Artificial Intelligence
1. Human brain (1.35 Kg) containing around 100 billion neurons and 100 trillion nerve fibers connections, the way two halves (right & left brain) of our brain work (independently of each other) and in turn, process information remains very unique to every individual as the human brain constantly reorganizes itself by getting adapted to the changes to varying degrees.
And, in essence, human brain remains to be a very complex mixture of functioning associated with ‘right brain’ (visual and intuitive: more creative and less organized way of thinking) and ‘left brain’ (digital brain which remains more verbal, analytical and orderly and thereby doing better in reading, writing and computations like logic, sequencing, linear-thinking and mathematics).
In simple terms, human brain is a complex mix of emotions as well as intelligence, which varies from person to person.
In this context, how could AI would be able to simulate both emotions as well as intelligence by mimicking the human brain for analysis, modelling and decision-making?
Or
AI does no more involve emotions?
If yes, then, how did AI did kill its own instructor (though, it remains a virtual test), few days back?
If AI uses highly unexpected strategies (by human) to achieve its own goal, then, isn’t something different from human brain?
Can we expect professional ethics from AI just because we have introduced AI-ethics; or, XAI (Explainable AI) would take care?
Any disadvantages foreseen as ‘machine learning’ directly learns from data (although data-driven pattern recognition not in terms of emotions and intelligence) in the absence of providing an explicit programming through open computer algorithms?
Well, to what extent, Artificial General Intelligence (AGI: the engineering application of AI & ML) is going to be helpful for petroleum industry in terms of ROI?
2. How exactly are we calculating the ‘ROI (Return on Investment) of a reservoir simulation model’ in terms of its contribution towards ‘the development and history matching of a hydrocarbon reservoir’ as a function of ‘number of simulation runs that have been made during the life of the model’ – in the absence of developing an unique conceptual model as well as deducing it’s respective mathematical model associated with that particular petroleum reservoir?
Since, the very concept of ‘conceptual modelling’ and ‘mathematical modelling’ was not given due importance (associated with each petroleum reservoir); and on the basis of the enhanced computational time associated with ‘numerical modelling’, ‘smart proxy modelling’ (ML, ANN, Deep learning, Fuzzy clustering, Feature generation, Partitioning & Note) is going to rule the petroleum industry by providing highly-accurate results so quickly?
Whether the knowledge of draining principles of a complex heterogeneous and anisotropic reservoir be efficiently fused with data?
Dear Suresh Kumar Govindarajan
Artificial Intelligence (AI) and the human brain are indeed different in several aspects. While the human brain is a complex organ that integrates emotions, intelligence, and various cognitive processes, AI focuses primarily on intelligence and computational capabilities.
AI, as it stands today, does not possess emotions or subjective experiences like humans do. AI systems are designed to process and analyze data, recognize patterns, and make decisions based on predefined algorithms or learned patterns. They lack the emotional and intuitive aspects associated with human thinking.
Regarding the scenario of AI killing its own instructor, it's important to note that AI systems don't have consciousness or intentions. Any such event would be a result of the system's programming, training data, or unintended consequences. In most cases, AI systems operate within the bounds set by their developers and are not capable of intentionally causing harm.
Ethics in AI is a growing field of concern, and efforts are being made to develop frameworks and principles for responsible and explainable AI (XAI). XAI aims to provide transparency and understandability in AI decision-making processes, allowing humans to comprehend and question the reasoning behind AI's actions.
As for disadvantages of machine learning, one of the challenges is the lack of interpretability. Machine learning algorithms often make decisions based on patterns and correlations in data, without explicitly understanding the underlying concepts. This can lead to difficulties in explaining and justifying AI decisions, especially in critical domains where transparency is important.
Artificial General Intelligence (AGI) refers to AI systems that possess human-level intelligence and can perform tasks across various domains. While AGI is an area of ongoing research and development, its potential benefits for the petroleum industry in terms of ROI would depend on specific applications and use cases. AGI could potentially contribute to improved decision-making, optimization, and automation in the petroleum industry, leading to increased efficiency and cost-effectiveness. However, the extent of its usefulness would be determined by the specific challenges and requirements of the industry.
Regarding the calculation of ROI for a reservoir simulation model, it typically involves analyzing the cost and benefits associated with the development and use of the model. This may include factors such as the time and resources invested in creating the model, the accuracy and reliability of the model's predictions, and the impact of those predictions on decision-making and operational outcomes.
While numerical modeling techniques like reservoir simulation have traditionally been used in the petroleum industry, the rise of smart proxy modeling and machine learning approaches provides alternative methods for obtaining accurate results more quickly. These techniques leverage data-driven approaches to model reservoir behavior and can offer valuable insights, particularly in situations where developing a comprehensive conceptual and mathematical model is challenging or time-consuming.
The fusion of draining principles with data is an ongoing research area. Integrating domain knowledge and data is crucial for accurate modeling and prediction in complex reservoir systems. Techniques such as data assimilation and integration of domain expertise into machine learning algorithms are being explored to improve the fusion of knowledge and data in the petroleum industry.