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.

--

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.

FxGqlC: Added support for DateTime datatype

SELECT convert(string, convert(datetime, '2012-07-13'), 'yyyyMMdd HH:mm:ss') 
-- Formats datetime using a format string, as defined by the .net Framework
--   "Standard Date and Time Format Strings" (http://msdn.microsoft.com/en-us/library/az4se3k1), and 
--   "Custom Date and Time Format Strings" (http://msdn.microsoft.com/en-us/library/8kb3ddd4.aspx)
SELECT datepart(day, '2012-07-13') -- returns 13
-- valid datepart values are: (with examples for '2012-07-12 23:59:50.1234567')
--    year,        yy, yyyy :   2012
--    quarter,     qq, q    :         3  (1 ... 4)
--    month,       mm, m    :         7  (1 ... 12)
--    dayofyear,   dy, y    :       194  (1 ... 366) 
--    day,         dd, d    :        12  (1 ... 31)
--    weekday,     dw, w    :         5  (1 = Sunday ... 7 = Saturday)
--    hour,        hh, h    :        23  (0 ... 23)
--    minute,      mi, n    :        59  (0 ... 59)
--    second,      ss, s    :        50  (0 ... 59)
--    millisecond, ms       :       123  (0 ... 999)
--    microsecond, mcs      :    123456  (0 ... 999999)
--    nanosecond,  ns       : 123456700  (0 ... 999999900)
SELECT dateadd(day, 10, '2012-07-03')
-- returns 2012-07-13
SELECT datediff(day, '2012-07-03', '2012-07-13') 
-- returns 10
SELECT datediff(day, '2012-07-12 23:59', '2012-07-13 00:01') 
-- returns 1, the number of day-boundaries crossed (as in T-SQL)
SELECT datediff(day, '2012-07-13 23:59', '2012-07-13 00:01') 
-- returns 0
SELECT datediff(day, '2012-07-14 23:59', '2012-07-13 00:00')  
-- returns -1
SELECT getdate(), getutcdate() 
-- returns current DateTime in local and UTC/GMT time

Reflections on Digital Interactions: Thoughts from the 2012 NA Faculty Summit

Posted by Alfred Spector, Vice President of Research and Special Initiatives

Last week, we held our eighth annual North America Computer Science Faculty Summit at our headquarters in Mountain View. Over 100 leading faculty joined us from 65 universities located in North America, Asia Pacific and Latin America to attend the two-day Summit, which focused on new interactions in our increasingly digital world.

In my introductory remarks, I shared some themes that are shaping our research agenda. The first relates to the amazing scale of systems we now can contemplate. How can we get to computational clouds of, perhaps, a billion cores (or processing elements)? How can such clouds be efficient and manageable, and what will they be capable of? Google is actively working on most aspects of large scale systems, and we continue to look for opportunities to collaborate with our academic colleagues. I note that we announced a cloud-based program to support Education based on Google App Engine technology.

Another theme in my introduction was semantic understanding. With the introduction of our Knowledge Graph and other work, we are making great progress toward data-driven analysis of the meaning of information. Users, who provide a continual stream of subtle feedback, drive continuous improvement in the quality of our systems, whether about a celebrity, the meaning of a word in context, or a historical event. In addition, we have found that the combination of information from multiple sources helps us understand meaning more efficiently. When multiple signals are aggregated, particularly with different types of analysis, we have fewer errors and improved semantic understanding. Applying the “combination hypothesis,” makes systems more intelligent.

Finally, I talked about User Experience. Our field is developing ever more creative user interfaces (which both present information to users, and accept information from them), partially due to the revolution in mobile computing but also due in-part to the availability of large-scale processing in the cloud and deeper semantic understanding. There is no doubt that our interactions with computers will be vastly different 10 years from now, and they will be significantly more fluid, or natural.

This page lists the Googler and Faculty presentations at the summit.

One of the highest intensity sessions we had was the panel on online learning with Daphne Koller from Stanford/Coursera, and Peter Norvig and Bradley Horowitz from Google. While there is a long way to go, I am so pleased that academicians are now thinking seriously about how information technology can be used to make education more effective and efficient. The infrastructure and user-device building blocks are there, and I think the community can now quickly get creative and provide the experiences we want for our students. Certainly, our own recent experience with our online Power Searching Course shows that the baseline approach works, but it also illustrates how much more can be done.

I asked Elliot Solloway (University of Michigan) and Cathleen Norris (University of North Texas), two faculty attendees, to provide their perspective on the panel and they have posted their reflections on their blog.

The digital era is changing the human experience. The summit talks and sessions exemplified the new ways in which we interact with devices, each other, and the world around us, and revealed the vast potential for further innovation in this space. Events such as these keep ideas flowing and it’s immensely fun to be part of very broadly-based, computer science community.

Natural Language in Voice Search

Posted by Jakob Uszkoreit, Software Engineer

On July 26 and 27, we held our eighth annual Computer Science Faculty Summit on our Mountain View Campus. During the event, we brought you a series of blog posts dedicated to sharing the Summit's talks, panels and sessions, and we continue with this glimpse into natural language in voice search. --Ed

At this year’s Faculty Summit, I had the opportunity to showcase the newest version of Google Voice Search. This version hints at how Google Search, in particular on mobile devices and by voice, will become increasingly capable of responding to natural language queries.

I first outlined the trajectory of Google Voice Search, which was initially released in 2007. Voice actions, launched in 2010 for Android devices, made it possible to control your device by speaking to it. For example, if you wanted to set your device alarm for 10:00 AM, you could say “set alarm for 10:00 AM. Label: meeting on voice actions.” To indicate the subject of the alarm, a meeting about voice actions, you would have to use the keyword “label”! Certainly not everyone would think to frame the requested action this way. What if you could speak to your device in a more natural way and have it understand you?

At last month’s Google I/O 2012, we announced a version of voice actions that supports much more natural commands. For instance, your device will now set an alarm if you say “my meeting is at 10:00 AM, remind me”. This makes even previously existing functionality, such as sending a text message or calling someone, more discoverable on the device -- that is, if you express a voice command in whatever way feels natural to you, whether it be “let David know I’ll be late via text” or “make sure I buy milk by 3 pm”, there is now a good chance that your device will respond how you anticipated it to.

I then discussed some of the possibly unexpected decisions we made when designing the system we now use for interpreting natural language queries or requests. For example, as you would expect from Google, our approach to interpreting natural language queries is data-driven and relies heavily on machine learning. In complex machine learning systems, however, it is often difficult to figure out the underlying cause for an error: after supplying them with training and test data, you merely obtain a set of metrics that hopefully give a reasonable indication about the system’s quality but they fail to provide an explanation for why a certain input lead to a given, possibly wrong output.

As a result, even understanding why some mistakes were made requires experts in the field and detailed analysis, rendering it nearly impossible to harness non-experts in analyzing and improving such systems. To avoid this, we aim to make every partial decision of the system as interpretable as possible. In many cases, any random speaker of English could look at its possibly erroneous behavior in response to some input and quickly identify the underlying issue - and in some cases even fix it!

We are especially interested in working with our academic colleagues on some of the many fascinating research and engineering challenges in building large-scale, yet interpretable natural language understanding systems and devising the machine learning algorithms this requires.

Using WinForms from a Console application

It is perfectly possible to use WinForms from a Console application.  You just need to add a Reference to System.Windows.Forms.  Thereafter, you can add any code that uses WinForms.  The only pitfall is that you need add the [STAThread] attribute to your Main method.  Otherwise, the OpenFileDialog instantiation will block/deadlock when running on Microsoft .net framework.  On Mono, everything runs fine without the [STAThread] attribute, but to eliminate portability issues, you probably want to add it.

[STAThread]

public static void Main (string[] args)
{
    OpenFileDialog openDialog = new OpenFileDialog ();
    openDialog.ShowDialog ();
}