Is now the right time for Nepal to start addressing artificial intelligence? Back in 1998, the Asia Regional Office of the International Telecommunication Union was pressuring the just established Telecom Regulatory Commission of Sri Lanka to put its limited resources behind telecentres. As director general of Telecom, I said no. In 2005, the United Nations Development Programme (UNDP) regional office offered funding support to LIRNEasia, just as it was being established as a regional think tank, conditional on pivoting to internet governance research. The legitimacy that would come from UNDP support was valuable, but I declined. We needed to get people connected at least to voice telephony before taking on internet issues. By that time, LIRNEasia was conducting research on telecentres, including those just established in eastern Nepal. Telecentres merited our attention in 2005, but would have been a distraction when we were struggling to stabilise the nascent competitive telecom sector. LIRNEasia, which said no to internet research in 2005, now does substantive work on the subject. Can today’s governments choose to pass on artificial intelligence or connected devices (also known as the Internet of Things)?

Why policy?

Today, a Nepali with hearing disability (or even those who are not so disabled) can use Google’s Live Transcribe, recently introduced in over 70 languages including Nepali. This allows the person who cannot hear to read on her smartphone what the other person is saying in real time. Live transcribe is based on artificial intelligence. The person using Live Transcribe would have to be literate, possess a smartphone, have some kind of data connectivity and possess awareness of, and the ability to download, the free app. Nepal has the highest penetration of smartphones in South Asia (52 percent of the population between 15 and 65), but the other preconditions are more challenging as shown by a recent nationally representative survey conducted by LIRNEasia.

Among the disabled, around half have never been to school and may be presumed illiterate. Data connectivity is rare outside the Kathmandu Valley and not perfect even within. Half the persons with disabilities who did not own mobile phones (almost 70 percent in the 15-65 age group) saw no need for a phone, indicating lack of awareness about the enormous potential of smartphones and of apps such as Live Transcribe.

The government does not have to authorise a Nepali person with hearing disability to use Live Transcribe. Short of blocking Google or the Play Store, there is nothing the government can do to prevent its use. That also means that there is nothing to stop data from that person’s conversations from being used to ‘train’ the artificial intelligence that powers the app that helps a disabled person understand what is being said. The more the Nepali app is used, the better trained will be the underlying artificial intelligence.

Over time, real-time transcription in Nepali will improve. The point is that artificial intelligence is seeping into our lives whether a strategy is in place or not.

A country needs an artificial intelligence strategy to proactively adopt artificial intelligence in its business processes, to position itself as a supplier of artificial intelligence technology to other users, or both. Even now, Nepali firms must be considering how to use artificial intelligence to improve business processes. But there is little incentive for most businesses and the consultants who advise them to talk about the skills needed to optimise the value of artificial intelligence in organisations.

This has historical parallels. We first talked about adopting computers back in the 1980s; it is later that we addressed the supply side. Now, many countries have adopted national strategies on how to increase export earnings from information technology and information technology enabled services. But with one of the youngest populations on the planet, many with information and communications technology credentials, can Nepal afford to wait?

Artificial intelligence refers to machines that show some behaviors that mimic human intelligence. These days, what we have is narrow artificial intelligence in specific domains. It is based on deep learning wherein the software is trained on massive amounts of domain-specific data. Artificial intelligence can make decisions/or advise those who are making decisions on creditworthiness or diagnose medical conditions faster and with fewer errors. Artificial intelligence has been used to generate Tang dynasty poetry by a Sri Lankan data scientist. The possibilities are endless.

The rules by which the software reaches its conclusions are opaque, so the results have to be verified against ground truth. Issues of bias or error have to be addressed. What works in California may not necessarily work in Nepal.

All these elements could be addressed through a well-formulated national strategy. Before artificial intelligence, big data and data analytics were the buzzwords. It was possible to ask a consultant to, for example, run a company’s customer records through a ‘black box’ proprietary software to identify the most valuable customers, predict the ones most likely to defect and so on. Today, these things are likely to be marketed as artificial intelligence. Data cleaning would most likely be necessary. Because analysis would be done in-house, data protection issues were unlikely to crop up. It would be good to have a data scientist on staff, but not essential. Though the underlying software would be open source, the consultants would have little incentive to open the black box unless the company or the in-house data scientist insists.

Things would get more complicated when the company starts working with external data sets, such as when it seeks to gain insights for marketing. Here, issues of representivity (does the data accurately depict the target population?) and also limitations, if any, on how the data may be used. If the former, patterns can be identified, even if the individual cannot be. There would be a greater necessity for domain knowledge and possibly also for in-house expertise in analytics. For artificial intelligence, training data is critically important. Many experts believe that China will lead in artificial intelligence because of the greater availability of training data (China’s digitalisation is highly advanced, for example, in payments and facial recognition). Europe is likely to lag because of excessive restrictions on data use and consent requirements.

Supply side

Barriers to entry are low in most information technology domains, including in artificial intelligence. Given the need for training data and skilled artificial intelligence engineers even on the demand side, it makes sense to also explore the opportunities of becoming suppliers of artificial intelligence solutions and artificial intelligence-infused products. Now, as many governments are considering data-protection legislation, the time is right to discuss artificial intelligence strategies. Otherwise, Nepal may find the many opportunities of artificial intelligence foreclosed by short-term considerations associated with doing business with Europe.

Developing policy requires resources and skills. When both are scarce, prioritisation is even more important. Unless the conversation is started now, it may be too late for Nepal’s youth and businesses.

Samarajiva is the chair of the ICT Agency, the apex body for ICT within the government of Sri Lanka, and founding chair of LIRNEasia