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Can AI create a better tomorrow?

If used ethically, AI advancements can help society to solve problems of income inequality and food insecurity to create a more inclusive human centred future

Can AI create a better tomorrow?
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Can AI create a better tomorrow?

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Over the last few years AI (Artificial Intelligence) has become a force to reckon with. According to the World Economic Forum (WEF), "The fourth industrial Revolution will represent a new era of partnership between humans and AI, with potentially positive global impact. AI advancements can help society to solve problems of income inequality and food insecurity to create a more inclusive human centred future."AI is an important innovation for society. It improves the quality of life and will disrupt the world.

AI is the simulation of human intelligence processes by machines, especially computer systems. Machine learning (ML) is a subset of AI and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning is a subset of machine learning that enables computers to solve more complex problems. Data science is the practice of organizing and analyzing data to gain insights that may prove helpful for human decision making.

AI is widely used to provide personalised recommendations to people based for example on their previous searches and purchases or other online behaviour. AI is hugely important in commerce: optimising products, planning inventory, logistics etc. AI enhances the speed, precision, and effectiveness of human efforts.

Some of the use cases of AI are improving customer satisfaction, fraud detection. AI based diffusion models have been introduced recently to improve the quality of low resolution images and convert them to high fidelity ones.

AI in healthcare

AI is going to impact many fields and health care is the most important field, AI is going to transform. AI in healthcare mimics human cognition in the analysis, presentation and comprehension of complex medical and health care data. For example using AI, cardio risk of a person can be predicted. Cardiovascular events for the coming five years can be anticipated by analysing the retina scan in a non-invasive way. Doctors can predict medical events of a person by analysing medical records. They can predict when the patient is going to be sick and requires readmission, getting more time to act.

The world is going through a disruptive Covid-19 pandemic. Knowing the vital numbers is very important for the government to judge the severity of this pandemic. Using AI, models are developed to forecast and predict the trends in the spread of the disease.

Machine learning can improve the efficiency of Covid-19 vaccine supply chain management (SCM) through automated quality check, streamlining production planning, warehouse management and reduction in forecast errors. ML, when implemented in the various stages of vaccine SCM, can help to achieve the goal of 'vaccination for every individual'.

AI in telecom

AI is used for radio resource optimization. It can be used to predict Radio Frequency (RF) signal coverage. AI applications in the telecom industry use advanced algorithms to look for patterns within the data to detect and predict network anomalies (say network overload) and fix problems before customers are negatively impacted. In Ericsson Radio system AI algorithms run on the baseband to predict traffic patterns and automatically turn off antennas as required to reduce energy usage.

Path loss prediction is important to optimize the performance of radio networks. For 5G networks (which use new frequency bands-sub C band, millimeter band) path loss prediction methods with high accuracy and low complexity are required. ML based path loss prediction models are more accurate and more computational efficient than other models. Based on historical data, ML based models can build relationships between path loss and input features such as antenna separation, distance, frequency.

Every telecom service provider uses AI and ML to improve its customer service by using virtual assistants and chatbots. Virtual assistant automate and provides response to the support requests which cut business expenses and improve customer satisfaction. The ability to offer speech and voice services through chatbots based on AI are available.

Chatbots contain three layers - knowledge base, storing the data and Natural Language Processing (NLP). NLP enables machines to understand the human language. It helps computers measure sentiment and determine which parts of human language are important. BSNL has introduced its online chatbot - BSNL Automated Virtual Assistant (BAVA) on BSNL's website to answer customers' queries related to services. This is partly rule based and partly AI based. Customers can also register or track their pending complaints. It will also help customers with online payments. Services of booking a new connection, choosing the plan and registering mobile numbers are also offered.

AI in financial sector

Economic indicators like GDP can be predicted using AI methods. Financial services firms are using ML to predict cash flow events and credit scores. AI powered financial apps help users manage their money better and learn smart financial behaviour.

Ethical issues of AI

AI is ultimately an advanced tool for computation and analysis which may be susceptible to errors and bias when it is developed with malicious intent or trained with adversarial data inputs and that is the reason why ethical issues are very important in AI. The four ethical principles in the implementation of AI are respect to human values, fairness, transparency and explainability and privacy and security. The seven key EU requirements for achieving trustworthy AI are: 1. Human agency and oversight 2.Robustness and safety 3.Privacy and data governance 4.Transparency 5. Diversity, non discrimination and fairness 6.Societal and environmental well being 7. Accountability

Green AI

This is an emerging tropic. Shared learning has to be encouraged to save computational power and to avoid 'reinventing the wheel'. AI implementation should be carbon compliant, to become responsible AI. By going one step ahead, the heat energy developed in AI devices should be gainfully utilised elsewhere in the industry so that the AI implementation will be carbon negative.

Conclusions

1. AI and ML have a lot of applications. They are playing an important role in Covid-19 management.

2. AI and ML are data hungry techniques whose performance heavily depends on the amount and quality of training data.

3. Service industries like telecom can deploy virtual assistants and chatbots, by leveraging AI/ML technologies, to improve customer delight. These virtual assistants/chatbots will augment /replace call centres and IVRSs (Interactive Voice Response systems). Telecom sector has allotted a lot of funds for utilising AI/ML technologies.

4. Ethical issues should not lose sight of while implementing AI, especially in the medical field and financial sector.

5. AI/ML implementation should cause zero carbon footprint, if possible negative carbon footprint.

(The author is a former Advisor, Department of Telecommunications (DoT), Government of India)

L Anantharam
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