NLP Models Powered Human-Machine Conversation
A bit more about NLP, the questions like Can machines think? or Can I speak to a machine? There have been numerous debates concerning machine cognitive abilities ever since the creation of digital computers (Turing, A.M. 1950; Newell, A. & Simon, H. 1976; Searle, J.R., 1980). The development of conversational AI has been underway for more than 60 years, in large part driven by research done in the field of natural language processing (NLP). In the 1980s, the departure from hand-written rules and shift to statistical approaches enabled NLP to be more effective and versatile in handling real data (Nadkarni, P.M. et al. 2011, p. 545). Since then, this trend has only grown in popularity, notably fuelled by the wide application of deep learning technologies. NLP in recent years finds remarkable success in classification, matching, translation, and structured prediction (Li, H. 2017, p. 2), tasks easier accomplished through statistical models.
How is it done? NLP techniques are mostly based on ML algorithms, following conclusions produced by AI. Companies looking forward to acting upon these insights can be benefited by these AI ML conclusions on speech recognition, sentiment analysis, etc. The future of NLP however is dependent on the development of Artificial Intelligence (AI). In today’s world working for NLP at its full potential is something that is required which can be completed by AI, hence we can say that the future of NLP is based on AI .
How does it work?
What are its components? First, collecting the input from humans in the form of speech or handwritten text. If the input is in the form of speech, ASR is the tech that makes sense of the input and converts it into a machine readable format.
Second, to understand the meaning of the text machines use Natural Language Understanding, a part of Natural Language Processing (NLP).
Next, responses are generated based on understanding of the text’s intent using Dialog Management, which further converts it into human readable format. Then a response is generated in either speech or text format.
Lastly, the application inputs all the mistakes made previously in order to learn from them in future.
In the past, restricted machine intelligence and insufficient human-machine interaction have often placed machines into less self-governing roles such as that of an assistant, an appliance, or a servant. A far more stimulating future is now visualized because of the recent developments in technology, where a conversational AI could gain rolling access to knowledge and extend its precision in foretell tendencies. With such furtherance, it surely requires a change in thinking as we design for a new offspring of machines to operate along the side of us in human housing. The rehabilitated system architectonics for a conversational AI should employ preferable systematic delegation to supply its wide range of conversations. It would draw from its various depictions to generate the appropriate response in each moment. It should maintain its local autonomy, keeping “organisationally closed” and “informationally open”. As the conversation continues, it would become mutuality with the human participant, and both will concurrently experience a progression in its system company until an agreeable domain is reached. The conversational AI would progress to fit the human need more grandly, and yet it should at times surprise the human with its perceptions. The model forward in this abstract is already partially doable insofar as neural networks can now hold on to schematic formations making use of deep learning technologies. Future study in this field should bend more on the structural coupling procedure and aim to clear the identifiable parts of local organisation forms. Achieving this with no doubt would be helpful further to the imitation machine in its quest for intelligence.
- Email filters
- Smart assistants
- Google Search results
- Predictive text
- Language translations
And much more yet to come…