"Microsoft" entries

Four short links: 28 January 2016

Four short links: 28 January 2016

Augmented Intelligence, Social Network Limits, Microsoft Research, and Google's Go

  1. Chimera (Paper a Day) — the authors summarise six main lessons learned while building Chimera: (1) Things break down at large scale; (2) Both learning and hand-crafted rules are critical; (3) Crowdsourcing is critical, but must be closely monitored; (4) Crowdsourcing must be coupled with in-house analysts and developers; (5) Outsourcing does not work at a very large scale; (6) Hybrid human-machine systems are here to stay.
  2. Do Online Social Media Remove Constraints That Limit the Size of Offline Social Networks? (Royal Society) — paper by Robin Dunbar. Answer: The data show that the size and range of online egocentric social networks, indexed as the number of Facebook friends, is similar to that of offline face-to-face networks.
  3. Microsoft Embedding ResearchTo break down the walls between its research group and the rest of the company, Microsoft reassigned about half of its more than 1,000 research staff in September 2014 to a new group called MSR NExT. Its focus is on projects with greater impact to the company rather than pure research. Meanwhile, the other half of Microsoft Research is getting pushed to find more significant ways it can contribute to the company’s products. The challenge is how to avoid short-term thinking from your research team. For instance, Facebook assigns some staff to focus on long-term research, and Google’s DeepMind group in London conducts pure AI research without immediate commercial considerations.
  4. Google’s Go-Playing AIThe key to AlphaGo is reducing the enormous search space to something more manageable. To do this, it combines a state-of-the-art tree search with two deep neural networks, each of which contains many layers with millions of neuron-like connections. One neural network, the “policy network,” predicts the next move, and is used to narrow the search to consider only the moves most likely to lead to a win. The other neural network, the “value network,” is then used to reduce the depth of the search tree — estimating the winner in each position in place of searching all the way to the end of the game.

JavaScript shares its ubiquity

WebAssembly changes the rules of the JavaScript game.


I’ve never seen a technology lay down its primary advantage and prepare to hand over its ubiquity. I’m proud of JavaScript for doing this, and I’m sure that in the long run this will be good for the Web, but in the meantime I’m wondering where WebAssembly will take us.

Brendan Eich’s announcement of the effort makes clear that this builds on the earlier asm.js (and Google’s similar PNaCl), a highly efficient JavaScript subset that compilers of other languages could target. Eich enjoyed using Unreal Engine for demos of the speed asm.js could provide, but compiling to JavaScript, even weird JavaScript, still needed to go through a JavaScript parser. (Other approaches compiled to more comprehensible but less optimized JavaScript.)

WebAssembly – wasm – skips that final step, producing a binary format, technically a compressed AST encoding. Unless you’re going to be building compilers, you can compare wasm to a bytecode system. There is a text format for debugging, but the binary emphasis yields substantial extra speed as it skips parsing and minimizes decompression.
Read more…

Comments: 2

.NET open source

Microsoft .Net Team Program Manager, Beth Massi, on the open source .NET Core.

wire mesh

You might have heard the news that .NET is open source. In this post I’m going to explain what exactly we open sourced, why we did it, and how you can get involved.

Defining .NET

If you’re not familiar with .NET, it’s a managed execution environment that provides a variety of services to its running applications including things like automatic memory management, type safety, native interop, and multiple modern programming languages that make it easier to build all kinds of apps, for nearly any device, quickly. The first version of .NET was initially released in 2002 and quickly picked up steam in many businesses. Today there are over 1.8 billion active installs of the .NET Framework and 6 million .NET developers in the world.

The .NET Framework consists of these major components: the common language runtime (CLR), which is the execution engine that handles running applications; the .NET Framework Base Class Libraries (BCL), which provides a library of tested, reusable code that developers can call from their own applications; and the managed languages and compilers for C#, F#, and Visual Basic. Application models extend the common libraries of the .NET Framework to provide additional libraries that developers can use to build specific types of applications, like web, desktop, mobile device apps, etc. For more information on all the components in .NET 2015 see: Understanding .NET 2015.

There are multiple implementations of .NET, some from Microsoft and others from other companies or open source projects. In this post, I’ll focus on .NET Core from Microsoft.

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Static analysis with C#

Complement a good testing program and identify hard-to-find bugs with static analysis.

Static analysis is, quite simply, any analysis you perform on software without actually running it. (Analyzing software as it runs is dynamic analysis.) There are many reasons to do static analysis, but almost all of them boil down to the desire to improve software quality. As a designer of developer tools, improving software quality by any means is keenly important to me.

Let’s consider compiler warnings. They are produced without executing the code, so the compiler is doing static analysis. Their aim is to inform the developer that the code, though legal, is probably wrong. Suppose you were a compiler developer and you wanted to add a new warning; what characteristics must that warning have?

  • There must be some statically identifiable pattern to the suspicious code.
  • The pattern must be common and plausibly written by a developer; developing a warning for a too-rare pattern or completely unrealistic code is effort that could be better spent on other features.
  • The warning must have a low “false positive” rate; a warning must actually identify defective code more than, say, 99% of the time. False positives encourage developers to eliminate the warning by turning the warning off, or worse, by incorrectly changing the code. There must be a way to eliminate the warning without introducing a bug into the code.
  • The pattern must be identified extremely Slowing the build process by anything more than a few percent is unacceptable.

I always recommend that everyone use the strictest warning settings on their compiler, to pay attention to warnings, and to (carefully) fix them all. Even fix the false positives; if the code was weird enough to fool the compiler then it’s weird enough to fool a human, and you don’t want to have “expected” warnings distracting you from actual warnings.
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Comments: 5

Building applications in Azure

Identifying the key requirements of a web application cloud architecture.

Download a free copy of “Azure for Developers,” an O’Reilly report by experienced .NET developer John Adams that breaks down Microsoft’s Azure platform in plain language, so that you can quickly get up to speed.

One of the most natural uses of the cloud is for web applications. You may already be using virtual machines on your own systems to make deploying your applications easier, either to new hardware or to additional servers. Microsoft Azure uses virtualization too, but it also brings useful benefits that virtualization cannot deliver alone. By hosting your application in the cloud, you can leverage automatic scaling, load balancing, system health monitoring, and logging. You also benefit from the fact that managed cloud platforms help narrow the attack surface of your system by automatically patching the operating system and runtimes and by keeping systems sandboxed. Let’s look at some examples of how to build some common web applications inside of Microsoft Azure.

Online store

Imagine that you work for a retailer who generates a significant amount of revenue through online sales. Imagine also that this retailer has been around for long enough that it already has an established web architecture that runs in a private data center. This retailer has decided that it wants to move to a hosted platform so that it no longer has any data center responsibilities and it can focus on its core business. How do you replatform this web application into Microsoft Azure? Let’s first identify some requirements for this system:

  • It has high utilization and needs to serve a large number of concurrent users without timing out, even during peak hours such as Black Friday sales.
  • It needs to accommodate a wide variety of products in its database that do not necessarily all follow the same schema.
  • It needs a fast and intelligent search bar so that customers can find products easily.
  • It needs to be able to recommend products to customers as they shop to help generate additional revenue.

However these requirements are being met today in the private data center, I can suggest some guidelines on how to reproduce this system in Microsoft Azure so you can boost performance instead of just replicating it. I will take each of these requirements in order and explain how to leverage certain Azure components so that these requirements are properly met.

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Four short links: 2 March 2015

Four short links: 2 March 2015

Onboarding UX, Productivity Vision, Bad ML, and Lifelong Learning

  1. User Onboarding Teardowns — the UX of new users. (via Andy Baio)
  2. Microsoft’s Productivity Vision — always-on thinged-up Internet everywhere, with predictions and magic by the dozen.
  3. Machine Learning Done WrongWhen dealing with small amounts of data, it’s reasonable to try as many algorithms as possible and to pick the best one since the cost of experimentation is low. But as we hit “big data,” it pays off to analyze the data upfront and then design the modeling pipeline (pre-processing, modeling, optimization algorithm, evaluation, productionization) accordingly.
  4. Ten Simple Rules for Lifelong Learning According to Richard Hamming (PLoScompBio) — Exponential growth of the amount of knowledge is a central feature of the modern era. As Hamming points out, since the time of Isaac Newton (1642/3-1726/7), the total amount of knowledge (including but not limited to technical fields) has doubled about every 17 years. At the same time, the half-life of technical knowledge has been estimated to be about 15 years. If the total amount of knowledge available today is x, then in 15 years the total amount of knowledge can be expected to be nearly 2x, while the amount of knowledge that has become obsolete will be about 0.5x. This means that the total amount of knowledge thought to be valid has increased from x to nearly 1.5x. Taken together, this means that if your daughter or son was born when you were 34 years old, the amount of knowledge she or he will be faced with on entering university at age 17 will be more than twice the amount you faced when you started college.
Four short links: 20 February 2015

Four short links: 20 February 2015

Robotic Garden, Kids Toys, MSFT ML, and Twitter Scale

  1. The Distributed Robotic Garden (MIT) — We consider plants, pots, and robots to be systems with different levels of mobility, sensing, actuation, and autonomy. (via Robohub)
  2. CogniToys Leverages Watson’s Brain to Befriend, Teach Your Kids (IEEE) — Through the dino, Watson’s algorithms can get to know each child that it interacts with, tailoring those interactions to the child’s age and interests.
  3. How Machine Learning Ate Microsoft (Infoworld) — Azure ML didn’t merely take the machine learning algorithms MSR had already handed over to product teams and stick them into a drag-and-drop visual designer. Microsoft has made the functionality available to developers who know the R statistical programming language and Python, which together are widely used in academic machine learning. Microsoft plans to integrate Azure ML closely with Revolution Analytics, the R startup it recently acquired.
  4. Handling Five Billion Sessions a Day in Real Time (Twitter) — infrastructure porn.
Comments: 2
Four short links: 23 October 2014

Four short links: 23 October 2014

Hard Javascript, Responsive Progress, Software Experiments, and Facebook Emotions

  1. You Don’t Know JSa series of [CC-licensed] books [to be published by O’Reilly] diving deep into the core mechanisms of the JavaScript language.
  2. progressbar.js — responsive progress bar.
  3. Microsoft Garage — Microsoft software experiments, in public. This is awesome.
  4. Creating Empathy on Facebook (NY Times) — On Facebook, teenagers are presented with more options than just “it’s embarrassing” when they want to remove a post. They are asked what’s happening in the post, how they feel about it and how sad they are. In addition, they are given a text box with a polite pre-written response that can be sent to the friend who hurt their feelings. (In early versions of this feature, only 20 percent of teenagers filled out the form. When Facebook added more descriptive language like “feelings” and “sadness,” the figure grew to 80 percent.)
Four short links: 16 June 2014

Four short links: 16 June 2014

Decision Trees, Decision Modifications, Mobile Patents, Web Client

  1. Quick DT — open source (Java) decision tree learner.
  2. Revealing Hidden Changes to Supreme Court OpinionsWHEREAS, It is now well-documented that the Supreme Court of the United States makes changes to its opinions after the opinion is published; and WHEREAS, Only “Four legal publishers are granted access to “change pages” that show all revisions. Those documents are not made public, and the court refused to provide copies to The New York Times”; and WHEREAS, git makes it easy to identify when changes have been made; RESOLVED, I shall apply a cron job to at least identify when the actual PDF has changed so everyone can see which documents have changed.
  3. Microsoft’s “Killer” Android Patents Revealed (Ars Technica) — Chinese Government required them disclosed as part of MSFT-Nokia merger. The patent lists are strategically significant, because Microsoft has managed to build a huge patent-licensing business by taxing Android phones without revealing what kind of legal leverage they really have over those phones.
  4. HTTPiea command line HTTP client, a user-friendly HTTP client.

Your money or your life

Microsoft, Google and pushing business models too far.

Photo by Didier, used under a Creative Commons license.I know it’s hard to run a large company. I know that organizations can get too deep into their own visions to imagine conflicting values.

I realized yesterday, though, that:

  • Microsoft ruined their brand for me by holding too tightly to things that they considered theirs. (Software.)
  • Google is ruining their brand for me by holding too tightly to things that I consider mine. (Identity, everything they can possibly learn about me.)

It’s a weird difference, but the Google version makes me much sadder about the world. As I’d tell a mugger, “You can have my wallet, just don’t take me.”

Photo by Didier, used under a Creative Commons license.