Vendors Survival: Why Wolfram Alpha is not a Google killer?

A new killer in town: Wolfram Alpha is the Google Killer.

Is it really a Google Killer as dubbed by some people? Although, my knowledge of Wolfram Alpha is less than limited, I am sure it is not.

Some arguments supporting my view:

1. Wolfram Alpha is not available yet for public use even as a Beta.

People impressions are based on previous achievements of Stephen Wolfram, Demos, an empty Web site and a blog.

As far as marketing is concerned, it is a huge success: so many references in the Web before it was released.

2. Is it a Search Engine? A Knowledge Base? An encyclopedia?

If it is not a search engine it is not competing directly with Google.

3. Frequently IT application Killers or Infrastructure Killers were doomed to death before their competitors they tried to kill.

For examples, IBM's 9370 computers described in the nineties as VAX killers fade away about 10 or 15 years before VAX computers end of life.

An article I read many years ago was titles: RDBMS death. Object Oriented Databases (OODBMS) should replace them according to the article. Today some people may argue that OODBMS are not dead, but RDBMS are still alive and kicking.

I also read some articles about C# as Java Killer. Java is still with us and will be around for many years.

4. Only Alpha's first part will be introduced.

I am quoting Wolfram Alpha blog: "And—like Mathematica, or NKS—the project will never be finished. But I’m happy to say that we’ve almost reached the point where we feel we can expose the first part of it."

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5. It aims at addressing theoretical scientific problems e.g. Natural Language understanding.

Translating a scientific breakthrough (and my scientific background is too limited for judging if it is a breakthrough) to commercial products is always a challenge.

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What is Wolfram Alpha?

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My understanding is based upon Wolfram's blog, demo screen shots, a YouTube video including demo presentation by Stephen Wolfram and few other Web items.

It is a kind of search engine, but the search is based upon questions instead of a search by keywords. The answer to a question summarizes data on the topic of that question.

For example if you ask: What is the GDP (Gross Domestic Product) of France? The answer will include a number representing that value and some histograms and related variables values. It is possible to drill down to more specific questions and get more detailed data.

The knowledge supplied resembles Wikipedia more than Google search engine.

The following factors distinguish between Wolfram Alpha and Wikipedia: -->

• The Alpha's knowledge base could be deeper and more scientific than encyclopedic data.

• No open community is mentioned as participating in Wolfram Alpha

• Wikipedia's search mechanism is based on keywords

• No explicit digging or drill down mechanism is inherent in Wikipedia. However, in some articles in Wikipedia the text includes references and hyperlink to other articles describing embedded topics. For example an article on SOA may refer to an article on SOA Contract or on SOA Government.

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As already mentioned, the questions are asked in natural language (English in this specific example).

The answers are based upon the vast knowledge available in the Web.

Wolphram Alpha utilize methods and algorithms of a previous project by the same company: Mathematica.

Mathematica is used for mathematical computation, modeling, simulation, visualization, development, documentation, and deployment.

The approach is based on calculation algorithms and is different from the Semantic Web approach.

My Take
  

•  Wolfram Alpha could be an innovative and successful product in the future, but it will not be a Google killer or even Wikipedia killer.
• It is true that rarely a new product may be a killer of an older one, but special conditions are required for supporting this process. I do not notice such special conditions in Wolfram's case.

. I can think of two well known examples:

1. Microsoft's Internet Explorer as Netscape's browser killer. The special conditions in this case were a dominant and stronger vendor (Microsoft) and a wrong approach of the market leader (Netscape). Notice that after Microsoft's victory, its market share is cannibalized by newer solutions (Open Source's FireFox and Google's Chrome).

2. Google's Search engine was a killer of some of preceding products. Google's first success
upon better innovative search algorithm (Page Rank), Larry Page & was based Sergey Brin's decisiveness, and relationships which enabled sponsorship of adequate Venture Capitals, as well as hiring of a an experienced
CEO (Eric Schmidt).

• Understanding Natural Language is quite a big challenge

Wolfram blog refers to Alpha as something that "almost gets us to what people thought computers would be able to do 50 years ago!"

Looking back only 25 years, I learned a little bit about academic disputes on computers abilities to understand natural human languages.

It is relatively easy to understand the syntactic layer but understanding the Semantics is more difficult. Simple example (I do not remember who the originator of that example is) cans illustrate the difficulty.

The following two syntactically identical sentences have different semantics:

The glass fell on the table and it was broken.

The rock fell on the table and it was broken.

The first sentence tells us that the first object (a glass) was broken.

The second sentence tells us that the last object (a table) was broken.

I do not know if Semantic Web is a good enough solution to understanding the semantics of natural languages, but it seems reasonable that a solution for that problem is required.

• Wolfram Alpha may provide good understanding of mathematical and physical (and probably other scientific) questions but probably less good understanding of natural language based questions in other fields.

Questions in scientific fields are usually more accurate and formalized than questions in more fuzzy topics. Therefore it is easier to understand and interpret questions in these fields.

• A lot of data is available in the Web but not all data was created equal. Some of it is knowledge or valuable information and some of it is useless.

The challenge is to distinguish between reliable information and non-reliable data. I do not know yet, how Wolfram Alpha is going to address it. In any case, it seems like its ability to distinguish between different kinds of data will be better in scientific fields than in other fields.

• Google is not a Search engine only company therefore a fierce competition of a new Search Engine not necessarily kills it.

For example, an improved search engine without proper advertising mechanisms and advertising mind share may require cooperation with Google in order to use its advertisement expertise and tools.

• A breakthrough in Search Engines algorithms could hurt Google even if it will not kill it.

A breakthrough will probably be based upon some kind of semantic search instead of keyword search. Wolfram Alpha could be an example of product including more semantic search and search results capabilities. Expect for other semantic search tools in the future.

In order to survive (see my post Vnedors Survival: Will Google survive until 2018?), Google should continue researching and inventing new searching algorithms, as well as coping with new algorithms used by competitors.

A final concluding remark

Wolfram Alpha may be a promising inventive product, but looking back to 1957, it could also be another General problem Solver, i.e. a pretentious general purpose effort, capable to answer formalized mathematical questions, but far away from answering real world problems.