The professor believes in reaching the people who truly have an aptitude for his material rather than just those who have the financial means. His goal is to reach students all over the World. Also, the professor would like to cover a wider range of topics than he could offer at Stanford.

Udacity:

We believe university-level education can be both high quality and low cost. Using the economics of the Internet, we’ve connected some of the greatest teachers to hundreds of thousands of students in almost every country on Earth.

Starting on February 20th Udacity will begin online learning offering two classes:

CS 101: BUILDING A SEARCH ENGINE
Learn programming in seven weeks. We’ll teach you enough about computer science that you can build a web search engine like Google or Yahoo!

CS 373: PROGRAMMING A ROBOTIC CAR
In seven weeks you’ll learn how to program all the major systems of a robotic car, by the leader of Google and Stanford’s autonomous driving teams.

Head of the program Professor Yishay Mansour began this project earlier this year at the International Conference on Learning Theory in Haifa, Israel.  He and the other researchers are working on algorithms that would allow computers to learn from their past failures in an effort to make better predictions.  This is referred to as “minimizing virtual regret” by Mansour.

“If the servers and routing systems of the Internet could see and evaluate all the relevant variables in advance, they could more efficiently prioritize server resource requests, load documents and route visitors to an Internet site, for instance,” says Mansour.

“Regret” is not really comparable to the human emotion that follows a night of heavy drinking or a bad relationship, but is more along the lines of measuring the distance between the desired outcome and the actual outcome.

Since the actions of people are wildly unpredictable, the algorithm would need to allow adaptation on the fly, and real-time decision making.

“We are able to change and influence the decision-making of computers in real-time. Compared to human beings, help systems can much more quickly process all the available information to estimate the future as events unfold – whether it’s a bidding war on an online auction site, a sudden spike of traffic to a media website, or demand for an online product,” says Mansour.

All of this research greatly interests Google, as would be expected.  As Google grows, their need to be able to process large amounts of data in real-time also grows.  Apparently the search giant is particularly interested in how this new technology can benefit AdWords and AdSense.

Masour will work with a 20 person team on the project, headed by Professor Noam Nisan of Hebrew University.  Also involved will be the head of Google Israel, Professor Yossi Matias, a Tel Aviv University faculty member.

I, for one, welcome our new computer overlords.

Google considers understanding human language to be one of the hardest problems in artificial intelligence, and the key to returning the best possible search results. While it is far from perfect now, Google has invested a great deal of time into this (5 years of research to be exact).

To cut to the chase, here are some things pertaining to Google’s handling of synonyms that you should keep in mind:

1. Google contantly monitors its system for handling synonyms with regard to search result relevance.

2. Google says synonyms affect 70% of user searches across over 100 languages.

3. For every 50 queries where synonyms significantly improve search results, Google has only found one "truly bad" synonym.

4. Google does not normally fix bad synonyms by hand, but rather makes changes to its algorithms to try and correct the problem. "We hope it will be fixed automatically in some future changes," Baker says.

5. Google has recently made a change to how its synonyms are displayed: in SERP snippets, terms are bolded, just like the actual words you searched for.

6. Google uses "many techniques" to extract synonyms. Its systems analyze perabytes of data to build "an intricate understanding of what words can mean in different contexts"

7. Some words or initials can have tons of different meanings, and Google uses other words in the query to help determine the correct ones. For example, there are over 20 possible meanings for the term "GM" that Google’s system knows something about.

8. Google includes variants on terms (such as singular and plural versions) within its "umbrella of synonyms".

9. Google still makes mistakes with synonyms.

10. You can turn off a synonym in a search by adding a "+" before the term or by putting the words in quotation marks.

Google wants feedback on algorithm mistakes. They’ll take it through the web search help center forum, or through a Twitter hashtag: #googlesyns.

It will be interesting to see how far Google progresses in the area of natural language search, because Baker is absolutely right in that it is a key to providing more relevant results. If they can understand exactly what we want from our language, without us having to tweak it too much, that will be a tremendous stride for search. Instead of us trying to figure out what Google wants us to say, Google would just understand what we say. Luckily people have gotten much better at searching over the years, learning to enter longer, more specific queries.

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"Since 2004, when Google began to have annual reports, Larry and I have taken turns writing an annual letter," the letter begins. "I never imagined I would be writing one in the midst of an economic crisis unlike any we have seen in decades. As I write this, search queries are reflecting economic hardship, the major market indexes are one half of what they were less than 18 months ago, and unemployment is at record levels."

The letter discusses the company’s past as well as the future. It specifically discusses search, YouTube, Books, Geo, Ads, Apps, Chrome, Android, and AI. Here are just a few interesting nuggets to pull away from it:

- In the past year alone we have made 359 changes to our web search — nearly one per day.

- Today, almost a third of all Google searches in Japan are coming from mobile devices — a leading indicator of where the rest of the world will soon be.

Within each category, the letter talks about ways that Google aims to improve, and that is perhaps the most intriguing part. The letter is a lengthy document, but if you’ve got some time to kill, it’s very interesting and worth reading. It’s pretty much a look at the state of Google on its many technological fronts.
 

All right, so that’s more than a little bit interesting. “We have some people at Google [who] are really trying to build artificial intelligence and to do it on a large scale,” the Google co-founder declared. “It’s not as far off as people think.”

What kind of product does that comment foreshadow, and what kind of timeframe does it imply? Well, nobody knows, but there are a lot of Terminator references floating around the Web, balanced out by numerous repetitions of the “do no evil” mantra. Some people have already picked out a name for the AI; Data seems to be a popular choice.

Google is known for its search engine, of course, and not for its androids, but Page led listeners in that direction. According to CNET’s Stefanie Olsen, the Google co-founder explained that “[t]he programming language of humans . . . would include the workings of your brain.” Page also guessed “that the brain’s algorithms weren’t all that complicated and could be approximated, eventually, with a lot of computational power.”

It sounds as if Page is anticipating a walking, talking Googlebot, or at least some sort of disconnected head. Given that there’s already a life-size spaceship model at the Googleplex, it doesn’t seem like a huge leap.

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