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ARTIFICIAL INTELLIGENCE VERSUS HUMAN INTELLIGENCE

Context

  • Recently, Kerala police inducted a robot for police work.
  • Similarly, Chennai got its second robot-themed restaurant, where robots not only serve as waiters but also interact with customers in English and Tamil.
  • In Ahmedabad, in December 2018, a cardiologist performed the world’s first in-human telerobotic coronary intervention on a patient nearly 32 km away.

All these examples symbolise the arrival of Artificial Intelligence (AI) in our everyday lives. AI has several positive applications, as seen in these examples. But the capability of AI systems to learn from experience and to perform autonomously for humans makes AI the most disruptive and self-transformative technology of the 21st century.

                                                                                                                                                     (The Hindu)

  • AI might just be the single largest technology revolution of our life times, with the potential to disrupt almost all aspects of human existence.
  • Andrew Ng, the co-founder of Coursera and formerly head of Baidu AI Group / Google Brain, compares the transformational impact of AI to that of electricity 100 years back.
  • With many industries aggressively investing in cognitive and AI solutions, global investments are forecast to achieve a compound annual growth rate (CAGR) of 50.1% to reach USD57.6 billion in 2021.

 Definition:

  • AI is a constellation of technologies that enable machines to act with higher levels of intelligence and emulate the human capabilities of sense, comprehend and act.
  • Thus, computer vision and audio processing can actively perceive the world around them by acquiring and processing images, sound and speech. The natural language processing and inference engines can enable AI systems to analyse and understand the information collected.
  • An AI system can also take action through technologies such as expert systems and inference engines or undertake actions in the physical world. These human capabilities are augmented by the ability to learn from experience and keep adapting over time.
  • AI systems are finding ever-wider application to supplement these capabilities across enterprises as they grow in sophistication.
  • Irrespective of the type of AI being used, however, every application begins with large amounts of training data. In the past, this kind of performance was driven by rules-based data analytics programs, statistical regressions, and early “expert systems.” But the explosion of powerful deep neural networks now gives AI something a mere program doesn’t have: the ability to do the unexpected.

How and why did it rise?

  • Due to the rapid growth of machines and deep learning algorithm
  • Big data availability
  • improved the efficiency of computers or processors, etc.
  • AI reduces error due to big data and logical analysis hence improves accuracy and saves time.
  • AI can work in a hostile environment efficiently without threat to human example space, mines, deep oceans.
  • AI replaces humans in repetitive and tedious work and reduces fear of inconsistency example automation robot.
  • It doesn't get tired, unlike humans, for example, performing car assembly or long medical surgeries.
  • Reduces delay and cost, improves response and quality, for eg., crime-solving,  weather predictions.
  • Increases certainty and stability in decisions.
  • Natural language processing:: Which increases interaction; personal assistants, for eg., Siri (Apple Inc.).
  • Machine vision, for example, Valera based system to drive driverless cars, medical imaging.
  • Smart robots and prosthetics to augment disabled.

In Economy:

Fourth Industrial Revolution:

  • Artificial Intelligence (AI) is a key driver of the Fourth Industrial Revolution. Its effect can be seen in homes, businesses and even public spaces.
  • In its embodied form of robots, it will soon be driving cars, stocking warehouses and caring for the young and elderly.
  • AI holds the promise of solving some of society’s most pressing issues but also presents challenges such as inscrutable “black box” algorithms, unethical use of data and potential job displacement.

Manufacturing:

  • The manufacturing industry is expected to be one of the biggest beneficiaries of AI-based solutions, thus enabling 'Factory of the Future' through flexible and adaptable technical systems to automate processes and machinery to respond to unfamiliar or unexpected situations by making smart decisions.
  • Impact areas include engineering (AI for R&D efforts), supply chain management (demand forecasting), production (AI can achieve cost reduction and increase efficiency), maintenance (predictive maintenance and increased asset utilisation), quality assurance (for example, vision systems with machine learning algorithms to identify defects and deviations in product features), and in-plant logistics and warehousing.

 Smart Mobility:

  • It includes transports and logistics like autonomous fleets for ride-sharing, semi-autonomous features such as driver assist, and predictive engine monitoring and maintenance.
  • Other areas that AI can impact include autonomous trucking and delivery, and improved traffic management.

Retail:

  • The retail sector has been one of the early adopters of AI solutions, with applications such as improving user experience by providing personalised suggestions, preference-based browsing and image-based product search.
  • Other use cases include customer demand anticipation, improved inventory management, and efficient delivery management.

Energy:

  • Potential use cases in the energy sector include energy system modelling and forecasting to decrease unpredictability and increase efficiency in power balancing and usage.
  • In renewable energy systems, AI can enable storage of energy through intelligent grids enabled by smart meters, and also improve the reliability and affordability of photovoltaic energy.
  • Similar to the manufacturing sector, AI may also be deployed for predictive maintenance of grid infrastructure.

Banking:

  • AI as customer care, for example, Laxmi at Union Bank, Eva at HDFC Bank.
  • Artificial Intelligence is the future of banking as it brings the power of advanced data analytics to combat fraudulent transactions and improve compliance.
  • AI algorithms accomplish anti-money laundering activities in few seconds, which otherwise take much more time.

In the Social Sphere:

Healthcare:

  • Application of AI in healthcare can help address issues of high barriers to access to healthcare facilities, particularly in rural areas that suffer from poor connectivity and limited supply of healthcare professionals.
  • This can be achieved through implementation of use cases such as AI-driven diagnostics (Babylon app analysis), personalised treatment, early identification of potential pandemics, and imaging diagnostics, among others.

Education and Skilling:

  • AI can potentially solve for quality and access issues observed in the Indian education sector.
  • Potential use cases include augmenting and enhancing the learning experience through personalised learning, automating and expediting administrative tasks, and predicting the need for student intervention to reduce dropouts or recommend vocational training.
  • For example, "Thinker Maths" (personalised teaching app) useful in rural areas where teachers are not willing to go.

Caste:

  • AI performs minuscule work e.g waste management thus reduces scavenging.
  • Speech recognition and translation can certainly reduce language barriers and promote harmony.

Agriculture:

  • AI holds the promise of driving a food revolution and meeting the increased demand for food (global need to produce 50% more food and cater to an additional 2 billion people by 2050 as compared to today).
  • It also has the potential to address challenges such as inadequate demand prediction, lack of assured irrigation, and overuse/misuse of pesticides and fertilisers.
  • Some use cases include improvement in crop yield through real-time advisory, advanced detection of pest attacks, and prediction of crop prices to inform sowing practices.For example, IBM Thunder for weather forecasting.

In Security:

Cybersecurity:

  • User behaviour analysis to predict and detect threat and reduce fraud.
  • Reduces crime by studying crime pattern using NCRB data, etc., this improves law and order.
  • Psycho-analysis and lie detector tests

Smart Cities:

  • Integration of AI in newly developed smart cities and infrastructure could also help.
  • Meet the demands of a rapidly urbanising population and providing them with enhanced quality of life.
  • Potential use cases include traffic control to reduce congestion and enhanced security through improved crowd management.

In the Political sphere:

  • Employing AI can predict voting patterns or polls. For example, the US election in which the MogIA used facebook and twitter, etc., along with big data to come up with poll predictions.
  • Germany has come up with ethical rules for autonomous vehicles stipulating that human life should always have priority over property or animal life.
  • China, Japan and Korea are following Germany in developing a law on self-driven cars.
  • The latest set of policy guidelines about Ethics in AI is the Recommendation on Artificial Intelligence from the Organisation for Economic Co-operation and Development (OECD).
  • It promotes five principles for the responsible development of trustworthy AI.
  • It also includes five complementary strategies for developing national policy and international cooperation.
  • The five AI principles encourage:
    • inclusive growth, sustainable development and well-being
    • human-centred values and fairness
    • transparency and explainability
    • robustness, security and safety
    • accountability
  • Over the past few years, the MIT-hosted “Moral Machine” study has surveyed public preferences regarding the applications and behaviour of Artificial Intelligence in various settings.
  • One conclusion from the data is that when an autonomous vehicle (AV) encounters a life-or-death scenario, it’s expected response largely depends on where one is from, and what one knows about the pedestrians or passengers involved.
  • For example, in an AV version of the classic “trolley problem,” some might prefer that the car strike a convicted murderer before harming others, or that it hit a senior citizen before a child.
  • Still, others might argue that the AV should simply roll the dice so as to avoid data-driven discrimination.

These recommendations are broad and do not carry the force of laws or even rules. Instead, they seek to encourage member countries to incorporate these values or ethics in the development of AI.

Threat /Negatives of AI

Though there are vast prospects, a nascent technology can't be left unchecked especially after cases  where:

  • Google translator developed own system to process data and bypassed the supervisor.
  • Facebook AI developed own language and started communicating which had to be shut down.
  • Human intelligence versus machine processing: machines process at high speeds and can learn fast and adapt to it, thus outperform humans.
  • Morality issues: AI can be programmed to do any specific task and cannot differentiate between right or wrong.
  • Goal-oriented: It may, therefore, use unethical means. The AI is programmed to do something beneficial, but it develops a destructive method for achieving its goal. This can happen whenever we fail to fully align the AI’s goals with ours, which is strikingly difficult.
  • If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for.
  • If a super-intelligent system is tasked with an ambitious geo-engineering project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it as a threat to be met.
  • The AI is programmed to do something devastating: Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties.
  • Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of such a situation.
  • Multi-tasking, consistency and speed have threatened jobs, especially semi-skilled. Thus, it can lead to increasing poverty leading widening inequality.
  • Costly and expensive: Especially in India where to invest in costly AI or reducing poverty would still be a dilemma.
  • The frenetic speed of digital adoption and the entry of machines is making the skills of millions of people irrelevant almost overnight
  • It also raises the need to determine what a machine should do and which tasks should remain under a human’s purview.
  • Supporting human values through technology, building consumer trust in an era of robotic solutions and the limits to the deployment of artificial intelligence (AI) are the issues generating debate among the corporate world today.
  • Our civilization will flourish as long as we win the race between the growing power of technology and the wisdom with which we manage it. In the case of AI technology, the best way to win that race is not to impede the former, but to accelerate the latter, by supporting AI safety research.

A few immediate steps can aid the transition, as technologists and business leaders strive to humanize the digital revolution.

Building trust:

  • Around the world, the trust deficit is growing. Concerns about the misuse of data by government institutions and businesses — especially with the frequent incidents of cyber attacks — have eroded people’s faith in the way data is being stored and managed.
  • The 2019 Edelman Trust Barometer reveals that people trust only those relationships that are within their control.
  • Only true transparency on the part of business leaders about data protection and usage can now regain public confidence.
  • Proactive policies are also urgently needed to restore people’s faith in new technologies.

Cultivating empathy:

  • An advocate of empathy-driven AI, Microsoft CEO Satya Nadella believes the world will have an abundance of ‘artificial’ intelligence but a scarcity of ‘real’ intelligence and human qualities like empathy.
  • The need, therefore, is to apply AI with a human touch. Every solution should be evaluated through the lens of the person who will be impacted by it, rather than only for its technological capabilities.

Being fair and equitable:

  • The connected world and the democratization of technology gives us a wonderful opportunity to remove disparities, provide open access to information, services, capital and skills, and create a more equitable and inclusive society.
  • However, we must learn from the previous Industrial Revolutions and ensure that technology does not serve the interests of only a few.
  • Hidden biases or imbalances in data can skew algorithms, leading in turn to skewed decision-making.
  • Wikipedia is a prime example of how even an open platform, that crowdsources content from all around the world, can perpetuate existing prejudices.
  • Only around 18 per cent of the biographies on Wikipedia are of women — a fact that prompted Jess Wade, a research scholar from UK’s Imperial College, to write a page a day through 2018 to correct the bias.

However, it doesn't matter how much AI excels as it cannot replace humans as it:

  • Lacks creativity and human spontaneity: AI is programmed for routine work, rule-based thus Lacks creativity and human spontaneity.
  • Lacks emotions/values/morals: example a kid and an adult may be drowning. Here, AI would save life based on calculated percentage of survival chances, but a human would go for the child first)
  • Socialisation issues: AI doesn't have five human senses.

Therefore, there is a need for balance of both artificial intelligence(logic) and human intelligence (emotions); both have their own benefits to taken sure world progress to the next level.

  • India isn’t exactly a hub for cutting-edge research in Artificial Intelligence (AI). There are still misconceptions about the fact that Machine Learning, Data Science, and Artificial Intelligence spells to loss of human jobs, hence the hesitation.
  • That is probably the reason, India hasn’t spelt out its long-term vision regarding the implementation of Artificial Intelligence. But recent developments have shown that contrary to everyone’s belief, people willing to work in the field of AI, get the support they need. Be it on Government level or on a personal level.
  • The focus is on areas such as agriculture, healthcare, education, and infrastructure. There are also moves to channel global Artificial Intelligence talent and resources to develop solutions that can benefit millions at the grassroots.

The scope of Artificial Intelligence in India:

  • Globally, no one is doing AI innovation for the social sector, India can lead here. That’s indeed the overarching vision in the first major blueprint on AI that was released this month—a discussion paper from government think-tank NITI Aayog, titled National Strategy for Artificial Intelligence.
  • The priorities are as evident as they can be. India’s agriculture is weirdly inefficient, as it employs just a little under 50% of the total population but contributes only around 18% to the GDP.
  • Access to healthcare is poor—India’s life expectancy (68 in 2015) is among the lowest for BRICS nations, and so are its hospital beds per thousand people, a KPMG report said last year.
  • Artificial Intelligence, is like the new age wheel or fire, which can revolutionize the entire landscape of industries. It has the potential to transform every sector as and when needed. From something as basic to Agriculture to Healthcare and defence, it has the potential to do it all.
  • While Artificial Intelligence is commonly understood as a piece of esoteric high technology that could get too powerful for our own good, it’s really a suite of technologies like machine learning, pattern recognition, big data, neural networks and self-improving algorithms, many of which have been around for a while and have been maturing with time.
  • So, AI has always been there, without actually causing the issues that we think it would cause, it is just that we are more aware of it now than we were before.
  • For a country at India’s level of socio-economic development, the suite of AI technologies can be applied effectively to relatively prosaic concerns. And it’s already happening. States like Andhra Pradesh, have started embracing this technology.
  • The state govt. with the help of Microsoft is using the technology to monitor and curb the drop out rates of students from schools.
  • Not just Andhra Pradesh, a few other govts. are beginning to test drive the technology, but still, there are miles and miles and miles to go before India actually embraces the positives of the technology.

Challenges Ahead

  • Stakeholders buy-in: NITI Ayog has glorified the importance of AI but the real question remains, will the government recognize the urgency.
  • Policy Action Gap: The present government has been accused of poor execution and probably one of the reasons, corporates and organizations are also hesitant in taking this technology ahead in India.
  • Bureaucratic Delays: India is already delayed in the adoption and thus, execution of AI. But now if it has to catch up, it needs to move fast. So, does India’s bureaucratic loopholes allow the desired pace?
  • Falling between stools: The implementation of these policies does not need to change hands from one ministry or one administration to another. It should rather take a more central and directed route so that the execution time is shortened and made more efficient and effective.
  • Funding Constraints: There is no surprise that this technology will need funding, proper funding for implementation
  • In academia, I still believe it is a high time people started equipping themselves with the knowledge of the various constituents of Artificial Intelligence. Things might seem a little slow right now, but the reality AI is just around the corner and once it is out, it would mean a plethora of opportunities for trained professionals, so being ready for what is about to happen gives you the necessary first-mover advantage.

Recommendations of  the NITI Aayog's National Strategy for AI :

  • India’s unique challenges and aspirations, combined with the advancement in AI, and a desire to assume leadership in this nascent technology means India’s approach towards AI strategy has to be balanced for both local needs and the greater good. The way forward for India in AI has to factor in our current strengths in AI, or a lack thereof, and thus requires large scale transformational interventions, primarily led by the government, with private sector providing able support.

The set of recommendation to address the biggest challenges and opportunities for India in the field of AI

  • Analysis of focus sectors in NITI AAYOG's National strategy for AI leads us to the assertion that the efforts need to be concentrated across major themes of research, data democratisation, accelerating adoption and re-skilling – with privacy, security, ethics and intellectual property rights permeating as common denominators for all our recommended initiatives.
  • These challenges, while by no means exhaustive, if addressed in an expeditious manner through concerted collaborative efforts by relevant stakeholders, with the government playing a catalytic role, could lead to fundamental building blocks that can form the core to India’s march towards achieving its goal of #AIforAll.
  • The first set of recommendations focus on turbocharging both core and applied research. In addition, two frameworks for solving some of AI’s biggest research challenges through collaborative, market-oriented approach have been proposed.
  • The new age of AI and related frontier technologies would disrupt the nature of jobs of tomorrow and the skills required to realise the true potential of these transformative technologies.
  • Thus, re-skilling of the existing workforce and preparing students for developing applied set of skills for the changing world of technology.
  • Among the several impediments towards large scale adoption of AI in India, the primary ones include difficulty in access to data (more specifically, structured and intelligent data), high cost and low availability of computing infrastructure, lack of collaborative approach to solving for AI combined with low awareness.
  • To address these challenges include developing large foundational annotated data sets to democratize data and multi-stakeholder marketplaces across the AI value chain (data, annotated data and AI models).
  • One of the key aspects of our ambition of #AIforAll includes responsible AI: ensuring adequate privacy, security and IP related concerns and balancing ethical considerations with the need for innovation.
  • For example, establishing a data protection framework with legal backing, establish sectoral regulatory frameworks, benchmark national data protection and privacy laws with international standards encourage AI developers to adhere to international standards and spread awareness.
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