Difference between revisions of "Fadhil Fabiano Hendis"

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'''ChatGPT :''' As an AI language model, I strive to provide accurate information based on the data and information available to me. However, as with any source of information, there is always the potential for errors or inaccuracies. If you have any specific concerns or questions about the data I have presented, please feel free to let me know and I can try to provide further clarification or sources to support my statements.
 
'''ChatGPT :''' As an AI language model, I strive to provide accurate information based on the data and information available to me. However, as with any source of information, there is always the potential for errors or inaccuracies. If you have any specific concerns or questions about the data I have presented, please feel free to let me know and I can try to provide further clarification or sources to support my statements.
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'''SUMMARY'''
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What I can think of is that AI can only be used as a supporting tool, never as the "main" one. due to the alleged uncertainly or unsureness of AI. When an artificial intelligence system is unable to confidently respond to a question or task, this is referred to as AI unsureness. Numerous factors, such as insufficient or conflicting data, ambiguous or unclear instructions, or restrictions in the algorithms or models the system is using, can cause this to happen.
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AI systems are frequently designed to provide a level of certainty or confidence in their responses, such as by providing a probability score or confidence interval. However, in some cases, the system may be unable to provide a confident response and must instead indicate its uncertainty.
 +
 +
Users who are expecting a definitive answer or response may find this difficult, as uncertainty can lead to confusion or frustration. However, it is critical to recognize that uncertainty is a natural part of any decision-making process, whether performed by a human or an AI system.
 +
 +
To address AI uncertainty, researchers and developers are investigating new techniques and algorithms, such as probabilistic reasoning, fuzzy logic, and Bayesian networks, that can assist AI systems in dealing with ambiguity and uncertainty. These approaches aim to provide more robust and reliable AI systems capable of dealing with complex and uncertain situations.
 +
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Butvinik, D. (n.d.). Uncertainty Quantification in Artificial Intelligence-based Systems - KDnuggets. KDnuggets. Retrieved February 24, 2023, from https://www.kdnuggets.com/uncertainty-quantification-in-artificial-intelligence-based-systems.html

Revision as of 04:21, 24 February 2023

My name is Fadhil Fabiano Hendis NPM 2006489110, I've been referred to as Fadhil or Bian. I am 19 years old, I was born on 25 February 2003 in south Jakarta and I’m currently in my fifth semester of study at Universitas Indonesia.

I have plenty interest in life, but the one I'd like to share is photography. Having a small memory kept in the form of a picture appeals to me, and I believe that one photograph may hold many story for other people. Even if I'm still a beginner, I want to learn more!

I appreciate you taking the time to read this brief essay about me. Thank you.

CHAT SESSION WITH CHATGPT

Fadhil : What’s the correlation between battery factory, sugar factory and fuel?

ChatGPT : The correlation between a battery factory, sugar factory, and fuel is related to the production and use of ethanol, which is a type of alcohol that can be used as a fuel or as a component of battery electrolytes. Sugar factories are known for producing sugar from sugar cane or sugar beets. However, during the process of extracting sugar from these plants, a byproduct known as molasses is produced. Molasses is a thick, dark syrup that is rich in sugars, minerals, and other nutrients. Molasses can be used as a feedstock for the production of ethanol, which is typically produced through a process of fermentation and distillation. In some cases, sugar factories themselves may operate ethanol plants as a way to generate additional revenue from their molasses byproduct. Ethanol can be used as a fuel for vehicles, either on its own or blended with gasoline. It is also used as a component of battery electrolytes in some types of batteries, such as those used in electric vehicles. Ethanol is particularly useful in battery electrolytes because it is a highly polar solvent that can dissolve a variety of salts, allowing for the transfer of charged ions between the battery's electrodes. In summary, the correlation between a battery factory, sugar factory, and fuel is that sugar factories can produce molasses, which can be used as a feedstock for ethanol production. Ethanol can then be used as a fuel or as a component of battery electrolytes, which are used in batteries produced by battery factories. This connection highlights the interrelatedness of various industries in the production and use of different types of energy and materials.

Fadhil : Can sugarcane be the main source of ethanol?

ChatGPT : Sugarcane is one of the most important sources of ethanol today and it has the potential to be a major source of biofuel in the future.

Sugarcane is an ideal source of ethanol because it is a high-yielding and energy-efficient crop, meaning that it produces a large amount of energy in relation to the amount of energy required to grow and process it. Additionally, sugarcane is relatively easy to grow and has a wide range of uses beyond ethanol production, such as producing sugar, molasses, and other products.

Fadhil : Can you show me the literature regarding "sugarcane as a source of ethanol" ?

ChatGPT : Certainly! Here are some references that provide more information about the use of sugarcane as a source of ethanol:

Ferreira-Leitão, V., Rodrigues, J., & Maciel Filho, R. (2016). A review on ethanol production from sugarcane bagasse: Chemical and technological perspectives. Chemical Engineering Transactions, 52, 1-22. https://doi.org/10.3303/CET1652001

Goldemberg, J., Coelho, S. T., & Guardabassi, P. (2004). The sustainability of ethanol production from sugarcane. Energy Policy, 32(6), 677-687. https://doi.org/10.1016/S0301-4215(03)00121-5

Macedo, I. C., Seabra, J. E. A., & Silva, J. E. A. R. (2008). Greenhouse gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020. Biomass and Bioenergy, 32(7), 582-595. https://doi.org/10.1016/j.biombioe.2007.12.006

Rajendran, K., Rajoli, S. R., & Taherzadeh, M. J. (2016). Current status and future prospects of bioethanol production from sugarcane and cassava wastes in Thailand. Bioresource Technology Reports, 1, 50-56. https://doi.org/10.1016/j.biteb.2016.02.004

Rosillo-Calle, F., & Cortez, L. A. B. (2017). Sugarcane ethanol: Contributions to climate change mitigation and the environment. In S. E. Jørgensen, F. Rosillo-Calle, & J. Fargione (Eds.), Biofuels for a more sustainable future: An assessment of the sustainability of biofuels using selected sustainability indicators (pp. 49-76). Academic Press. https://doi.org/10.1016/B978-0-12-804195-9.00003-6

These references cover a range of topics related to sugarcane ethanol production and use, including the technological and chemical aspects of production, environmental impacts, and the current status and future prospects of bioethanol production from sugarcane wastes in different regions.

Fadhil : How did you know sugarcane is an ideal source for ethanol?

ChatGPT : As an AI language model, I have been trained on vast amounts of data and information. The statement that sugarcane is an ideal source for ethanol is based on scientific research and industry practice, which has been documented in academic papers, reports, and other sources.

Sugarcane is a highly productive crop that is grown in tropical and subtropical regions around the world, particularly in countries such as Brazil, India, and Thailand. It is rich in fermentable sugars, which can be converted into ethanol through a relatively simple process. Additionally, sugarcane has a high energy yield, meaning that it produces more energy than is required to grow and process it.

Research has also shown that sugarcane ethanol has a lower carbon footprint than gasoline and other fossil fuels, making it an attractive option for reducing greenhouse gas emissions and mitigating climate change.

Overall, the suitability of sugarcane as a source of ethanol is based on a combination of factors, including its productivity, energy efficiency, and sustainability.

Fadhil : Are you sure about your data?

ChatGPT : As an AI language model, I strive to provide accurate information based on the data and information available to me. However, as with any source of information, there is always the potential for errors or inaccuracies. If you have any specific concerns or questions about the data I have presented, please feel free to let me know and I can try to provide further clarification or sources to support my statements.

SUMMARY What I can think of is that AI can only be used as a supporting tool, never as the "main" one. due to the alleged uncertainly or unsureness of AI. When an artificial intelligence system is unable to confidently respond to a question or task, this is referred to as AI unsureness. Numerous factors, such as insufficient or conflicting data, ambiguous or unclear instructions, or restrictions in the algorithms or models the system is using, can cause this to happen.

AI systems are frequently designed to provide a level of certainty or confidence in their responses, such as by providing a probability score or confidence interval. However, in some cases, the system may be unable to provide a confident response and must instead indicate its uncertainty.

Users who are expecting a definitive answer or response may find this difficult, as uncertainty can lead to confusion or frustration. However, it is critical to recognize that uncertainty is a natural part of any decision-making process, whether performed by a human or an AI system.

To address AI uncertainty, researchers and developers are investigating new techniques and algorithms, such as probabilistic reasoning, fuzzy logic, and Bayesian networks, that can assist AI systems in dealing with ambiguity and uncertainty. These approaches aim to provide more robust and reliable AI systems capable of dealing with complex and uncertain situations.

Butvinik, D. (n.d.). Uncertainty Quantification in Artificial Intelligence-based Systems - KDnuggets. KDnuggets. Retrieved February 24, 2023, from https://www.kdnuggets.com/uncertainty-quantification-in-artificial-intelligence-based-systems.html