Finding The Laws That Govern Us

A Google post, Finding the laws that govern us, describes a new feature that enables users to read U.S. federal and state court opinions on line.

See: Google Scholar.

You now can use the feature to search by the name of a well-known case, such as Roe v. Wade or Miranda, or you can search by an interesting topic such as the "right to vote", or "freedom of the press".

My suggestion: Digitize legislation and court opinions from many countries in the world and also enable language translation.

This would allow users world-wide to search for topics such as "right to vote", or "freedom of religion" and see and learn how the myriad legal structures of the world compare.

Here is my original SETI research.

Rethinking How To Select Optimal Keywords For Internet Ad Auctions

Most internet ad buyers probably apply a commonly held belief that search volume equates to consumer interest so they bid on the most popular keywords that represent their product line.

A Google.org post appears to refute this strategy, at least in regard to swine flu search volume trends.

See: Google Flu Trends expands to 16 additional countries.

The Google post states that "An important aspect of Google Flu Trends is that we filter out terms that may be popular because people hear about them in the news.

What we do not use in the models is a term like (swine flu) since people are more likely to type that into Google because they want to know more information about it, given the news headlines, and not because they actually have H1N1 or swine flu."

If popular keywords such as "swine flu" do not correspond closely to real world activities (e.g. CDC ILI contacts), it may be preferable for internet ad bidders to invest in R&D to find other sets of keywords that have a stronger correlation with consumer activities and bid on those instead.

For example, a cola soft drink manufacturer may find that its yearly sales pattern is a closer match to Google Trends graphs for "sunscreen" or "ice cubes", than to "cola".

Users may run searches for "cola" to get more information about it, making it a high-volume keyword, but not because they are going to consume it.

Rethinking the keyword search volume to consumer action correlation is a significant paradigm shift. It is already applied in Google Flu Trends, but probably not yet by most bidders in Google ad auctions.
 
See: Search engine ads: auctions or page rank?

Here is my original SETI research.

Predicting Search Trends Based On Historical Data

A Google R&D post describes analyzing historical search trends patterns to predict future search trends. See, On the predictability of Search Trends.

Predicting search trends based on historical search trend patterns is a difficult challenge. There may be insurmountable obstacles embedded in these sayings: 1) Expect the unexpected, and 2) The only thing that is constant is change.

These are my previous posts about Google Trends:

http://googcomments.blogspot.com/2009/08/public-sector-todays-hot-trends.html

http://googcomments.blogspot.com/2009/01/google-trends-comparing-ice-cream.html

http://googcomments.blogspot.com/2008/12/comment-on-letters-to-nature-detecting.html

http://googcomments.blogspot.com/2008/11/google-trends-measures-what.html

http://googcomments.blogspot.com/2008/06/does-google-trends-data-equate-to.html

Public Sector Today's Hot Trends

A Google public sector team post describes how they would like to "build consumer products and applications that connect citizens to public information and services". See, Hello, world!

Suggestion: Create a Public Sector Today's Hot Trends page - only displaying links, not the trends search box. This would enable citizens and public service providers to see at a glance the top 20 search volume trends for public sector issues.

It is, however, important to keep in mind that Google search trends do not equate to users casting a vote, responding to a poll, or purchasing a consumer product or service.

A vote usually indicates a person is in favor of 1) electing an official or 2) implementing a legislative proposal.

Unlike a vote, if "town halls" or "health care" links appear on a Google Public Sector Today's Hot Trends page, those trends include an aggregation of users with wide-ranging opinions on the issue.

Google Chrome: Mixing Metaphors?

Google is starting a contest: "Film yourself building the Google Chrome icon in a creative way..."

See: Get creative with the Google Chrome icon.

I like the color of shiny, metallic chrome but I don't see chrome anywhere. Where's the chrome? Cars have chrome, motorcycles have chrome - the Google Chrome icon doesn't.

The Google Chrome icon (red, yellow, blue, green) looks a lot like many toys and is almost identical to other product icons.

Here is my favorite icon that I consider fun and creative:

Here is my original SETI research.

A Mom’s Day Brunch With A Side Order Of CAPTCHA

Google’s executive chef serves up a banquet of Mother’s Day recipes on the official Google blog. See: A Mom’s Day menu.

The Mother’s Day brunch includes:

• Chilled Asparagus & Avocado Soup with Tomato Confit
• Warm Wild Mushroom, Goat Cheese and Caramelized Onion Strudel
• Crab Cakes
• Molten Chocolate Cakes

Perhaps… Chef Giambastiani has also prepared a side order – a CAPTCHA. 

The Chef relates this childhood story:

“When I was about three years old, my mom and I had a game. Mom would show me things around the house. "Look, Scotto, this is a picture," she said. "Can you eat it?" I asked. "No, honey," she said. "Look, Scotto, this is a flower." "Can you eat it?" I said.”

My suggestion: Although not CA (Completely Automated), Chef Giambastiani appears to offer a potential CAPTCHA – perhaps better than Microsoft’s ASIRRA CAPTCHA.

The chef’s CAPTCHA can draw from a larger and more diverse set of images. That is Can you eat it? / Can you not eat it? may provide greater range and diversity than Is it a dog? / Is it a cat?

See:
http://research.microsoft.com/en-us/um/redmond/projects/asirra

http://crypto.stanford.edu/~pgolle/papers/dogcat.pdf

Upright Rotation CAPTCHAs

A Google R&D post describes a new type of CAPTCHA based on asking users to rotate a set of images to a natural upright position.

See: Socially Adjusted CAPTCHAs and the complete R&D paper What’s Up CAPTCHA? A CAPTCHA Based On Image Orientation.

After filtering out images that contain features easily recognizable by computers - such as faces, cars, pedestrians, sky, grass - performing this task successfully is easily done by people but not by computers.

Displaying three images and requiring the upright rotation to be within a 16-degree window (8-degrees on each side) results in random computer-generated guesses being correct in fewer than 1 in 10,000 tries.

An issue I see in the researchers’ reasoning is that a computer does not have to rotate the images to a naturally upright position to break this What’s Up CAPTCHA.

If a computer can detect the overwhelming vertical or horizontal lines or edges in an image the computer can rotate the image on the X or Y axis to an upright, upside-down, left-facing, or right-facing orientation. This would mean three images could be guessed correctly 1 in 64 (not 10,000) tries.

Search Engine Ads: Auctions Or Page Rank?

A Google R&D post provides a glimpse at the complexity of internet ad auctions. The research considers advertiser perspectives such as budgets and bidding strategies.

See: Market Algorithms and Optimization Meeting

The auction model for advertising links on a search engine is only one of two tracks: 

• The auction model is pay for play
• Organic page rank growth is web page relevance for play

How does an advertiser split resources across these distinct strategies? 

Perhaps, advertisers should use their budget to build their web site's page rank instead of participating in ad-words auctions.

Robotic Pets And The Future Of Search

A Google R&D post describes an artificial pet project (PEPE) with goals of 1) creating a robotic pet, and 2) making consumer electronics, such as toasters and VCRs, easier to configure. 

See: Maybe your computer just needs a hug.

I don't know if I want my toaster to bark at me. However, it would be nice when my refrigerator is low on food if it would say: Feed me I'm hungry!

Google search has one emotional component: the I'm Feeling Lucky! button.

Suggestion: On the Google search page add a drop-down filter users can select to return pages with emotional content. Here's a set of basic emotions:

• lucky
• happy
• adventurous
• romantic
• creative
• sociable

If a user searches for "vacation vermont" and applies the "adventurous" filter the results could return parachuting and cliff climbing. 

However, if the user searches for "vacation vermont" and applies the "romantic" filter the results could instead include spas and clubs.

An Ecological Perspective: Google Search

A Google R&D post describes the ecological perspective of Google search: Powering a Google search.

The Google post says: "Tools like email, online books and photos, and video chat all increase productivity while decreasing our reliance on car trips, pulp and paper." And "In terms of greenhouse gases, one Google search is equivalent to about 0.2 grams of CO2."

An important but omitted part of the equation is what people do as a result of running a Google search query.

Some folks may decide to sprinkle a pasture with carbon-friendly windmills, but others will decide to buy that gas-guzzling SUV they've been hankering for, or plan their next trip abroad by jet.

The post also glosses over other Google products such as Google Maps, Find Businesses, and Get Directions. These apps are probably pollution facilitators. Surely some Google Maps users will walk, jog, zip-line, or bicycle to their destinations. However, most users will probably drive a polluting car.

I use Google search quite often, and I am glad that Google is trying to organize the world's knowledge - however I'm not convinced the net impact of Google is green.

Google Trends: Comparing Ice Cream Flavors

A Google R&D post from a few months ago A new flavor of Google Trends interpreted the Google trends graphs for vanilla ice cream and chocolate ice cream.

I commented about the post here: Does Google Trends data equate to consumer interest?

Taking another look at the Google R&D post, an additional problem is the queries the R&D poster uses are imprecise for a conclusion about ice cream.

My suggestion: Try these instead: "vanilla ice cream" and "chocolate ice cream".

Probably, there are far more chocolate products  in the world than vanilla. For example: cakes, candy bars, cookies. However, folks still like vanilla ice cream more than chocolate ice cream.

The surrounding quotes enable Google Trends to return ice cream rather than a mixture of products.

Comment On Letters To Nature: Detecting Influenza epidemics using search engine query data

Google's R&D identified the top search queries that accurately model the CDC's data on influenza-like illness (ILI) physician contacts in various geographic regions. 

See, Nature: Detecting influenza epidemics using search engine query data.

The CDC's US Influenza Sentinel Provider Surveillance Network publishes on its Web site physician related ILI activities with a 1-2 week reporting lag. Google's automated procedure reduces the reporting to a day. See, Google Flu Trends.

In my opinion, there is a period of time, probably a few days to a week or more, between the onset of flu-like symptoms and actual physician contact. Folks can feel sick or have a cough or a runny nose which is going to result in flu before actually initiating ILI physician contact.

R&D might be able to identify the top search queries that occur a week or two before either CDC or Google Flu Trends data show ILI physician contacts.

Going from detection to prediction would be a useful step to prevent influenza-like illness.

 btw check out my original SETI research

How To Improve Mobile Language Translation

A Google R&D post describes a cool new translation application for IPhones. While you are touring, it enables you to text in a word or a phrase and get it instantly translated.

You can translate key phrases such as these:

• Can you take me to the airport please?
• How much does it cost?
• May I have a large coffee please?

This translation tool has a clear need for predictive input. It would be really cool if you could get common phrases displayed quickly by entering a single word: airport, cost, coffee.

However…asking folks questions is only part of the communication hurdle. While mobile, you also need to understand in real time the answers you get – and they are in the same foreign language. 

Suggestion: In addition to providing a phrase translation, display a set of the most common answers. 

This way you may have a good chance of understanding at least part of what a taxi driver, sales person, or waiter says after you ask: ¿Puedo tener un gran café por favor?

Understanding Queries: Keywords And Natural Language

A Google R&D post Technologies behind Google ranking describes how search keywords are algorithmically interpreted.

The post states: “our algorithms understand that in the query [new york times square church] the user is looking for the well-known church in Times Square and not for articles from the New York Times.”

Suggestion: Assume in this example that many users are actually looking for a well-known church in Times Square when they enter the query: [new york times square church].

Now try the following simple experiment – run both of these searches:

• new york times square church
• well-known church in Times Square

The first search clearly returns higher quality results than the second search. The results list the well-known church in Times Square. 

The problem is that according to the post, the second set of search keywords, which is closer to natural language than the first set of keywords, expresses more precisely what the user is attempting to search for.

Why doesn't the set of keywords that expresses more precisely what the user is attempting to search for return superior results?

btw check out my original SETI research

Collaborative Software As A Green Initiative

Google is supporting electric cars: Plug-ins converge on Washington.

Commuting by car, whether by a gas-guzzler or an electric vehicle, consumes energy and wastes scads of time.

A different way to reduce our dependency on oil is to enable telecommuting for jobs that are primarily computer-based. 

However, remote connections offer a lack of transparency and a less than robust environment for face-to-face meetings and other serendipitous interactions.

Isn't it a touch more Googley to put some more zing into social collaboration apps than to promote the use of electric vehicles?

Relevance: SERPs and Related Searches

Google R&D discusses an enhancement to its related search links in an article: Fresher related search suggestions.

Let’s try to search for the animal: cat.

If you enter into Google search the keyword cat, the following links to relevant SERPs display down the page:

• Caterpillar Inc.
• Cat - Wikipedia, the free encyclopedia
• Centre for Alternative Technology Home Page
• Lolcats ‘n’ Funny Pictures of Cats

The following related search links are displayed at the top of the page:

• cat deeley
• cat stevens
• cat unix
• cat exam

Why is there such a divergence between the concept of “relevance” for SERP links and for related searches links?

If Cat Deeley, a DJ, is relevant to related searches, why doesn’t she appear as a SERP link?
If the Unix command cat is relevant to related searches, why isn’t it listed as a SERP link?
Similarly, if Caterpillar Inc. is a relevant SERP link, why isn't there a related search link to building equipment?

In my opinion, applying two widely divergent standards for “relevance” to SERPs and to related search links introduces the risk of reducing the coherence of the Google page rank algorithm.

Relevance to a keyword/s such as cat means one thing across the top of a page, and something else down the page.

btw check out my original SETI research

Does Google Trends Data Equate To Consumer Interest?

Google labs is as passionate about providing cool apps as I am about using them. Thus said, a new post by Google Trends A new flavor of Google Trends draws, in my opinion, a clearly incorrect conclusion. 

The post tries to relate search volume to consumer interest. According to the post, ice cream shops and supermarkets should be sure to stock up on chocolate ice cream:

"Google Trends is not only a fun tool; it also offers some practical uses as well. Suppose you own an ice cream shop and don't know which flavors to serve, or suppose you're responsible for stocking supermarkets across the country; Trends can help you explore the popularity and seasonality of your products."

"As the numbers on the top of the graph indicate, vanilla ice cream has about 30 percent less search traffic than chocolate ice cream."

However, if you perform a Google search for ice cream popular flavors and drill down to a sampling of articles, you will be able to easily see that vanilla ice cream is between 2 and 3 times globally more popular among consumers than chocolate ice cream.

Frontiers To Computer Search!

Google provides the public with a glance into how it is improving search: "A peek into our search factory". Breaking through search limits is, of course, a worthy goal.

Increasing the amount/types of accessible data, enhancing user input, and improving search retrieval algorithms should result in a continuous improvement of search result pages.

However… there may be a few limits to creating a perfect search engine:

• Some media types are probably totally inaccessible. For example: human memory. Billions of folks are walking around with enormous amounts of valuable, but unsearchable, human memory. It will be quite some time into the future before Google can search human memory.
• Some media types are partially inaccessible. For example: proprietary intellectual property, geophysical and space data, and the contents of an incoming foreign cargo ship.
• Some search results may be nearly impossible to achieve because of search algorithm limits. For example: searching for a cure to AIDS.

Here's my original SETI research

Connecting Members And Excluding Nonmembers

Google's Friend Connect is a plug-and-play mashup of Web site hubs with social networking features, gadgets, and apps. 

Visitors who sign in to a Web site get to network along the theme of the Web site - for instance sharing guacamole recipes, or mountain bike maps.

Web masters are supposed to benefit by attracting new visitors as members bring along their dozens or hundreds of friends from their social networks.

Google provides some easy to use social gadgets that are described here: http://www.google.com/friendconnect/home/moreinfo

• Sign-in with their existing Google, Yahoo, AIM, or OpenID account
• Invite and show activity to existing friends from social networks such as Facebook, Google Talk, hi5, orkut, Plaxo, and more
• Browse member profiles across social networks
• Connect with new friends on your site

According to Google "The key gadget is the members gadget".

Although membership can enhance the connection of friends to a Web site, it can also exclude those visitors on the outside who do not want to become Web site members.

Probably, the number of Web site members, including all of their social networking friends, will usually be much, much smaller than the number of Web site visitors who do not want to sign in to become a member.

The problem: If a Web site offers their rich content only after member sign-in, won't that limit the total number of useful visitors to a Web site?

Will Friend Connect be a layer that actually excludes?

Here's my original SETI research

Privacy Of Aggregated Non-Personal Information When Performing Google Searches

A Google VP of Engineering discusses this interesting topic: How Google keeps your information secure.

I have a comment about Google’s privacy policy.

When a user performs Google searches, there is an expectation of privacy regarding at least two types of information:

• Personally identifiable information
• Searches

Google's privacy policy for using aggregated search data is described here:

"We may share with third parties certain pieces of aggregated, non-personal information, such as the number of users who searched for a particular term, for example, or how many users clicked on a particular advertisement. Such information does not identify you individually."

http://www.google.com/intl/en/privacypolicy.html

"Aggregate non-personal information is information that is recorded about users and collected into groups so that it no longer reflects or references an individually identifiable user."

http://www.google.com/intl/en/privacy_faq.html#aggregatedinfo

Does the Google policy on the use of aggregate non-personal information prevent Google from using search information in the following example scenarios:

A team (i.e. aggregate) of research scientists is doing research to patent a new router, or a lunar spacecraft engine.

It appears that Google's privacy policy allows the searches of aggregated research scientists to be shared with third parties.

Is this a huge flaw in the privacy policy?