ARTIFICIAL INTELLIGENCE VERSUS HUMAN INTELLIGENCE

• 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.

• 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.

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.