2nd July 2018 THE HINDU Editorial Analysis ha ii
The dream of being an Al powerhouse o In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (Al) powerhouse. Al is the use of computers to make decisions that are normally made by humans . Many forms of Al surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity. But NITI Aayog envisions Al solutions for India on a scale not seen anywhere in the world today, especially in five key sectors-agriculture, healthcare, education, smart cities and infrastructure, and transport
In agriculture, for example, machines will provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations e It's an idea with great potential: India has 30 million farmers with smartphones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution Machine learning, the set of technologies used to create Al, is a data- guzzling monster. It takes lots of historical data as input, identifies the relationships among data elements, and makes predictions. More sophisticated forms of machine learning, like "deep learning", attempt to mimic the human brain. And even though they promise greater accuracy, they also need more data than what is required by traditional machine
...learning. Unfortunately, India has sparse data in sectors like agriculture and this is already hampering Al-based businesses today o Take the Bengaluru-based Intello Labs, for instance. This is a start-up which helps buyers at agricultural mandis evaluate the quality of grains, fruits or vegetables. In the normal course, a buyer determines visually how much wheat is destroyed by pests, and if foreign particles are present, before offering the farmer a price o But this process is subjective and prone to error. Visual inspection relies too much on the buyer's expertise, and corrupt middlemen may cheat farmers. So, a smartphone-based Al product, such as Intello Labs' grading app, can help. To develop this product, the Intello Labs team had to photograph 2.5 million agricultural samples. Experts then identified the contents of these photos a laborious process called annotation. Next...
.the team wrote a deep learning algorithm, which was trained using the photos. Today, the algorithm can predict the quality of 12 foods over 95% of the time in a few markets like Delhi and Rajasthan . But in order to expand their basket beyond 12 products and a few States, Intello Labs will need millions of more such images. This can be challenging for a private firm, unless such images are collected, digitised and annotated automatically by the government at agricultural mandis. Such data collection doesn't happen today. "The biggest agricultural data today resides with the government. It's entirely up to them to annotate it and make it usable," Climate-Connect, a Delhi-based firm, which uses Al to predict the amount of power a solar plant will generate every 15 minutes. This is critical because solar electricity generation can change dramatically every hour...
..depending on weather conditions and the position of the sun. When this happens, the plant must communicate expected changes to power distributors, which will then switch to alternative sources. With India planning to install 100 GW of solar power by 2022, such Al will play a central role in power planning But to generate such data, Climate-Connect needs historical inputs like the time of sunrise and sunset, and cloud cover where the plant is located. Unfortunately, since most Indian solar plants are recent, data are available only for a couple of years, whereas deep learning needs data over many years to predict generation Today, the firm uses traditional machine learning technologies such as regression analysis that work with less data. These methods have an accuracy of around 95%, while deep learning can boost accuracy for such
....perations NITI Aayog's report has bleak news: only about 50 Indian scientists carry out "serious research" and they are concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. e, only about 4% of Al professionals have worked in emerging technologies like deep learning. A survey of Linkedin found 386 out of the 22,000 people with PhDs in Al across the world to be Indians Can India then really become an "Al garage" for 40% of the world, as NIT Aayog envisions? The discussion paper mentions no timeline for this goal. But for any reasonable time frame for execution, much needs to change immediately NITI Aayog suggests setting up a network of basic and applied Al research
..institutes. But if these institutes are to fulfil their mandate, they must collaborate closely with agricultural universities, medical colleges and infrastructure planners NITI Aayog's ambitious road map does not mention deadlines or funding. Without these, it lacks accountability
Bhima-Koregaon and the fault in our laws Multiple "Public Safety Acts" and "Defence of India Acts" had been the favourite weapons of the colonial regime. Many speakers expressed the concern that, despite the best intentions of the Constituent Assembly, the Constitution could easily be interpreted to authorise the continuation of these hated laws An examination of the UAPA shows how, in one overarching "anti- terrorism law", vast discretionary powers are conferred upon state agencies, judicial oversight is rendered toothless, and personal liberty is set at naught
The UAPA authorises the government to ban "unlawful organisations" and "terrorist organisations" (subject to judicial review), and penalises membership of such organisations e problems begin with the definitional clause itself. The definition of "unlawful activities" includes "disclaiming" or "questioning" the territorial integrity of India, and causing "disaffection" against India. These words are staggeringly vague and broad, and come close to establishing a regime of thought-crimes "Membership" of unlawful and terrorist organisations is a criminal offence, and in the latter case, it can be punished with life imprisonment. But the Act fails entirely to define what "membership" entails. Are you a "member" if you possess literature or books about a banned organisation? If you express sympathy with its aims? If you've met other, "active"
....power to arrest people under boundlessly manipulable justifications, such as "having suspected Maoist links" The second serious problem with the UAPA regime kicks in: Section 43D(5) of the Act prohibits courts from granting bail to a person if "on a perusal f the case diary or the [police] report .. [the court] is of the opinion that there are reasonable grounds for believing that the accusation against such person is prima facie true." The case diary and the charge sheet is the version of the state. Therefore, under the UAPA, as long as the state's version appears to make out an offence, a court cannot, under law, grant bail When we juxtapose this with the inordinately slow pace at which criminal trials progress, Section 43D(5) of the UAPA is effectively a warrant for..
Seychelles templat New Delhi has clearly opted for a charm offensive in the Indian Ocean Region (IOR). The red carpet laid out for the visiting Seychelles President Danny Faure last week came against the backdrop of setbacks in the bilateral relationship owing to the Assumption Island agreement being put on hold The pact, to build a naval base on the island, was seen as a major strategic enhancement of India's IOR naval capacities and had been under discussion since 2003. It was finally signed during Prime Minister Narendra Modi's visit to the Seychelles in 2015 The deal was to include 30-year access to the base as well as permission to station Indian military personnel on the ground, with facilities on the
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