Why are Indian startups looking for talent abroad? Well, the answer to this is the dearth of homegrown professionals on the ground with knowledge of UI/UX (user interface/user experience), artificial intelligence, machine learning, reports The Economic Times.

Industry experts are of the opinion that roles in data science and data engineering are not typically part of the curriculum of Indian colleges.

The ratio of the number of people to jobs in deep learning is 0.53, while for machine learning it’s 0.63 and for NLP it’s 0.71. Only 4 per cent of AI (Artificial Intelligence) professionals in India have worked on cutting-edge technologies such as deep learning and neural networks, the key ingredients in building advanced AI-related solutions, said Rishabh Kaul, co-founder of recruitment startup Belong.

Another reason for Indian startups to hunt for talent from overseas is to increase their global presence. Hence, these companies are either hiring foreign professionals or those Indians who wish to come back to India.

For the past two years, there have been a lot of global uncertainties, especially in the US. A lot of people are thinking of working from India and not the US. “The funds are normally coming from global companies and the startups are here, so it is best of both worlds as there’s a reverse brain drain,” said Rajeev Banduni, chief executive of advisory services firm GrowthEnabler, who has a lot of requests from European students, particularly the UK and Spain, for internships. He said 80-85 per cent talent is coming back to India.

UpGrad, started by UTV co-founder Ronnie Screwvala, is among those looking to expand presence in global markets. It has a team of three Chinese Singaporeans focused on analysing the Southeast Asian market and deciding upon its market-entry strategy. The firm is also talking to some Chinese education professionals to join it to help bring learning from Chinese markets.

As many as 22 members in Nykaa’s 80-people business and marketing team are those who have come back to India from the US and Europe.

But why are Indian startups more interested in hiring Indian professionals? According to these firms, the availability of such global talent is tougher and their salaries are higher. A similar talent in the US/western world coming to India would be 30-50 per cent costlier than what’s available in India.

These companies also believe that a change in the talent pool can be brought through government initiatives just like China where they have a three-year plan to dominate in AI.

A breather for the startup ecosystem hunting for top talents is that that in his budget speech in February, finance minister Arun Jaitley had announced that the government will launch a national programme on AI and doubled the budget allocation for Digital India — the government’s main initiative for promoting AI, machine learning and other innovations — to Rs 3,073 crore for this year.

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In the summer of 2011, I was giving a lecture on machine translation at a small college in Kathmandu. That afternoon, one student asked me a question that was so complex and required such fundamental understanding of computer science that it made me realize how some students in the most remote corner of the world — with hardly any access to advanced technology and academic privileges — have the level of talent and curiosity as some of my students at Columbia University. Two years later, I hired the student to work for me at Fusemachines, where he currently leads a team of 90 engineers.

Chinese tech giant Tencent, in a study compiled by its research institute, estimates there are around 300,000 AI professionals in the world — but millions more are needed. As artificial intelligence permeates through every field in every industry, there is a war for AI talent. In fact, Silicon Valley giants are fighting and paying an exorbitant amount of money to lure the best AI engineers to work for them. “Salaries are spiraling so fast that some joke the tech industry needs a National Football League-style salary cap on AI specialists,” Cade Metz wrote in a New York Times article.

But instead of upping the salaries to millions of dollars and fighting for the same small pool of talent, we should be training engineers in artificial intelligence around the world. Young students and engineers in remote developing countries also have the ability to perform — and, at times, outperform — the ones who have degrees from elite institutions in the West. There is untapped talent in these places, and we are neglecting it to our detriment.

Educating engineers from across the globe in machine learning, deep learning and natural language processing — the most common sub-disciplines within AI — will help increase access to AI talent. Someone who experiences complex problems in his/her own country could be more suited to try and solve those problems with AI. For example, a Nepali engineer who wants to use machine learning to predict crop yields of their community will be better informed about Nepal’s farmlands than a graduate from Silicon Valley.

Similarly, engineers working for companies like Zipline and Fuse machine in Nepal and Rwanda are able to build and adapt autonomous drones to deliver medicine in remote villages in countries with poor road infrastructure. This would not only save lives but also remarkably change the livelihood of villagers who would otherwise have to trek for days to get to the nearest pharmacy. This is one example of the numerous ways artificial intelligence can be used to improve health care, fight poverty and raise the standards of living in developing countries. For that, it’s important that we invest in educating and enabling talented young engineers in such countries.

But how do we train local engineers in far-flung places to build drones, robots and complex systems? The answer is in a combination of online courses and some on-site training. Two years ago, Fusemachines launched a fellowship program that allows students in Nepal to develop high-level skills in programming and solving machine learning algorithms — eventually leading to a MicroMasters in Artificial Intelligence from Columbia University. Today, the program has expanded to three additional locations: the Dominican Republic, New York City and Rwanda.

As they complete the course, enrolled students come to class once a week and discuss the homework assignments and problems. What we’ve found is that this mix of an online course with on-site guidance works very well with the students. They learn on their own time throughout the week but still feel like a part of a class when they meet with other students in a physical location. With this model of learning, we have had many engineers graduate with certificates in AI from Columbia University.

The software development company Andela is another example that this type of talent pipeline works. Andela focuses solely on training developers in Africa before pairing them with Western companies, thus helping to reduce the opportunity gap in tech jobs. Online MOOCs like Edx, Coursera and Udacity are also doing a tremendous job of making highly sought-after specialized skill sets available for the masses through online courses taught by talented professors, scientists and engineers.

The world is rapidly moving into a new era of technology where AI will transform many aspects of human life. But if we want everyone to benefit from the development of AI, and not just a few select countries, we need to make AI equally accessible around the world. This is why we need to democratize artificial intelligence.

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healthcare. There have been many success stories around the use of AI for medical imaging, data mining and implementation of bots that have conversations with patients. AI also offers hospitals an opportunity to enhance quality of care and improve patient outcomes. However, there are several obstacles standing in the way of artificial intelligence adoption in healthcare.

Many key drivers behind the use of AI stems from the need for healthcare organizations to improve patient outcomes and deliver more effective treatments and care. Mayo Clinic was one of the early adopters of one of IBM’s flagship AI platform Watson and implemented it as part of its clinical team to help match patients to the appropriate cancer treatments and trials. Their implementation proved that AI could provide valuable insights into accurately matching patients with specific needs to treatment plans that match their conditions. Another use was at the Cleveland Clinic, which adopted Microsoft AI technology to help determine high-risk patients in their ICU. The process included the use of machine learning and advanced analytics to determine, based on different indicators, who needed attention and would be considered high risk.

Despite these successful use cases for AI in healthcare, several hospital IT executives find it challenging to implement this technology in their current environment. The difficulties hospitals face when implementing AI are the result of a few challenges that healthcare as a whole is dealing with. Here are six common barriers to artificial intelligence adoption in healthcare.

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