Computer science is changing: the amount of data available for processing is growing exponentially, and so must the emphasis towards its handling. Like the 19th century change in physics from mechanics to statistical mechanics, the new algorithms sacrifice the precision of a unique answer for the fast search of statistical properties. The following draft of a book by Hopcroft and Kannan breaks the path of what most future algorithms manuals may look like:
Heavy on proofs, many topics have been selected for their mathematical elegance, not their pragmatism. On the final version of this much anticipated book, I would love to see more content on hash algorithms, parallel algorithms, graph spanners or a more extensive discussion on Support Vector Machines.
After Edward Snowden’s revelations had been publically discussed for months, I can only claim: did nobody see this coming? Because there is a clear precedent: NSA’s Project SHAMROCK collected all telegraphic data entering or exiting the United States from 1945 to 1975. And more recently, the gargantuan scale of the Great Firewall of China was an omen of what more advanced countries could built to spy on its citizenship.
Privacy will be gradually eroded, being a weak private right and a strong public wrong whenever facing the presumption of illegalities, and prosecution laws are already being modified to be more accepting of evidence gathered through warrantless surveillance programs, especially in common-law countries where police forces have ample power to interpret the law. And if in the future all surveillance is perfectly automated with almost no human intervention, who would really care of the invasion of privacy if done by amoral and unsentient machines?
What I really find fascinating is that Snowden’s revelations haven’t brought us any advanced technology: it’s almost like the NSA didn’t have any real technology edge over current commercial technologies, which I don’t really buy into. Meanwhile, the private sector develops and markets some technologies like PredPol for real-time crime prediction: an excellent predictor of what’s to come.
Cloud Deployment Manager is the first and only tool to automagically deploy Microsoft clouds from a diagram, with just the click of a button: it represents the cutting edge of DevOps research and innovation on the Windows platform.
It’s amazing what can be achieved to improve how systems are currently architected with the right tools and some code.
- Rooting SIM cards: the latest research of Karsten Nohl on GSM security (as if the typical problems of cellular encryption weren’t enough)
- The early history of computing to automate cryptanalysis (declassified)
- TLS forward secrecy has been proposed as the easiest solution to PRISM-like attacks, but it may be very easy to botch
- Buggy and poorly implemented protocols are the quickest path to break any encrypted voice protocol, as the recent weaknesses in ZRTPCPP shows
- DNSCrypt: I remember playing with a similar tool a decade ago, but this one is incredibly advanced
I’ve just ended these three books published this year on the intersection of finance and programming:
- C# for Financial Markets. This recently published book is just the translation to C# of all the previous books by the same author, especially the ones on the intersection between finance and C++. As such, one-third of the book delves into the implementation of basic mathematical finance (bonds, futures, options, swaptions, curve and surface modelling, finite difference method, …) and two-thirds of the book delves into teaching the C# language and its interoperability with Excel/LINQ/C++: note that if you’re already a pro on C#, you’ll better skip these parts since they are far from being the best authoritative source, although the sections on interoperability are really instructive. The best point of this book is that really full of examples to enlighten every concept (850 pages long!), although they never manage to compose a full application of any financial worth (that’s left as an exercise to the reader!): thus, and only on this technology angle, it’s the best book for beginners.
- Financial Modelling – Theory, Implementation and Practice (MATLAB). Do you need a book to quickly acquaint yourself with the state-of-the-art in financial mathematics for asset allocations, hedging and derivatives pricing, skipping the study of dozens of papers? Then this book is your definitive shortcut: it encompasses all from the derivation of the models to their implementation in Matlab, demonstrating that this language can also be used for prototyping purposes in the financial industry, efficiency and interoperability aside (if you don’t know Matlab, a third of the book delves into that). My favourite part of the book it’s the first one on models (stochastic, jump-models, multi-dimensional, copulas) that reads lightly and fast in just an afternoon, but the book is also overfocused on the numerical implementation of the models (a third of the book), when most of these details are just left to some library in the real world. Even so, just running over all its examples is worth its full price.
- Financial Risk Modelling and Portfolio Optimization with R. R is the lingua franca of statistical research, and its under-utilization in the financial industry it’s a real puzzle, the truth being that the sheer number of packages dealing with every imaginable statistical function should be enough to justify a much deeper penetration into daily use. This book is best suited for quantitative risk managers, and it surveys the latest techniques for modelling and measuring financial risk, besides portfolio optimisation techniques. Every topic follows a precisely defined structure: after a brief overview, an explanation is offered and a very interesting synopsis of R packages and their empirical applications ends the discussion. My favourite parts are at the end, on constructing optimal portfolios subject to risk constraints and tactical asset allocation based on variations of the Black-Litterman approach.
Charles Handy divided the management cultures in four types on his now classic book on business administration, Gods of Management:
- Zeus culture, a club culture with centralized power in which speed of decisions and accountability are prioritized over any other virtue
- Apollo culture, a role-based culture with many rules and procedures, in which stability and predictability are prioritized, creating resistance to change
- Athena culture, a task-based culture focused on providing solutions to concrete problems, ideal in times of expansion when new products and technologies get introduced
- Dionysus culture, an existential culture in which the organization exists for the sole purpose of helping the professionals to achieve their personal goals
In the last reorganization, Microsoft has shifted from an Athena-like culture to an Apollo-like culture, a typical change in business re-organizations also predicted by Handy in his book. However, the general consensus is that this move towards this kind of management culture is inferior, as shown in multiple studies:
- Managerial overload and organization design: a divisional organization is more efficient than the functional organization when the number of projects is large, and Microsoft has 13 products and services with revenues of more than 1 billion dollar per year.
- Organization Design: different management structures are compared based on the internal co-ordination costs imposed on the firm, very relevant to the Microsoft reorganization
- A double moral hazard model of organization design: a model based on moral hazard to explain why sub-optimal business structures are chosen is introduced, starting from general fact that the divisional structure is more efficient than the functional structure.
Only the malleable nature of software and the command to deploy a common software platform on any device and any type of screen may outstrip the previously presented disadvantages: be that as it may, MSFT is pursuing a much-needed goal for the whole computer industry.
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