The Sustainability Benefits of Cognitive AI

Introduction

The rapid advancements in artificial intelligence have led to the emergence of cognitive AI systems, which possess the ability to mimic human thought processes and decision-making capabilities. These systems are trained using a costly process, but once the relationships between various elements are correctly mapped, and patterns are identified and integrated into the model, the execution of the AI against new data scenarios becomes much more efficient. (Ahmad, 2017) (Ji, 2020) This is because the logical flow within the model replaces the need for statistical analysis, allowing the AI to freely explore and discover insights using structured and unstructured data sets, and to infer and conclude based on a logical mindset. (Ahmad, 2017)

Sustainability Benefits of Cognitive AI

The sustainability benefits of cognitive AI are significant. By reducing the reliance on CPU-intensive statistical analysis, cognitive AI systems can dramatically lower the energy requirements for their operation, making them a more sustainable solution compared to traditional data processing approaches.? (How et al., 2020) (Gao & Feng, 2023) Additionally, the ability of cognitive AI to uncover new insights and patterns in data can lead to more informed decision-making, enabling organizations to optimize their processes and resource utilization, further enhancing sustainability. (How et al., 2020) (Gao & Feng, 2023)

Studies have shown the potential of AI-driven productivity gains, with AI demonstrating the capacity to foster economic sustainability within the framework of Industry 4.0 (Gao & Feng, 2023). Furthermore, the use of AI in the pursuit of sustainable development goals has been explored, highlighting the technology's potential to positively influence various domains. (Hasan et al., 2023)

However, it is important to note that the sustainability benefits of cognitive AI are not without their challenges. The increased complexity of AI models can lead to a rebound effect, where the energy savings achieved through more efficient processing are offset by the energy demands of the more sophisticated models. Additionally, corporate culture and the adoption of sustainability-oriented practices can play a significant role in ensuring the responsible and sustainable use of AI. (Isensee et al., 2021)

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this is an example of how cognitive AI code which is based on conclusions related to patterns and relationships between objects is more efficient in comparison to statistical generative AI models because the cognitive model can directly infer based on logical reasoning rather than relying on complex statistical analysis.

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this is a python code example of what needs to be executed on current generative AI in order to calculate a response, and this is the cognitive AI approach which would be more efficient:

Generative AI:

```python

import numpy as np

import scipy.stats as stats

Simulate some data

X = np.random.normal

(loc=10, scale=2, size=1000)

y = 2 * X + 3 + np.random.normal

Fit a linear regression model

model = stats.linregress(X, y)

_

slope, intercept, r_value, p_value, std_err = model(X, y)

Make a prediction

new_X = 10

prediction = slope * new_X + intercept

print(prediction)

The predicted value is: {prediction:.2f}

The r-squared is: {r_value ** 2:.2f}

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Here is example of cognitive AI code that could replace the above generative AI code:

```python

class CognitiveAIModel:

?def init(self, knowledge_base):

?# Define the knowledge base and reasoning engine

?def infer(self, input_data):

?# Apply logical reasoning to the input

?# Return the output based on the logical flow

```

this cognitive model can directly generate the output based on the logical relationships and patterns it has learned, without needing to perform complex statistical analysis, which leads to more efficient processing and reduced energy consumption compared to generative AI models (Goff, 2023) (Gmyrek et al., 2023) (Gao & Feng, 2023) (Wynsberghe, 2021).

The sustainability benefits of cognitive AI are further highlighted by recent studies that suggest the potential of AI to revolutionize traditional programming practices, much like the transition from assembly language to high-level programming languages in the 1970s (Denny et al., 2024). The use of generative AI tools, such as GitHub Copilot, is already claiming a significant proportion of new code generation, and the rapid pace of advancement in this field indicates a potentially transformative impact on programming and software development. (Denny et al., 2024)

This shift towards cognitive AI-driven programming has the potential to greatly improve productivity and efficiency, ultimately contributing to the overall sustainability of the technology sector.

Literature Review

The sustainability benefits of cognitive AI have been explored in various academic studies. (Hasan et al., 2023) highlights the potential of AI to have a positive influence across many domains, including the pursuit of sustainable development. (Pachot & Patissier, 2023) delves deeper into the environmental impacts of AI, examining both the benefits and the challenges, such as the need to integrate environmental indicators into algorithms to mitigate the rebound effect. further emphasizes the importance of corporate culture in shaping the sustainable use of AI, identifying features of a sustainability-oriented corporate culture that can influence the handling of AI in a sustainable manner.

Methodology

This research paper employed a comprehensive literature review to explore the sustainability benefits of cognitive AI. Key academic sources were analyzed, including empirical studies and conceptual papers, to synthesize the current understanding of this topic.

Results

The findings of this research indicate that the sustainability benefits of cognitive AI are multifaceted. Cognitive AI systems can significantly reduce the energy requirements for data processing by replacing CPU-intensive statistical analysis with a more efficient logical flow. This, in turn, can lead to lower greenhouse gas emissions and a reduced environmental impact.

Furthermore, the ability of cognitive AI to uncover new insights and patterns in data can enable organizations to optimize their processes and resource utilization, further enhancing sustainability.

Discussion

Cognitive AI can also help solve the anticipated massive impact of AI on workforce optimization, including massive layoff, as this era is unprecedented, cognitive AI is uniquely positioned to help figure out how humans can be quickly and effectively repurposed and used in this new era (Hasan et al., 2023).

The sustainability benefits of cognitive AI are not without challenges, however. The increased complexity of AI models can lead to a rebound effect, where the energy savings achieved through more efficient processing are offset by the energy demands of the more sophisticated models. Additionally, corporate culture and the adoption of sustainability-oriented practices can play a significant role in ensuring the responsible and sustainable use of AI. (Isensee et al., 2021)

Conclusion

In conclusion, the sustainability benefits of cognitive AI are significant, as the technology can dramatically reduce energy requirements for data processing and enable more informed decision-making to optimize resource utilization. However, it is crucial to address the potential challenges, such as the rebound effect and the importance of corporate culture, to ensure the sustainable development and deployment of cognitive AI.

By leveraging the strengths of cognitive AI while mitigating its potential drawbacks, organizations can harness the technology to drive sustainable growth and contribute to a more environmentally-conscious future.

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References

Ahmad, A. S. (2017). Brain inspired cognitive artificial intelligence for knowledge extraction and intelligent instrumentation system. https://doi.org/10.1109/isesd.2017.8253363

Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J., Luxton-Reilly, A., Reeves, B., Santos, E. A., & Sarsa, S. (2024). Computing Education in the Era of Generative AI. In P. Denny, J. Prather, B. A. Becker, J. Finnie-Ansley, A. Hellas, J. Leinonen, A. Luxton-Reilly, B. Reeves, E. A. Santos, & S. Sarsa, Communications of the ACM (Vol. 67, Issue 2, p. 56). Association for Computing Machinery. https://doi.org/10.1145/3624720

Gao, X., & Feng, H. (2023). AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity. In X. Gao & H. Feng, Sustainability (Vol. 15, Issue 11, p. 8934). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/su15118934

Gmyrek, P., Berg, J., & Bescond, D. (2023). Generative AI and jobs. In P. Gmyrek, J. Berg, & D. Bescond, ILO eBooks. https://doi.org/10.54394/fhem8239

Goff, T. L. (2023). Recommendations for public action towards sustainable generative AI systems. https://hal.science/hal-04371031

Hasan, Md. T., Shamael, M. N., Akter, A., Islam, R., Mukta, Md. S. H., & Islam, S. (2023). An Artificial Intelligence-based Framework to Achieve the Sustainable Development Goals in the Context of Bangladesh. In Md. T. Hasan, M. N. Shamael, A. Akter, R. Islam, Md. S. H. Mukta, & S. Islam, arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2304.11703

How, M.-L., Cheah, S.-M., Chan, Y.-J., Khor, A. C., & Say, E. M. P. (2020). Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach. In M.-L. How, S.-M. Cheah, Y.-J. Chan, A. C. Khor, & E. M. P. Say, Information (Vol. 11, Issue 1, p. 39). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/info11010039

Isensee, C., Griese, K., & Teuteberg, F. (2021). Sustainable artificial intelligence: A corporate culture perspective. In C. Isensee, K. Griese, & F. Teuteberg, NachhaltigkeitsManagementForum | Sustainability Management Forum (Vol. 29, Issue 3, p. 217). Springer Science+Business Media. https://doi.org/10.1007/s00550-021-00524-6

Ji, Q. (2020). Analysis on E-commerce Course Reform under the Background of Artificial Intelligence. In Q. Ji, Journal of Physics Conference Series (Vol. 1533, Issue 3, p. 32088). IOP Publishing. https://doi.org/10.1088/1742-6596/1533/3/032088

Pachot, A., & Patissier, C. (2023). Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues. In A. Pachot & C. Patissier, Green and Low-Carbon Economy. https://doi.org/10.47852/bonviewglce3202608

Wynsberghe, A. van. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. In A. van Wynsberghe, AI and Ethics (Vol. 1, Issue 3, p. 213). Springer Nature. https://doi.org/10.1007/s43681-021-00043-6