Replace all occurrences of a character in a std::string with another character

in one line of C++ code, using C++11/C++0x:

std::string str = "my#string";
std::for_each(str.begin(), str.end(), [] (char &ch) { if (ch == '#') ch = '\\'; }  );
// str now contains my\string

The for_each function code calls the lambda expression (indicated in yellow) for every character, and the lambda expression replaces the '#' with a '\'

The future of technology?

Click the image to make it bigger:

Source: http://envisioningtech.com/

2012 2013 2014 2015 2016 2017 2018 2019 2020 2030 2040 2012 2013 2014 2015 2016 2017 2019 2020 2030 2040 ROBOTICS BIOTECH MATERIALS ENERGY ARTIFICIAL INTELLIGENCE SENSORS GEOENGINEERING QUANTITATIVE FORECASTS INTERNET INTERFACES UBICOMP SPACE BITS ATOMS RELATIVE IMPORTANCE CONSUMER IMPACT CLUSTER OF TECHNOLOGIES The node size indicates the predicted importance of a technology. The outline of a node indicates a consumer impact larger than the technological novelty. A jagged outline indicates a cluster of similar technologies grouped together. World population: 8 billion Source: U.N. – http://bit.ly/7nqQkS World population: 7 billion BRICs GDP overtakes the G7 Source: Goldman Sachs – http://bit.ly/nc9Wqj Petabyte storage standard Source: http://bit.ly/r9BYQc Exabyte storage standard Source: http://bit.ly/kPMKMb Terabit internet speed standard Source: http://bit.ly/kPMKMb World population: 9 billion Source: U.N. – http://bit.ly/7nqQkS Source: http://bit.ly/6MoQJc Sources: Intel – http://intel.ly/pWbH04 Ericsson – http://bit.ly/avvVok Alan Conroy – http://bit.ly/pofHp5 FutureTimeline – http://bit.ly/qz4ben Sources: Intel – http://intel.ly/pWbH04 InternetWorldStats – http://bit.ly/AKbO5 Source: U.N. – http://bit.ly/7nqQkS Global online population: ± 2 billion Connected devices: ±10 billion Global online population: 4-5 billion Connected devices: 30-50 billion $150 Hard disk: ±200 Tb Standard RAM: ±750Gb Global online population: ± 2.5 billion Connected devices: ±15 billion $ 1.000 computer reaches the capacity of the human brain (± 10 15 calculations per second) Vertical farming Weather engineering Seasteading Desalination Carbon sequestration Climate engineering Arcologies Commercial spaceflight Sub-orbital spaceflight Lunar outpost Mars mission Solar sail Space elevator Space tourism Inductive chargers Thorium reactor Traveling wave reactor Fuel cells Multi-segmented smart grids Biomechanical harvesting Bio-enhanced fuels Artificial photosynthesis Space-based solar power Piezoelectricity Photovoltaic glass Nanogenerators Enernet Tidal turbines Programmable matter Personal fabricators Molecular assembler Metamaterials Additive manufacturing Graphene Optical invisibility cloaks Biomaterials Carbon nanotubes Self-healing materials Nanowires Antiaging drugs Stem-cell treatments In-vitro meat Nanomedicine Artificial retinas Rapid personal gene sequencing Synthetic biology Personalized medicine Gene therapy Hybrid assisted limbs Smart drugs Synthetic blood Organ printing Smart toys Robotic surgery Telematics Appliance robots Self-driving vehicles Domestic robots Powered exoskeleton Embodied avatars Swarm robotics Utility fog Commercial UAVs Fabric-embedded screens Reprogrammable chips Picoprojectors Volumetric (3D) screens Flexible screens Skin-embedded screens Modular computers Tablets Boards Retinal screens Eyewear-embedded screens Context-aware computing Smart power meters Biometric sensors Machine vision Optogenetics Depth imaging Biomarkers Neuroinformatics Near-field communication Pervasive video capture Computational photography Speech recognition Haptics 4K Augmented reality Gesture recognition Multi touch Immersive virtual reality Holography Telepresence 4G 5G Cloud computing Interplanetary internet Exocortex Photonics Virtual currencies Cyberwarfare Mesh networking Reputation economy Remote presence VR-only lifeforms Machineaugmented cognition Software agents High-frequency trading Natural language interpretation Procedural storytelling Machine translation Research & visualization by Michell Zappa mz@envisioningtech mz@envisioningtech.com mz@envisioningtech.com Envisioning emerging technology for 2012 and beyond Last updated: 2012-02-10 Understanding where technology is heading is more than guesswork. Looking at emerging trends and research, one can predict and draw conclusions about how the technological sphere is developing, and which technologies should become mainstream in the coming years. Envisioning technology is meant to facilitate these observations by taking a step back and seeing the wider context. By speculating about what lies beyond the horizon we can make better decisions of what to create today. BY SA

Improving Google Patents with European Patent Office patents and the Prior Art Finder

Posted by Jon Orwant, Engineering Manager

Cross-posted with the US Public Policy Blog, the European Public Policy Blog, and Inside Search Blog

At Google, we're constantly trying to make important collections of information more useful to the world. Since 2006, we’ve let people discover, search, and read United States patents online. Starting this week, you can do the same for the millions of ideas that have been submitted to the European Patent Office, such as this one.

Typically, patents are granted only if an invention is new and not obvious. To explain why an invention is new, inventors will usually cite prior art such as earlier patent applications or journal articles. Determining the novelty of a patent can be difficult, requiring a laborious search through many sources, and so we’ve built a Prior Art Finder to make this process easier. With a single click, it searches multiple sources for related content that existed at the time the patent was filed.

Patent pages now feature a “Find prior art” button that instantly pulls together information relevant to the patent application.

The Prior Art Finder identifies key phrases from the text of the patent, combines them into a search query, and displays relevant results from Google Patents, Google Scholar, Google Books, and the rest of the web. You’ll start to see the blue “Find prior art” button on individual patent pages starting today.

Our hope is that this tool will give patent searchers another way to discover information relevant to a patent application, supplementing the search techniques they use today. We’ll be refining and extending the Prior Art Finder as we develop a better understanding of how to analyze patent claims and how to integrate the results into the workflow of patent searchers.

These are small steps toward making this collection of important but complex documents better understood. Sometimes language can be a barrier to understanding, which is why earlier this year we released an update to Google Translate that incorporates the European Patent Office’s parallel patent texts, allowing the EPO to provide translation between English, French, German, Spanish, Italian, Portuguese, and Swedish, with more languages scheduled for the future. And with the help of the United States Patent & Trademark Office, we’ve continued to add to our repository of USPTO bulk data, making it easier for researchers and law firms to analyze the entire corpus of US patents. More to come!

Export of Office Outlook contacts to GMail

To import your Microsoft Office Outlook contacts to GMail or Google Apps, you need to export them first to a CSV file.

• In Outlook, go to the "File" tab in the ribbon menu, and click "Options" in the left sidebar.
• In the Outlook Options dialog, click on "Advanced" in the sidebar, and click the "Export" button. 
• In the first step of the Import and Export wizard, select "Export to a file", and click "Next".
• In the second step, select "Comma Separated Values (Windows)", and click "Next".
• In the third step, select your Contacts folder that you want to export (normally "Contacts"), and click "Next".
• In the fourth step, enter or select the filename, e.g. "contacts.csv".
• Click "Finish" to start the export.

When you import this file in GMail, and you are a member of a Windows Active Directory domain, the e-mail addresses are not imported.  Instead, the e-mail address field in GMail contains the "distinguished name" of your contact as known to your ActiveDirectory.  E.g. "cn=jsmith,ou=promotions,ou=marketing,dc=noam,dc=reskit,dc=com".
The real e-mail address is however included in the CSV file, as part of the column "E-mail Display Name", which contains the full name and the regular e-mail address between parentheses, but this column isn't used by the GMail import.

You could replace all E-Mail Addresses in the file using an Excel formula, or manually in a text-editor.
Or you can simply use this FxGqlC command to replace all e-mail address columns with the e-mail address taken from the display name:

select replaceregex($line, '\"/o=.*?\",\"EX\",(\".*?\((.*?)\)\")', '"$2","EX",$1') into [contacts2.csv] from [contacts.csv]

The same method can be used to replace national telephone numbers into an international format:
select replaceregex($line, '\+?(32\d{8,9})', '+$1') into [meucci3.csv] from [meucci2.csv]

You need to adopt the regular expression to a format appropriate for your contacts.

Import the resulting file in GMail, and that's it.

Cleanup old files from your harddisk using PowerShell

This script removes all files from the current folder (in this case 'c:\temp') that are not accessed during the last 3 months.  A confirmation is asked because -Confirm is included.

Get-ChildItem -Recurse | where { $_.LastAccessTime -lt (Get-Date).AddMonths(-3) } | Remove-Item -Recurse -Force -Confirm

Teaching the World to Search

Posted by Maggie Johnson, Director of Education and University Relations

For two weeks in July, we ran Power Searching with Google, a MOOC (Massive Open Online Course) similar to those pioneered by Stanford and MIT. We blended this format with our social and communication tools to create a community learning experience around search. The course covered tips and tricks for Google Search, like using the search box as a calculator, or color filtering to find images.

The course had interactive activities to practice new skills and reinforce learning, and many opportunities to connect with other students using tools such as Google Groups, Moderator and Google+. Two of our search experts, Dan Russell and Matt Cutts, moderated Hangouts on Air, answering dozens of questions from students in the course. There were pre-, mid- and post-class assessments that students were required to pass to receive a certificate of completion. The course content is still available.

We had 155,000 students register for the course, from 196 countries. Of these, 29% of those who completed the first assessment passed the course and received a certificate. What was especially surprising was 96% of the students who completed the course liked the format and would be interested in taking other MOOCs.

This learning format is not new, as anyone who has worked in eLearning over the past 20 years knows. But what makes it different now is the large, global cohort of students who go through the class together. The discussion forums and Google+ streams were very active with students asking and answering questions, and providing additional ideas and content beyond what’s offered by the instructor. This learning interaction enabled by a massive “classroom”, is truly a new experience for students and teachers in an online environment.

Going forward, we will be offering Power Searching with Google again, so if you missed the first opportunity to get your certificate, you’ll have a second chance. Watch here for news about Power Searching as well as some educational ideas that we are exploring.

Speech Recognition and Deep Learning

Posted by Vincent Vanhoucke, Research Scientist, Speech Team

The New York Times recently published an article about Google’s large scale deep learning project, which learns to discover patterns in large datasets, including... cats on YouTube!

What’s the point of building a gigantic cat detector you might ask? When you combine large amounts of data, large-scale distributed computing and powerful machine learning algorithms, you can apply the technology to address a large variety of practical problems.

With the launch of the latest Android platform release, Jelly Bean, we’ve taken a significant step towards making that technology useful: when you speak to your Android phone, chances are, you are talking to a neural network trained to recognize your speech.

Using neural networks for speech recognition is nothing new: the first proofs of concept were developed in the late 1980s(1), and after what can only be described as a 20-year dry-spell, evidence that the technology could scale to modern computing resources has recently begun to emerge(2). What changed? Access to larger and larger databases of speech, advances in computing power, including GPUs and fast distributed computing clusters such as the Google Compute Engine, unveiled at Google I/O this year, and a better understanding of how to scale the algorithms to make them effective learners.

The research, which reduces the error rate by over 20%, will be presented(3) at a conference this September, but true to our philosophy of integrated research, we’re delighted to bring the bleeding edge to our users first.

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1 Phoneme recognition using time-delay neural networks, A. Waibel, T. Hanazawa, G. Hinton, K. Shikano and K.J. Lang. IEEE Transactions on Acoustics, Speech and Signal Processing, vol.37, no.3, pp.328-339, Mar 1989.

2 Acoustic Modeling using Deep Belief Networks, A. Mohamed, G. Dahl and G. Hinton. Accepted for publication in IEEE Transactions on Audio, Speech and Language Processing.

3 Application Of Pretrained Deep Neural Networks To Large Vocabulary Speech Recognition, N. Jaitly, P. Nguyen, A. Senior and V. Vanhoucke, Accepted for publication in the Proceedings of Interspeech 2012.