Thanks to a strong IT presence and enviable customer base, India has solidified its position as a leader in artificial intelligence (AI) during the past few years. IDC1 predicts that by 2025, the AI market in India will be worth 7.8 billion USD. Government organisations have also expressed interest despite low adoption rates, particularly in Natural Language Processing (NLP) models. The government has established national-level programmes, funding, collaborations, and research centres with the goal of fostering innovation because it recognises the potential of NLP.
Why investing in NLP is profitable
Given how difficult it is for computers to comprehend and use human languages, NLP is essential in bridging the gap between languages and computer science. Applications like text summarization, email filtering, and machine translation have all been around for a while. Additionally, text-based chatbots and virtual assistants have developed into more complex versions that can handle laborious tasks.
fundamental models to early training models
The first chatbot, Eliza, which employed pattern matching and substitution methods to loosely imitate human communication, was an early example of NLP. Being a rule-based paradigm, however, required a significant amount of human work to encode rules, making systems inflexible, fragile, and anything but natural.
Later, NLP changed into supervised machine learning, which needs lots of training data sets with clear labels for each task. Systematic mistakes, such model bias, are a potential problem due to data quality. Additionally, the development of new applications is slower because labelling data requires a lot of work and is more difficult to obtain.
While foundational models are making a big shift, large corporations are increasing their investments in computational resources to allow AI models to ingest data effectively. Transfer learning is used by foundational models, a phrase made popular by the Stanford Institute for Human-Centered Artificial Intelligence, to complete tasks and get around limitations of prior models.
Understanding the basics of models
Foundation models learn representations of all words using a large amount of training data and self-supervised learning techniques, including predicting the next word in a running text or filling in a missing word. A large amount of data can be used for this purpose because the knowledge does not require any labelled data.
The model can then be adjusted for a particular use case using a very small amount of tagged data. For instance, a sentiment analysis model that just contains a small sample of reviews and their sentiment polarity can be trained. While human resource departments use the same technique to increase employee retention rates, some banks utilise sentiment analysis to evaluate borrower sentiment and boost debt collection rates.
The future’s secret
Given the high cost of AI resources, it is unlikely that applications with human capabilities will ever be developed, but basic models can influence how AI is applied across different fields, languages, and industries. These concepts make sense when growing AI in terms of functionality and potential costs. Imagine completing things with the same fundamental model and only prompts!
These models help businesses in the following ways:
self-directed instruction
The cost of developing applications can be decreased by using foundational models, which can train at scale and reduce the amount of effort needed to classify data sets. Additionally, after training, only a small amount of supervised data is required to produce specialised applications, greatly lowering the time, effort, and resources required for training.
availability of trained models
As communities become more democratic, businesses can create particular NLP applications using pre-trained core models. OPT by Meta AI, Bloom by Big Science, GPT3 and ChatGPT by OpenAI, for instance, are well-known open foundational models on which many NLP applications are based. Open models can occasionally be made API-enabled for simple integration.
the use of less resources
The price of specialised hardware is frequently a barrier to developing and deploying AI models. To swiftly train and fine-tune models, clusters of graphics processing units (GPUs) or tensor processing units (TPUs) are frequently required. But basic models, once trained, can serve various functions, so businesses might anticipate a decrease in overall computing costs.
linguistic instruction
The National Language Translation Mission’s intentions to develop next-generation conversational apps and the government’s vision to provide e-services in Indian languages are tied together, which is why NLP is exciting for India. To do this, we must develop foundation models in each of the nation’s official languages. The collection of sufficient digital data in India’s 22 Official Languages is a challenge to developing these models in Indian languages. But as 4G, internet connectivity, and social networking platforms advance, digital data will eventually become more widely available.
NLP language models in automation, analytics, speech synthesis, and conversational AI are becoming more and more common thanks to a slew of specialised products. As more advancements take place, new use cases will manifest, strengthening India’s presence internationally.
