I am not aware of any open-source products that can readily replace IBM Watson, but the following projects could be of interest to you in that regard:
OAQA (Open Advancement of Question Answering Systems)
Stanford CoreNLP – Natural language software
Watson starts off by searching its massive database of sources for stuff that might be pertinent to the question. Next, it searches through all of the search results and turns them into candidate answers. For example, if one of the search results is an article, Watson might pick the title of the article as a possible answer. After finding all of these ...
As a person who works with people who work on Watson, perhaps I can give some insight.
The name Watson is casually thrown around a lot whilst many people aren't aware of its evolution into a larger suite of systems and services. We now have Chef Watson, Watson Health, and many other developing projects along the "cognitive" route. Watson is really an ...
It seems easy for this to be sublinear growth or superlinear growth, depending on context.
If we imagine the space of the complex AI as split into two parts--the context model and the content model (that is, information and structure that is expected to be shared across entries vs. information and structure that is local to particular entries), then ...
I cannot say for certain, but I know of no such other uses (I work at the building where Watson is developing but do not directly work with it).
The DeepQA team's page (https://www.research.ibm.com/deepqa/deepqa.shtml) only ever references Watson as the implementation, and based on the structure of the FAQ there I would imagine they'd be eager to list any ...
I get the impression that (perhaps even more than Bluemix) this is what the Wolfram Language is looking to offer in the longer term.
Seems to me that the main pros and cons are two sides of the same coin:
With Wikipedia, there's no 'search filter' between you and the text. Adding an algorithmic level of indirection between the user and the knowledge that ...
IBM clearly don't provide all the details / "secret sauce" but there is some information out there on how Watson works. Some of the text search / retrieval stuff uses a technology called UIMA which IBM open-sourced a few years ago. It also uses Prolog and some custom C++ code. Some more information can be found here.
Try UIMA and GATE, both of them are open source.
Watson Content Analytics implements UIMA framework, according to this:
Open, scalable analytics pipeline | IBM Watson Content Analytics 3.5.0
UIMA takes care of the management of NLP pipeline, but the intelligence is actually comes from ‘annotators’.
You can rely on annotators from projects like GATE, Apache ...
Yes, it does and at many parts of the solution. For one of the core components - intent detection - Intento did a benchmark comparing IBM Watson and other similar products: https://www.slideshare.net/KonstantinSavenkov/nlu-intent-detection-benchmark-by-intento-august-2017
Outside of intent detection, there are other areas where AI techniques help - e.g. ...
Have a look at the ChatScript project, whose description is
ChatScript is a "next Generation" chatbot engine, based on the one that powered Suzette, that won the 2010 Loebner Competition. ChatScript has many advanced features and capabilities that, when properly utilitized, permit extremely clever bots to be programmed. There is also a potentially useful ...
I know it seems like a cop-out answer to every question on AI, but "it depends". For example, if the bulk of the storage space is storing learned concepts, and attributes of example entities, then it stands to reason that concepts and entities could be reused. In that scenario, learning from an additional 10G of text would use less storage than the ...