What askabl is about

askabl will bring about a whole bunch of new technologies - everywhere, where spoken or written text is essential. Here are some examples:

  • answer any and all questions, including sophisticated ones, like: "What were the socio-economic implications of the French Revolution?"
  • help researchers by letting askabl read through all the current research publications on a certain topic, and sum up, or focus on a certain issue
  • help school or college students with writing papers, going though books, or help them conceive of original ideas

These are just a few examples how askabl can be employed - whenever there is a difficult question, or lots of text involved, askabl can help tremendously ...

How is this possible?

askabl embodies a completely new concept in the field of NLP (natural language processing - a subsection of Artificial Intelligence) - the basic ideas first arose within a small team of experts about ten years ago. Over time, it got more and more sophisticated. Now, as we are closing up to the finish line, we would like to introduce askabl to the world - the paradigm shift we were so fortunate to have discovered ...

Standrad procedure in NLP

Standard NLP techniques use all sorts of AI techniques: a management layer acts like a director, passing along instructions to thousands of subroutines, which may consist of statistical analyses, which are oftentimes combined with a whole pile of pre-trained neuronal networks. In addition, these networks are supplemented by classical AI techniques, like hidden Markov models or optimization methods. Each element in the array is employed to fulfill one specific task, and notifies the management layer of its result. There, the results are combined, evaluated, and an overall output is created.

askable procedure

askabl's architecture is a lot simpler, and much more efficient: Basically, we invented a new kind of neuronal net. At first, the management layer decides, which task is to be done, and hands it off to one very large neural network, which is structurally a novelty, the first of its kind: instead of being limited one specific task, our network can be trained to handle ALL sorts of linguistic problems. Once all calculations are done, the result is passed up to the management layer, which presents the output.

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