NLP has been recognised by the NSCI’s NLP Recognition Committee and by the ICSN.
In general, the NSCI accepts applications from individuals within the general community and from organizations involved with research and development of NLP systems (for example, clinical or business organisations), and from institutions and researchers in areas of NLP. The NSCI also considers applications from independent scholars.
Applications for NLP certification shall include:
An indication of the NLP-specific training the applicant has received, which will cover any topics relevant to the type of research under the certification
Evidence that the individual (including a representative sample of researchers) is qualified to give scientific advice on NLP issues. Evidence may include an application for a teaching qualification under section 3 of the Clinical Skills for Healthcare Leaders Research Excellence Framework (CSHEREF) or a postgraduate award.
If an application has been received from an institution and there is no evidence of an NLP-specific qualification, the NSCI will not recognise the application
Where no evidence of a NLP-specific qualification is provided by the NSCI, candidates will be assessed on the basis of the best scientific evidence available and will be required to demonstrate that they have sufficient skill and knowledge to assist in the evaluation of clinical knowledge, skill and knowledge gained, and experience.
The NSCI also accepts applications from persons wishing to develop and disseminate NLP information to academic institutions, organisations, professionals and researchers. Applications for NLP certification shall include:
A team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), led by Junichi Nakagawa, has devised a way to build a simple platform that can analyze billions of data points and extract insights from it.
They call their system “Hearbot.” It can be found in the research paper on their website. If you click on a certain image it will reveal the source data; if you click on an audio image, it will reveal the source image; and if you click on a video image, you can see the source video.
But the big breakthrough of this project is how the system can combine data in a way that enables it to learn. It does this by working with a number of small nodes that are connected with the goal of extracting insights using the data.
Image: Junichi Nakagawa/MIT
It will do this by first using artificial intelligence techniques to identify all the ”
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