In these columns, I have earlier attempted to highlight the possibilities of multiple game-changing applications in the field of artificial intelligence (AI) that hold the potential to deliver positive benefits to humanity. AI can do much more beyond enabling a transformative business impact. Every technological advancement brings with it an opportunity to deliver positive changes to society. It is no different with AI. With massive increases in computing power and data deluge, it is possible for AI to be a harbinger of change for the society we live in.

A complex problem that we see is Autism Spectrum Disorder. As of 2015, this neurodevelopment disorder is estimated to have affected the lives of 24.8 million people globally. Worse, despite all the advances in medical sciences, there is no conclusive understanding of its causes and cures. While we do see cases where autism gets resolved, we are still unable to point concretely to any medical option that works better than others.

At best, autism can be managed – possibly through a mix of early diagnosis and continuous therapy. Thankfully, we live in an era where the awareness of autism is on the rise and the associated stigma is declining. By harnessing the power of AI to detect and manage cases of autism, we could potentially help those suffering from it lead a fulfilling life of dignity and respect.

While not conclusively proven, it is likely that an early diagnosis of autism is hugely beneficial for managing it in the future. An early detection would also help the people around these children be better attuned to their condition and enable them to set an environment that is conducive to their development.

One such application is applying AI to analyse vocal and behavioural cues exhibited by children. Stephen Sheinkopf — an autism researcher and psychologist at the Brown University Center for the Study of Children at Risk — believes anomalous crying patterns of babies might serve as an early warning sign of autism.

Neurological cues present in the acoustic features of cries — pitch, energy and resonance — might hold the key to early detection. Combining vocal, behavioural and physiological data, we might be able to piece together a pathway to early detection. That’s where AI and machine learning can add really great value, in integrating these disparate pieces of information that might otherwise be hard to make sense of.

An excellent example of this in action is Chatterbaby — conceptualised and developed by Ariana Anderson, a computational neuropsychologist at UCLA. Earlier, functionality enabled identification of potential causes for why a baby is crying by monitoring crying patterns. In addition, Chatterbaby is also attempting to identify if there are discernible differences in the crying patterns of autistic children and neurotypical ones — ultimately aiming to isolate the characteristics of each group to detect autism early.

Early ‘Testing’

Researchers in the UK and Italy have turned to AI for developing what might be the world’s first ‘test’ for autism. In their study, they leveraged AI to compare the protein levels in the blood samples of two groups of children — one group comprising 38 children diagnosed with autism, and the other with 31 children without the diagnosis. Their findings helped develop an algorithm that could test autism — with a 90% accuracy for which children have autistic spectrum disorder and an 87% accuracy for which children do not have it.

Another example is through tracking changes in brain function of six-month-old babies, which researchers from UNC Chapel Hill and Washington University’s School of Medicine believe can help early detection. They recently published a paper wherein they examined the brain scans of 59 high-risk babies to understand the connections and interactions between different regions of the brain. Post this, they analysed the brain scans of the 11 babies that were eventually diagnosed with autism.

By combining the data with AI and deep learning, they developed an algorithm able to detect the possibility of autism with an accuracy of 9 out of 11.

Chatbots and Virtual Assistants

Across studies, we see children with ASD have high levels of comfort with computers due to their predictable and logical nature. Autistic children can perceive humans to be emotional and unpredictable but computer-based systems (even those with human expression) to be rational and non-judgmental.

Systems such as chatbots and social robots can help capture and track the progress of autistic children, continuously monitor their social behaviours and make quick, informal assessments in school and at home. The big promise of these systems is allowing autistic children the opportunity to navigate social interactions, unfamiliar environments while aiding them to reach the developmental goals usually set for neurotypical children.

An example of this is an app called Companion, featuring a virtual assistant named Abby. Abby helps identify interests and needs of autistic children and provides support throughout the day. Beyond this app, Identifier identifies talents of autistic children through interactive game-play; eventual results are compiled onto a dashboard detailing the skills and shortcomings of the child.

A Strict No-No

While AI can bring these benefits, it is also important to ensure that these systems are designed with empathy for the autistic population. For instance, the use of bright, jarring colours is an absolute no-no for the design of such applications. Secondly, virtual agents should be enabled to speak in simple language, without the use of idioms, euphemisms or figures of speech to help such people understand instructions much better.

For a problem that has no apparent cause or cure, AI could be a breakthrough in improving the quality of life of those who suffer from autism and the people around them. By using AI for early diagnosis, disease management and people enablement, we would be able to help bring dignity to the lives of affected people.

In the academic, industry forums and conferences, India is being positioned as a potential superpower in the field of AI on the world stage. It is our collective vision to see India as the premier destination of AI in the foreseeable future. There is seemingly a lot of work to be done if we are to overtake our formidable competitors and eventually the current leaders in the space of AI – the US and China. But no intervention is perhaps as urgent as the need to promote natively developed AI research and intellectual property within our academic institutions, universities and corporate enterprises. 

China spends 2.07% of its GDP on core research and development. In India, that number is a meagre 0.6%. Given this, it should not be surprising that China produced 4.5 times the number of citable research documents in the field of AI between 2010 and 2016. In terms of citations, the H-index of papers published in India (100) lags well behind China (195) and the US (413) – for the same period. At this juncture, it is evident that India’s contribution to the overall body of knowledge of Artificial Intelligence has been both quantitatively and qualitatively disappointing.

The contribution from Indian corporate enterprises has also been found to be wanting. For the period between 2001 and 2016, corporates contributed to merely 14.42% of the AI research done in India, with almost 70% of that being done by foreign multinationals doing business in India – the likes of Microsoft, Google,  IBM and others. 

There is a definite need for both academia and Indian industry to step up and contribute to India’s vibrancy in the domain of artificial intelligence. Here are 5 critical interventions needed urgently to take our research capabilities to the next level: 

Foster a collaborative approach to IP development

There is often a pattern of multiple research projects happening in silos – through collaboration between like-minded researchers from the same field of science. Artificial Intelligence on the other is an interdisciplinary subject. It essentially requires researchers from different walks of life – data engineers, machine learning scientists and those conversant with real-life challenges that AI can solve – to come together to solve common problems and add to the overall body of knowledge.

Universities need to quickly recognize that and build research capabilities in AI in an interdisciplinary, collaborative manner. The good thing is, universities by their very nature tend to host brilliant minds from multiple fields, but it is imperative that these minds be brought together for real, cutting-edge research to be published in the field of AI

Boost Funding for Research at Universities

Whilst India does not lag in the number of STEM graduates, our research capability is still inadequate. This is partly because of a lack of funding for research. For millennials, the allure of a corporate job or opting for entrepreneurship is far too high due to the compensation and benefits on offer.

It is critical that local and national governments intervene to help academic research in the field of AI be a viable career option for those who are interested in research. By expanding the budget allocated towards research and development, more researchers would be able to tap into public grants to expand the research in AI. 

Promote Corporate Funding and Empanelment

The onus of funding AI research cannot only fall on public institutions. Private Indian corporations would also be a huge beneficiary of locally developed AI competency. It is critical that we continue the work in building the bridge between academia and corporations to fund and promote AI research.

Several large companies, as well as startups, recognize the need for indigenously developed research in Artificial Intelligence. Corporate empanelment programs take multiple forms – from the low touch speaker arrangements to corporations helping setup topical centers of excellence at universities in a technology area that is key to their business success. More arrangements of the latter form are necessary, in addition to individual research grants and scholarships that corporations provide.

Release government datasets for AI algorithm development

Activating the development and learning for AI algorithms requires access to a lot of data. Here again, the government should step in to provide the relevant data sets to researchers working on complex, India-specific problems. 

In multiple circumstances, the government holds a treasure trove of data which can be hugely beneficial for the learning cycle of algorithms and promoting their development. The government should take a positive stand on sharing data sets, all the while keeping in mind data security measures and privacy rights of citizens.

Two-Pronged Approach – Core AI as well as Applications-Led IP Development

Research in AI needs to be supported at two levels. First, the development of patented core AI algorithms that will have broad, cross-industry applicability. The second is the development of intellectual property that is topical to industry or sector-specific problems.

India suffers from numerous topical problems that need an AI-led solution – from providing health care services to our burgeoning population, support for those engaged in the agricultural sector and provision of basic public infrastructure – roads, hospitals, schools and sanitation facilities. In addition to developing core mathematical capability, significant value can be unlocked through by developing expertise around these large, complex problems in AI, with solutions that can be applied to help countries facing similar problems.

Building core R&D capabilities and more IPs in Artificial Intelligence is key to cementing India’s position and competitiveness in this space. Building strong capabilities in AI is now a mandate for most of the world’s most powerful countries and it is imperative that India does not fall by the wayside. The good news is that NITI Aayog, MEITY, NASSCOM and others are already taking concrete steps to engage the stakeholder community – researchers, educational and corporate institutions – for AI research. The foundational aspects – an inclination towards STEM streams and an orientation towards key subject areas among the current students is there as well. It is time we harness their wisdom and knowledge to catapult India to the forefront in the field of AI. 

For the many potential application areas where artificial intelligence can deliver breakthrough transformation, unlock efficiencies and augment human life, one of the most impactful areas – at least in the Indian context – will be the value it can bring to the field of agriculture.

According to Indian Brand Equity Foundation, nearly 58% of India’s population relies on agriculture as their primary source of livelihood. The total export of agricultural commodities for India is expected to hit $38.1 billion in FY18, making the country one of the top 15 exporters globally.

However, agriculture in India is riddled with systemic problems, which AI is well placed to address. First, the traditionally unorganised agriculture sector continues to be difficult to organise due to our vast geography combined with our cultural and linguistic diversity. Second, farmers tend to be severely cash-strapped owing to smaller landholding, leading to lower farm productivity. Due to this poor cash position, capital expenditure associated with mechanisation and technology adoption continues to remain slow – thus precluding the possibility of improving farm productivity. Third, farmers are unable to tap a wider base of knowledge and tend to rely on community-based sources of knowledge. This handicap keeps them insulated from the progress of their peers.

Almost all the other large and small topical problems faced by farmers in India flow from one of these three macro-level problems – a lack of organisation, weaker capital muscle to introduce modernisation initiatives and poorer access to a larger, refined body of knowledge. On the bright side, with improved data collection techniques and easier access to cutting-edge research, enabled with better access to telecommunications infrastructure and improving technology literacy, these endemic problems might finally see an amenable solution; combined with the burgeoning interest of the private sector and thrust by the government for modernising farming.

So, it is time for artificial intelligence to step in and deliver a smarter, leaner and productive agrarian sector. Here are the three novel ideas for improving and augmenting farm productivity output using AI:

Communication in Local Languages
AI-powered technology provides a unique opportunity for agriculturists to break down traditional linguistic barriers. Through the infusion of natural language processing (NLP) and machine learning in agriculture, farmers would be able to communicate and learn from their peers and with civic bodies in their local language.

One such example is the development of ‘Kisan Helplines’. Farmers typically need to reach out to civic bodies for information and guidance but this may get hampered due to the poor availability and limited scalability offered by human-centric contact centres. Step in, chatbot. Chatbots can offer high availability, high scalability and reduce the wait time for farmers in need of advice or warnings. Through multi-lingual chatbots powered by NLP, farmers could get easier access to information that may be extremely critical for their activities.

Further, using an NLP-powered communication exchange, they would be able to connect with peers across States for information. Such a communication platform would help crowdsource information on weather progression and potential pest attacks. It would also help seek reviews and feedback on a multitude of farm procurement – seeds, fertilizers, pesticides and mechanical equipment – as well as trade information on successes and practices. By reducing this communication gap and enabling farmers to organise, they would be empowered to make better decisions from sowing to selling and everything in between.

Improving Farming Productivity
While increasing landholding might be challenging, AI can help make the most out of the land available by improving the yield through farm productivity interventions.

By using AI techniques such as machine vision and deep learning, farmers will be able to make many small tweaks to their farming activity that will ultimately result in sizeable gains. For instance, in the pre-sowing and sowing period, machine vision will help farmers monitor soil health and defects – helping them take corrective action early. During the sowing period, farmers can use AI to predict the near-term weather conditions, which can inform the optimal time to sow their seeds. Prior to the harvest, they could harness the power of machine vision to mitigate the impact of pest attacks. Finally, at the time of harvest, machine vision can help farmers grade their produce and demand the right market price.

One such brilliant example of this in action is Microsoft’s initiative with the International Crop Research Institute for the Semi-Arid Tropics (Icrisat), Hyderabad. Their AI sowing app uses machine learning and Microsoft’s PowerBI platform to predict and inform farmers the optimal date to sow. A feature phone capable of receiving text messages is all they need to receive this information. Early results from the use of this technology suggest a 30% improvement in yield per hectare achieved through 175 farmers, who were part of this programme.

Another example is the work being done by Bengaluru-based startup CropIn. Their suite of solutions captures data from multiple sources to provide real-time actionable insights – around weather information, scheduling and monitoring farm activity, monitoring crop health and estimating size of harvest. AI-enabled solutions such as these have great potential to reduce inefficiencies and uncertainties.

Pricing and Supply Chain
While pricing strategies and supply chain optimisation have reached a high level of maturity in the corporate sphere, there is a huge scope for them to impact the agricultural space in India. Using AI, farmers would be able to sense market demand for their goods and keep a pulse of customer choices and seasonality. Market sensing around consumer demand would help them aggressively price their goods and yield a better return.

AI-powered supply chains, on the other hand, can help improve their bottom line by reducing the costs incurred in managing distributed logistics and a multitude of middlemen. Through technology-powered communication networks and smarter routing, smaller farmers too will be able to organise their route-to-market more efficiently and gain benefits of scale. They would also be able to get their perishable goods to market quicker without the intervention of middlemen – thus reducing wastage and losses. An efficient, synchronous farm-to-table supply chain would help bring down their costs and improve their financial position.

At present, AI is only making its first inroads into agriculture. A multitude of use cases exists beyond these as well – from accurately forecasting commodities in the futures market to speeding up loan approval requests through faster credit scoring. AI for agriculture is not only a huge opportunity for startups and large organisations. It also offers the chance to make a huge impact in a sector crying for outside help to improve its operations and productivity.

We are truly entering the Golden Age of Artificial Intelligence. With data and computational power making giant strides year on year, AI promises to unlock untold benefits for business and transform human life as we know it – a transformation that will play out in our professional and personal lives. Data Science and AI careers are in high demand as students and working professionals flock to courses in these subjects to ride the wave and build their careers. Enough has been written and consumed around the potential of AI and how corporations and universities need to enable their students to make the most of this emerging new technology.

Actively Seek Private and Public Funding for Research

Many countries globally do provide public funding programs for educational institutions. However, at the present level, this may be insufficient, and the exchequer may not be in a position to fill the massive capital gap required to improve research capabilities and labs.

To this end, it is critical that universities actively seek out ways to secure funding from public and private sector institutions. Several creative collaboration opportunities are surfacing to the instrument such partnerships. Corporations are always interested to seek inputs from the leading scientific minds to add to their portfolio of cutting-edge solutions and intellectual property. Some of the commonly seen engagement models include – securing research grants for topical research allied with a challenging business problem, setting up technology incubation labs to work on bleeding-edge technologies with exponential potential and sponsoring hiring hackathons to identify the best of talent. 

To stem the brain drain from academia to corporate, universities need to offer corporations a model where academicians can add value to corporations while staying inside the university and keep the pipeline brimming with young talent. Privately funded research from a corporate perspective could be a useful way to engage professors while keeping them available to be able to develop fresh professionals. Data Science and AI professors at institutions may not simply be interested in studying, but also generating research with wide applicability. Universities with a strong financial muscle and backing of public and private agencies would be able to support such aspirations of professors and help them continue to stay relevant in the subjects that are highly relevant to the workforce today.

Re-Educate Academicians in Data Science and Artificial Intelligence

While universities make strategic moves required to increase their muscle to improve research capabilities, they also need to consider training more of their faculty members to address classroom requirements of students wanting to study AI. Universities need to augment their training curriculum for faculty to infuse subjects that can help them take up AI as a subject for students.

For instance, technical institutions are typically rich in academics that impact computer science curriculum; additional subjects such as machine learning, deep learning, statistical methods and data engineering will help them become better-rounded professors, able to teach AI concepts to students. Similarly interested candidates from the pure science faculty – such as math and statistics – can be trained in computer science methods. Such cross-pollination of skills would help create a better talent pool available to serve a larger base of students.

Engage Industry for Academic Internship Programs

Finally, universities need to promote hands-on skills in artificial intelligence among academia by developing corporate internship programs. Through this intervention, university faculty will be able to broaden their understanding of real-life applications of AI – the application of topical AI solutions to solve relevant business problems.

At present, a small number of universities do provide their professors with opportunities to collaborate on industry-specific use cases. For faculty that gain exposure to such programs, it can be a truly transformational learning experience – and one that they can replicate in their classrooms for enabling better guidance for their students. Universities that boast of such industry connects become automatically more appealing to prospective students – as they enter the campus knowing that they will learn material that is truly relevant to the age that we live in, rather than having just a cursory, booking understanding of AI-related concepts.

Reorienting existing academia and bringing in a supply of talented young researchers in the field of Artificial Intelligence should be the top priority for universities today globally. With the high demand for this technology today and abundance of impactful use cases, it is critical that we keep the tap running and bringing in more researchers and academicians is a critical part of the solution that can help keep the AI revolution going. 

Since this is an Engineers’ day Special, we have used some quotes from different professionals 

“On this Engineers’ Day, we pledge to make engineers intelligent designers with ideas instead of making them screwdrivers,” says. Ravi Raj, Brand Head, Director, Sales & Support at NetRack 

With the advancement of technology, both the industry and the government is focusing and welcoming the fourth state of Industry revolution: Industry 4.0 which enables the wide range of digital concepts especially in ESDM Industry in multiple ways by making engineers and the technology leaders more flexible to adapt and meet the new demands of the market easily. On this special occasion of Engineers day, we at NetRack would like to congratulate all engineers across the globe for bringing the wave of innovation and solution leading to faster sustainable and profitable future of India. 

Every year, more than 20 lakh engineering graduates passed out from their colleges but without having their practical or skillful experience to contribute to the industry as a whole. And, in this dynamic industry, the scenario is witnessing more in a magnified way and which needs specialized and skills to cater its requirements. The only solution is emphasizing on their skills and offering them specialized training from the operational level to even the engineers’ level.   We have also come across, very few colleges/ engineering schools have not stressed this issue so far. 

On this special occasion, we as one the key Industry leader should take the pledge to not only focus to make them skillful but intelligent designers with new ideas. However, this, in turn, helps in fulfilling make & create (in)n India initiative with innovation.      

However, we are thankful to all the engineers for their highly valuable expertise and dedication and wish them all the very best for future endeavors!! 

“Emphasizing more on hands-on training to expose engineers’ to the real world to make them job ready”, says Adam Paclt, CEO, IceWarp on this Engineers’ Day

It is the fact that science and technology are the spine of any country to scale-up its growth development.  Similarly, for any country’ economy, investment in skilling and reskilling the engineers’ is the necessity to enhance their knowledge both technical and vocational skills along with transferable and digital skills to make them job ready  

we have to train our young and aspiring engineers who are committed to driving development by adopting the best practices of Industry 4.0 to transform the industry. For this, the major area where we at IceWarp believes that the Industry and academia have to jointly take a step forward in building and filling the Industry-academia gap by incorporating skills-based courses in their curriculum of engineering degree. 

On this Engineers’ Day, we pledge to help the young engineers to unleash their true potential and discover their true self by giving more emphasis on the principle of hands-on practical training exposing them to real-world situations and reasoning.    

Companies should also change their working culture by offering an apprenticeship programme which in turn will provide hands-on exposure to high-value engineering skills in an industrial environment.   Moreover, Industry ’s the mission must promote the cooperation, not competition by adopting the holistic approach to connect with a variety of personas and to become an agent of change.  

“We salutes the spirit of all Indian Engineers, whose innovations have contributed to the world’s Digital Transformation journey across industries,” says Mr Krishna Raj Sharma, Director & CEO at iValue InfoSolutions:

We at iValue have solution offerings which cater to the Digital transformation needs of the customers. It is important to skill the engineers and re-skill them time and again on the latest technologies so that they are abreast and capable of giving better and optimum solutions in order to address a customer’s DX journey. We firmly believe in enabling our women employees on technology and we began this exercise by hiring campus recruits and ensured they travel through the complete training cycle of solution sales journey and are ready for facing customers and partners addressing Industry Revolution 4.0. across multiple continents. There is a paradigm shift in the way the business is done in the IT fraternity. Hence, it is of prime importance that the channel community ensures there is a constant innovation in GTM and technology adaption as it will play a major role in creating a differentiator in the market. iValue salutes the spirit of all Indian Engineers, whose innovations have contributed to the world’s Digital Transformation journey across industries.”

We are well and truly in the midst of the AI revolution. Research houses, academicians, think-tanks, business and technology leaders all agree upon the significant value waiting to be unlocked through the positive and progressive use of Artificial Intelligence – by re-engineering the old and envisioning the new. According to a research by Gartner, organizations using cognitive ergonomics and system design in new artificial intelligence projects will achieve long term success four times more often than others. Citing research by the MIT Center for Digital Business, from a competitive standpoint, companies that embrace digital transformation are 26 percent more profitable than their average industry competitors and enjoy a 12 percent higher market valuation.

The writing is on the wall. Intelligent business interventions made through AI will, to a large extent, define if your business will be an industry leader or a laggard tomorrow. And with that end in mind, businesses are rapidly changing their mindset and approach to AI – from topical experiments performed by forward-thinking business units, to more of a strategic mandate for enabling competitive differentiation. Businesses realize that for truly unlocking business value, they need to not only weave AI into the fabric of their enterprise, but also operationalize it – with the right personnel and change management initiatives. Given that AI can bring both cost efficiencies to business as well as potentially new revenue streams, businesses today are exploring an ‘AI Transformation’ – moving the dial on what is truly possible through a business model, engineered around AI. To enable your organization to do so, here are three powerful ideas to map the AI Transformation journey of your business.

Ensure Enterprise Readiness to Build and Adopt AI

The first step in the journey to AI Transformation for your enterprise is to understand and address if there are any disparities between your vision for AI and the ability of your organization to follow through with it. To that end, it is important to assess just how ready your enterprise is, in its current state, to build, deploy, adopt and benefit from AI-centric solutions. Ideas for AI Transformation need to be communicated clearly and grounded in the realities of organizational capabilities. When they are not, even the best intentions can go awry.

To do so, it is critical that business leaders measure their current AI maturity and assess the availability of internal skills. This will enable you to baseline just how empowered your current workforce is to develop industry-leading AI solutions. Once such a baseline is established on workforce readiness for building and adopting AI-led solutions, organizations need to start improving on these metrics – through internal trainings and external capability augmentation.

By developing this baseline score for AI readiness – organizations can have an objective view of where they are, how far they need to go and what the potential milestones to be achieved are in the journey to AI Transformation. This sort of pre-survey, combined with relevant training and assessment can help organizations craft a relevant roadmap with realistic timelines, as well as concrete actionables.

Build an AI ‘Win Team’

An AI Transformation is not unlike an extremely complex business re-engineering exercise. It entails massive changes – from the way you do business to how you run internal processes and staff multiple business units. Not only is it important to reskill a huge section of the workforce, there is also an important aspect of enabling change management to reinforce the importance of an AI-centric mindset.

To overcome this challenge, enterprises need to foster the consensus and engagement of a ‘win-team.’ This win-team would typically comprise functional and technical leaders who would be responsible for enabling the AI Transformation within their business units – from orienting the employees to the new mindset and ensuring capability readiness for the tasks at hand. On one hand, functional leaders can help their teams identify the processes that can be re-imagined using AI and manage resistance to change. On the other hand, technical leaders would lead the solutioning of technical components, while setting the training priorities and calendars for the workforce.

On change management, enterprises need employees to clearly appreciate the topline and bottomline benefits of an AI Transformation and focus towards enabling it. Employees stand to benefit themselves – as the professional benefits of making this transformation will accrue for their future. To further explore how companies can reduce the defensiveness in implementing AI-led processes further, they could also set innovation objectives for stakeholders as part of their performance metrics. Doing this will help create a strong alignment between individual, team and organizational objectives. A key aspect of AI transformation is ensuring large-scale adoption and usage of AI-powered solutions. AI applications typically fare better with every incremental user feedback and enriched data sources. Adoption and continuous use is a key parameter for the success of this transformation.

Integrated Business Processes over Siloed Business Functions

For years, the view of technology transformation and procurement has been of one that happens at a department / functional level – HR teams buy talent management software, finance teams sanction the purchase of accounting software, and CRMs get implemented to aid the efforts of sales teams. While this serves small technology initiatives, a sea-change is required for progressing an AI Transformation. To foster this, enterprises need to make a shift from a siloed, function-centric mindset to an integrated, process-centric mindset.

This is because AI use cases can often span multiple business units and functions, while tapping into multiple data sources for providing cross-team value, seamlessly. The very nature of AI deployments thus requires a process-centric view, with a strong consensus and buy-in from multiple stakeholders. Furthermore, the budget for purchasing AI services / applications is likely to come from the allocations of multiple beneficiaries across functions. This makes it all the more imperative that enterprises deprioritize functions in favor of processes.

An AI Transformation is doubtless the most strategic subject to be tackled by organizations today. Successful transformations will ensure enterprises go beyond mere automation and cost-cutting strategies and unveil previously unseen business and revenue opportunities. It is also extremely important to consider the role of digitization in building a new technology infrastructure that is AI-ready – possibly decentralized, cloud-based and highly available. There is now an urgent need for business leaders to have more than just a superficial understanding of AI and its successes. They will now be tasked with building and delivering a concrete, value-oriented roadmap for enabling a key transformation in the history of their organizations.

Artificial intelligence is slowly, and steadily, making its way into mainstream education. And not simply as part of educational curricula. We are seeing increasing instances of schools, colleges and other academic institutions leveraging AI as a crucial part of the process in which they deliver education to their students. In the West, numerous examples abound of these educational institutions leaning heavily on AI – from delivering personalized educational curricula to automating routine tasks that classroom teachers have to routinely perform. 

Tech luminaries such as Bill Gates are enthused by the idea of Artificially Intelligent Tutoring Systems – which can ensure impactful delivery of course content and improved internalization of that content among students. The education sector in India, currently reeling from endemic problems – from static curricula to dated pedagogical methods – has much to gain through an AI-driven facelift. Let us look at some of the areas where AI can make its way into education and revolutionize the way the next generation of students learns.

Augment Planning of Curricula and Lesson Plans

The present-day paradigm of a teacher delivering pre-designed, standardized content to a classroom of students with diverse aptitudes and interest levels – is remarkably inefficient. We’ve seen the negative impact that the current pedagogical methods have had on the employability levels of the current generation. To this end, by leveraging the variegated applications of artificial intelligence techniques, academia would be able to deliver more personalized curricula and lesson plans, improve students’ understanding and retention of the coursework and in turn improve educational outcomes. Here are a few examples of how we could enable those:

By infusing AI into the skills assessment and aptitude assessment process for students, schools and universities will be able to better judge both – the current level of understanding among students for a specific subject area and where their innate inclinations lie. Often, students are unclear or unsure about where they see their career graph moving and what they would like to do in the future. Through AI, schools and universities can map out the data of previous students and their career achievements and tie that back to educational research. This way, schools, and universities may be able to accurately predict which subjects a student has a natural inclination towards and then coach them for a career in that direction.

Going in the same vein, AI can also use data around student attention, interest, aptitude, and ability to recommend customized coursework. This will help build the capability of students towards a specific career path and bring better value to the time of students. This AI-centric approach would help foster more personalized training pathways and enable students with the skills they need to succeed in their future professions, rather than burdening our students and staggering their confidence as done by the current system.

Furthermore, AI can also be instrumental in enabling continuous improvement for teachers. By tracking their performance across a variety of metrics, schools will be able to better uncover the areas where teachers need support and coaching. AI can also help curate the coursework for teacher improvement, thus making sure that teachers are continuously updated and continuously refine their craft

Automating Routine, Low-Value Tasks

Teachers today are overburdened by all manner of menial, low-value tasks that neither improve student experience nor deliver better learning outcomes. Enormous time is spent by our teachers worrying about and performing hygiene activities – from taking the attendance of the class, evaluating and grading tests and assignment and performing peer reviews. We can unlock this time spent by teachers and help them focus on what they do best – teaching and coaching for success. By incorporating AI into the core way-of-working of schools today, we can eliminate these burdensome tasks in the following ways:

By automatically curating tests for students based on the aptitude of students in the classroom. Rather than relying on teachers to conjure up questions in the classroom, AI could help understand the learning level of students and fire up the questions. By using a gradational question bank, teachers would be able to administer tests much more easily.

The other related time-consuming area for teachers tends to be grading the administered tests and assignments. These tasks can much easily be eliminated by using the AI administered tests. AI can help automate the repetitive task of grading tests, thus helping teachers focus more on coaching, solving questions from students and helping create a better platform for learning. AI-graded tests can also help surface patterns of errors (i.e. are students mainly making the same mistakes?), thus providing input to teachers on which areas of training require more impetus in the next class.

Among other several administrative tasks – teachers also spend hours over the year taking attendance, peer reviewing the efficacy of the other teachers and submitting periodic reviews to their supervisors and coordinators. This workload can also be supported by artificial intelligence – by maintaining automated attendance logs, summarizing the test scores of students and reporting the performance of teachers.

Optimizing the Classroom Experience

AI in education can go well beyond simply personalizing course content and unburdening teachers. To fully inform and unleash the creativity and expertise of teachers, we also need to imbue AI-led applications in the classroom on a day-to-day basis so that teachers can work at full-throttle. Time spent on minding students and reorienting classroom methods to ensure better student engagement can be saved by using AI in the following ways:

AI can help improve the tracking of students’ attention levels and help teachers intervene before students lose interest in the classroom content. While teachers are conversant in minding students that actively disrupt the classroom, engaging students who are quietly inattentive is a comparatively difficult task. Using attention trackers, teachers can much easily monitor the attentiveness of the class and mind them before they tune out.

Finally, by aggregating the attention scores of a particular classroom, AI can help teachers devise a more potent mix of teaching, testing, and activities – to continuously ensure better class performance and engagement

Using AI to augment classroom and educational institutions is of interest to everyone – students, teachers, and parents – and can help bolster educational outcomes. By personalizing the curriculum, optimizing the time of teachers and effective in-class monitoring and assistance, AI can be a game-changer in the way coursework is consumed and leveraged by the next generation of students. 

Over the last few years, technology has been rapidly permeating the sporting arena — from DRS in cricket to VAR (video assistant referee) and goal-line technology in football. Moneyball – the seminal book and movie on the use of analytics for smarter player acquisition – was the tipping point in how analytics and AI could be gainfully used in managing the business of sports better.

For years, avid sports fans around the world have had the topline analytics for their favourite players and teams at their fingertips. From there, today carrying analytics on player performance is seeing a massive data detonation. Sports analytics is no longer a mere water-cooler conversation. It is increasingly a specialised science that is rapidly informing the way professional sports team pick, monitor and coach players – in the process, even transforming the way a game is played.

As sports become more competitive, the margin for error is becoming smaller than ever. Teams need to incorporate every possible scientific element that helps improve player performance for a sustained improvement in their rankings. One such science being co-opted in sports is Artificial Intelligence – which is now a key component in the way teams identify players with high potential, monitor in-game progress to provide feedback and provide coaching for long-term success to players. Let us unpack these three crucial areas and see how AI is playing a key role in managing sports teams.

1. Identifying Top Stars

With a wealth of data available right from lower rungs to youth team records, AI can be hugely decisive in scouting for players likely to be mainstays of the senior levels of sport for years to come. By ingesting performance data for each player from their very first game, AI can uncover youth players who have a strong potential or can complement existing players in the team.

Union Minister of State for Youth Affairs and Sports Rajyavardhan Rathore believes that AI will help sports pick future stars. His plan is for the Indian government to create a database of nearly 3 crore young children in the age group of 5 to 18 years, who will be further refined and trained based on their abilities in different sports.

2. Performance and Insights

The major chunk of identified AI use cases falls in the arena of monitoring key performance indicators of sportspersons and providing insights for improvement. A lot of investment has already gone into the development of various sensors and devices that can track speed, accuracy, motion during practise sessions and games.

These monitoring devices – coupled with computer vision and machine learning – will be crucial interventions in the way coaches and players refine their approaches to their sport. Whether it be identifying potential areas to work on during training, or an analysis of situations within the game, AI can combine well with coaches to uncover how to extract the best performance from their teams and players.

An example of this in action is Dutch company SciSports. The company has developed a product called BallJames. Leveraging computer vision and machine learning, the product uses 3D images to provide insights on movements and tactics for football players.

3. Coaching for Success

Augmenting coaches and long-term training programme is the third and rapidly emerging use case of AI in sports. Depending on the sport being played, elite athletes spend between 10 years and 20 years at the very senior level, and possibly 10 years before at junior levels before becoming elite pros. All of which means that these athletes require continuous mental and physical conditioning to stay at the top of their game for nearly 30 years – depending on the sport they are playing.

AI can be an important intervention in building customised training routines for sportspersons – helping them take a long-term view of their health and diet. For instance, using AI, players could identify areas of improvement on the physical side and strengthen muscles, which may be adjudged as below-par for their sport. On similar lines, AI-led apps could also help suggest dietary options based on the conditioning required and provide personalised pathways for these athletes depending on their unique physical characteristics.

Mental Health

The other equally, if not more important aspect of managing the long-term well-being of athletes, is mental health. Sports players often live away from their families, go through swings in form alongside enormous highs and lows of success and failure. Through all of this, it is critical that we also manage the long-term psychological health of athletes to ensure that they stay at the top of their game. Apps such as jolt.ai – chatbots for motivation and tracking workout adherence – could be instrumental if created specifically to manage the mentality of sports players.

While the above mostly dealt with the ‘game’ aspects of sports management, there is also a plethora of topical applications that can be utilised in the management of sports franchisees (fan loyalty and engagement, player acquisition and abilities matching and uplift from sponsorship deals) and sports administrators (spend and procurement management, asset management and tracking legal compliance and fraud). When you combine these two areas, it is evident that Artificial Intelligence could very well be the next MVP in sports!

A combination of economic, business and technology factors have led to a steady rise in synergistic partnerships between industry and academia in India. Whilst a strong academia-industry partnership model has existed for several years in USA, UK, Canada, Singapore and few other countries.  India Inc. is catching up quickly to the transformative benefits that academia-industry engagements can bring to both parties. Rather than co-opt existing technology, corporates are under increasing pressure to incubate and deliver bleeding edge technology solutions to ensure continued competitive advantage and they are turning to some of the brightest minds in universities today for ideas on how to do that.

While there may be several drivers for corporates inking strategic, operational partnership modes with an academic institution; I see three common themes: First, academic institutions are under increasing pressure to deliver bleeding edge research that has commercial viability and real-world applications. They simply cannot ignore events in the business spheres anymore. For this, they rely on enterprises to provide contextualized understanding within which they can frame their problem statements and hypotheses. Second, we are also witnessing a muted public-sector funding for research, combined with the proliferation of private universities. As a result, academics need the patronage of corporates to fund their long-term research endeavors and goals. Lastly, R&D departments at organizations need the bright minds from academia to deliver results in a shorter time-frame and lower cost. These three critical drivers are spurring a healthy interest in developing academia-industry engagements. 

From AI, analytics perspective; research and innovation are the key differentiators. Let us dig deeper into the academia-industry engagements and dwell on building robust and synergistic engagement model and framework between academia-industry: 

Collaboration for Data Sets

This engagement is primarily intended for gaining access to data for running analysis and hypothesis building. Usually, an organization may need access to multiple varieties of data sets that are available with universities, to refine and improve their statistical models. These engagements can be often seen between enterprises and university hospitals – a hot-bed of structured and unstructured patient data. Healthcare-focused technology companies typically need access to tons of data to build and improve their AI systems – to capture every possible variation of the data and ensure that their model accounts for the best results.

An example of such a collaboration can be seen between Google DeepMind and University College London (UCL) for the use of AI in radiotherapy. The key to this partnership is UCL’s hospital and the availability of researchers in a real-world medical environment. 

Applied Research

The second area of collaboration is for industry and academia to create real-world applicability for research. Academics tend to be extremely visionary in their ability to add to a body of knowledge through thorough and intelligent research but may often lack visibility into challenges faced by businesses. By leveraging business context provided by corporates, they can add a flavor of high applicability to their research. Additionally, solving relevant, business-critical problems, researchers can also improve their visibility among their community, while potentially improving their H-Index scores through highly citable research.

An example of this collaboration is CA Technologies and IIIT Hyderabad engagement, they recently signed an agreement to set up a co-innovation lab. They intend to work together on topical problems in areas of Natural Language Processing, AI and Machine Learning, as per the company statement. For the researchers, this agreement would help improve their visibility through publications in scientific journals and CA Technologies can identify reference architectures and prototypes that will enable faster development timelines.

Co-Curriculum development and learning programs

This alliance between industry and academia is for cross-pollinating and co-creating AI, analytics academic curricula. Given the dynamic nature of business today, enterprises are collaborating with universities for providing continuous AI, analytics training to their employees across disciplines. This ensures that their employees have a contemporary understanding of the best practices in their field of work, while also promoting employee satisfaction. On the other hand, universities carry this understanding of the needs of the corporate sector and incorporate the same into their AI, analytics academic curriculum. For universities, this is a critical way in which they can create a comprehensive coursework that is exceeding relevant in the job market today.

Whilst, these are few prevalent areas of collaboration; other ones may look at mutually inking long-term strategic initiatives that involve academic institutions adding a cross-dimensional flavor to multiple analytics projects and requirements at organizations. The journey between academic – institutions collaboration has evolved and will witness several novel engagement models in the future. The continuous evolution of learning, unlearning and relearning phase will usher a new paradigm in academia-industry collaboration  

We are on the definitive cusp of the 4th Industrial Revolution. Earlier industrial revolutions ushered mechanization of previously manual tasks, leading to a huge shift in production output and increased operational efficiencies while creating a new range of skills for the workforce to master. According to Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, the transformation driven through this technological revolution will be unlike anything that humankind has experienced before and will require an integrated and comprehensive response involving all stakeholders of the global polity – from the public and private sector businesses, academia, and civil society.

Industry 4.0 – defined by breakthroughs in emerging technologies such as Robotics, Artificial Intelligence, Internet of Things, 3D Printing, Autonomous Vehicles and Quantum Computing – will yet again create a massive shift. It is increasingly common news that a manufacturing major is introducing robots on the production line. With smarter factories, smarter production and smarter supply chains, running autonomous production and delivery of manufactured goods, the question is bound to arise – what are the engineers supposed to do?

Engineering has long been a highly sought-after stream of education in India. Consider this – approximately 1.5mn students graduate out of 3,000+ AICTE-affiliated institutions in India, every year. [However, endemic problems surround their quality and technical output. Research after research confirms the disconnect between the education imparted to students, and the skills required on the job. According to the National Employability Report 2016, conducted by Aspiring Minds, 80% of engineers are considered unemployable. Even in India’s highly vaunted software industry, 95% of engineers are thought to be unfit to take up software jobs.

If the average Indian engineer is unfit to perform the tasks expected from him today, what hope is there for him to be able to perform the jobs of tomorrow? The 4th Industrial Revolution is only going to complicate matters by contracting the number of available jobs, while looking for specialized skills that Indian engineers most likely won’t have. One example, according to Talent Supply Index 2017 published by hiring startup Belong, there are only 8 data scientists for every 10 data scientist jobs in India.

It is undoubtedly a matter that needs urgent attention from educational institutes. The need for alignment between the skill-suppliers (colleges) and skill-consumers (businesses) is greater than ever before, and it is critical that educators stay in step with this new wave of industrialization, or risk falling by the wayside.

Embedding AI in engineering streams

Artificial Intelligence is the cornerstone of this new wave of industrialization. Embracing emerging technology areas will ensure that the engineering workforce is relevant for the jobs of the future and their knowledge needs to be embedded in traditional engineering syllabi. It is commonly assumed that AI only happens at the intersection of computer science and mathematics. While that is somewhat true at present, other streams too are looking at developing topical AI programs. Let’s look at these other engineering fields and how AI can be embedded into their existing coursework.
Civil or Construction Engineering is often considered to be far removed from AI disruption. However, AI is already making in-roads into this field. With geo-spatial intelligence and historical earthquake data, civil engineers can make better decisions on assessing the landscape available for projects, understand the materials required to withstand environmental conditions, or at times drop a risky project that might be too dangerous to develop. AI-driven predictive maintenance helps engineers optimally predict maintenance schedules for civil infrastructure developed – mitigating the risks posed by damaged infrastructure to civilians. AI can also help parse image data to detect damage to property, assess the extent of repairs required, and the costs of that repair work. Beyond these, AI can also help design smarter buildings – optimally utilizing electricity and water resources, while also bringing efficiencies to construction costs by automating inventory procurement decisions.

Another stream of engineering assumed to be immune from AI intervention is Chemical Engineering. Chemical Engineers with an understanding of AI can reduce the time for new chemical development, by modeling the impact of chemical combinations. AI can help predict and test the quality and resilience of new formulations. Chemical engineers with a knowledge of how to operationalize robotics technology for combining potentially dangerous chemicals – will again be an important intervention in this area of engineering.

Even across diverse engineering domains – metallurgy, oceanology and aerospace engineering, knowledge of artificial intelligence will be critical. Metallurgists with a knowledge of AI can run models to understand the properties of various metals and build stronger and more purpose-driven alloys. Oceanographers can leverage AI technology to parse geospatial information to better understand sea-beds and model the chemical and physical properties of oceans. In Aerospace Engineering, AI can bring untold efficiencies through robotics for assembling components. AI can predict failures and maintenance schedules required for aerospace equipment. In each of these domains, knowledge of AI, Robotics, Predictive Analytics, Computer Vision and Deep Learning will help ingest large volumes of unstructured disparate data, autonomously generating insights in a much lower time span – while improving the speed of the production process.

Finally, in certain streams within engineering – Mechanical, E&TC and Electrical – AI lends itself more naturally. Mechanical Engineers need to upskill themselves to develop and run autonomous robots that can do complex assembly and integration tasks. Education in Electronics Engineering needs to tend more in favor of developing Industrial IoT, Quantum Computers and advanced chipsets that can handle the large-scale processing required to run cross-platform AI applications. Electrical and Telecommunications engineers, given an education in Artificial Intelligence, can automate, monitor and improve the uptime and performance of their respective systems.

Leading the Way

A substantial chunk of upskilling needs of existing engineering graduates are handled by online courses. We are seeing an increased proliferation of AI, Machine Learning, cybersecurity, IoT, and Robotics courses delivered by online educational platforms: Coursera, Udemy, Udacity, UpGrad, while their programs are well serving the current crop of engineers, some of the other prominent academic institutions and academies  – ISB , Manipal global education , Jigsaw Academy , IFIM , Institute of Product leadership (IPL) ,UPES, Praxis Business School , Shiv Nadar engineering school, IITD are few listed ones that have taken vantage position in imparting AI , Analytics programs . A structured learning and innovative pedagogy approach are needed from traditional educational institutes for skilling new engineering graduates, to be able to master these new means of learning. They need to alter their curriculum to ensure that the next generation of engineers is equipped to handle the next generation of opportunities. However, it is very important that other institutions also follow suit and promote the cause of AI education.AI  will usher a   new beginning in the education arena and the ones that have the ability to learn, unlearn and relearn will succeed in the professional spheres.

The AI Race is fast heating up. While private enterprises tend to view this through a lens of achieving competitive advantage through breakthrough business and process innovation, there is a much larger play between nations competing to achieve supremacy in the domain of Artificial Intelligence. Across the globe – from Japan in the east to United States in the west – every major industrialized nation is ramping up their efforts (and rhetoric) to build indigenous AI capability. These economies have shown great interest, from the federal to the local levels, to achieving the much-vaunted status as the world leader in Artificial Intelligence. While the approaches by each country may differ – the end goal is some variation of achieving a preeminent position as the single distinguished player in the field of AI.

At this point, it is natural to ask – why? Why are entire economies and governments frantically organizing themselves to win in this race? The answer lies mainly in the size of what is at stake. According to a recent report by global consulting company PwC, AI’s contribution to the global economy is expected to be $15.7 trillion by 2030. The nation that serves the largest portion of this need will not only have the highest revenue, but also the highest number of in-demand professionals, the lowest dependency on other nations in this massive field of work, alongside being the singular force to reckon with in the future of the world.

This might explain why, today, the US and China are at the forefront of this technology. According to the same report, China and North America will see the largest part of the global value-pie ($7trillion and $3.7 trillion respectively). When the stakes are this high, you probably do not want to depend on the benevolence of others. You ought to ensure that every capability you require is available within your own shores. In China, the government stands strongly behind AI adoption, announcing their intention to become “a principal world center of artificial intelligence innovation” by 2030. On the other hand, the US has the highest number of AI startups and one of the deepest wells of venture capital to fund the startups’ endeavors. Not to mention, they are also home to larger tech corporations – Google, Amazon, Facebook, Microsoft, IBM etc. – which are also pioneering AI research in their own way.

While the US and China have taken a quantum leap ahead over their other competitors, the field of AI is not exactly a duopoly. While these two are clearly the leaders across any measurement criteria that you would employ, there are several others in the fray – Japan, South Korea, Germany, France, the UK, Canada, Israel, Russia and India – who are all in various stages of launching their visionary plans and developing on-ground leadership through either private enterprise, public support – or both.

With the size of the prize outlined, the next logical question would be – how is India doing in this space? What steps is India taking to ensure that we do not fall by the wayside as the world runs to win this monumentally important race?

There’s some good news and some not so good news on that front. For one, India is not yet considered among the absolute top rung of AI superpowers today. While we do have significant numbers of STEM graduates passing through academia each year, most of them are currently involved in the so-called lower end of the IT value chain – infrastructure services and maintenance etc. On the bright side, India is uniquely positioned to deliver strong AI leadership, assuming we take steps in the right direction on the policy side, as well as in industry-academic collaboration.

Why do I feel India is uniquely positioned? Consider the following:

• India continues to have a strong continuing focus on STEM education. As AI enters the mainstream curricula of our universities, we will realize the benefits of having a robust intellectual capital in this arena.
• Typically, it is data that powers an AI application. India, with the second largest population in the world (and increasingly connected to smart devices) has the potential to not only generate massive data sets, but also one of the most diverse set of data due to the inherent diversity across class, language and other cultural aspects – which can power the most enriched applications of AI
• There is a strong impetus on the policy front in India for AI – with Digital India, Skill India programs started by this government, in addition to constituting NITI Aayog – a national-level think-tank to execute on a vision rich with emerging technology

So how can we combine India’s inherent advantages, with some strong moves already made in the AI space, to possibly achieve AI supremacy in the near future? Here are three clear areas that require a high degree of attention and action to fulfil that vision.

1. Lead with Policy

With a strong, forward-looking government, India is already making the right noises on the development of AI. NITI Aayog – the think-tank I had mentioned earlier – has constituted a committee to study and deliver a National AI Strategy for India. In their June 2018 discussion paper, they identified 5 areas where India is uniquely poised to deliver AI leadership due to our intrinsic advantages – healthcare, agriculture, education, smart cities and smart mobility and transportation. While the Aadhar program has had its critics, it is likely to be instrumental in building a massive training set of citizen data, enabling India to build some thought-leading application in AI. The government has also pledged to put their money where their mouth is – with $480mn projected to be spent on the Digital India program in 2018. While this spending pales in comparison to the spending of other countries (China has committed $150bn up to 2030), it will be instrumental for founding a strong test-bed for incubating our AI vision. The government is also planning a national data and analytics platform in collaboration with private players to utilize the huge amount of data with the help of AI.

2. Facilitate through Academia

Close to 2.6mn students graduated out of STEM fields from India in 2016. While I mentioned that these graduates have anywhere between no to a rudimentary understanding of AI today – it does represent the huge footfall seen in these fields, who would be well-served through a healthy training in AI-centric technologies.

The more pressing problem can be seen in core AI research. While India is ranked 5th in the world today terms of number of papers published (14,864 between 2010-16), we are still a fair way behind the US (63,344) and China (39,820) on this metric. Worse still, India ranks a distant 19th on the metric of H-Index (measured between 1996 and 2016), which leads to a concern on whether our current research is citation-worthy or rooted in business applicability. So, while the appetite for research exists, the contribution to the overall body of knowledge still needs some upgrading.

To address this, the aforementioned NITI Aayog discussion paper, recommends the set-up of a 2-tier integrated approach for boosting research in both core AI and applied AI. The first – COREs (Centers of Research Excellence in Artificial Intelligence) will be focused on developing a better understanding of existing core research and pushing technology frontiers through creation of new knowledge. The second – ICTAI (International Centre for Transformational Artificial Intelligence) will have a mandate of developing and deploying application-based research through Private sector collaboration. This framework would also consist an umbrella organization addressing issues relating to access to finance, social sustainability and the global competitiveness of the technologies developed. This body would be similar to the Campus for Research Excellence and Technological Enterprise (CREATE), Singapore program or Innovate UK.

3. Implement through Private Industry

While the first two points deal with strengthening the backbone of AI research and education, this final aspect deals with building high-class industry-grade IP with wide applicability. Due to a huge democratization in information, both large tech corporations and startups are aware of the challenges that can be solved through AI and are building solutions to address these challenges. Behemoths IT and consulting players are already investing in academic partnerships to set up a base for IP development and workforce training. Startups too, while not similarly endowed, are looking to build visionary products that will transform the industry through collaboration with academia. Through such an industry-academia collaboration, Indian technology companies would be able to foster synergy by developing bleeding edge research in India which can be gainfully employed to solve global challenges. Extending the Make in India initiative would be crucial to ensure that the intellectual property of the work done by Indians stays in the home country, boosting our credibility in this space.

In conclusion, while India is already among some of the top nations in the world today in the field of Artificial Intelligence, there still is a long way to go to hit the absolute pinnacle in this space. However, given that AI is still is in a nascent stage, there is significant scope for India to still emerge as the leading light in this space. With this sustained and rapid pace of progress, I am certain that India will soon emerge as the preeminent leader in the field of AI.