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How will AI shape our future?

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Over the past few years, AI (artificial intelligence) has become a force to be reckoned 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. Advances in AI can help society address issues of income inequality and food insecurity to create a more inclusive and people-centered future. “AI is an important innovation for society. It improves the quality of life and will change 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 understand things from 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 obtain information that can be useful for human decision making.

AI is widely used to provide personalized recommendations to people based on, for example, their previous searches and purchases or other online behaviors. AI is extremely important in commerce: product optimization, inventory planning, logistics, etc. AI improves the speed, accuracy and efficiency of human efforts.

Some of the use cases of AI improve customer satisfaction, fraud detection. Recently AI-based broadcast models have been introduced to improve the quality of low-resolution images and convert them to high-fidelity images.

AI in healthcare: AI is going to impact many areas and healthcare is the most important area, AI is going to transform. AI in healthcare mimics human cognition in analyzing, presenting and understanding complex medical and healthcare data. For example, using AI, a person’s cardio risk can be predicted. Cardiovascular events for the next five years can be anticipated by scanning the retina in a non-invasive manner. Doctors can predict a person’s medical events by analyzing medical records. They can predict when the patient will get sick and need to be readmitted, giving them more time to act.

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

Machine learning can improve the efficiency of Covid-19 vaccine supply chain management (SCM) through automated quality control, streamlined production planning, warehouse management and reduction of forecast errors. ML, when implemented at different stages of vaccine MCS, can help achieve the goal of “vaccination for every individual”.

AI in telecommunications: AI is used for the optimization of radio resources. It can be used to predict radio frequency (RF) signal coverage. AI applications in the telecommunications industry use advanced algorithms to find patterns in data to detect and predict network anomalies (e.g., network overload) and resolve problems before customers. are not adversely affected. In the Ericsson Radio system, AI algorithms run on the baseband to predict traffic patterns and automatically turn off antennas if necessary to reduce power consumption.

Prediction of path loss is important for optimizing 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 computationally efficient than other models. Based on historical data, ML-based models can relate path loss to input characteristics such as antenna separation, distance, and frequency. Every telecom service provider uses AI and ML to improve customer service using virtual assistants and chatbots. The virtual assistant automates and responds to support requests, reducing business expenses and improving customer satisfaction. The ability to offer voice and voice services through AI-based chatbots is available.

Chatbots contain three layers – the knowledge base, data storage, and natural language processing (NLP). NLP enables machines to understand human language. It helps computers measure feelings and determine which parts of human language are important. BSNL has introduced its online chatbot – BSNL Automated Virtual Assistant (BAVA) on the BSNL website to answer customer questions related to services. This is partly rules-based and partly AI-based. Customers can also register or track their pending complaints. It will also help customers to make payments online. Reservation of a new connection, choice of plan and registration of mobile numbers are also offered.

AI in the financial sector: Economic indicators such as GDP can be predicted using AI methods. Financial services companies use ML to predict treasury events and credit scores. AI-powered financial apps help users manage their money better and learn smart financial behavior.

AI Ethical Issues: AI is ultimately an advanced computational and analytical tool that can be prone to error and bias when developed with malicious intent or trained with input from conflicting data and this is why ethical issues are so important in AI.

The four ethical principles in the implementation of AI are respect for human values, fairness, transparency and explainability, as well as confidentiality and security. The EU’s seven key requirements for achieving trustworthy AI are: 1. Human agency and oversight 2. Robustness and security 3. Confidentiality and data governance 4. Transparency 5. Diversity, non-discrimination and fairness 6. Societal and environmental well-being 7. Responsibility.

Green AI: it’s an emerging tropic. Shared learning should be encouraged to save computing power and avoid “reinventing the wheel”. The implementation of AI must be carbon compliant, to become responsible AI. Taking a step forward, thermal energy developed in AI devices should be used profitably elsewhere in the industry so that the implementation of AI is carbon negative.

Conclusion:

1. AI and ML have many applications. They play an important role in the management of Covid-19.

2. AI and ML are data-intensive techniques whose performance is highly dependent on the quantity and quality of training data.

3. Service industries like telecommunications can deploy virtual assistants and chatbots, leveraging AI / ML technologies, to enhance customer enjoyment. These virtual assistants / chatbots will augment / replace call centers and IVRS (interactive voice response systems). The telecommunications industry has allocated a lot of funds for the use of AI / ML technologies.

4. Ethical issues should not be overlooked when implementing AI, especially in the medical and financial sectors.

5. The implementation of AI / ML should result in a zero carbon footprint, if possible a negative carbon footprint.

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


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