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Data science is a new interdisciplinary field of research that focuses on extracting value from data, integrating knowledge and methods from computer science, mathematics and statistics, and an application domain. Machine learning is the field at the intersection of computer science and statistics, with many applications in data science when the application domain is considered. From a historical perspective, machine learning has been considered part of artificial intelligence. It was taught mainly in computer science departments to scientists and engineers and the focus was placed on the mathematical and algorithmic aspects of machine learning, regardless of application domain. Thus, although machine learning deals also with statistics, which focuses on data and considers the application domain, until recently most machine learning activities took place in the context of computer science, where it began, and focused traditionally on algorithms.

Artificial intelligence (AI) and data science (DS) centers are becoming ubiquitous in academic institutions around the globe. These centers serve to focus research efforts and bring together large teams to address important problems. AI centers in more mature research ecosystems tend to be multi-institutional, such as the Alan Turing Institute in the U.K. with 13 academic partners12 and Mila in Montreal with four academic partners and numerous industry partners.8 Often such centers are also focused on a specific theme, such as the 18 AI institutes funded by NSF.10 In contrast, the centers in India tend to be contained in only one institute--this facilitates the institute to identify AI/DS as a growth area and an area of interest to the Institute.

The problem of underrepresentation of women studying STEM subjects is well known and is being faced by several nations across the world. The field of computer science is no exception to this deteriorating gender ratio, nor is the Indian case. The male:female population ratio in India is 1.06, but the ratio of females making it to engineering institutions is lower, at 1.79.1 In absolute numbers, India produces around 1.5 million engineers from its 6,000 engineering institutions across the country.2 When it comes to employ-ability, 4.03% of male engineering students are employable by IT product firms, while only 2.54% of females are employable by these firms, and 16.67% of males as against 15.49% of females are employable by IT Services organizations. If we shift our focus to the employability of the graduates of top engineering institutions in the country--Indian Institutes of Technology (IITs), National Institutes of Technology (NITs), and other leading engineering educational institutions including International Institutes of Information Technology (IIITs)--employability among fresh graduates in IT product roles increases to 22.67%, and in IT Services roles, it is 36.29%.1

Programming education in India faces an uphill task of educating two-plus million students who enroll in degree programs with coding as a core skill.10 Fulfilling this demand presents unique challenges due to inadequate infrastructure and the unavailability of technical content in regional languages6 with unfortunate outcomes: more than 90% of Indian graduates have coding skills inadequate for IT roles, and more than 37% struggle to write code that even compiles.13 Studies indicate a steep decline in coding skills between graduates from top-100 colleges and the rest.12 While concerning, since only a tiny minority of students enroll in top-tier colleges, this is not entirely surprising. A recent study indicates that even experienced instructors at non-top-tier colleges in India struggle to write code.10

The National Digital Library of India was conceptualized with an aim to bring equity of access to educational resources for every Indian through a single window access mechanism.

The idea of "reinventing the wheel" is very often looked down upon in research. But many devices and solutions in the assistive technology (AT) space have been available for nearly half a century and still have not reached most users in low-income countries. Two such examples are refreshable Braille displays, which make digital data accessible in Braille through touch rather than audio, and tactile diagrams, which are critical to helping visually impaired people to pursue subjects, such as science, where diagrams are crucial to understanding the concepts. While accessibility normally refers only to the modality for making information accessible, in the Indian context, it is tightly tied to affordability. No market exists in the AT space in low-income countries, though the need is very high, because the user's ability to pay is either low or non-existent.

Computational sciences in the India region are going through an exciting time. While India has always had significant strength in theoretical computer science (CS), in recent years it has developed substantial presence and maturity in other, more applied areas of CS such as hardware and computer architecture, data science and artificial intelligence (AI), and cyber-security. Alongside pure research, there has been a significant push toward lab-to-field projects and technology transfer and deployment, creating broad impact to the region and beyond. Significant efforts have been made on the democratization of education through online courses, enabling the vast population to learn from a relatively limited number of available experts. All these activities have continued to bolster India's already strong IT industry and been a factor in the huge increase in the number of startups (under 1,000 in 2016 to over 60,000 in 2022a), with the number of unicorn startups reaching 100.b

Is #AI really intelligent while it is in any case programmed? Programming or training, educating or learning, it is the alternative ways to become knowledgeable or intelligent. What is necessary is if it has a world model and inference mechanism, be it encoded or trained and learnt. The world model machine is an implementation of real intelligence, be it in the rudimentary forms, as the internet, narrow AI systems, NLP applications, ML models or DL algorithms as DNNs, as well as human minds. As with the natural case of human intelligence, real AI should be both programmed with the world knowledge/learning/inference model and trained with the world data semantic model to become powerful machine intelligence.

Unlike the carefully scripted dialogue found in most books and movies, the language of everyday interaction tends to be messy and incomplete, full of false starts, interruptions, and people talking over each other. From casual conversations between friends, to bickering between siblings, to formal discussions in a boardroom, authentic conversation is chaotic. It seems miraculous that anyone can learn language at all given the haphazard nature of the linguistic experience. For this reason, many language scientists--including Noam Chomsky, a founder of modern linguistics--believe that language learners require a kind of glue to rein in the unruly nature of everyday language. And that glue is grammar: a system of rules for generating grammatical sentences.