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Fundamental Reflections on Minds and Machines
No full textWe live in a complex world, and the complexity exists not just in degree but in diversity that is pluralistically rich and spectral
Being “LaMDA” and the Person of the Self in AI
No full textThe emergence of self in an artificial entity is a topic that is greeted with disbelief, fear, and finally dismissal of the topic itself as a scientific impossibility. The presence of sentience in a large language model (LLM) chatbot such as LaMDA inspires to examine the notions and theories of self, its construction, and reconstruction in the digital space as a result of interaction. The question whether the concept of sentience can be correlated with a digital self without a place for personhood undermines the place of sapience and such/their/other high-order capabilities. The concepts of sentience, self, personhood, and consciousness require discrete reflections and theorisations
Testing for Causality in Artificial Intelligence (AI)
No full textIn the 1950 in a landmark paper on artificial intelligence (AI), Alan Turing posed a fundamental question “Can machines think?” Towards answering this, he devised a three-party ‘imitation game’ (now famously dubbed as the Turing Test) where a human interrogator is tasked to correctly identify a machine from another human by employing only written questions to make this determination. Turing went on and argued against all the major objections to the proposition that ‘machines can think’. In this chapter, we investigate whether machines can think causally. Having come a long way since Turing, today’s AI systems and algorithms such as deep learning (DL), machine learning (ML), and artificial neural networks (ANN) are very efficient in finding patterns in data by means of heavy computation and sophisticated information processing via probabilistic and statistical inference, not to mention the recent stunning human-like performance of large language models (ChatGPT and others). However, they lack an inherent ability for true causal reasoning and judgement. Heralding our entry into an era of causal revolution from information revolution, Judea Pearl proposed a “Ladder of Causation” to characterize graded levels of intelligence, based on the power of causal reasoning. Despite tremendous success of today’s AI systems, Judea Pearl placed these algorithms (DL/ML/ANN) at the lowest rung of this ladder since they learn only by associations and statistical correlations (like most animals and babies). On the other hand, intelligent humans are capable of interventional learning (second rung) as well as counterfactual and retrospective reasoning (third rung) aided with imagination, creativity, and intuitive reasoning. It is acknowledged that humans have a highly adaptable, rich, and dynamic causal model of reality which is non-trivial to be programmed in machines. What are the specific factors that make causal thinking so difficult for machines to learn? Is it possible to design an imitation game for causal intelligence machines (a causal Turing Test)? This chapter will explore some possible ways to address these challenging and fascinating questions
National analysis on variations in estimates of forest cover dynamics over India (2001–2020) using multiple techniques and data sources
No full textThis study evaluated multiple methodologies for monitoring forest and tree cover dynamics in India using remote sensing. The Forest Survey of India (FSI) biennially maps forest and tree cover, reporting areas under three crown density classes along with a pixel-level change matrix. Global Forest Watch (GFW) data use an annual Landsat time series to detect tree loss pixels from 2001 to 2020 and global data on new plantations. Cumulative forest to non-forest class transitions of 12.1 Mha was estimated by counting pixel-level using a change matrix reported by FSI and 2.43 Mha was estimated through tree loss using GFW data. However, FSI and global plantations data indicated new areas brought under tree cover/forest 14.5 Mha and 11.1 Mha, respectively. Part of these variations was due to differences in definition and methodology. This study highlights the need for mapping the regular loss and new areas under tree cover, which simple statistics of net forest cover change are unable to capture. Additionally, the locations of loss and plantations were visualized as spatial layers of a 1 × 1 km grid. Geo-located loss and gain areas would be of great interest in spatially capturing dynamics of forest biomass and carbon cycle. Enhanced greening of India reported in many studies is also supported. The nature of interventions leading to additional tree cover has also been highlighted
Enhancing domestic coking coal availability to reduce the import of coking coal (NIAS/NSE/EECP/U/RR/01/2024)
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The Emerging Threats to the Himalayan Environment
No full textThe predictions on the disaster scenarios in the Himalayan states have come true. We are now witnessing the consequences of human interventions that have contributed to the intensity of disasters, impacting the lives and livelihoods of the people