person to know. Facts about ourselves are not peculiarly solid and resistant to skeptical dissolution. Our natures are, indeed, elusively insubstantial -- notoriously less stable and less inherent than the natures of other things. And insofar as this is the case, sincerity itself is bullshit.
- Harry G. Frankfurt, On Bullshit
Sunday, July 9, 2017
John Roberts’ Commencement Speech @ his Son’s 9th Grade Graduation
Now the commencement speakers will typically also wish you good luck and extend good wishes to you. I will not do that, and I’ll tell you why. From time to time in the years to come, I hope you will be treated unfairly, so that you will come to know the value of justice. I hope that you will suffer betrayal because that will teach you the importance of loyalty. Sorry to say, but I hope you will be lonely from time to time so that you don’t take friends for granted. I wish you bad luck, again, from time to time so that you will be conscious of the role of chance in life and understand that your success is not completely deserved and that the failure of others is not completely deserved either. And when you lose, as you will from time to time, I hope every now and then, your opponent will gloat over your failure. It is a way for you to understand the importance of sportsmanship. I hope you’ll be ignored so you know the importance of listening to others, and I hope you will have just enough pain to learn compassion.
Whether I wish these things or not, they’re going to happen. And whether you benefit from them or not will depend upon your ability to see the message in your misfortunes.
- via MR
Whether I wish these things or not, they’re going to happen. And whether you benefit from them or not will depend upon your ability to see the message in your misfortunes.
- via MR
Saturday, July 8, 2017
Wisdom Of The Week
The METI group aims to improve on the Arecibo message not just by targeting specific planets, like that super-earth orbiting Gliese, but also by rethinking the nature of the message itself. ‘‘Drake’s original design plays into the bias that vision is universal among intelligent life,’’ Vakoch told me. Visual diagrams — whether formed through semiprime grids or engraved on plaques — seem like a compelling way to encode information to us because humans happen to have evolved an unusually acute sense of vision. But perhaps the aliens followed a different evolutionary path and found their way to a technologically advanced civilization with an intelligence that was rooted in some other sense: hearing, for example, or some other way of perceiving the world around them for which there is no earthly equivalent.
Like so much of the SETI/METI debate, the question of visual messaging quickly spirals out into a deeper meditation, in this instance on the connection between intelligence and visual acuity. It is no accident that eyes developed independently so many times over the course of evolution on Earth, given the fact that light conveys information faster than any other conduit. That transmission-speed advantage would presumably apply on other planets in the Goldilocks zone, even if they happened to be on the other side of the Milky Way, and so it seems plausible that intelligent creatures would evolve some sort of visual system as well.
But even more universal than sight would be the experience of time. Hans Freudenthal’s ‘‘Lincos: Design of a Language for Cosmic Intercourse,’’ a seminal book of exosemiotics published more than a half-century ago, relied heavily on temporal cues in its primer stage. Vakoch and his collaborators have been working with Freudenthal’s language in their early drafts for the message. In Lincos, duration is used as a key building block. A pulse that lasts for a certain stretch (say, in human terms, one second) is followed by a sequence of pulses that signify the ‘‘word’’ for one; a pulse that lasts for six seconds is followed by the word for six. The words for basic math properties can be conveyed by combining pulses of different lengths. You might demonstrate the property of addition by sending the word for ‘‘three’’ and ‘‘six’’ and then sending a pulse that lasts for nine seconds. ‘‘It’s a way of being able to point at objects when you don’t have anything right in front of you,’’ Vakoch explains.
[---]
All of which takes us back to a much more down-to-earth, but no less challenging, question: Who gets to decide? After many years of debate, the SETI community established an agreed-upon procedure that scientists and government agencies should follow in the event that the SETI searches actually stumble upon an intelligible signal from space. The protocols specifically ordain that ‘‘no response to a signal or other evidence of extraterrestrial intelligence should be sent until appropriate international consultations have taken place.’’ But an equivalent set of guidelines does not yet exist to govern our own interstellar outreach.
One of the most thoughtful participants in the METI debate, Kathryn Denning, an anthropologist at York University in Toronto, has argued that our decisions about extraterrestrial contact are ultimately more political than scientific. ‘‘If I had to take a position, I’d say that broad consultation regarding METI is essential, and so I greatly respect the efforts in that direction,’’ Denning says. ‘‘But no matter how much consultation there is, it’s inevitable that there will be significant disagreement about the advisability of transmitting, and I don’t think this is the sort of thing where a simple majority vote or even supermajority should carry the day . . . so this keeps bringing us back to the same key question: Is it O.K. for some people to transmit messages at significant power when other people don’t want them to?’’
In a sense, the METI debate runs parallel to other existential decisions that we will be confronting in the coming decades, as our technological and scientific powers increase. Should we create superintelligent machines that exceed our own intellectual capabilities by such a wide margin that we cease to understand how their intelligence works? Should we ‘‘cure’’ death, as many technologists are proposing? Like METI, these are potentially among the most momentous decisions human beings will ever make, and yet the number of people actively participating in those decisions — or even aware such decisions are being made — is minuscule.
‘‘I think we need to rethink the message process so that we are sending a series of increasingly inclusive messages,’’ Vakoch says. ‘‘Any message that we initially send would be too narrow, too incomplete. But that’s O.K. Instead, what we should be doing is thinking about how to make the next round of messages better and more inclusive. We ideally want a way to incorporate both technical expertise — people who have been thinking about these issues from a range of different disciplines — and also getting lay input. I think it’s often been one or the other. One way we can get lay input in a way that makes a difference in terms of message content is to survey people about what sorts of things they would want to say. It’s important to see what the general themes are that people would want to say and then translate those into a Lincos-like message.’’
When I asked Denning where she stands on the METI issue, she told me: ‘‘I have to answer that question with a question: Why are you asking me? Why should my opinion matter more than that of a 6-year-old girl in Namibia? We both have exactly the same amount at stake, arguably, she more than I, since the odds of being dead before any consequences of transmission occur are probably a bit higher for me, assuming she has access to clean water and decent health care and isn’t killed far too young in war.’’ She continued: ‘‘I think the METI debate may be one of those rare topics where scientific knowledge is highly relevant to the discussion, but its connection to obvious policy is tenuous at best, because in the final analysis, it’s all about how much risk the people of Earth are willing to tolerate. . . . And why exactly should astronomers, cosmologists, physicists, anthropologists, psychologists, sociologists, biologists, sci-fi authors or anyone else (in no particular order), get to decide what those tolerances should be?’’
- Greetings, E.T. (Please Don’t Murder Us.) by Steven Johnson
Like so much of the SETI/METI debate, the question of visual messaging quickly spirals out into a deeper meditation, in this instance on the connection between intelligence and visual acuity. It is no accident that eyes developed independently so many times over the course of evolution on Earth, given the fact that light conveys information faster than any other conduit. That transmission-speed advantage would presumably apply on other planets in the Goldilocks zone, even if they happened to be on the other side of the Milky Way, and so it seems plausible that intelligent creatures would evolve some sort of visual system as well.
But even more universal than sight would be the experience of time. Hans Freudenthal’s ‘‘Lincos: Design of a Language for Cosmic Intercourse,’’ a seminal book of exosemiotics published more than a half-century ago, relied heavily on temporal cues in its primer stage. Vakoch and his collaborators have been working with Freudenthal’s language in their early drafts for the message. In Lincos, duration is used as a key building block. A pulse that lasts for a certain stretch (say, in human terms, one second) is followed by a sequence of pulses that signify the ‘‘word’’ for one; a pulse that lasts for six seconds is followed by the word for six. The words for basic math properties can be conveyed by combining pulses of different lengths. You might demonstrate the property of addition by sending the word for ‘‘three’’ and ‘‘six’’ and then sending a pulse that lasts for nine seconds. ‘‘It’s a way of being able to point at objects when you don’t have anything right in front of you,’’ Vakoch explains.
[---]
All of which takes us back to a much more down-to-earth, but no less challenging, question: Who gets to decide? After many years of debate, the SETI community established an agreed-upon procedure that scientists and government agencies should follow in the event that the SETI searches actually stumble upon an intelligible signal from space. The protocols specifically ordain that ‘‘no response to a signal or other evidence of extraterrestrial intelligence should be sent until appropriate international consultations have taken place.’’ But an equivalent set of guidelines does not yet exist to govern our own interstellar outreach.
One of the most thoughtful participants in the METI debate, Kathryn Denning, an anthropologist at York University in Toronto, has argued that our decisions about extraterrestrial contact are ultimately more political than scientific. ‘‘If I had to take a position, I’d say that broad consultation regarding METI is essential, and so I greatly respect the efforts in that direction,’’ Denning says. ‘‘But no matter how much consultation there is, it’s inevitable that there will be significant disagreement about the advisability of transmitting, and I don’t think this is the sort of thing where a simple majority vote or even supermajority should carry the day . . . so this keeps bringing us back to the same key question: Is it O.K. for some people to transmit messages at significant power when other people don’t want them to?’’
In a sense, the METI debate runs parallel to other existential decisions that we will be confronting in the coming decades, as our technological and scientific powers increase. Should we create superintelligent machines that exceed our own intellectual capabilities by such a wide margin that we cease to understand how their intelligence works? Should we ‘‘cure’’ death, as many technologists are proposing? Like METI, these are potentially among the most momentous decisions human beings will ever make, and yet the number of people actively participating in those decisions — or even aware such decisions are being made — is minuscule.
‘‘I think we need to rethink the message process so that we are sending a series of increasingly inclusive messages,’’ Vakoch says. ‘‘Any message that we initially send would be too narrow, too incomplete. But that’s O.K. Instead, what we should be doing is thinking about how to make the next round of messages better and more inclusive. We ideally want a way to incorporate both technical expertise — people who have been thinking about these issues from a range of different disciplines — and also getting lay input. I think it’s often been one or the other. One way we can get lay input in a way that makes a difference in terms of message content is to survey people about what sorts of things they would want to say. It’s important to see what the general themes are that people would want to say and then translate those into a Lincos-like message.’’
When I asked Denning where she stands on the METI issue, she told me: ‘‘I have to answer that question with a question: Why are you asking me? Why should my opinion matter more than that of a 6-year-old girl in Namibia? We both have exactly the same amount at stake, arguably, she more than I, since the odds of being dead before any consequences of transmission occur are probably a bit higher for me, assuming she has access to clean water and decent health care and isn’t killed far too young in war.’’ She continued: ‘‘I think the METI debate may be one of those rare topics where scientific knowledge is highly relevant to the discussion, but its connection to obvious policy is tenuous at best, because in the final analysis, it’s all about how much risk the people of Earth are willing to tolerate. . . . And why exactly should astronomers, cosmologists, physicists, anthropologists, psychologists, sociologists, biologists, sci-fi authors or anyone else (in no particular order), get to decide what those tolerances should be?’’
- Greetings, E.T. (Please Don’t Murder Us.) by Steven Johnson
Quote of the Day
That which can be asserted without evidence, can be dismissed without evidence.
- Christopher Hitchens
- Christopher Hitchens
Friday, July 7, 2017
The Two Problems With Science
On the one hand, we have outright fraud – i.e. making up data, or otherwise lying, breaking the basic rules of science.
On the other hand we have questionable practices such as: publication bias, p-value fishing, the File Drawer, sample size peeking, post-hoc storytelling, and all of the other dark arts that can lead to false positive science. These are permissible, even encouraged, by the current rules of doing and publishing science.
These two problems are similar in some ways – they’re both “bad science”, they both lead to failures to replicate, etc. – but in underlying essence they’re very different, so much so that I’m not sure they can be usefully discussed in the same breath.
Fraud and questionable practices are different in terms of their harms. Fraud is a more serious act and it causes local harm, introducing major errors into the record. But in terms of its overall effects, I believe questionable practices are worse, as they systematically distort science: ensuring that, in some cases, it is difficult to publish anything but errors.
[---]
In finance, you have some people who break the rules. Bernie Madoff is the current poster boy for this. Such people harm others by outright criminal acts. But then we have the people who play by the rules, and still cause harm. The global financial crisis was in essence caused by all of the major American banks going all-in on a bet, and losing. Yet no-one broke the rules: the regulations allowed banks to gamble. The problem was not rule-breaking, but the rules (or lack thereof).
Here’s the curious thing: the financial crisis did more harm than Madoff’s scam, even though what Madoff did – theft by fraud – was more immoral than what the bankers did – gambling unwisely.
That’s confusing to our ethical sense and our emotions (who should we feel more angry at? Who’s ‘worse’?) but it’s really no surprise: precisely because what the banks did was above board, everyone did it so the damage was huge. If it had been illegal for banks to gamble all their money at once, individual banks might still have broken that rule, locally, but it’s unlikely that the system would have been threatened.
Maybe you can see where I’m going with this: everyone following bad rules is often worse than individuals breaking good rules.
- More Here
On the other hand we have questionable practices such as: publication bias, p-value fishing, the File Drawer, sample size peeking, post-hoc storytelling, and all of the other dark arts that can lead to false positive science. These are permissible, even encouraged, by the current rules of doing and publishing science.
These two problems are similar in some ways – they’re both “bad science”, they both lead to failures to replicate, etc. – but in underlying essence they’re very different, so much so that I’m not sure they can be usefully discussed in the same breath.
Fraud and questionable practices are different in terms of their harms. Fraud is a more serious act and it causes local harm, introducing major errors into the record. But in terms of its overall effects, I believe questionable practices are worse, as they systematically distort science: ensuring that, in some cases, it is difficult to publish anything but errors.
[---]
In finance, you have some people who break the rules. Bernie Madoff is the current poster boy for this. Such people harm others by outright criminal acts. But then we have the people who play by the rules, and still cause harm. The global financial crisis was in essence caused by all of the major American banks going all-in on a bet, and losing. Yet no-one broke the rules: the regulations allowed banks to gamble. The problem was not rule-breaking, but the rules (or lack thereof).
Here’s the curious thing: the financial crisis did more harm than Madoff’s scam, even though what Madoff did – theft by fraud – was more immoral than what the bankers did – gambling unwisely.
That’s confusing to our ethical sense and our emotions (who should we feel more angry at? Who’s ‘worse’?) but it’s really no surprise: precisely because what the banks did was above board, everyone did it so the damage was huge. If it had been illegal for banks to gamble all their money at once, individual banks might still have broken that rule, locally, but it’s unlikely that the system would have been threatened.
Maybe you can see where I’m going with this: everyone following bad rules is often worse than individuals breaking good rules.
- More Here
Quote of the Day
If we knew what it was we were doing, it would not be called research, would it?
- Albert Einstein
- Albert Einstein
Thursday, July 6, 2017
Quote of the Day
10 percent of any population is cruel, no matter what, and 10 percent is merciful, no matter what, and the remaining 80 percent can be moved in either direction.
- Susan Sontag
- Susan Sontag
Wednesday, July 5, 2017
Quote of the Day
The next time that you want to motivate yourself to go for a run or pick up the weights, try making up your own ritual that you perform every time you begin.
- Dan Ariely
- Dan Ariely
Tuesday, July 4, 2017
Quote of the Day
It’s a self-organizing process, by which selection will arrive at some standard of beauty all by itself, in the absence of any adaptive benefit—or, indeed, despite maladaptive disadvantage.
- The Evolution of Beauty: How Darwin's Forgotten Theory of Mate Choice Shapes the Animal World and Us by Richard Prum
- The Evolution of Beauty: How Darwin's Forgotten Theory of Mate Choice Shapes the Animal World and Us by Richard Prum
Monday, July 3, 2017
Quote of the Day
Epictetus told his students, when they’d quote some great thinker, to picture themselves observing the person having sex. It’s funny, you should try it the next time someone intimidates you or makes you feel insecure. See them in your mind, grunting, groaning, and awkward in their private life—just like the rest of us.
- Ryan Holiday, The Obstacle Is the Way: The Timeless Art of Turning Adversity to Advantage
- Ryan Holiday, The Obstacle Is the Way: The Timeless Art of Turning Adversity to Advantage
Saturday, July 1, 2017
Wisdom Of The Week
Cyber warfare makes things even worse for would-be imperialists. As recently as the days of George W. Bush, the U.S. could wreak havoc in far-off Fallujah while the Iraqis had no means of retaliating against San Francisco. But if the U.S. now attacks a country possessing even moderate cyber warfare capabilities, malware and logic bombs could stop air traffic in Dallas, cause trains to collide in Philadelphia and bring down the electric grid in Michigan.
In the great age of conquerors, warfare was a low-damage, high-profit affair. At the battle of Hastings in 1066, William the Conqueror gained the whole of England in a single day for the cost of a few thousand dead. Nuclear weapons and cyber warfare, by contrast, are high-damage, low-profit technologies. You could use such tools to destroy entire countries, but not to build profitable empires.
Hence in a world filling up with saber-rattling and bad vibes, perhaps our best guarantee of peace is that major powers aren’t familiar with any recent example of a successful war. While Genghis Khan or Julius Caesar would invade a foreign country at the drop of a hat, present-day strongmen talk loud but are very careful about actually launching wars. Of course, if somebody does find a formula to wage successful wars under twenty-first-century conditions, the gates of hell might open with a rush. This is what makes the Russian success in the Crimea a particularly frightening omen. Let’s hope it remains an isolated example. Though, even if it is impossible to wage successful wars in the twenty-first century, that does not give us an absolute guarantee for peace. We should never underestimate human stupidity.
- Why It’s No Longer Possible for Any Country to Win a War by Yuval Noah Harari
In the great age of conquerors, warfare was a low-damage, high-profit affair. At the battle of Hastings in 1066, William the Conqueror gained the whole of England in a single day for the cost of a few thousand dead. Nuclear weapons and cyber warfare, by contrast, are high-damage, low-profit technologies. You could use such tools to destroy entire countries, but not to build profitable empires.
Hence in a world filling up with saber-rattling and bad vibes, perhaps our best guarantee of peace is that major powers aren’t familiar with any recent example of a successful war. While Genghis Khan or Julius Caesar would invade a foreign country at the drop of a hat, present-day strongmen talk loud but are very careful about actually launching wars. Of course, if somebody does find a formula to wage successful wars under twenty-first-century conditions, the gates of hell might open with a rush. This is what makes the Russian success in the Crimea a particularly frightening omen. Let’s hope it remains an isolated example. Though, even if it is impossible to wage successful wars in the twenty-first century, that does not give us an absolute guarantee for peace. We should never underestimate human stupidity.
- Why It’s No Longer Possible for Any Country to Win a War by Yuval Noah Harari
Quote of the Day
If you don’t like change, you’re going to like irrelevance even less.
– General Erick Shinseki, Chief of Staff, U.S. Army
– General Erick Shinseki, Chief of Staff, U.S. Army
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