How Conversational AI Can Contribute To Improving The Lives Of Underprivileged In India
Innovation happens faster than we can even imagine. The brightest and fastest of minds assemble and research and work on new technologies than can benefit us today and shape our tomorrows. It enhances our ability to do more for ourselves and society, intelligently.
Now, when we think of "technological intelligence", we think of Artificial Intelligence. Majority of our lives today are stored on our computers in cloud. Even more so, in our pockets our mobile phones. All of this is using Artificial Intelligence in big way.
The benefits of Conversational AI can be reaped only when it can help the deprived people. If such solutions can make major changes in their lives and solves their common problems
Amazon Alexa, Apple Siri, and google talk are some of the basic examples of conversational AI where verbal interaction between humans and computers is taking place. Most of us may have noted predictive text or auto type functions in on phones while using WhatsApp or typing a mail in gmail. This are some of the latest examples of conversational AI. Most of these solutions are based on NLP (Natural Language Processing). NLP is the most complex field of research in AI. Especially the predictions based on voice has the maximum complexity due to variations in dialect which limits the ability of machines to learn and respond.
Most of the work done in this area are done on English language and in recent time we find some responses on Alexa in Hindi also. However, the solutions are targeted at high income group as they have resources to buy the solution.
The benefits of Conversational AI can be reaped only when it can help the deprived people. If such solutions can make major changes in their lives and solves their common problems. Major research if done in vernacular language to develop solution, will benefit people in many ways, some of those are mentioned below:
Conversational AI in healthcare can emulate the role of a healthcare worker for common ailments. We all know that our healthcare system is mostly concentrated in cities and we find very few doctors working in remote villages. While government has focused expanding network to remotest places, the basic health care and doctors availability will be challenging. Conversational AI based application using vernacular language may greatly help in the remotest places to have first point of contact with healthcare system. While making the medicines available will still be a challenges but some proactive consultation may help bring timely action of critical ailments for poor. Moreover such solution will be empathetic, scalable, informative, and since they are bots- they are available 24x7.
Education is the right of every individual. Since poverty-stricken families cannot afford to send their children to schools and colleges, Conversational AI can make education accessible to them too. It can estimate algorithms to analyze the requirement of the students in these areas and teach the students in the desired language(s). It thus eliminates the very criteria for investing large amounts of funds in the education of children and overcome the lack of specially trained teachers thereof. It facilitates the augmenting and enhancing of the learning experience through personalized learning, automating and expediting administrative tasks.
Agriculture is the major source of income in India. Even though more than half the population is involved in agriculture we yet fail to monetize the entire produce. Conversation AI based solutions can help farmers to get timely advice on harvesting, use of fertilizers, consulting on irrigation and help them market their produce at better terms. This will also help them enhance their skills as the advice will be available in their own language. Key is how do we train the models to capture the large amount of queries and their responses.
An extension of education is developing the skill-set to be able to draw survival from it. The potential use of Conversational AI includes predicting the need for student intervention to reduce dropouts and recommend vocational training. Vocational training will help develop the skill-set of children and adults who want to either learn and/or hone skills to make use of it as a way(s) to earn a living.
Finally, developing such systems and training the NLP models require large amount of data for research work and large computational power like GPUs. This not only requires skilled data scientists but also statisticians, data collection agents, and systems. Further, return on investment period is likely to be very long due to the social natural of such projects, any research this field cannot move without active support from government. Hope the government will look in to these aspects and commit investment to develop solutions based on Conversational AI which can reap the benefits for deprived sections.