**Should-Read**: We are moving from a statistical culture that focused on the taming of sampling variation to a different statistical culture that... what? Guarding against overfitting and computational economy seem to be the most important goals, and they are linked And behind everything lurks the problem of induction: in what ways are we justified in assuming that the future will be like the past, and in what ways are we not? For if we assume the future will be like the past in ways that it will not, we are simply hosed: **Michael Jordan**: [On Computational Thinking, Inferential Thinking and Data Science](https://bids.berkeley.edu/events/computational-thinking-inferential-thinking-and-data-science): "The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis... >...That classical perspectives from these fields are not adequate to address emerging problems in Data Science is apparent from their sharply divergent nature at an elementary level—in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and...
DXC Technology (DXC) got early termination of the waiting period under the Hart-Scott-Rodino Antitrust Improvements Act of 1976 (HSR Act) by the U.S. Federal Trade Commission (FTC).
Mathematics is very unlike any other discipline, and 2017 demonstrated it in spades! Imagine, if you will, what it takes to prove out a hypothesis or conjecture in science. Years of careful and often costly experimentation or observation produces results that, should any tiny detail lead to results that might overturn part or all of the hypothesis and its predictions through new observations and experiments, will lead to the hypothesis being tested more acutely or perhaps continue to be used after having its limitations considered (the best case scenario), shelved, or unceremoniously dumped into the rubbish bin of failed scientific propositions. Starts With a Bang's Ethan Siegel recently lamented that scientific proof is a myth: You've heard of our greatest scientific theories: the theory of evolution, the Big Bang theory, the theory of gravity. You've also heard of the concept of a proof, and the claims that certain pieces of evidence prove the validities of these theories. Fossils, genetic inheritance, and DNA prove the theory of evolution. The Hubble expansion of the Universe, the evolution of stars, galaxies, and heavy elements, and the existence of the cosmic microwave background prove the Big Bang theory. And falling objects, GPS clocks, planetary motion, and the deflection of starlight prove the theory of gravity. Except that's a complete lie. While they provide very strong evidence for those theories, they aren't proof. In fact, when it comes to science, proving anything is an impossibility. The reason that absolute scientific proof is an impossibility is because reality is, in reality, very complicated. Our limitations as real people living in the real world constrains our ability to see, test or even to imagine every possible detail of existence that might affect how reality works. As a result, every genuine scientific theory and hypothesis can potentially be shown to be false, which is a major part of what separates science from pseudoscience. There is only one serious human discipline that doesn't have that limitation: mathematics. One of the most fun descriptions that gets into what mathematical proofs can do that scientific proofs cannot that we saw this year was presented in Thomas Oléron Evans and Hannah Fry's book The Indisputable Existence of Santa Claus: The Mathematics of Christmas: The scientific method takes a theory - in our case that Santa is real - and sets about trying to prove that it is false. Although this may seem a little counterintuitive on the surface, it actually does make a lot of sense. If you go out looking for evidence that Santa doesn't exist and don't find any ... well then, that is pretty revealing. The harder you try, and fail, to show that Santa cannot exist, the more support youy have for your theory that he must. Eventually, when enough evidence has been gathered that all points in the same direction, your original theory is accepted as fact. Mathematical proof is different. In mathematics, proving something "beyond all reasonable doubt" isn't good enough. You have to prove it beyond all unreasonable doubt as well. Mathematicians aren't happy unless they have demonstrated the truth of a theory absolutely, irrefutably, irrevocably, categorically, indubitably, unequivocally, and indisputably. In mathematics, proof really means proof, and once something is mathematically true, it is true forever. Unlike, say, the theory of gravity - hey, Newton? So with the differences between the nature of scientific and mathematical proofs in mind, let's get to the biggest math stories of 2017 where, because we're practical people who live in the real world, we've focused upon the stories where the outcome of maths can make a practical difference to people's lives. Have you ever walked around with an open-topped cup of coffee? If so, you probably have run into the problem of having the contents of your mug slosh and spill out as you attempted to walk with it, which wastes both your precious coffee and, if it gets on your hands, potentially causes burns requiring first aid. Fortunately, 2017 is the year in which mathematics solved the problem of sloshing coffee! Americans drink an average of 3.1 cups of coffee per day; for many people, the popular beverage is a morning necessity. When carrying a liquid, common sense says to walk slowly and refrain from overfilling the container. But when commuters rush out the door with coffee in hand, chances are their hastiness causes some of the hot liquid to slosh out of the cup. The resulting spills, messes, and mild burns undoubtedly counteract coffee's savory benefits. Sloshing occurs when a vessel of liquid—coffee in a mug, water in a bucket, liquid natural gas in a tanker, etc.—oscillates horizontally around a fixed position near a resonant frequency; this motion occurs when the containers are carried or moved. While nearly all transport containers have rigid handles, a bucket with a pivoted handle allows rotation around a central axis and greatly reduces the chances of spilling. Although this is not necessarily a realistic on-the-go solution for most beverages, the mitigation or elimination of sloshing is certainly desirable. In a recent article published in SIAM Review, Hilary and John Ockendon use surprisingly simple mathematics to develop a model for sloshing. Their model comprises a mug on a smooth horizontal table that oscillates in a single direction via a spring connection. "We chose the mathematically simplest model with which to understand the basic mechanics of pendulum action on sloshing problems," J. Ockendon said. But that's not the best part of the story! That part comes from where the idea for the study originated, which illustrates Evans and Fry's point of the extremes to which mathematicians will go that scientists do not: The authors derive their inspiration from an Ig Nobel prize-winning paper describing a basic mechanical model that investigates the results of walking backwards while carrying a cup of coffee.... The authors evaluate this scenario rather than the more realistic but complicated use of a mug as a cradle that moves like a simple pendulum. To further simplify their model, they assume that the mug in question is rectangular and engaged in two-dimensional motion, i.e., motion perpendicular to the direction of the spring's action is absent. Because the coffee is initially at rest, the flow is always irrotational. "Our model considers sloshing in a tank suspended from a pivot that oscillates horizontally at a frequency close to the lowest sloshing frequency of the liquid in the tank," Ockendon said. "Together we have written several papers on classical sloshing over the last 40 years, but only recently were we stimulated by these observations to consider the pendulum effect." The Ockendons focused on rectangular containers in and indicate that their mathematical study may be extended to include cylindrical cups in the future, but rest assured, the mathematical work won't stop there until cups of every possible geometry have been considered! Still, while the maths that might lead to preventing sloshing and spilling may be terribly important for coffee drinkers, it's not the biggest math story of the year, so we much continue our search! In the sport of basketball, the practical ability to skillfully place a round ball through an elevated circular ring with netting attached to it is one that can determine whether a player earns millions of dollars a season as a professional athlete or does not. Sometimes however, the limits of skilled players seem to stretch as they appear have an easier time in consistently shooting baskets than at other times, where they seem to have what's called a "hot hand" as they exceed their usual level of performance over an extended period of time during a game. Scientists and statisticians have studied this apparent phenomenon over the years and have chalked it all up to randomness, where statistically speaking, it is something that periodically happens when a player's performance is much closer to the long end of the tails of a normal distribution describing their performance than it is to their mean performance, where the apparent "hot hand" is little more than a cognitive illusion as people who see it are really being fooled by randomness. That consensus view was challenged in a paper by Joshua Miller and Adam Sanjurjo, who instead argue that earlier finding was based on a misreading of the math of probabilities. Our surprising finding is that this appealing intuition is incorrect. For example, imagine flipping a coin 100 times and then collecting all the flips in which the preceding three flips are heads. While one would intuitively expect that the percentage of heads on these flips would be 50 percent, instead, it's less. Here's why. Suppose a researcher looks at the data from a sequence of 100 coin flips, collects all the flips for which the previous three flips are heads and inspects one of these flips. To visualize this, imagine the researcher taking these collected flips, putting them in a bucket and choosing one at random. The chance the chosen flip is a heads – equal to the percentage of heads in the bucket – we claim is less than 50 percent. If flip 42 were heads, then flips 39, 40, 41 and 42 would be HHHH. This would mean that flip 43 would also follow three heads, and the researcher could have chosen flip 43 rather than flip 42 (but didn't). If flip 42 were tails, then flips 39 through 42 would be HHHT, and the researcher would be restricted from choosing flip 43 (or 44, or 45). This implies that in the world in which flip 42 is tails (HHHT) flip 42 is more likely to be chosen as there are (on average) fewer eligible flips in the sequence from which to choose than in the world in which flip 42 is heads (HHHH). This reasoning holds for any flip the researcher might choose from the bucket (unless it happens to be the final flip of the sequence). The world HHHT, in which the researcher has fewer eligible flips besides the chosen flip, restricts his choice more than world HHHH, and makes him more likely to choose the flip that he chose. This makes world HHHT more likely, and consequentially makes tails more likely than heads on the chosen flip. In other words, selecting which part of the data to analyze based on information regarding where streaks are located within the data, restricts your choice, and changes the odds. Similar mathematical reasoning applies for the statistics behind the counterintuitive phenomenon described by the Monty Hall problem. It's a really cool insight, although one that we're afraid has limited potential for practical application, which is why we cannot call this the biggest math story of the year. The field of mathematics is notorious for developing conjectures that defy proof for centuries. 2017 saw the delivery of a formal proof of the Kepler Conjecture, which identifies the maximum density by which spherical objects of equal size can be packed together within a given space, which was first proposed by Johannes Kepler in 1611. In the real world, the results can be seen anywhere spherically-shaped objects are packed together, such as oranges packed into a rectangular crate, which if optimally packed according to the Kepler Conjecture, will mean that a little over 74% of the available space will be filled by orange, while the rest of the space would be empty. 306 years later, a team of 19 researchers led by Thomas Hales appears to have finally cracked it and published a formal proof that can be confirmed by mathematician referees. Or rather, by their computers, because Hales' team's proof is so sufficently complex that modern computing technology is the only way that humans have to verify the findings. In 2003, Hales anticipated that it would take 20-person years of labor for computers to verify every step of the proof in launching the project that finally delivered the proof 14 calendar years later. Not every mathematical conjecture involving discrete geometry endures for centuries however. Some only last for decades, as was the case in Zilin Jiang's and Alexandr Polyanskii's proof of László Fejes Tóth’s zone conjecture, which says that if a unit sphere is completely covered by several zones, their combined width is at least equal to the irrational mathematical constant pi. At first glance, these kinds of proofs may not seem to to have terribly practical applications, but effective solutions to discrete geometry problems like these do have real world impact. Discrete geometry studies the combinatorial properties of points, lines, circles, polygons and other geometric objects. What is the largest number of equally sized balls that can fit around another ball of the same size? What is the densest way to pack equally sized circles in a plane, or balls in a containing space? These questions and others are addressed by discrete geometry. Solutions to problems like these have practical applications. Thus, the dense packing problem has helped optimize coding and correct mistakes in data transmission. A further example is the four-color theorem, which says that four colors suffice to plot any map on a sphere so that no two adjacent regions have the same color. It has prompted mathematicians to introduce concepts important for graph theory, which is crucial for many of the recent developments in chemistry, biology and computer science, as well as logistics systems. And also secure, garble-free, long distance (including interplanetary) communications, to name an up-and-coming application that would be an outcome for doing this kind of math! Perhaps the biggest mathematical breakthrough honored in 2017 was the discovery by Maryanthe Malliaris and Saharon Shelah that two different variants of infinity, long thought to be different in nature, are actually equal in size. In a breakthrough that disproves decades of conventional wisdom, two mathematicians have shown that two different variants of infinity are actually the same size. The advance touches on one of the most famous and intractable problems in mathematics: whether there exist infinities between the infinite size of the natural numbers and the larger infinite size of the real numbers. The problem was first identified over a century ago. At the time, mathematicians knew that “the real numbers are bigger than the natural numbers, but not how much bigger. Is it the next biggest size, or is there a size in between?” said Maryanthe Malliaris of the University of Chicago, co-author of the new work along with Saharon Shelah of the Hebrew University of Jerusalem and Rutgers University. In their new work, Malliaris and Shelah resolve a related 70-year-old question about whether one infinity (call it p) is smaller than another infinity (call it t). They proved the two are in fact equal, much to the surprise of mathematicians.... Most mathematicians had expected that p was less than t, and that a proof of that inequality would be impossible within the framework of set theory. Malliaris and Shelah proved that the two infinities are equal. Their work also revealed that the relationship between p and t has much more depth to it than mathematicians had realized. Surprise is the right word, because Malliaris' and Shelah's result is very counterintuitional. And incredibly cool. But alas, not the biggest math story of 2017! There is a class of mathematical problems named after Diophantus of Alexandria that are, unsurprisingly, known as Diophantine equations, whose components are made up of only sums, products, and powers in which all the constants are integers, and where the only solutions of interest are expressed as either integers or as rational numbers. If you think back to when you might have taken a class in algebra and recall those really wicked polynomial equations that you encountered or had to factor, that's the kind of problem that we're talking about. Wicked being the operative word, because there's a really difficult Diaphantine equation that mathematicians have been working to solve for over four decades called the "cursed curve", where they've been seeking to prove that the equation only has a limited number of rational solutions. In November 2017, a team of mathematicians succeeded. Last month a team of mathematicians — Jennifer Balakrishnan, Netan Dogra, J. Steffen Müller, Jan Tuitman and Jan Vonk — identified the rational solutions for a famously difficult Diophantine equation known as the “cursed curve.” The curve’s importance in mathematics stems from a question raised by the influential mathematician Jean-Pierre Serre in 1972. Mathematicians have made steady progress on Serre’s question over the last 40-plus years, but it involves an equation they just couldn’t handle — the cursed curve. (To give you a sense of how complicated these Diophantine equations can get, it’s worth just stating the equation for the cursed curve: y4 + 5x4 − 6x2y2 + 6x3z + 26x2yz + 10xy2z − 10y3z − 32x2z2 − 40xyz2 + 24y2z2 + 32xz3 − 16yz3 = 0.) In 2002 the mathematician Steven Galbraith identified seven rational solutions to the cursed curve, but a harder and more important task remained: to prove that those seven are the only ones (or to find the rest if there are in fact more). The authors of the new work followed Kim’s general approach. They constructed a specific geometric object that intersects the graph of the cursed curve at exactly the points associated to rational solutions. “Minhyong does very foundational theoretical work in his papers. We’re translating the objects in Kim’s work into structures we can turn into computer code and explicitly calculate,” said Balakrishnan, a mathematician at Boston University. The process proved that those seven rational solutions are indeed the only ones. "Kim's general approach" in this case refers to the work of the University of Oxford's Minhyong Kim, who has been working to apply concepts derived from the science of physics to the solution of difficult mathematical problems. The proof for the seven solutions of the cursed curve is an exciting development for number theory, where the intersection of physics and mathematics brings us up to the biggest math story of the year. We began this article with a discussion of the main difference between the standards of scientific proof and mathematical proof. Nowhere in 2017 is that difference more on display than in the biggest math story of the year, in which mathematicians have demonstrated that the famed Navier-Stokes equations that describe the flow of fluids in the real world, break down under "certain extreme conditions". The Navier-Stokes equations capture in a few succinct terms one of the most ubiquitous features of the physical world: the flow of fluids. The equations, which date to the 1820s, are today used to model everything from ocean currents to turbulence in the wake of an airplane to the flow of blood in the heart. While physicists consider the equations to be as reliable as a hammer, mathematicians eye them warily. To a mathematician, it means little that the equations appear to work. They want proof that the equations are unfailing: that no matter the fluid, and no matter how far into the future you forecast its flow, the mathematics of the equations will still hold. Such a guarantee has proved elusive. The first person (or team) to prove that the Navier-Stokes equations will always work — or to provide an example where they don’t — stands to win one of seven Millennium Prize Problems endowed by the Clay Mathematics Institute, along with the associated $1 million reward. Mathematicians have developed many ways of trying to solve the problem. New work posted online in September raises serious questions about whether one of the main approaches pursued over the years will succeed. The paper, by Tristan Buckmaster and Vlad Vicol of Princeton University, is the first result to find that under certain assumptions, the Navier-Stokes equations provide inconsistent descriptions of the physical world. By "inconsistent descriptions of the physical world", Buckmaster's and Vicol's work is pointing to the situation where, when given exactly the same fluid and starting conditions, instead of providing a single, unique solution for what the flow of fluids will be at a particular point of time in the future, the Navier-Stokes equations will instead provide two or more non-unique solutions, where they cannot accurately predict the future state of the resulting fluid flow. That puts the physics associated with the Navier-Stokes equations into the situation where physicists and engineers must consider the limitations of where they can be shown to be valid in using them in their applications. Since Navier-Stokes is considered to be the "gold standard of the mathematical description of a fluid flow", that finding could potentially disrupt a whole lot of apple carts in multiple sciences and engineering disciplines whose applications might approach those limits. That disruptive potential is why the story of the mathematical proof of the ability of the Navier-Stokes equations to describe unique solutions for all fluid flows under all conditions counts as being the biggest math story of 2017! Previously on Political CalculationsThe Biggest Math Story of the Year is how we've traditionally marked the end of our posting year since 2014. Here are links to our previous editions, along with our coverage of other math stories during 2017: The Biggest Math Story of the Year (2014) The Biggest Math Story of 2015 The Biggest Math Story of 2016 The Biggest Math Story of 2017 Connecting the Lonely Dots The Map of Mathematics Illegal Numbers in America Big Data Gone Bad for Fighting Crime Think Outside of the Box Composition of Functions Babylonian Trig Powers of Ten Computing All the Prime Numbers from Here to Infinity How to Use Math to Optimally Divide Up Resources Have a Merry Christmas, and we'll see you again in the New Year!
Hewlett Packard (HPE) reported earnings about a month ago. What's next for the stock? We take a look at earnings estimates for some clues.
Within unemployment rates at a 17-year low, job markets have become more constrained. These industries continue to grow and pay really well.
Aron Vellekoop Len/Getty Images Because I teach a course on Product Management at Harvard Business School, I am routinely asked “what is the role of a Product Manager?” The role of a Product Manager (PM) is often referred to as the “CEO of the Product.” I disagree because, as Martin Eriksson points out, “Product managers simply don’t have any direct authority over most of the things needed to make their products successful – from user and data research through design and development to marketing, sales, and support”. PMs are not the CEO of product and their roles vary widely depending on a number of factors. So, what should you consider if you’re thinking of pursuing a PM role? As an aspiring PM, there are three primary considerations when evaluating the role: Core Competencies, Emotional Intelligence (EQ), and Company Fit. The best PMs I have worked with have mastered the core competencies, have a high EQ, and work for the right company for them. Beyond shipping new features on a regular cadence and keeping the peace between engineering and the design team, the best PMs create products with strong user adoption that have exponential revenue growth and perhaps even disrupt an industry. Core Competencies There are core competencies that every PM must have – many of which can start in the classroom – but most are developed with experience and good role models and mentoring. Some examples of these competencies include: Conducting customer interviews and user testing Running design sprints Feature prioritization and roadmap planning The art of resource allocation (it is not a science!) Performing market assessments Translating business-to-technical requirements, and vice versa Pricing and revenue modeling Defining and tracking success metrics These core competencies are the baseline for any PM and the best PMs hone these skills over years of defining, shipping, and iterating on products. These PMs excel at reflecting on where each of these competencies contributed to the success or failure of their products and continuously adjust their approach based on customer feedback. Emotional Intelligence (EQ) A good PM may know the Do’s and Don’ts of a customer interview, but the best PMs have the ability to empathize with customers in that interview, are tuned into their body language and emotions, and can astutely suss out the true pain-points that their product or feature will address. A PM with a high EQ has strong relationships within their organization and they have a keen sense of how to navigate both internal and external hurdles to ship a great product. Here’s a deeper look at how the four key traits of EQ, as defined by Daniel Goleman, relate to the PM role: Relationship management: Probably one of the most important characteristics of a great PM is their relationship management skills. By forming authentic and trustworthy connections with both internal and external stakeholders, the best PMs inspire people and help them reach their full potential. Relationship management is also vital in successful negotiation, resolving conflicts and working with others toward a shared goal which is especially challenging when a PM is tasked with balancing the needs of customers, resource-constrained engineering teams, and the company’s revenue goals. Authentic and trusting relationships within an organization can lead to more support if additional funding is needed for a product or to sway an engineer to include a quick bug fix in the next sprint. Outside an organization, these skills could encourage existing customers to beta test a new feature for early feedback or convince a target customer to try the MVP of a product still in stealth mode. These relationship skills can also be what makes the difference between having irate customers because of a bug introduced into the product and those who say, “No worries, we know you’ll fix this!” Self-awareness: PMs must be self-aware so as to remain objective and avoid projecting their own preferences onto users of their products. If a PM is in love with a feature because it addresses their own pain points — PMs are often super users of the products for which they are responsible — they may cause a user to say they love it too, just to please the PM (“False positive feature validation”). If not self-aware, a PM may push to prioritize a feature they conceived even when all the customer interviews and evidence is stacked against it. This lack of self-awareness could derail more important priorities and/or damage the PM’s relationship with engineers who may lose confidence in their PM when the feature isn’t readily adopted by users. Self-management: Being a PM can be incredibly stressful. The CEO wants one thing, the engineering team another, and customers have their own opinions about feature priorities. Managing tight deadlines, revenue targets, market demands, prioritization conflicts, and resource constraints all at once is not for the faint of heart. If a PM cannot maintain their emotions and keep it cool under pressure, they can quickly lose the confidence of all their constituents. The best PMs know how to push hard on the right priorities, with urgency, but without conveying a sense of panic or stress. These PMs also know when to take a breath and step away if needed, to regroup. Social awareness: According to Goleman, the competencies associated with being socially aware are Empathy, Organizational Awareness, and Service. PMs must understand customers’ emotions and concerns about their product as much as they understand the concerns of the sales team on how to sell that product, or the support team on how to support it, or the engineering on how to build it. PMs have to have a deep understanding of how the organization operates and must build social capital to influence the success of their product – from obtaining budget and staffing to securing a top engineer to work on their product. Finally, social awareness ensures the best PMs service their customers with a product that addresses their jobs to be done which is ultimately what drives product market fit. (Read more about what Paul Jackson has to say about EQ and PMs here. And here’s an interview with Sam Lessin, former VP of Product Management at Facebook, who says he has “never successfully trained empathy.”) Company Fit If the best PMs have well developed core competencies and a high EQ, does that mean that they are then destined for success no matter where they work? Not necessarily. In fact, it is taking these skills and personality traits and applying them to the right company that will ultimately guarantee success. I have yet to see a standard job description for a Product Manager because each role is ultimately defined by the size, type of product, stage, industry, and even culture of the company. If you possess the core competencies and high EQ needed to be a successful PM, the next step is to unpack who’s hiring and what they are truly looking for. Here are a few of the key areas in which companies differ in what they want from a PM: Technical skill – The type of product, who uses it and/or the type of company will determine how technical a PM needs to be. For example, Google requires PMs to pass a technical skills test regardless of what product they’ll work on. If the company is building a SaaS CRM, there may be more requirements around experience with go-to-market and customer lifecycles than how the product itself is built. By contrast, if it’s a data science product with machine learning algorithms and APIs, the role may require a lot more technical depth to not only understand how to build the product but also how to talk credibly with the customers who will use it. That said, having a basic technical understanding of what is “under the hood” and mastery of the tools that PMs use is definitely important for the role, anywhere. Colin Lernell has more to say about these necessary skills here. If you are an aspiring PM concerned you lack the basic tech skills for the role, you might consider taking online courses such as the renowned Introduction to Computer Science (CS50) course offered by Harvard University or one of the many intro and advanced technology courses offered by The Flatiron School. Company philosophy about PM – Every company has a different philosophy about the product development process and where PMs fit into that process. Below are the three most common types with pros and cons: PM Drives Engineering: This is a “throw it over the wall” approach where PMs gather requirements, write the quintessential PRD (Product Requirements Document) and hand it off to Engineering to spec out the technical requirements. Contemporary organizations may do this process in a more agile and collaborative way, but the expectation is that PMs know best about what customers need and engineering is there to serve. Pro: Engineering can focus on coding without a lot of distraction; this tends to work well for Waterfalldevelopment shops with long lifecycles. Con: Engineers lose sight of the big picture and do not develop empathy for customers, which can lead to a poor user experience. Often there are unhealthy tensions when technical debtand “plumbing” work needs to be prioritized against customer requirements. Engineering Drives Product: More technically-oriented product companies (e.g., cloud, big data, networking) tend to be engineering driven, where engineers are advancing the science in their domain and PMs validate solutions or create front end access points (UIs, APIs) to tap into this new technology. There can be a collaborative relationship and feedback loop between customers, PM, and engineering, but typically PMs are serving engineering in these companies. Pro: Breakthrough technology can offer customers things they didn’t even know they needed. VMotionat VMware was a great example of this. An engineer thought it would be cool to do, a PM figured out how to monetize it and it became a billion-dollar game changer for the company. Cons: Engineers chase the shiny new thing, over-architect the solution, or iterate forever seeking perfection before getting customer feedback. PM input on priorities are ignored, which sometimes includes the most basic needs of customers. The PMEngineering Partnership: In these cases, there is a strong yin-yang between PM and Engineering where there is joint discovery, decision-making, and shared accountability. Engineers join PMs in customer interviews and PMs are in sprint meetings to help unblock tasks or bring clarity to requirements. But the two roles respect the line where one starts and the other stops. PMs understand what’s being coded, but don’t tell engineers how to code, and engineers have empathy for customers’ needs, but leave the prioritization to the PMs. Pros: A streamlined prioritization process that values technical debt and plumbing projects; better design processes leading to a more positive user experience; higher performing teams with improved product velocity, quality, and, typically, happier customers. Con: Breakthrough innovation may not get greenlit; time-to-market may seem to lag (though I’d argue what’s released is far better aligned with customer needs and more likely to successfully scale). I’m clearly biased in favor of the third type of philosophy about PM (as is venture capitalist Fred Wilson) as I’ve experienced all three and found the yin-yang to be most effective. But that’s not to say the others are notably bad — it really depends on what type of product you’re building, the company stage, and more. Regardless, when considering a PM role, the philosophy of PM at the company could be the deciding factor on fit for the role. Stage of company – The role of the PM at a startup is far likely to be responsible for “all the things” vs. at a mature company where their role will be more distinctly defined. (Eriksson, Banfield and Walkingshaw’s book Product Leadership has a section that has a lot more detail on this topic). Startup: Beyond discovery, definition, and shipping, PMs may also be responsible for pricing, marketing, support, and potentially even sales of the product. These PMs thrive in a scrappy environment and are comfortable with ambiguity and frequent changes to direction as the company works towards product-market fit and learns to operate at scale.Pros: PMs are likely to be more involved with company strategy, get exposure to senior leadership and the board, are able to take more risks and make a bigger impact. They also have more influence and authority over company resources. Cons: There’s typically little-to-no mentorship or role models or best practice within the company. (You may have to seek it externally.) Budgets are typically tight, and PMs may not have the requisite experience to succeed at some of the things they’re tasked to do. Mature Company: The PM may have a more narrow scope, have co-workers who handle pricing, go-to-market strategies, etc. And they are likely part of a larger team of Product Managers.Pros: PMs are more likely to have mentoring and role models as well as development standards and best practice Close association with an engineering team can create strong relationships over time, which is great for long term impact/career growth. And if the product has market fit, there is an established customer base and performance baseline to work from vs. guessing until you get it right. Cons: PMs have less exposure to company strategy and are just one of many voices of the customer. They can get “lost” in the system and have to deal with more politics and tight budgets. Founder/CTO/CEO relationship with PM – Especially in earlier stage companies, it’s important to know how involved the Founder/CEO/CTO is in the product process. If they are deeply involved, the PM role may play more of a support role to flesh out their ideas or validate concepts with customers vs. conceiving and driving ideas of their own. This can be great fun for some PMs who enjoy partnering with founders and c-level executives and collaborating on the product evolution. But for other PMs, it can be very frustrating if they prefer to take more ownership of the product direction. It can also be challenging if the more technical founders/c-levels prefer working directly with engineers. This can leave PMs out of the loop or undermined (sometimes unintentionally) causing not just personal frustrations but delays. When considering a PM role that may work closely with the founding leadership team, be sure to find out their expectations of the PM function and decide whether this is the right fit with your interests. There are of course many other factors to consider for any role such as the type of product you are building (B2B, B2C, industry, etc.), the humans with whom you’ll work, the overall company culture (diverse, inclusive, flexible work hours, remote culture, etc.), and of course the compensation and benefits. There are also lots of articles on hiring product managers to get perspective on what the hiring managers are looking for – I especially recommend my friend Ken Norton’s piece How to Hire a Product Manager. However, if you are striving to be a great Product Manager, consider all of the above before signing on to your next gig. Developing core competencies will be an ongoing activity throughout your career and leveraging EQ will ensure a more positive experience. But where you work, how they work, and who you work with and for will ultimately determine your long term success. A version of this article first appeared on the author’s website.
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States [Computer Sciences]
The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the...
Technology continues to be one of the outperformers. The sector is benefiting from increasing demand for cloud-based platforms as well as growing adoption of AI solutions.
Aaron Tilley/Getty Images It seems like every week brings news of yet another major cybersecurity breach. Evidence suggests that the bad guys are getting smarter and more professional. Nowhere is the problem tougher than in national defense, where sophisticated actors, including nation states, engage in cyberwarfare. A big part of the problem: There simply aren’t enough great cyberdefense analysts to go around. The Australian Defence Organization (ADO), which consists of the Australian Defence Force and the civilian Australian Department of Defence personnel supporting the ADF, has the same escalating challenge. To help address it, ADO has, with the help of some innovative business firms, leapt to the forefront with a new approach to sourcing cybersecurity talent: “Dandelion programs.” They tap non-traditional talent sources — especially people on the autism spectrum who, because of the social difficulties that accompany their disorder, can have trouble getting hired and remain unemployed. As the pioneering Danish firm Specialisterne showed first in the early 2000s, however, and as the Australian Defence Organization’s partner DXC Technology has demonstrated through deployments in Australia, if you manage things right, you can recruit great talent and activate it to a maximum degree from populations of autistic people. Insight Center The Human Element of Cybersecurity Sponsored by Varonis Shore up your company’s first line of defense. The dandelion metaphor comes from Thorkil Sonne, the founder of Specialisterne, who observes that dandelions, despite being very valuable plants, are considered weeds, primarily because they turn up in green lawns that are supposed to be uniformly green. The analogy to people with autism suggests that they are weeds only if we try to fit them into organizations in standard ways, using traditional management. If we adapt a different managerial approach, however, we can access superior talent. DXC developed the Dandelion Program based on this metaphor, in collaboration with a number leading universities and independent advisors. Although we’re talking here about support staff, not people in uniform, it helps that the military has a lot of success taking in people who don’t “fit in” and providing them with opportunity, skills, and a strong sense of self-worth. The story of the ne’er-do-well teenager transformed by military training and service into a highly productive member of society has been recognized for decades, if not centuries. Though the challenges of people with autism spectrum disorder (ASD) are of a different kind, some of the military’s capabilities for integrating people of different backgrounds and talents into effective organizational units are relevant to people with autism. The Israeli Defense Forces (IDF) pioneered the idea of recruiting analysts from populations on the autism spectrum. The IDF’s Intelligence Division’s Unit 9900 primarily concerns itself with the analysis of visual information from surveillance satellites. It’s “Roim Rachok” (Hebrew for “beyond horizons”) team is composed entirely of people on the spectrum. According to the IDF blog, these specialized analysts are “gifted with an incredible ability to analyze, interpret, and understand satellite images and maps.” In other words, this is not about compromise or settling for whatever talent you can get in an overly competitive labor market. These are true A-teams; it’s just that the “A,” in this case, stands for “autism.” Perhaps surprisingly, when it comes to skills required for cybersecurity analysis, (some) people with autism excel. Of course, this all must be done right and not in traditional ways. Going to populations of largely unemployed people with autism spectrum disorder, DXC administered psychometric tests and discovered that in some areas many of these future analysts are “off the charts” in terms of skill potential. They are not, however, classically well-rounded. Often their talents are very deep in specific areas but not broad and are near zero in some areas. Also, some analysts exhibit eccentricities. One, for example, is comforted, and her talent is enabled, by keeping sand in her pockets, and she only wants to walk on grass, not pavement. Though these, as well as other elements of her interaction style, might seem odd, there is no mistaking her talent with data. In her spare time, at home or wherever she is, she categorizes, charts, and otherwise sifts through data. Not surprisingly, she’s very good at it in professional settings as well, given proper conditions. When DXC found her, she had been struggling in an undergraduate, computer science program not because she couldn’t do the work but because she was bored. Much has been learned about how to manage these programs. Many people on the spectrum don’t do well in standard interview processes, so recruitment and assessment processes have had to be changed. Interviews are out; longer-term “hangouts” and project work “tryouts” are in — these give observers opportunities to watch individuals’ analyst abilities in action, a more accurate gauge than what might come out in an interview. La Trobe University’s Olga Tennison Autism Research Centre, working in conjunction with DXC and the Australian Defence Organization, has been translating, studying, and adapting the IDF’s psychometric assessment tools, figuring what works and what doesn’t in cybersecurity. Specially developed training provides unique preparation for the job. Once composed, dandelion teams work in highly effective “pods” (a DXC innovation), which have built-in support systems, including an autism specialist, to keep workers on track. Though it remains early days, so far this works well. Preliminary evidence suggests that these new cybersecurity analysts are doing an outstanding job. Analysts with ASD are mostly very hard workers; it is difficult, in fact, to get them to take breaks. They are able to spot patterns others cannot see. And, in part because they do not like changes in routines, they have very high retention rates so far, across DXC’s dandelion programs (some of which are outside the Australian Defence Organization). The softer benefits are also impressive. Data from La Trobe’s Tennison Centre confirms significant improvement in the quality of life that dandelion analysts report. The government and society benefits a lot as well every time someone on public assistance can be transitioned into a tax-paying tech worker. A study by PwC, commissioned by DXC, shows that even limited deployment of such programs can throw off hundreds of millions of dollars of benefits for the country’s economy. The general managerial templates being developed as part of this project are so promising that the Australian Defence Organization is thinking now in terms of “talent incubators” — creating a broad capability to staff A-teams in many areas of military and intelligence data analysis from populations that, remarkably, had been mostly unemployed.
Anecdotal evidence suggests that offering different avenues for computer science engagement can help extend participation in the field.
В Университет ИТМО в Санкт-Петербурге, где проходили соревнования, приехали 128 команд. Рекордное количество команд представил Физтех — на полуфинал приехали целых семь.
President Donald J. Trump Announces Intent to Nominate Gregory Slavonic to the Department of the Navy Gregory J. Slavonic of Oklahoma to be an Assistant Secretary of the Navy, Manpower and Reserve Affairs. Mr. Slavonic most recently served as Chief of Staff for U.S. Senator James Lankford (R-OK). Prior to this role, he was a senior leader at the Computer Sciences Corporation, where he planned and executed several nationwide U.S. Navy community outreach engagements. He also served as Executive Director of the Jim Thorpe Association; and as President of Flagbridge Strategic Communications, a consulting company focused on strategic communications and leadership. He has written two books on leadership development and co-authored a book on American Olympian Jim Thorpe. Mr. Slavonic retired from the U.S. Navy after a 34-year career, where he originally enlisted as a Seaman Recruit and, after repeatedly distinguishing himself, was promoted to the rank of Rear Admiral. During his Navy career, he held four command assignments, served in combat deployments to Vietnam, Operation Desert Shield/Storm, and Operation Iraqi Freedom, and was awarded numerous decorations including the Legion of Merit, Bronze Star, Presidential Unit Citation, and Combat Action Ribbon. Mr. Slavonic earned a B.S. from Oklahoma State University and M.Ed. from the University of Central Oklahoma, where he was recognized with a Distinguished Alumni Award. ___ President Donald J. Trump Announces Intent to Appoint Personnel to Key Administration Posts The following individuals to be Members of the American Battle Monuments Commission: • William M. Matz Jr. of Virginia to be the Secretary • Thomas O. Hicks of Texas • John P. McGoff of Indiana • Col. Evans C. Spiceland of Louisiana • Robert O. Wefald of North Dakota • Jennifer Sandra Carroll of Florida • Dorothy Gray of Virginia • Luis Rodolfo Quinonez of Florida The following individuals to be Members of the Board of Trustees of the James Madison Memorial Fellowship Foundation: • Terrence G. Berg of Michigan for the remainder of a six-year term expiring October 3, 2018 and an additional six-year term expiring October 3, 2024 • Diane S. Sykes of Wisconsin for the remainder of a six-year term expiring November 14, 2021 The following individuals to be Members of the President’s Commission on White House Fellowships: • Paris P. Dennard of Arizona • Linda M. Springer of Pennsylvania • Robert J. Smullen of New York • Daniel Caine of New York
Iran on Sunday awarded its biennial $500,000 Mustafa Prize to two computer science experts, an Iranian and a Turkish-French national.
● Big Mind: How Collective Intelligence Can Change Our World By Geoff Mulgan Summary via publisher (Princeton University Press) A new field of collective intelligence has emerged in the last few years, prompted by a wave of digital technologies that make it possible for organizations and societies to think at large scale. This “bigger mind”—human […]
Москва, 29 ноября - "Вести.Экономика". Криптовалюты могут стать одной из самых больших угроз для правительств, системы безопасности и всей финансовой системы. Они помогут финансировать терроризм, так как принцип анонимности делает почти невозможным отслеживание операций. Самое главное - криптовалюты могут произвести революцию в банковском деле.
Криптовалюты могут стать одной из самых больших угроз для правительств, системы безопасности и всей финансовой системы. Они помогут финансировать терроризм, так как принцип анонимности делает почти невозможным отслеживание операций.
Криптовалюты могут стать одной из самых больших угроз для правительств, системы безопасности и всей финансовой системы. Они помогут финансировать терроризм, так как принцип анонимности делает почти невозможным отслеживание операций.
Peer review may be “single-blind,” in which reviewers are aware of the names and affiliations of paper authors, or “double-blind,” in which this information is hidden. Noting that computer science research often appears first or exclusively in peer-reviewed conferences rather than journals, we study these two reviewing models in the...
С этой Украиной народ совсем все запустил. Учитывая степень накала можно предположить, что ньюсмейкеры искусственно нагоняют истерию, чтобы отвлечь внимание от более глобальных тенденций, как например развал Еврозоны, провал «японского чуда» и политики Абе, затяжная рецессия в США, очередной провал корпоративных отчетов. Кстати, в последнее время говорят о чем угодно, но только не о последних результатах крупнейших мировых гигантов. Что там с ними? Из 30 наиболее крупных ИТ компаний в США 11 компаний сокращают годовую выручку по сравнению к 2013 году. Это HPQ, IBM, Intel, Western Digital, Computer Sciences, Seagate Technology, Texas Instruments и другие. Наибольшее годовое сокращение выручки у Seagate Technology – почти 15%. С оценкой 5 летних тенденций, то в наихудшем положении Hewlett-Packard, IBM, Computer Sciences и Texas Instruments, у которых выручка находится на 5 летних минимумах. В таблице данные, как сумма за 4 квартала. Но есть и те, кто вырываются вперед – Microsoft, Google, Ingram Micro, Qualcomm. Apple замедляет в росте и переходит в фазу стагнации с последующим сокрушительным обвалом на фоне роста конкуренции. Intel в стагнации, как 3 года. Данные за 1 квартал предварительные, т.к. еще далеко не все отчитались. Но общие тенденции нащупать можно. Примерно 35-40% крупных компаний сокращают бизнес активность, 25-35% компаний в стагнации и еще столько же растут. Отмечу, что рост отмечает в отрасли, связанной так или иначе с мобильными девайсами – либо производство софта, либо реклама на них, или поставки аппаратной части, как Qualcomm. По прибыли. Здесь еще хуже. Мало компаний, показывающих приращение эффективности. Около 60% компаний сокращают прибыль, либо стагнируют. Относительно стабильный тренд увеличения прибыли у Google, Oracle, Qualcomm. Хотя темпы прироста наименьшие за 3 года.