Dec 07, 2024 02:16 PM IST Share Via AI (Getty Images/iStockphoto) This umbrella term encompasses a range of capabilities and approaches, divided into four distinct categories based on functionality: Reactive AI - The simplest form, reactive AI systems focus on specific tasks but lack the capacity for learning. They perform assigned tasks without improving or adapting over time. Limited memory AI - This type of AI can process and store past data to improve its outputs. Leveraging techniques like deep learning, limited memory AI drives the functionality of generative AI models, which can produce human-like text, music, and even code. ChatGPT, LLaMA, and Bard represent prominent examples, capable of generating content through a blend of machine learning and algorithmic programming. Theory of Mind AI - Although still in development, this ambitious category seeks to emulate emotions, motivations, and social intelligence—a crucial leap toward interactive, empathetic AI. Self-aware AI - The ultimate frontier, self-aware AI would possess a sense of identity and consciousness. Hypothetically, such systems could assist in complex diagnostics, emotional support, and much more. However, for now, self-awareness in machines remains within the realm of speculation. AI, despite its potential, is fraught with challenges. Ethical considerations, legal frameworks, and transparency issues must be tackled to ensure AI benefits are widely realised while minimising risks. Bias and discrimination - AI systems are not immune to biases present in training data. Whether in hiring, law enforcement, or financial services, improper data handling can embed biases within AI algorithms, exacerbating inequalities instead of resolving them. Data privacy and security - With vast data troves powering AI, ensuring robust encryption, anonymisation, and compliance with global data protection laws is critical. AI can be both a safeguard and a vulnerability, with cyber-attacks and privacy violations posing significant risks. Transparency and interpretability - Many AI models, especially in deep learning, operate as ‘black boxes,’ where the rationale behind their decisions remains opaque. This lack of interpretability makes it difficult for users to trust AI outputs, emphasising the need for more transparent and explainable AI solutions. Regulatory and legal hurdles - The rapid advancement of AI challenges existing legal structures, especially regarding liability and intellectual property. New frameworks are essential to provide clarity on AI accountability. The potential of AI is vast, with market forecasts underscoring a meteoric rise in adoption. According to reports, the global AI market is expected to grow from $184 billion in 2024 to $415 billion by 2027. Canada, a prominent player in the AI arena, anticipates its market will reach $18.5 billion by 2030. Meanwhile, India’s AI market, projected to grow to $8 billion by 2025, is transforming sectors from health care to agriculture. A 2023 survey by EY revealed that nearly all CEOs planned substantial investments in generative AI, while a McKinsey study confirmed that 79% of respondents have been exposed to some form of generative AI. From predictive diagnostics in health care to fraud detection in finance, AI applications are revolutionising industries, creating unprecedented efficiencies and new avenues for innovation. AI is seamlessly embedding itself into daily life and various industries, transforming the way we interact with technology and make decisions. In e-commerce, AI personalises shopping experiences by predicting customer preferences, while many entertainment platforms use AI to offer customised content that boosts engagement. In finance, AI streamlines risk assessment and fraud detection, improving accuracy and efficiency. Healthcare benefits from AI-powered predictive diagnostics, allowing for earlier and more accurate disease detection. Additionally, manufacturing leverages AI for predictive maintenance, quality control, and production optimisation, significantly enhancing operational efficiency. As AI systems become more entrenched in society, the importance of diligent governance cannot be overstated. This technology, while potent, has profound risks, including misinformation, deep fakes, and bias. AI applications should be designed and deployed with accountability and transparency at their core. Policymakers and industry leaders must work together to forge a regulatory framework that ensures ethical AI development. The possibilities AI offers are vast, from transforming industries to shaping our everyday lives. However, to harness this potential responsibly, a balance must be struck between innovation and accountability. As we step further into the era of AI, a collective effort across industries and governments will be crucial in navigating the potential—and pitfalls—of this groundbreaking technology. This article is authored by Nirpendra Ajmera, chief audit executive, Qulliq Energy Corporation. See more