Friday, September 15, 2017

How AI Can Help Small Business Solve Big Problems 

Small business owners and self-employed individuals typically face financial and operational challenges. Artificial intelligence is giving them a leg up through applications such as smarter accounting software and fintech services like expanded access to capital. At the recent AI Frontiers conference in Silicon Valley, Ashok Srivastava, chief data officer at financial software firm Intuit, the creator of TurboTax, QuickBooks and Mint, spoke to KnowledgeWharton about how his firm is using AI to “power prosperity for the current and future generations.”        An edited transcript of the conversation follows. KnowledgeWharton: How did you get interested in AI and data sciences? Ashok Srivastava: It’s an interesting story. In some ways you might say it was predestined. My father was a mathematician and a statistician who worked in many areas of information science, experimental design and so forth. When I was young, he bought me a book on artificial intelligence AI and told me that I had to read it during the summer. Being the good son, I took it and I read it in the university library. It made a tremendous impact on me. Ever since I was a child, I was interested in making things do things for themselves. That was just my way of thinking. I remember that I used to think like that even while playing with toys. AI seemed to be the way to do it. Well, I ended up reading that book and thinking about it, but frankly, I then put it aside and went about my journey in electrical engineering. I got a Ph.D. in electrical engineering and I focused on signal process and control theory and those types of fields. But towards the end of my Ph.D., I became interested in machine learning. That was the point where I started to work in machine learning and neural networks and bringing ideas from signal processing and time series into it. That got me into the field and I’ve been in it ever since. KnowledgeWharton: Intuit has more than 50 million customers. When you look at their financial data, what insights do you get about the economic challenges that young people, and especially small companies, face today? Srivastava: The challenges are extraordinary. You don’t have to look into a vast data set to see them. If you look around what’s happening in our country and around the world today, what you see is that people are trying to be successful. Some are successful, some are at the borderline, and some are not as successful as they would want to be. As you look at that and as you understand what’s happening in the economic fabric of our society, you see that oftentimes people are trying their very best, but they might lack access to capital or access to knowledge or to mentorship or to market forces. “Take QuickBooks Self–Employed. This is a platform that we’ve built which has AI behind it.” One of the key challenges — and I think one of the great opportunities we have at Intuit — is to help bring such insights to individual users so that they can do better and have a better understanding of how they can manage their finances, whether they be professional finances or small business finances or personal finances. KnowledgeWharton: What has Intuit been able to do to help individuals, especially young people, deal with these challenges using AI and machine learning? Srivastava: If you look at economy right now, you see the gig economy coming up. People are spending more time — both young people and older people — doing work for Uber, for Lyft and for other companies where they are parceling out their time to get a job. We’re building technology to help those people. Take QuickBooks Self–Employed. This is a platform that we’ve built which has AI behind it. For instance, if a person is driving for a ride-hailing company, they would not have to say: “This was a business trip this was not a business trip” when they’re doing tax categorization. The machine does it for them automatically. It takes a good deal of machine learning and AI and data to enable something like that. That’s just one of the ways that we’re helping people who are in these new areas of our economy. KnowledgeWharton: How is AI changing the way in which you work with small businesses? Srivastava: If you look at small businesses today, many of them need quick access to capital — for payroll, to buy inventory — to make the business run. Looking for loans or other opportunities to get money is very difficult for them. We have an AI-powered service — QuickBooks Capital — that allows small businesses to get rapid access to credit. If we do this at scale, it drives the entire ecosystem forward. This is one of the most exciting areas because it requires relatively little work on the part of an individual or a small business. We’re very proud of the results here. Some 60% of the members who use this service would not qualify for capital from other sources. It’s a process where we facilitate the capital flow for small businesses through other institutions, and we also provide capital ourselves. But the long-term goal is to do it through others. KnowledgeWharton: For borrowers, credit history is always one of the big challenges. There has been a move, especially by some of the fintechs, to come up with non-traditional measures of credit scoring. Has AI enabled Intuit to do these things? What are some of the lessons you’ve learned? Srivastava: The models we use to understand a person’s past history and to make credit recommendations are based on AI. It’s a combination of methods that we bring together. We bring together rules as well as statistical learning in order to make that happen. It’s very critical that these things be done as close to real time as possible. We want to avoid a situation where a person applies for a loan and then waits for such a long time that the value of the capital is diminished. KnowledgeWharton: Do you see other banks using AI in the same way that Intuit is doing? Srivastava: The financial services industry in total is starting to see that AI and machine learning are critical activities. That’s exciting for not only those of us in the business community, but also for those people who are consumers of these products, because it enables faster, more direct and much higher degree of personalization. KnowledgeWharton: How would you position what you’re doing at Intuit relative to some of the fintechs, especially some of the peer-to-peer lending groups? Srivastava: Our approach is very customer-specific. We think about things from the customer’s needs and then build outward, rather than starting with the technology and then trying to build it forward. This focuses us on the immediate problems that small businesses and consumers face. The fact is that the technology might have some similarity with what others are doing, but the origination is really from a deep understanding of the customer’s problems and what we can do and how we are uniquely positioned to solve that. KnowledgeWharton: Does AI help you to manage risk better than previous technology did? Srivastava: Indeed it does, because one of the things that’s happened, if you look over the last 20 years or so, is the advent of data. There’s also the advent of a tremendous number of rules, not only in the past 20 years, but probably ever since credit started many hundreds of years ago. These are rules on which we can make credit assignments. Well, what’s happening is that we’re starting to bring these two things together so that it can provide a better solution for the end customer. That’s one of the unique aspects of the work we’re doing. “Some 60% of the members who use this service would not qualify for capital from other sources.” KnowledgeWharton: How do you benchmark what you are doing with AI at Intuit against financial institutions in other parts of the world? For example, in China, companies like Ant Financial and Tencent have made huge strides using AI. How do you benchmark yourself against those initiatives? Srivastava: We keep a constant eye on what’s happening with our competitors and our partners and make sure that we have the right balance of technology. As far as benchmarking goes, we have a multi-fold, multi-pronged activity in which we’re not only focused on, let’s say, risk, but we’re also focused on security. We’re focused on governance. For the outside world, we’re also focused on products that can be enabled through chat interfaces, through interfaces that are alternatives to the traditional GUI. This gives us a differentiated portfolio. Each of these elements can be powered with AI and machine learning and statistical methods. That gives us a very rich portfolio to help the end consumer. KnowledgeWharton: Kai-Fu Lee’s book on AI Superpowers argues that Chinese companies are, in some ways, moving faster and are further ahead than American companies. Do you agree with that assessment? If so, what are some of the lessons that can be learned from some of the innovations you’re seeing in China? Srivastava: There’s no doubt that artificial intelligence and machine learning are at the forefront of entire nations’ R&D activities, certainly in China, certainly in the United States and other parts of the world. You’ll find that there are pockets of activity where different parties, different countries, different researchers could be leading. What I think is very critical in all of this is that we maintain the idea that within the developments that we’re doing — and now I’m talking about the AI community at large, regardless of the country that it’s originating from — the AI technology be powered in such a way that it helps the end consumer and the end business person or the end user in the most effective way. At present, everyone does not necessarily take this for granted. As a practitioner of AI, as a person who has done research in this field and the field of machine learning specifically, I think it’s very important that we do that. KnowledgeWharton: Lee mentions a company in China called Smart Finance, which is using AI to make microloans to small borrowers. Is Intuit looking at using AI for microfinance? Srivastava: We’re thinking about several ways to help end consumers. The specifics of whether it would be a microloan or not are things that we’re considering right now. But what I would say is that as we cast our vision out to understand what the key issues are for consumers and small businesses, if it’s something that we can do that’s differentiated, you can be sure that we’re thinking about it. If you look at Intuit’s origin about 35 years ago, it was extremely customer-driven. It was always trying to solve immediate customer problems and building the technology to do that. When you run things that way, it’s likely that the things where there is market demand for, where people have that urgent need, we’re going to be there to address it. “The models we use to understand a person’s past history and to make credit recommendations are based on AI.” KnowledgeWharton: How do you see the relationship between financial inclusion through AI and financial education? Srivastava: Let’s look at it as follows. When a person is starting up or running a small business, let’s say that they’ve decided to open up a new dry-cleaning service in Columbus, Ohio. They need to have the best data and tools available to run that business. That’s their enterprise. That’s what’s going to bring the money home to help their family and their children be successful. In that context, the way we’re thinking about it is that we have tremendous data, and also the ability to extract insights that would be relevant that helps that person make better financial decisions. It helps them bridge potential gaps that they might have in their financial literacy or education so that they can make better financial decisions. In the old days, this was done through mentorship. That person might work with somebody else who ran a dry-cleaning business, let’s say in another city or nearby, and they might compare notes in order to do it. Our society isn’t quite built that way anymore. This is another way that we think we can bring that level of knowledge and expertise to individuals through a high degree of personalization that’s essentially powered by AI and machine learning and data. KnowledgeWharton: What are some of the things that AI cannot do today but which you hope it will be able to do over the next few years? What will be the next big breakthrough? Srivastava: The conversation you and I are having this moment is not something that an AI system can do. Is it something that’s desirable? Well, we can discuss that another time. But I don’t think that we should assume that artificial intelligence capabilities are going to be able to do what humans can do well. As builders of these technologies, we need to see where they are best used and best suited, and then tailor them accordingly to drive those activities. I feel that the realm of creativity — music, art, poetry and literature — these are domains where humans will operate for a long time. I don’t mean to say that AI doesn’t have a role there, but I think we’re going to be very well-suited in those areas. One of the most important things for people to remember is that artificial intelligence is a tool that can be used for many purposes. Here, we’re trying to think about ways to use artificial intelligence to power prosperity for the current and future generations. People need to come together to think about how to solve these big, grand challenges. If we don’t, we will be worse off as a society.


SEX ON THURSDAY | Masturbation for the Working Mathematician 

It is a truth universally acknowledged that a single man in possession of good fortune must masturbate. Despite this, I had sex before I masturbated for the first time, and the masturbation took a great deal of effort. Masturbation is fucking hard, but fucking hard is not masturbation. To clarify, I learned calculus before I masturbated for the first time but I attempted and failed at masturbating before I attempted calculus. Thus, calculus is easier than masturbation but primarily because there are more guides for it. When I looked up how to do a calculus problem, I generally found a nice guide to walk me through it. I’ve never been able to find a good article that teaches boys how to masturbate. However, there are loads of articles for girls. Apparently and surprisingly, it’s more common for girls to struggle with the mysterious magic of masturbation based on my careful analysis of responses on Yahoo Answers. But here’s a perfunctory guide of what to do. There are two primary areas for attacking masturbation. The first is physical technique. The motion to masturbation takes practice and some degree of physical strength. I am a small, weak boy with flaccid arms and tight pants, so I struggled with this one at first. But it is a hurdle you may surprisingly easily overcome through focused practice. The second is inspiration. People often talk about what they masturbate to. Some masturbate to pictures, books, picture books, videos, comics, real-live people, and even poorly written Sun columns. You may just need to find the inspiration that’s right for you. Physical So first of all, you’re probably already familiar with the motion and the rhythm. You can get that shit from porn videos and Charlie Day’s performance in the uncomfortable after-allegations film I Love You, Daddy. But did you know that you can use both hands? This is already a game changer. All the little, literal bastards at my local regional high school would ask me jokingly whether I go left or right, and I never thought to innovate until the summer after my sophomore year at college while doing research on C*-algebras. There’s just something about noncommutative geometry that makes me think “I need both hands for this.” So step one if you’re having trouble with the generally suggested, standard run of the mill, pumping method is to try breaking out your leftover hand. After that, it would make sense to start taking advantage of the machinery well known for sexual things. By that, I of course mean the materials you can purchase at that one pretty sketch sex shop in Ithaca. Feel free to attempt butt stuff, introduce lubrication, put your dick in between your mattress and your bed. The world is your oyster, and oysters resemble vaginas when you look at them from the right angle. So go wild. Fuck a roll of toilet paper. Get creative. Inspiration Inspiration also requires some degree of creativity. I found this to be the easy part, but it’s primarily about finding out what you like and getting rid of your shame. The most important piece of advice I can provide is to feel no shame unless it can get you arrested. This is a core principle I live by in any area of my life but specifically when it comes to masturbation. If you’ve sacrificed your shame but are still struggling, you may just need to look around more and find things you kinda like and move forward. A good rule of thumb is that if you haven’t tried hentai, you haven’t tried hard enough. So step one if you’re having trouble with inspiration is to acknowledge that whatever you’re watching, I probably watched worse when I was 12, without masturbating, with no shame. And here I am, still surviving. After that, just explore, and you’ll likely find something that somewhat works for you. Putting it all together At this point, you have no shame, you’re using both hands, you’re getting creative, and you’veexplored a while. Now it’s just about figuring out how these things fit together. Likely in both of the inspiration and physical categories you’ve found some favorites or at least some things that work well but maybe just can’t get you to finish. I suggest putting them all together. If covering yourself in butter and watching butt stuff work well separately, maybe doing both at once will get you there. Finding out how to masturbate is similar to figuring out how to socialize. You have to develop a personality, figure out your needs, and find out how to get them met. Put all of these suggestions together, and you’ll be firing off loads like your peers in no time. Erogenous Jones is a student at Cornell University. Anals of Mathematics is a guest Sex on Thursday contributor.  
Twitter’s ban on ‘misgendering’ trans people will be used to hunt dissidents: mathematician 

November 28, 2018 LifeSiteNews – It was with great delight that progressive media outlets announced Twitter’s “pro-trans move”: The social media giant was formally banning “deadnaming” or “misgendering” transgender people. “Deadnaming,” it turns out, refers to mentioning the previous name of a transgender person: so mentioning the name “Bruce Jenner” would be “deadnaming,” and referring to Jenner as a “he” would be misgendering. Both are now forbidden.  Trans activists, who have been incredibly successful in their campaign to coerce people into using language that affirms the ideological premises of their movement, are celebrating this move: Pink News stated triumphantly that this sort of language is unacceptable, and has often been used “on Twitter to insult and erase trans people’s identities and right to exist.” One trans activist was quoted saying, “Excellent news everyone: Twitter has finally updated their TOS such that misgenderdeadnaming a trans person is against the site’s rules. Happy hunting.” The purge of heretics has already begun, with radical feminist Meghan Murphy getting shoved off the platform for questioning transgender orthodoxy and noting its inherent ideological opposition to traditional feminism. Trans activists frequently accuse those who disagree with them of attempting to “erase” them in order to justify violence against their opponents, Murphy noted, and physical altercations have already taken place between trans activists and feminists.  Murphy, incidentally, has been consistently targeted by trans activists, and has now been permanently banned from Twitter for making points like this: “My issue isn’t with ‘transgender people,’ per se, but, rather, with men. There is a reason certain spaces are sex-segregated—such as change rooms, bathrooms, women’s shelters, and prisons: because these are spaces where women are vulnerable, and where male predators might target women and girls. These are spaces where women and girls may be naked, and where they do not want to be exposed to a man’s penis, regardless of his insistence that his penis is actually ‘female.’” If such ideas are permitted to proliferate, people might see how reasonable they are—which is precisely why trans activists want them silenced. On Twitter, the probably soon-to-be-banned mathematician Eric Weinstein, who is himself a liberal, also objected strenuously to the new Terms of Service: There’s something VERY suspicious about the social media platforms & their new treatment of Trans issues. I now believe it’s being fashioned cynically as the preferred weapon with which to hunt those who will never give a single inch of scientific ground to political pressure. This banning of “deadnaming” is preposterous. We need to honor work attributed before transition! How does this differ from our need to discuss scientific papers published under a “maiden name”? Or contributions before a Muslim name is chosen e.g. Cassius Clay, Cat Stevens. This makes being a historian impossible. Further treating Trans MF *exactly* the same as born MF would be medical malpractice. Etc. So what you’re really doing is saying that biology, history, science and medicine are only allowed to exist at the whim of political activists. This is like Caligula making his horse a senator. Any competent independent person knows that if they don’t treat the horse as a senator, they will be disappeared. So it’s done to select against strong independent clear headed thinkers by forcing them to identify themselves. Weinstein is precisely right—Twitter is quite literally banning historical facts. Consider this: Under Twitter’s new rules, it is forbidden to post the following statement: “Bruce Jenner won the men’s decathlon at the 1976 Olympics.” Of course, it would be factually incorrect to state that Caitlyn Jenner secured victory at a men’s event, and Bruce hadn’t even conceptualized himself as Caitlyn yet. But under the new Orwellian rules governing one of mankind’s primary methods of communication—social media—these facts are now unsayable. Not all trans activists are on board with this new state of affairs. One tweeted in disbelief that, “Twitter is now suspending trans people who acknowledge their own birth sex. Yes, this is true, not speculation. I, like every other ‘trans woman’, am biologically male. I, like many other ‘trans women’, take hormones to make myself look more female. I, like few other ‘trans women’, have had surgery to ease body dysmorphia. We are what we are. Twitter can’t suspend reality.” They can, however, suppress it quite effectively. Trans activists, of course, are already hunting down heretics to make life easier for the censors over at Twitter headquarters. Twitter’s new rules are simply a sign of things to come. The social media monopolies now collectively possess the ability to tip the scales towards their preferred ideological allies, and they have given every indication that LGBT activists and social “progressives” are in. Christians, pro-lifers, and an assortment of old-guard liberals including a few beleaguered feminists who, like Murphy, are questioning many of their previous assumptions at the realization that only those on the Right are willing to defend their right to speak, on the other hand, are very much out.  Social media now connects much of the world, and over the next few years, I expect we will see more aggressive, wide-scale purges that seek to eliminate those of us with traditionalist values from these platforms by way of new rules that are designed to eliminate our voices. I’d like to say that I have the answer to what we should do to combat this or stop it, but I simply don’t have one. As long as the social media giants have the monopoly on communication, they control the narrative—and it does not appear likely that this will end well for those who have a narrative of their own. 
The most beautiful and important mathematical equations 

Math is more of a marathon than a sprint — it’s a long, slow and steady grind, with rare moments of breakthrough. Still, once in a while, we do get those prized “Eureka” moments, those short lines of letters and numbers which change science forever. Here are some of the most famous equations, from the ancient Greeks to modern physics. Pythagora’s theorem 530 BC This is pretty one of the foundating pillars all geometry: in a right triangle, the square of the hypotenuse the side opposite to the right angle is equal to the sum of the squares of the other two. The theory is generally attributed to the Greek mathematician Pythagoras, though there is some evidence that Babylonian mathematicians understood the formula. It’s also very possible that the theorem was known by many people, but he was the first to prove it. The theorem has been given numerous proofs — possibly the most for any mathematical theorem. They are very diverse, including both geometric proofs and algebraic proofs, with some dating back thousands of years. Complex numbers The Italian mathematician Gerolamo Cardano is the first known to have introduced complex numbers, calling them “fictitious” at the time. However, the mathematical development of “i” as the imaginary number representing the square root of -1 is attributed to Leonhard Euler, one of the most important mathematicians and scientists in human history. Complex numbers are basically numbers that don’t really exist, but which are very useful for a number of calculations. They consist of numbers with a real part the numbers we all know and an imaginary part the i represented here and have practical applications in many fields, including physics, chemistry, biology, economics, electrical engineering, and statistics. The logarithms Logarithms are basically the inverse function of exponentiation. You need a number N, a base a, and the logarithm of N in base a will be x, where N equals a to the power of x. It might seem like only a different way of writing the same thing and in a sense, it is, but logarithms have a myriad of practical applications, being used in psychology, economy, and measurements of many physical phenomena such as pH or earthquake magnitude. Logarithms were publicly propounded by John Napier in 1614, in a book titled Mirifici Logarithmorum Canonis Descriptio Description of the Wonderful Rule of Logarithms — a fitting title. A special case of logarithm is the natural logarithm —  e, where e is an irrational and transcendental number approximately equal to 2.71828182845. In fact, e itself has a fascinating history and an impressive number of applications, but that’s a story for another time. Calculus Few fields of mathematics have been as impactful as calculus. Developed in the 17th century by Isaac Newton and Gottfried Wilhelm Leibniz, calculus is widely used in science, engineering, and economics. Calculus usually focuses on dealing with small quantities, particularly infinitely small quantities. Through calculus, these can be treated as real numbers, even though they are technically infinitely small. For a simpler visualization, integration, depicted above, can be thought of as measuring the area under a curve, defined by a function. The Law of Gravity Speaking of Newton, he is also “responsible” for one of the world’s most famous and spectacular equations: the law of gravity. The law basically describes how any two bodies of masses m1 and m2 are attracted to each other. The force F1, F2 is inversely proportional to the square of the distance between them r. The only remaining factor, G, is a gravitational constant. The nature of this constant remains elusive. General Relativity For almost 200 years, Newton’s law defined our level of understanding of mechanics. Einstein’s work in the 20th century took things to the next level — these two achievements tower on the highest pedestals in the world of physics. General relativity is essentially a geometric theory of gravitation, generalizing Newton’s theory providing a unified description of gravity as a geometric property of space and time — or spacetime. In particular, Einstein showed not only that there is such a thing as “spacetime” merging the three dimensions with the 4th dimension of time, but he also showed that this spacetime can be curved by gravity, with the curvature being directly related to the energy and momentum of whatever matter and radiation are present. Second law of thermodynamics The Second Law of Thermodynamics is why we can’t have nice things in the Universe. Jokes aside, the four laws of thermodynamics define fundamental physical quantities temperature, energy, and entropy that characterize thermodynamic systems. The second one, in particular, stands out here due to its simplicity, but absolutely massive implications. The law essentially states that the sum of the entropies of the interacting thermodynamic systems must always increase, or at the very most remain constant. When energy changes from one form to another or matter moves around, the entropy or disorder in a closed system increases. All differences in temperature, pressure, and density tend to flat out after a while Maxwell’s Equations Simply put, Maxwell’s equations are to electromagnetism what Newton’s law is to mechanics. They provide a mathematical foundation for classical electromagnetism, classical optics, and electric circuits. They are widely used in the very device you are reading this on — basically, all electronic devices. Maxwell’s laws describe how electric and magnetic fields are generated by charges, currents, and changes of the fields. A significant breakthrough was the demonstration that electric and magnetic fields propagate at the speed of light. Euler’s Identity Lastly, this is quite possibly the most elegant equation, a thing of supreme beauty, because it involves all the “basic” numbers: 0, which is neutral for addition and subtraction; 1, which is neutral for multiplication and division; e, which is Euler’s number see above, the base of natural logarithms; i is the imaginary unit see above; and π is pi, the ratio of the circumference of a circle to its diameter. Finding a relation that unifies all these numbers is nothing short of breathtaking, and seems quite unlikely. The demonstration isn’t exactly simple, but you can see it here. It’s only fitting that Stanford University mathematics professor Keith Devlin described the equation, saying that “like a Shakespearean sonnet that captures the very essence of love, or a painting that brings out the beauty of the human form that is far more than just skin deep, Euler’s equation reaches down into the very depths of existence” It’s not often that mathematics and physics boil down to simple and elegant equations — but when they do, it’s quite a sight to behold. Enjoyed this article? Join 40,000+ subscribers to the ZME Science newsletter. Subscribe now!
Made in (ancient) China: amazing inventions from the Far East 

The phrase ‘Made in China’ probably makes you think of poorly-made plastic tat, probably unfairly given the amount of high-end goods coming from the country at the moment the iPhone being an excellent example. But as James M. Russell discovers in his new book, Plato’s Alarm Clock, which hosts a collection of amazing breakthroughs and devices from throughout history, some incredible inventions have come from the the Far East. Here are three such inventions that show just how long China has been at the forefront of science and technology. The Mechanical Clock First Invented 8th Century AD Sometimes the joy of history lies in the small details, such as the original names of inventions. The world’s first mechanical clock went by the name of the ‘Waterdriven Spherical Birds’-Eye-View Map of the Heavens’. Invented by Yi Xing, a Buddhist mathematician and monk, in 725 AD, it was developed as an astronomical instrument that incidentally also worked as a clock. In spite of the name it wasn’t strictly speaking a water clock one in which the quantity of water is used to directly measure time. However, it was water-powered – a stream of falling water drove a wheel through a full revolution in twenty-four hours. The Chinese engineer Su Song’s hydro-mechanical clock tower The internal mechanism was made of gold and bronze, and contained a network of wheels, hooks, pins, shafts, locks and rods. A bell chimed automatically on the hour, while a drumbeat marked each quarter-hour. Another splendidly named clock was the ‘Cosmic Engine’ built by the Chinese inventor Su Song between 1086 AD and 1092 for an emperor of the Sung Dynasty. This was also a mechanical astronomical clock, but it was huge, spreading over several storeys in a tower that was over 10 metres 35 feet high. It was made of bronze and powered by water. At the top, a sphere on a platform kept track of the motion of the planets. The clock remained in place and working until 1126 when it was lost in a Tatar invasion. The Crossbow First Invented c. 6th Century BC The crossbow is a mechanical application of the bow-and-arrow principle. It generally consists of a horizontal bow known as a ‘prod’, which is mounted on a stock. The projectiles that it fires are called bolts or quarrels. Crossbows were another significant step in people’s ability to wage war. While archery was a highly skilled craft that generally had to be learned from childhood by dedicated archers, the crossbow could be mastered by any soldier or new recruit with a few weeks’ training. This enabled far more armies to get up to fighting condition within a short amount of time. A Roman ballista crossbow from Discorso della Religione Antica de Romani, 1570 The earliest definite evidence we have of the crossbow comes from the 6th Century BC in ancient China and the neighbouring areas. A 4th-Century BC text mentions a giant crossbow being used in the 6th or 5th Century BC, while Sun Tzu’s classic text on military tactics, The Art of War, which dates to 500–300 BC, mentions the crossbow several times. When it comes to artefacts, bronze crossbow bolts that date to the mid-5th Century BC have been discovered in burial sites around China, while crossbow stocks small enough to be handheld have been found at a dig in Qufu, Shandong, dating to the 6th Century BC. A more controversial question is when repeating crossbows were first used. These are crossbows that can rapidly fire multiple bolts. There is some suggestion these might date to earlier, but they are generally credited to the famous military adviser Zhuge Liang Ad 181–234. His version, which would be deadly when used by massed ranks of soldiers, could fire two to three bolts at once. It had a magazine loaded with bolts over the bow, and a lever driven mechanism to replenish the bolts. The weapons of this period had a range of about 100 metres 330 feet. In the medieval period, the Chinese also developed a 12-round repeater crossbow that continued to be used until the nineteenth century, and has been compared to the machine gun in terms of its destructive capacity. Zhang’s Seismoscope Date invented 2nd Century AD The seismoscope: a modern recreation of Zhang Heng’s apparatus for detecting earthquakes SSPL Images A seismometer or seismograph is a scientific instrument that measures distant earthquakes and volcanic activity through the tiny movements of the ground that they cause. Remarkably, the first seismometer was invented nearly 2,000 years ago in China. It is known as Zhang Heng’s seismoscope. Its inventor Zhang Heng thought that the main cause of earthquakes was chaotic air motion, theorizing that: … as long as air is not stirred, but lurks in a vacant space, it reposes innocently, giving no trouble to objects around it. But any cause coming upon it from without rouses it, or compresses it, and drives it into a narrow space … and when opportunity of escape is cut off, then ‘With deep murmur of the Mountain it roars around the barriers’, which after long battering it dislodges and tosses on high, growing more fierce the stronger the obstacle … In Zhang’s device the earth tremors made a bronze ball fall out of any one of eight tubes in the shape of dragons’ heads. The ball then fell into the mouth of a metal toad, whose position indicated the orientation of the seismic wave. It isn’t fully known how the device worked. The eight mobile arms definitely raised a catch via a crank, and a lever, which released the ball. Apparently the device also included a pendulum hung from a bar. This suggests that the driving force was inertia – a small movement in the pendulum perhaps triggered the motion that was transformed into a slight force on the correct lever. However, there are no clear historical documents or remaining examples, so modern attempts at reconstructions involve a considerable degree of speculation and interpretation of the few mentions that were made of the device in contemporary texts. Nonetheless, it seems clear that Zhang’s seismoscope did use similar technology to early modern seismographs. After the death of Zhang, it was not until 1783 that a simple seismograph was deployed by an Italian scientist called Schiantarelli, who used it to measure a major earthquake in Calabria. Zhang Heng, the Chinese Da Vinci Porcelain statuette of the Chinese astronomer, mathematician and seismologist, Zhang Heng 78-139 AD SSPL Images It’s worth taking a moment to celebrate the life of Zhang Heng 78–139 AD, whose expertise across a wide range of fields – including maths, science, engineering, cartography, art and poetry – have led to him being described as the Leonardo da Vinci of ancient China. He was initially a minor civil servant, but rose to become the Chief Astronomer and Palace Attendant at the imperial court. As well as his seismoscope, he invented a water-powered astrolabe a three-dimensional model of the solar system, gave an improved estimate for the number pi, and catalogued over 2,500 stars. He also gave an advanced description of the Moon, its ‘dark side’, and how lunar and solar eclipses proved that the Moon must be a spherical object. And if all that wasn’t enough for one individual, he was also a renowned poet, whose work was still being studied years after his death. Plato’s Alarm Clock and Other Amazing Ancient Inventions by James M. Russell is out now in hardback, £9.99, Michael O’Mara Books Follow Science Focus on Twitter, ,  and Flipboard

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