This post about the research of natural language acquisition will be as short as previous parts. This time I want to describe how the current research that is too linguistically focused may benefit from being open up to other disciplines, such as Machine Learning, computer Science, Neurosciences and Physics.
Currently, the language acquisition research is predominantly done by linguists. In my opinion, it is the reason why the progress in this field is so slow. It is very clear that researches that trained only in linguistic alone cannot leverage advances in other fields that are related to natural language processing, such as Neural Machine Translation which is a part of Machine Learning, Neuroimaging which is a part of Neuroscience, Neuromorphic Chips which are part of Electronics, and Dynamical Systems which are part of Physics. The mere luck of mathematical modeling is a very constraining factor, and it propelled all fields mentioned above. That is why groups that consists of generalists that have good grasp of math, machine learning, neuroscience and engineering will be most efficient in advancing the research and practical implementation of language acquisition.
Clearly defined goal
As Jeff Hawkins from Numenta that is focused on developing general neocortex algorithm based on neurological evidence mentioned we have enough data to work on general theory of neocortex functioning. There is no lack of data, in opposite the data is in abundance. What lacks is the clear goal of what we want to achieve and clear plan how to move into right direction. It seems to me the best approach should be something along the lines of Lunar Program back in 60th and 70th of 20th century. Though there is no need to invest billions of dollars to make a progress, but dedicated people with right background and well defined goals.
- Numenta, ‘A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex‘
- Neuromorphic chips, Stanford University, ‘Silicon Neurons that Compute‘