Relevance: GS-3: Science and Technology- developments and their applications and effects in everyday life.

Key phrases: Artificial Intelligence, machine learning, Climate issue, Processing, Digital Assistants, natural language processing.

Why in News?

• While there is an allure to national dreams of economic prosperity and global competitiveness, underwritten by AI, there is an environmental cost and — like any issue at the nexus of technology, development, growth and security — a cost that comes with being locked into rules about said environmental impact set by powerful actors.

What is artificial intelligence?

• Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.
• Artificial Intelligence, often abbreviated as "AI", may connote robotics or futuristic scenes, AI goes well beyond the automatons of science fiction, into the non-fiction of modern day advanced computer science.

Why artificial intelligence is important?

The artificial intelligence is important in the following way:

• AI automates repetitive learning and discovery through data. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks. And it does so reliably and without fatigue. Of course, humans are still essential to set up the system and ask the right questions.
• AI adds intelligence to existing products. Many products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies. Upgrades at home and in the workplace, range from security intelligence and smart cams to investment analysis.
• AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that algorithms can acquire skills. Just as an algorithm can teach itself to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data.
• AI analyses more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers used to be impossible. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data.
• AI achieves incredible accuracy through deep neural networks. For example, your interactions with Alexa and Google are all based on deep learning. And these products keep getting more accurate the more you use them. In the medical field, AI techniques from deep learning and object recognition can now be used to pinpoint cancer on medical images with improved accuracy.
• AI gets the most out of data. When algorithms are self-learning, the data itself is an asset. The answers are in the data. You just have to apply AI to find them. Since the role of the data is now more important than ever, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.

Climate cost issue with artificial intelligence:

• AI seems destined to play a dual role. On the one hand, it can help reduce the effects of the climate crisis, such as in smart grid design, developing low-emission infrastructure, and modelling climate change predictions. On the other hand, AI is itself a significant emitter of carbon.
• In 2019 when researchers at the University of Massachusetts Amherst analysed various natural language processing (NLP) training models available online to estimate the energy cost in kilowatts required to train them. Converting this energy consumption in approximate carbon emissions and electricity costs, the authors estimated that the carbon footprint of training a single big language model is equal to around 300,000 kg of carbon dioxide emissions. This is of the order of 125 round-trip flights between New York and Beijing, a quantification that laypersons can visualize.
• Training artificial intelligence is an energy-intensive process. New estimates suggest that the carbon footprint of training a single AI is as much as 284 tonnes of carbon dioxide equivalent — five times the lifetime emissions of an average car.

Way forward:

• Like most nexus issues, the relationship between climate change and AI is still a whisper in the wind. It is understudied, not least because the largest companies working in this space are neither transparent nor meaningfully committed to studying, let alone acting, to substantively limit the climate impact of their operations.
• Governments of developing countries, India included, should also assess their technology-led growth priorities in the context of AI’s climate costs. It is argued that as developing nations are not plagued by legacy infrastructure it would be easier for them to “build up better”. These countries don’t have to follow the same AI-led growth paradigm as their Western counterparts. It may be worth thinking through what “solutions” would truly work for the unique social and economic contexts of the communities in our global village.

Source: Indian Express

Mains Question:

Q. In her budget speech, FM Nirmala Sitharaman’s described AI as a sunrise technology that would “assist sustainable development at scale and modernise the country. In this regard what are the social-economic implications of Artificial Intelligence? Critically Analyse.