- Diffusing the Ticking Time bomb called Diabetes
- Putting AI to Work in Business Environment
In June 2023, a study conducted by the Madras Diabetes Research Foundation in collaboration with the Indian Council of Medical Research and the Union Health Ministry unveiled that 11.4% of India’s population, equating to 10.13 crore individuals, is grappling with diabetes. Additionally, 15.3% of the population, or an extra 13.6 crore people, are identified as pre-diabetic. The study also highlighted that 28.6% of the populace falls under the category of obesity according to the BMI measure.
How is the present state of food industry in India responsible for rising cases of diabetes? What can be done to create a more responsible food industry ecosystem? (15 marks, 250 words).
Food industry and diabetes:
- The World Health Organization attributes a significant factor to the consumption of unhealthy ultra-processed foods and beverages, aggressively marketed to replace traditional diets.
- Such items include carbonated drinks, instant cereals, chips, fruit-flavored drinks, instant noodles, cookies, ice cream, bakery products, energy bars, sweetened yogurts, pizzas, processed meat products, and powdered infant formulas.
- Scientific evidence establishes a connection between diets high in ultra-processed foods, laden with sugar, fat, and salt, and the heightened risk of diabetes.
- A mere 10% increase in daily ultra-processed food consumption is associated with a 15% higher risk of type-2 diabetes among adults.
- Ultra-processed foods, due to their altered structure and the addition of cosmetic additives, colors, and flavors, contribute to overeating, weight gain, and an increased risk of diabetes and other chronic diseases.
- Obesity and diabetes, in turn, become key risk factors for heart disease and mortality.
- The sale of sugar-sweetened beverages has declined over the past two decades in high-income countries.
- To compensate, the food industry has shifted its focus to low- and middle-income countries, with India being a prominent market.
- Billions are spent on marketing ultra-processed foods and beverages, resulting in heightened consumption, especially among vulnerable populations.
- Marketing strategies target younger generations and the expanding middle class, making it challenging for individuals to opt for healthier food choices.
- Children, in particular, are exposed to marketing tactics involving cartoon characters, incentives, gifts, and celebrity endorsements, exacerbating the public health crisis, particularly the diabetes epidemic.
- Sugar-sweetened beverages are a significant source of added sugar, increasing the risk of type 2 diabetes. However, the food industry opposes restrictions on marketing, offering partnerships and economic development arguments.
- Despite participating in programs like ‘Eat Right,’ the food industry’s influence hampers strong regulations, including front-of-package labeling suggested by the Food Safety and Standards Authority of India, which is yet to be implemented.
To shield the public from the manipulative strategies of the food industry, the government must establish a legal framework or even an ordinance aimed at reducing or halting the consumption of ultra-processed foods. This could involve defining ‘healthy food,’ implementing warning labels on unhealthy food, and placing restrictions on the promotion and marketing of unhealthy items. Governments in South Africa, Norway, and Mexico have recently taken similar actions, and the Indian government can demonstrate its commitment to regulating food labeling and marketing through such legislation. This proposed law, akin to the Infant Milk Substitutes, Feeding Bottles, and Infant Foods Act, has the potential to curb the growth of commercial unhealthy foods and beverages—an idea whose time has come.
Generative AI is undergoing training for various commercial applications such as automated customer support, financial forecasting, and fraud detection.
GS3- Science and Technology-Awareness in the fields of IT, Space, Computers, Robotics, Nano-technology, Bio-technology and issues relating to Intellectual Property Rights.
GenAI can significantly transform enterprise growth and development. Comment. (10 marks, 150 words).
Statistics related to GenAI:
- In the context of enterprises, an estimated 60% of IT leaders are considering GenAI implementation. However, concerns, particularly regarding security (cited by 71% of IT leaders), pose a hurdle to adoption.
- The Dell Technologies 2023 Innovation Index report notes that 59% of Indian businesses are either investing or exploring the feasibility of investing in AI, Machine Learning, and advanced analytics for innovation.
- The key to widespread GenAI adoption lies in identifying and deploying purpose-built models that suit the specific needs of enterprises, such as automating customer support, financial forecasting, and fraud detection.
- The Dell Technologies 2023 Innovation Index report highlights that India leads in the adoption of AI-based optimization software for process automation (37% of businesses).
Purpose-built Gen AI:
- To drive this transformative change, enterprise utilization of GenAI is likely to differ from the broad application of general-purpose Large Language Models (LLMs) like ChatGPT.
- Instead, enterprises are expected to employ GenAI models tailored to address specific challenges, ensuring more accurate results than those achieved by general-purpose models.
Advantages of purpose-built GenAI models:
- Data Security: As enterprises leverage AI for handling vast datasets, the importance of securely managing this data becomes paramount. Industries with strict data privacy regulations, such as healthcare and finance, need purpose-built models to comply with these standards.
- Time to Market: Updating GenAI models is a frequent requirement for most enterprises, and purpose-built models streamline this process. General-purpose LLMs, like ChatGPT, have longer training times due to the extensive data required, compromising speed to market.
- Performance: Purpose-built models outperform general-purpose models, particularly in applications requiring real-time processing. Enterprises utilizing third-party LLMs may struggle to optimize performance and minimize latency for GenAI workloads.
- Cost: Purpose-built GenAI models, requiring less training data, translate to cost savings in terms of training and re-training compared to general-purpose LLMs.
GenAI, with its potential to automate intricate processes, enhance customer interactions, and provide superior machine intelligence, holds profound possibilities for enterprises worldwide, with CIOs playing a crucial role in its advancement. Unlocking the full capabilities of Generative AI (GenAI) demands tailored approaches to mitigate inherent adoption risks.