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. 

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.”

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  

The buzzword among the business and tech communities in China for the past year has been ‘AI’, or artificial intelligence. Artificial intelligence, which allows software to “learn” human ways of thinking, is being incorporated into the largest e-commerce platforms, including Baidu, Alibaba, and Tencent, as well as into data-intensive traditional sectors. With strong government backing and concentrated research in this area, AI is poised to drive China’s economy forward toward higher levels of growth.

China is developing artificial intelligence in improving the capabilities of robotics, developing driverless cars, divining consumer preferences, inventory forecasting, selling enhanced products, and marketing goods and services. According to Liu Lihua, Vice Minister of Industry and Information Technology, China has thus far applied for 15,745 AI patents.

China plans to launch a national AI plan, which will strengthen AI development and application, introduce policies to contain risks associated with AI, and work toward international cooperation. The plan will also provide funds to back these endeavors. Some municipalities also support AI research programs. Beijing, for example, is home to the CAS Institute of Automation, a consortium of universities and firms that provides venture capital funding of 1 billion RMB ($150 million) to AI development. Zhejiang province has also embraced AI programs. Already, Geely Automobile in Zhejiang is using intelligent manufacturing and internet marketing services based on AI to boost sales.

BAT – Chinese AI Frontier Giants

China’s BAT, or Baidu, Alibaba and Tencent, is leading the way for AI in China. Baidu was the first Chinese company to embark upon research in AI, using a system known as Duer to be used in home devices and driverless cars. Driverless auto software provided by Baidu will be made available to car manufacturers under the Apollo Project. Alibaba is using AI to forecast regional order quantities and to improve logistics efficiency, while Tencent has released a platform for deep learning using social data.

Baidu, Alibaba and Tencent have been vying for top talent in AI in order to become leaders in this area. Making headlines several days ago, Alibaba lured Ren Xiaofeng from Amazon.com to lead its own technology lab, which aims to make headway in artificial intelligence. Tencent brought Baidu’s AI expert Zhang Tong on board in March. In 2014, Baidu poached Andrew Ng from the Google Brain project to lead the Baidu Research Institute (though he recently stepped down).

Bay Area dominates this year’s AI funding

Venture investment in startups that are applying artificial intelligence or machine learning has more than tripled in the U.S. since 2013, according to PitchBook Data, with about 60 percent of that coming to founders in the Silicon Valley Bay Area.

The Seattle investment research firm put together a ranking of the top 20 AI deals done around the world this year for me while I was researching this week’s Silicon Valley Business Journal cover story. Almost half of the startups that were funded and nearly three-quarters of the investors involved were from San Francisco and the Silicon Valley region.

The new era in Silicon Valley centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

Silicon Valley’s financiers and entrepreneurs are digging into artificial intelligence with remarkable exuberance. The region now has at least 19 companies designing self-driving cars and trucks, up from a handful five years ago. There are also more than a half-dozen types of mobile robots, including robotic bellhops and aerial drones, being commercialized.

Funding in A.I. start-ups has increased more than fourfold to $681 million in 2015, from $145 million in 2011, according to the market research firm CB Insights. The firm estimates that new investments will reach $1.2 billion this year, up 76 percent from last year.

Even Silicon Valley’s biggest social media companies are now getting into artificial intelligence, as are other tech behemoths. Facebook is using A.I. to improve its products. Google will soon compete with Amazon’s Echo and Apple’s Siri, which are based on A.I., with a device that listens in the home, answers questions and places e-commerce orders. Satya Nadella, Microsoft’s chief executive, recently appeared at the Aspen Ideas Conference and called for a partnership between humans and artificial intelligence systems in which machines are designed to augment humans.

The auto industry has also set up camp in the valley to learn how to make cars that can do the driving for you. Both technology and car companies are making claims that increasingly powerful sensors and A.I. software will enable cars to drive themselves with the push of a button as soon as the end of this decade — despite recent Tesla crashes that have raised the question of how quickly human drivers will be completely replaced by the technology.

AI is in it for the long-haul

Whenever there is a new idea, the valley swarms it. But you have to wait for a good idea, and good ideas don’t happen every day. Silicon Valley’s new A.I. era underscores the region’s ability to opportunistically reinvent itself and quickly follow the latest tech trend. This is at the heart of the region’s culture that goes all the way back to the Gold Rush. The valley is built on the idea that there is always a way to start over and find a new beginning.