Category Archives: Science

Algorithms to Live By by Brian Christian and Tom Griffiths

In some real sense, computers are like brains. They take information in, process it in some way, and try to make sense of it. A key difference is that, with computers, we can explicitly lay out all of the rules for processing that information. For brains, the rules are already there, we can just try to figure out what they are. The central thesis of Brian Christian and Tom Griffiths’ book Algorithms to Live By: What Computers Can Teach Us About Solving Human Problems is that, by looking at how computers can be programmed to solve problems and what kinds of problems are easy and hard, we can learn something about how brains do the same.

Christian and Griffiths go systematically through a series of problem types that are central to computer science and applied math and describe how the insights into those problems give us insight into how brains handle information. One of their first examples relates to decision making. Say you have a choice you need to make from a pool of options — who to get married to, what house to buy, which secretary to hire. The basic conundrum is this: you want to make sure you get enough data to make an intelligent choice — you want to know that your choice is really a good one by comparing it to the other options — but the more information you gather, the longer you wait, the more likely the best one has already come and gone. So, you need to wait for some time to judge the quality of the pool and each candidate relative to the pool, but you can’t wait too long or you miss the best one. Under some assumptions, applied math has solved versions of this problem, a class of problems called “optimal stopping” problems. It turns out that, under certain conditions, the optimal stopping point is 37%. That is, you should use the first 37% of your options to help you build your knowledge base about the pool, and not choose any of them. But, you should choose the very first person after that 37% that is better than any of those in the first 37%. This maximizes your chances of choosing the very best person. You aren’t guaranteed to get the very best with this algorithm, but you have the best chance of getting the best.

This is just one example that Christian and Griffiths use to draw analogies between computer science and human thinking. They delve into a variety of topics:

  • Exploring versus Exploiting. Related to optimal stopping, this is the problem of relying on something you already know well versus trying out something new, such as a restaurant.
  • Sorting. If you have a large amount of information, how is it best to sort through it all.
  • Caching. Again, if you have a lot of information, how do you deal with it in the first place? How do you get the information you need now when you can’t have all of the information at your fingertips?
  • Scheduling. If you have a full to-do list, how do you optimize the best way of getting through your list? Do you want to keep the list as short as possible? Do you want to minimize how long others have to wait for you?
  • Bayes’s Rule. How do you use what you know now to make estimates about what will happen next?
  • Overfitting. What are the dangers of overthinking a problem?
  • Relaxation. Given a hard problem, how do you even begin to solve it? How do you find the best answer?
  • Randomness. When you have a huge problem, with a lot of data, so much that you can’t look at all of it, how do you figure out what it says? Think of polling.
  • Networking. In a large, interconnected world, how do you share information with everyone else?
  • Game Theory. How do we make choices when our choices involve other people and their choices?

All of these topics not only have direct relevance to how we program computers to work for us, to solve hard problems that computers are better at, but also give insight into how we can organize our own thinking and data processing. With the internet, 24-hour cable news, and ever-increasing media presence, the amount of information we are bombarded with continues to grow. Our lives become busier as we juggle work, our child’s soccer schedule, the maintenance we have to do on our house, our social lives. A lot of what we do is process information and try to make some sense out of it. While computer algorithms often don’t provide silver bullets — in fact, some problems are simply not solvable, at least not in a finite amount of time — they provide some insight into how to think about certain types of problems.

Algorithms to Live By provides a nice introduction into some of the problems of computer science in a way that is easily approachable. And, if the problems Christian and Griffiths describe might offer some insight into how our own brains work, at the same time, by making that connection between computers and us, they make the problems of computer science more relatable. That is, they provide an accessible pathway to learning about computer science and how we solve some of the biggest problems in computer science. Given the ubiquity of computers in our lives, it certainly doesn’t hurt to know more about how those machines work.

The Radium Girls by Kate Moore

Just yesterday, on August 10th, a jury awarded Dewayne Johnson nearly $300 million dollars in a case that argued he had contracted cancer from using Monsanto’s Roundup. It is an amazing verdict, especially compared to the story of the so-called Radium Girls.

Kate Moore’s The Radium Girls: The Dark Story of America’s Shining Women, chronicles the story of numerous women — and in some cases girls — and their own battle to find justice. These women, in two separate but eerily similar situations in New Jersey and Illinois, were employed to paint the face of dials to be used in military equipment and on watches and clocks. The critical thing about these faces was that the paint glowed, so they could easily be read in the dark. And, to make them glow, the paint contained the radioactive element radium.

These women were working primarily in the 1910s and 1920s. Radium had only been discovered in 1898 by the famed Marie and Pierre Curie. Radioactivity itself had only been discovered a few years earlier, in 1896 by Henri Becquerel. So, when the women began working with this element, not much was known. In some parts of the world, particularly Germany it seems, it had been recognized that radioactive substances can cause skin lesions. However, entrepreneurs touted the beneficial effects of radioactive substances, even selling drinks that had radium in it, promising it had health benefits.

The Radium Girls describes how the women used their mouths to shape the tip of their brushes to get the finest point to paint the dials. This meant they were ingesting radium. Ultimately, the radium settled into their bones, as it is chemically similar to calcium, and gave many of them cancer. The descriptions of the effects of the radium on their bodies is often gruesome. Suffice it to say, these women suffered considerably as their bodies deteriorated.

However, trying to get any kind of recognition that their employment had anything to do with their sicknesses was a herculean task. First, doctors had no idea what was going on to these women as they hadn’t seen these kinds of symptoms before. Radiation poisoning was entirely new to the profession. Second, industrial hygiene law severely limited the liability of the companies. Third, the women were often poor, a situation exacerbated by massive medical bills, and couldn’t afford lawyers. All of these factors came together to make justice elusive for these women.

Perhaps the worst part of this story, beyond the suffering of the women, was the way their employers attempted to shirk responsibility. In some cases, they even knew the women were sick, but did nothing to either alert them or help them. The lawyer the women in Illinois ultimately got to represent them stated that the behavior of the company they worked for was “an offense against Morals and Humanity and, just incidentally, against the law.”

Because of the perseverance and bravery of these women, eventually, the laws changed. Companies became more liable. Protections were put into place. At the time, however, there was nothing to help these women.

These women and the effect of radium on their bodies became the best source of the effects of radiation on human health. And, the dangers their deteriorating health warned of impacted the efforts of the scientists in the Manhattan Project. Knowing how these women had suffered, Glenn Seaborg insisted that the health effects of plutonium be studied and that safety guidelines be instituted for workers.

One husband remarked “We’ve got humane societies for dogs and cats, but they won’t do anything for human beings.” It is notable how much has changed. The Radium Girls is a stark reminder of how impersonal and profit-driven companies can become if there are no checks on their behavior. How easily human life can be discarded in the name of profits if no one is there to fight for the individual.

The Signal and the Noise by Nate Silver

Grey. Whenever my daughter asks me “What’s your favorite color?” my answer is always the same. Grey. My daughter asks me this often, maybe once a week, expecting my favorite color to change, maybe based on my mood, or the weather. But, no, my answer is always the same. Grey.

Why grey? First, grey is simply a cool color. Contrast is defined by shades of grey. Think of drawing with a pencil. All of the texture is conveyed by shades of grey. But, really, grey is my favorite color because of what it represents. Grey reminds me that the world is not black and white. It is nuance. It is not absolute. It is shades of grey. Our knowledge is not black and white. Science is not black and white. It adapts with new knowledge. It evolves. Science is, in some real sense, grey.

That’s, in some sense, the message of Nate Silver’s The Signal and the Noise. Nate Silver is most famous for running the FiveThirtyEight political forecasting website. He initially gained fame by contributing to the wave of analytics applied to baseball and his tools for forecasting baseball games and player performance. In The Signal and the Noise, he describes the inherent complexities involved in forecasting in a range of fields, from baseball and politics to the stock market, weather and hurricanes, earthquakes and, ultimately, national security issues such as terrorist attacks. He provides perspective into when we have proven successful in our ability of prediction, when we haven’t, and why some of these problems are so complex.

As a scientist, my job, in a real way, is about forecasting and prediction. I personally am not so involved in real forecasting, per se, but about understanding the phenomena that might be important to account for in forecasting. Others actually try to use that information to make real predictions. That said, the ultimate goal is to take knowledge we have today and make predictions about how materials would respond if the conditions were changed, either pushed further in time, or in slightly different environments, or for new applications. Thus, whether the results I uncover are useful is to be judged by if they help us understand and make predictions of materials that are better than they were yesterday. So, Silver’s treatment, while not delving into the my field per se, provides a nice overview of the idea of prediction more generally.

Silver takes the view that the world is inherently Bayesian. I’m certainly no expert on Bayes statistics, but, as far as I understand, the basic idea is that information doesn’t exist in isolation. Rather, there are certain biases or preconceptions or prior knowledge that we have and our experiments or experience modify those priors to give us new knowledge or allow us to make a better prediction. As one negative example of this, our intelligence agencies didn’t prepare for 9/11 not so much because they had no intelligence but because they had an implicit assumption that such an event could not happen. Their implicit probability that a group would hijack multiple planes and crash them into a building with no demands was effectively zero. So, all the intelligence of the world wouldn’t indicate an increased likelihood of such an event as it was simply beyond the realm of possibility for them.

Similarly, using what we do know to estimate probabilities and make predictions enhances our ability to make good predictions. As Wikipedia describes it, if we want to estimate the probability that someone might have cancer, it is important to account for the fact that cancer risk depends on age and that the probability is not uniform for all people. Knowing someone’s age lets us better predict if they might have cancer. Or, as Silver discusses, it allows us to better interpret test results.

Thus, the world isn’t black and white. It is a range of grey and the shade of grey depends on our experience and prior knowledge. Silver believes in an objective truth, but realizes that we will never fully be able to describe that truth. All of our models of reality are approximate, to varying degrees. None are fully faithful to reality itself. Realizing this, helps us make the best possible use of what we know and make the best possible prediction.

Silver delves into many examples and discussions of the state of prediction in several fields. Weather and hurricane prediction are success stories as we have improved our abilities significantly over the last several decades. In contrast, earthquake prediction has not advances at all. We are no better at predicting when an earthquake will happen than we were 20-30 years ago. Some of this is related to our ability to make relevant measurements — it is much easier to measure surface and atmospheric phenomena than the state of stress deep in the earth. We can’t directly measure the conditions that might allow us to make a good prediction.

Along the way, Silver makes a number of interesting points, a few of which I thought were worth noting:

  • “Human beings have an extraordinary capacity to ignore risks that threaten their livelihood, as though this will make them go away.”
  • “We forget — or we willfully ignore — that our models are simplifications of the world. We figure that if we make a mistake, it will be at the margin.”
  • “The key is in remembering that a model is a tool to help us understand the complexities of the universe, and never a substitute for the universe itself.”
  • A lot of pundits, such as the political talking heads on TV, are what might be termed “hedgehogs”: their predictions are not better than random and their views do not change with new evidence but rather remain entrenched, regardless of how bad their predictions have been in the past.
  • “If you have reason to think that yesterday’s forecast was wrong [about anything you might have forecast], there is no glory in sticking to it.”
  • A particularly difficult aspect of economic forecasting is that the prediction itself can influence the system. If a forecaster predicts that Facebook stock will tumble, that prediction itself may lead to a frenzy in trading Facebook stocks, impacting the price of those stocks and the market as a whole. So, while in weather and earthquake prediction, the forecast is outside of the system, that is not true of economics and other similar fields such as the prediction of epidemics. “The most effective flu prediction might be one that fails to come to fruition because it motivates people toward more healthful choices.”
  • In the era of Big Data, predictions might become worse rather than better, as much of the data might be noise that confounds our models rather than signal that leads to enhanced predictive capability.
  • “The need for prediction arises not necessarily because the world itself is uncertain, but because understanding it fully is beyond our capacity.”
  • “What I do know is that there isa ¬†fundamental difference between science and politics. In fact, I’ve come to view them more and more as opposites… In science, one rarely sees all the data point toward one precise conclusion.”
  • “The dysfunctional state of the American political system is the best reason to be pessimistic about our country’s future. Our scientific and technological prowess is the best reason to be optimistic.”
  • “Whatever range of abilities we have acquired, there will always be tasks sitting right at the edge of them. If we judge ourselves by what is hardest for us, we make take for granted those things that we do easily and routinely.”

In the lead up to the Iraq War, Donald Rumsfeld gave a speech that was widely criticized. Not for the argument that Iraq had weapons of mass destruction (though that was certainly a big criticism) but for this statement: “[T]here are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — there are things we do not know we don’t know.” He was criticized for this phrase “unknown unknowns.” I certainly don’t agree with his politics, but I never understood the backlash he got for this. The view of the world he espoused in this simple statement is one we should all have. It is precisely the “unknown unknowns” that led to our intelligence agencies being unable to fathom a 9/11-like attack. There are always things we haven’t even thought of that will impact our day, our job, our health, our life. We can’t know what these are, by definition. But, we have to at least acknowledge that such things are out there and that they can severely disrupt what we predicted might have happened. “By knowing more about what we don’t know, we may get a few more predictions right.”

Overall, Silver advocates for a view of the world that is probabilistic, one in which we don’t make black and white assertions about the world, but one in which we acknowledge what we know, how uncertain that knowledge is, and what we don’t know, and use it to make estimates/predictions/forecasts about the world around us. Only by admitting that the world is grey — or better said, at least our understanding of the world is grey — can we hope to make better sense of it. Silver’s book is an initial step in arguing for this view of the world. The next step is trying to take that view to heart and use it in our every day lives. As Silver concludes: “Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.”

The Violinist’s Thumb by Sam Kean

In The Violinist’s Thumb, Sam Kean takes us on a tour of how we have learned about the genes that define us and the implications of some of discoveries behind that science. I admit that biology is not my favorite branch of science, but Kean does an excellent job of telling an engaging story about the people, their world, and their discoveries that have led to our current understanding of how our genes impact everything from our behavior to our talents to our interactions with the rest of nature. For anyone with even a passing interest in our genes and DNA, what makes us tick biologically, and how we’ve learned what we learned, this is a great book.

Kean’s focus is on DNA, the science that, over many years, led to our discovery and subsequent understanding of what DNA does, and the people behind that discovery. As he guides us through the history of DNA science, he also takes us on interesting detours, introducing us to non-scientists whose lives demonstrate the point he is trying to make. These are people with either less common genes or mutations that gave them some benefit, such as more flexible hands for playing, for example, a violin, but often also made them rather sickly and not long for the world.

There are simply too many interesting tidbits in this book to really give them justice. But, I highlight a few that particularly piqued my interest:

  • Ultraviolet light can cause kinks in certain places in DNA. Nearly all animals and plants have enzymes that can fix these kinks. Mammals don’t. That is why mammals sunburn.
  • Women were typically not admitted into the science club. One exception were Catholic nuns, who were unmarried and had the financial support and independence from the church-run convents to pursue science.
  • Polar bears survive on eating seals. Seals have a high abundance of vitamin A, which allows them to survive in the cold, promoting growth of fat cells. Polar bears have adapted to this, and can tolerate high levels of vitamin A, which is stored in their liver. However, the concentrations of vitamin A in their liver is toxic to most all other animals, even other polar bears if they ate another polar bear liver: “As little as one ounce of polar bear liver can kill an adult human, and in a ghastly way.”
  • Our DNA isn’t entirely “human” – about 8 percent is ancient virus DNA, that was introduced as viruses attacked our ancient ancestors.
  • Toxoplasma gondil (Toxo) is a parasite that exists and thrives in the guts of cats. Rats who are infected with it are attracted to cat urine, making them easy prey for cats, and thus spreading the parasite to more cats. “Overall it infects one-third of people worldwide,” settling in our brains. There is evidence that it makes infected people less risk adverse: it can make dopamine and “Some emergency room doctors report that motorcycle crash victims often have unusually high numbers of Toxo cysts in their brains.
  • Viruses probably created the mammalian placenta, the interface between mother and child that allows us to give birth to live young and enables us to nuture our young.”
  • Every known ethnic group worldwide has one of two genetic signatures that help our bodies fight off certain diseases that cannibals catch, especially mad-cow-like diseases that come from eating each other’s brains. This defensive DNA almost certainly wouldn’t have become fixed worldwide if it hadn’t once been all too necessary.”
  • Trauma we experience can be passed down to our children and, even more amazingly, to their children. Women with PTSD from the 9/11 attacks who had kids, particularly those who were in their third trimester at the time, have kids with higher levels of anxiety and acute distress than others in some situations.
  • Possibly the most amazing fact is that a child’s health is more directly related to the father’s diet during his so-called slow growth period, about 9-12 years old: “Even more strangely, the child got a health boost only if the father faced starvation. If the father gorged himself, his children lived shorter lives with more diseases.

The Woman Who Smashed Codes by Jason Fagone

The true makers of history are often hidden from us, either owners of softer voices or casualties of the rhetoric of louder glory-seekers. More often than not, those lost voices below to women and that is the case for Elizebeth Smith Friedman, one of the first people to develop a science for code-breaking and a key, if not the key, figure in the development of the US’s intelligence services. As the author of her story, The Woman Who Smashed Codes, Jason Fagone writes, “It’s not quite true that history is written by the winners. It’s written by the best publicists on the winning team.”

The story of her and her husband, a leading code breaker in his own right, is fascinating, not only for the development of code-breaking as a science and their contributions to more than one war, but also because of the odd and eccentric characters that populate Elizebeth’s life. Her husband, William, was perpetually on the edge of a nervous breakdown, in part due to the extremely long hours both Friedmans put in service to the US government. Maybe most fascinating of all was Elizebeth’s first patron, George Fabyan, who created a compound outside of Chicago — the Riverbank Laboratories — which was a private laboratory researching a multitude of topics, some scientifically sound and others very much of the crack-pot variety. It was at Riverbank that Elizebeth first encounter cryptography and her future husband William. Riverbank was full of would-be scientists, studying a range of topics from hidden messages in Shakespeare’s plays to acoustics, for which it still exists. The compound raised all its own animals and grew much of its own crops for food.

During World War I, there was a dearth of people who understood encryption, much less could decipher the messages of the enemy. William and Elizebeth demonstrated their abilities and developed a true scientific approach to the problem. Both Friedmans had an uncanny knack of seeing patterns in data, at a time when computers weren’t available to help with the task. But, at the same time, one had to discern real patterns and not ones made up by their own brain. As Fagone writes, “Humans are so good at seeing patterns that we are often able to see patterns even when they aren’t really there” and “One way of thinking about science is that it’s a check against the natural human tendency to see patterns that might not be there.” Seeing and identifying real patterns was the first criterion for breaking a code.

During the time the Friedmans were developing the science of cryptography and creating the profession of the cryptanalyst (“a person who analyzes and reads secret communications without the knowledge of the system used”), the world was changing at an incredible pace. Radio communications meant that agents could speak to each other across the globe, without the need to exchange paper. The atom bomb was being developed. Politics were changing too. J. Edgar Hoover was accumulating power in the FBI and was at odds with the military in the use of cryptography. What do you do when you break a code? As was highlighted in the movie The Imitation Game about the life of another famous cryptanalyst, Alan Turing, if you act on the intelligence from the broken code, you reveal the fact that the code is broken to the enemy, leading them to change the code and breaking that stream of intelligence. Her husband called this dilemma “cryptologic schizophrenia.” It is a no-win situation for the cryptanalyst, especially since human lives were often at stake. The FBI was chasing sensationalist news rather than maximizing the benefit to the nation of the broken codes.

The story follows Elizebeth’s career from a scientist building the beginnings of a new scientific field to her work for the government, where she ultimately found a home with the US Navy, where she tracked Nazi spies in South America. She also worked for the US Treasury, intercepting the messages of crime lords working within the US. Throughout it all, Elizebeth simply did her work, serving her country, either not willing or even able to really tout her contributions and role in developing the field. In fact, after the death of her husband, she dedicated much of her life organizing his records and documents, his legacy, at the detriment of her own. However, her work, along with that of her husband, led directly to the spy agencies we have now, such as the CIA and NSA. What they created, however, ultimately led them to become uncomfortable, as the reach of agencies such as the NSA extended far into every aspect of our lives.

An interesting note that relates to our own times. In discussing the context of Germany in the lead-up to World War II, Fagone notes that “The international press covered him [Hitler] like a normal leader. Many Germans did not think he would really do the things he had said he would do.” Perhaps a caution for our own times.

Fagone weaves an excellent story, filling these larger-than-life characters with personality and telling an exciting story involving spies, drug dealers, and the future of Western Civilization. Learning about hidden heroes such as Elizebeth Smith Friedman is always a pleasure, even more so when the story is well executed.