NC

I negate resolved: The development of Artificial General Intelligence is immoral.

 Framework 🤤
My value is Morality
Therefore, my value criterion is utilitarianism. Prefer this value criterion for the
following reasons:
[1] Pain and pleasure are intrinsically valuable – they are reasons for actions in and of
themselves. Nagel 86
Nagel, The View From Nowhere, HUP, 1986: 156-168. [Brackets for gendered language]
I shall defend the unsurprising claim that sensory pleasure is good and pain bad, no matter whose they are. The point of the exercise is to see
how the pressures of objectification operate in a simple case. Physical
pleasure and pain do not usually depend on
activities or desires which themselves raise questions of justification and value. They are just
sensory experiences in relation to which we are fairly passive, but toward which we feel
involuntary desire or aversion. Almost everyone takes the avoidance of his own pain and the
promotion of his own pleasure as subjective reasons for action in a fairly simple way; they are not
back[ed] up by any further reasons. On the other hand if someone pursues pain or avoids pleasure [it is a means to their end], either it as a
means to some end or it is backed up by dark reasons like guilt or sexual masochism. What sort of general value, if any, ought to be assigned to
pleasure and pain when we consider these facts from an objective standpoint? What kind of judgment can we reasonably make about these
things when we view them in abstraction from who we are? We can begin by asking why there is no plausibility in the zero
position, that pleasure and pain have no value of any kind that can be objectively recognized. That would mean
that I have no reason to take aspirin for a severe headache, however I may in fact be motivated; and that looking at it from outside, you
couldn't even say that someone had a reason not to put his [her] hand on a hot stove, just because of the pain. Try looking at it from the
outside and see whether you can manage to withhold that judgment. If the idea of objective practical reason makes any sense at all, so that
there is some judgment to withhold, it does not seem possible. If the general arguments against the reality of objective reasons are no good,
then it is at least possible that I have a reason, and not just an inclination, to refrain from putting my hand on a hot stove. But given the
possibility, it seems meaningless to deny that this is so. Oddly enough, however, we can think of a story that would go with such a denial. It
might be suggested that the aversion to pain is a useful phobia—having nothing to do with the intrinsic undesirability of pain itself—which
helps us avoid or escape the injuries that are signaled by pain. (The same type of purely instrumental value might be ascribed to sensory
pleasure: the pleasures of food, drink, and sex might be regarded as having no value in themselves, though our natural attraction to them
assists survival and reproduction.) There would then be nothing wrong with pain in itself, and someone
who was never
motivated deliberately to do anything just because he knew it would reduce or avoid pain
would have nothing the matter with him. He would still have involuntary avoidance reactions,
otherwise it would be hard to say that he felt pain at all. And [t]he[y] would be motivated to
reduce pain for other reasons—because it was an effective way to avoid the danger being signaled, or because interfered with
some physical or mental activity that was important to him. He just wouldn't regard the pain as itself something he had any reason to avoid,
even though he hated the feeling just as much as the rest of us. (And of course he wouldn't be able to justify the avoidance of pain in the way
that we customarily justify avoiding what we hate without reason—that is, on the ground that even an irrational hatred makes its object very
unpleasant!) There is nothing self-contradictory in this proposal, but it seems nevertheless insane. Without some positive reason to think there
is nothing in itself good or bad about having an experience you intensely like or dislike, we can't seriously regard the common impression to the
contrary as a collective illusion. Such things are at least good or bad for us, if anything is. What seems to be going on here is that we cannot
from an objective standpoint withhold a certain kind of endorsement of the most direct and immediate subjective value judgments we make
concerning the contents of our own consciousness. We regard ourselves as too close to those things to be mistaken in our immediate,
nonideological evaluative impressions. No objective view we can attain could possibly overrule our subjective authority in such cases. There can
be no reason to reject the appearances here.
HEALTH 💔
AGI is key to improve health outcomes with better, customized, and earlier
treatments and diagnoses – heart disease proves
Haq et al. 25 [Rashid Ul Haq, researcher at the Abdul Wali Khan University Mardan, Hashim Ali,
researcher at the Abdul Wali Khan University Mardan, Mehak Mushtaq Malik, researcher at COMSATS
University Islamabad, Abdullah Akbar, researcher at the National University of Computer and Emerging
Sciences, Mariya Ouaissa, researcher at the Computer Systems Engineering Laboratory at Cadi Ayyad
University, Mariyam Ouaissa, researcher at the Laboratory of Information Technologies at Chouaib
Doukkali University, and Inam Ullah Khan, former visiting researcher at King’s College London and
General Chair at the International Conference on Trends and Innovations in Smart Technologies with a
Ph.D. in Electronics Engineering from Isra University and a Bachelor of Computer Science from Abdul
Wali Khan University, 2025, “Chapter 8 Review of Heart Disease Prediction Using AGI Models:
Advancements and Challenges Artificial General Intelligence (AGI) Security,
https://www.springerprofessional.de/en/artificial-general-intelligence-agi-security/27634942]/Kankee
Future Directions and Research Opportunities There are various issues and restrictions in current research on heart disease prediction. As
the models are still not enough to be deployed to the general population, further research is needed to improve their accuracy and
reliability. Future research on heart disease prediction might benefit from the use of Artificial General Intelligence
(AGI) since it could increase accuracy and dependability. The analytical powers of AGI can find new risk
variables and improve model performance. This integration provides the potential to discover important insights for more
accurate prediction and opens up new areas for investigation. AGI has the potential to anticipate and prevent cardiac
disease with greater accuracy and dependability. Integration of Multimodal Data for Comprehensive Prediction As a
consequence of technological improvements, a number of hospitals and clinics have recently acquired patient data; however, they do not
make it available on the internet or the quality is inadequate, so the researcher continues to use outdated data. As a result, increasing the
availability and quality of patient data may allow us to develop more effective algorithms and gain a better understanding of the risk factors
for heart disease. Modern technologies and tools are decreasing the error rate and hence will be helpful in the future with better prediction
[20]. Data rate, power constraints, and many other factors like these are delicate to deal with and are required to be handled carefully [21].
Similarly, if the input consists of images and heart sounds, the model can be trained to recognize trends and alert users to potential concerns
before they become serious [22]. This could lead to earlier detection and treatment of cardiac illness, as well as better patient outcomes and
cheaper healthcare expenditures. There is promise for more accurate and early identification, better patient outcomes, and efficiency when
Artificial General Intelligence
(AGI) is integrated with multiple modalities for complete prognosis in cardiac disease. The
capacity of AGI to examine many kinds of data, including pictures and heart sounds, can improveearly
warning systems and trend identification. Along with addressing issues with data availability and quality, this integration
improves prediction models and helps identify risk indicators. Healthcare costs can be lowered by
using AGI through preemptive interventions and efficient resource allocation. AGI holds the potential
to provide more accurate and effective comprehensive heart disease prediction . Personalized Medicine
Approaches in Heart Disease Prediction As the algorithms advance, they may be able to add individual patient information like age, gender,
and lifestyle aspects to produce even more personalized forecasts. These models could also be coupled to wearable devices or mobile apps
for easy and continuous monitoring of heart health. With
more accurate and customized predictions, the use of
Artificial General Intelligence (AGI) in personalized medicine techniques for cardiac disease prediction
has great potential to improve patient care. We can improve the precision and efficacy of forecasts, enabling proactive
therapies and enabling people to actively control their heart health, by combining specific patient data and utilizing AGI’s analytical skills.
Improved patient outcomes might result from this combination, which has the potential to transform
personalized therapy in heart disease prediction. Conclusion The goal of this review study is to provide helpful insights
into the advancements, limitations, and potential of heart disease prediction algorithms. It aims to contribute to ongoing research and
development efforts in heart disease prediction by critically analyzing the effectiveness and limitations of existing AGI
models,
resulting in improved early detection, prevention, and management of this vital health concern. The purpose of
this study is to discover limitations and potentials in the categorization of heart disease literature . The
accuracy of the algorithms was used as a comparison parameter. The number of data points utilized in the model, the number of attributes
used in the model, and the preprocessing processes used in the specific study were discovered to have the largest impact on accuracy. It also
depends on the algorithms used, which are determined by the dataset’s attributes. In the majority of research studies, the ANN technique
has the highest accuracy on the Cleveland dataset. With the exception of ANN, SVM and random forest outperformed the other approaches.
Our research and study show that SVM performs best when datasets can be divided linearly; however, alternative techniques, like MLP, may
perform better when datasets cannot be separated linearly. Before deciding on the optimal machine learning technique, the attributes of
the dataset must be evaluated. As a result, before choosing the best AGI approach, it is necessary to thoroughly examine the dataset and its
properties. The predictive power of heart disease algorithms can be greatly enhanced by AGI’s capacity
to decipher intricate patterns, manage missing values and outliers, and utilize a larger variety of data
sources and attributes. The constraints of linear separation can be addressed and complex
relationships within the dataset can be captured by combining AGI with conventional statistical
models. Furthermore, by taking into account unique patient features and customizing therapies accordingly, AGI can
support personalized medicine. But it’s imperative to integrate AGI sensibly, taking ethical issues like algorithmic bias, data
security, and privacy into consideration. The incorporation of AGI has the potential to completely transform the
prediction of cardiac disease, resulting in better early detection, more precise diagnosis, and better
management of this serious health issue with careful thought and appropriate application.

AGI helps solve neurodegenerative disorders – that saves millions of lives and
increases their quality of life
Qadri et al. 24 [Yazdan Ahmad Qadri, researcher at the School of Computer Science and Engineering
at Yeungnam University, Khurshid Ahmad, researcher at the Department of Health Informatics, College
of Applied Medical Sciences, Qassim University, and Sung Won Kim, researcher at the School of
Computer Science and Engineering at Yeungnam University, 10-15-2024, "Artificial General Intelligence
for the Detection of Neurodegenerative Disorders", MDPI, https://www.mdpi.com/1424-
8220/24/20/6658]/Kankee
1. Introduction Organization for Economic Co-operation and Development (OECD) nations have seen their populations decline along with an
increase in average life expectancy [1]. This high average life expectancy, paired with a low fertility rate, results in a rapidly aging
population, which threatens the national economy and healthcare systems. Statistics reveal that South Korea is facing the fastest decline in
population growth and is on the path to becoming a super-aged society by 2025. With this increase in age, the presumed percentage of the
population with dementia is expected to increase, as per a report by the National Health Insurance Service of the Republic of Korea [2]. The
incidence of Parkinson’s disease (PD) and Alzheimer’s disease (AD) in South Korea has notably risen, which
coincides with existing studies which correlated age with these neurodegenerative disorders (NDs) [3,4].
An estimated 8.5 million people were suffering from PD in 2019 according to the Global Burden of Diseases, Injuries,
and Risk Factors Study (GBD), which furthermore caused a 100% increase in the number of deaths between
2000 and 2019 [5]. An estimated 50 million people suffer from AD, while dementia is the fifth leading
cause of death across the globe [6]. Females are more likely to suffer from these diseases compared with males [7]. PD is a
progressive ND which significantly degrades the quality of life (QoL) due to its impact on motor and locomotive
functions. AD is a severe form of dementia which affects behavior, memory, and cognition [3,8]. In OECD
countries, the economic burden of these diseases is projected to rise significantly as the population ages
[9]. Early and accurate diagnosis of PD and AD is critical in managing symptoms and maintaining a
stable QoL. The diagnostic tools for detecting PD and AD include clinical evaluations, imaging, and biomarker analysis [10,11]. The
patients undergo an extensive clinical evaluation to identify changes in locomotion and cognitive abilities. On the other hand, radiological
tools include magnetic resonance imaging (MRI) and positron emission tomography (PET) scans [12,13]. Genetic testing and biomarker
analysis can further consolidate the reliability of the diagnosis of these diseases. Artificial intelligence ( AI) can assist in and
improve the accuracy of the diagnostic process. The United States Food and Drug Administration (FDA) has approved
several AI and machine learning (ML)-based radiological image analysis tools [14], therefore, AI and ML are coming to the forefront in the
early detection and diagnosis of NDs. Neural networks (NNs) can accurately detect patterns in biomedical signals and identify objects in
medical images. Convolutional neural networks (CNNs) can successfully identify abnormalities in the medial images [15]. Recurrent neural
networks (RNNs) can analyze time series data to identify anomalies in the normal functions of physiological functions. Deep learning (DL)
algorithms have demonstrated high accuracy in anomaly detection [16]. Additionally, large language models (LLMs) can analyze massive
tranches of image and clinical datasets, clinical studies, and the research literature and assist in accurately diagnosing NDs. AI algorithms can
analyze medical records, including imaging, genetics, and biomarkers, to identify patterns indicative of PD and AD [17,18]. The hope of
artificial general intelligence (AGI) leading to superhuman capabilities in machines is contended in [19]. AGI is the capability of an AI model
to mimic human cognitive processes. AGI allows machines to “think” and “learn”, which allows them to
understand concepts and apply them across several domains . Therefore, machines can transfer the knowledge they
have learned in one domain to another domain. AGI can possess four characteristics. Firstly, they can perform an unlimited
number and types of tasks. Secondly, they can generate new tasks within a context, and thirdly, the agents
operate using a value system which underpins task generation. Finally, they can visualize the world in a
global model, which they can use to interact with the physical world [20]. LLMs are exemplified by models like
ChatGPT, Gemini, and Llama, which exhibit the traits of AGI; they can gather knowledge from vast online resources and transfer their
learning from these large resources to perform an unlimited number of tasks [21]. These models can perform tasks across domains such as
mathematics, language processing, image, video, text generation, medicine, and software coding. Their application in the field of
healthcare is profound, ranging from drug discovery to medical image processing, genomics, and
clinical assistance[22]. Detecting NDs using next-generation AI models has gained traction in the past few years and attracted
significant research interest. The advent of transformer models has enabled AI-based systems to understand the context of data [23]. This
ability to contextualize information is critical in establishing correlation between the symptoms and
history and arriving at an accurate diagnosis [24]. This review is aimed at providing insight into the prediction, early
detection, and diagnosis of NDs using the latest AGI models, with a focus on PD and AD. Radiological data analysis in the context of clinical
evaluations and medical tests can assist in triangulating the underlying causes and hence determining a prudent treatment plan. The
American College of Radiology Data Science Institute’s AI Central database contains a list of 200 FDA-approved AI products to assist in
imaging-based diagnostic products from across 100 manufacturers [25]. Approval from regulators accelerates the integration of computer-
aided diagnosis in healthcare systems. A survey of DL-based approaches for detecting NDs was presented in [26]. The survey covered disease
detection along with severity analysis, presenting a CNN-based methodology for ND detection. The
accuracy of various DL-based
approaches in identifying these diseases wasbetween 89% and 97%. The severity of the disease was
estimated with a success rate greater than 90%. This work identifies the state-of-the-art AGI methodologies which can
diagnose PD and AD using massive repositories of clinical and experimental data, including the
research literature. Combining Internet of Things (IoT) with AGI can alleviate and streamline the process of
monitoring at-risk individuals for diagnosis of NDs. A primer on PD and AD is presented, describing the diagnostic tools
used to predict and diagnose these disorders, in Section 2. The role of AGI in the diagnosis of PD and AD is identified. To the best of our
knowledge, this review is the first to present a detailed discussion on the role of AGI in ND diagnosis (Section 3). An IoT-based framework
based on our previous work is presented for the ubiquitous monitoring and diagnosis of NDs in Section 4. Section 5 presents a roadmap for
the future to identify the avenues for mitigation of challenges in this area. Section 6 concludes the discussion. 2. Background 2.1.
Neurodegenerative Disorders

AGI telehealth improves diagnostic treatment and reduces healthcare costs

Asif et al. 25 [Ali Asif, researcher at the National University of Sciences and Technology, Hassan Asif,
researcher at the Pakistan Institute of Engineering and Applied Sciences, Abdullah Akbar, researcher at
the National University of Computer and Emerging Sciences, Maqsood M. Khan, researcher at
the ,Shahzad Latif, researcher at the Department of Computer Science at Szabist University,
Muhammad Ameer Hamza, researcher at the Department of Civil Engineering at COMSATS University
Islamabad, and Abdur Rehman Khan, researcher at the Air University in Pakistan, 2025, “ Chapter 16
AGI-Enabled Robotics for Healthcare Industry,” Artificial General Intelligence (AGI) Security,
https://www.springerprofessional.de/en/artificial-general-intelligence-agi-security/27634942]/Kankee
Transforming Healthcare Industry Using AGI-Based Robots The healthcare industry has long been marked with complexity and intricate
treatment procedures. With the advent of technologies like AGI and ML, this sector is transforming as smart algorithms provide innovative
solutions to long-standing challenges. AGI-based robots are assisting medical professionals in many ways. The use of externally wearable
sensors provides useful data regarding the health and wellness condition of the user. Such devices are also useful for providing health alerts
for the user based on the collected data. They also allow medical professionals to remotely monitor the patient and provide real-time
feedback and consultation [60]. This transformation, however, also comes with some challenges. Privacy concerns arise as users’ data is
used to gather useful output from the ML algorithm. Other concerns include ethical issues and the risk of system failure. These challenges
must be tackled in order for there to be more trust and widespread implementation of the use of such technologies in the healthcare
industry. Such transformation has good implications for health care as well. Studies show that AGI technology can diagnose skin
cancer more accuratelythan a professional dermatologist [61]. AGI-based systems are now also being used to
diagnose breast cancer [62]. Such examples show how AGI can introduce novel and useful applications in the
healthcare industry and make the process of patient monitoring and treatment more efficient . The use
of AGI has economic implications for the healthcare industry as well. In most cases, technology-backed
solutions prove to be more cost-efficient as compared to traditional methodologies. As an example,
Grady Hospital, a public hospital in Atlanta, USA, saved over $4 million over two years by
implementingan AGI-enabled tool in their workflow [62]. The implementation of AGI-based robotics in
health care means that patients can perform routine consultations remotely and get instant help from
a medical professional from the comfort of their homes. Expensive visits for routine checkups are also
no longer needed, as the diagnosis can be performed from a distance with the help of mobile robots and sensors.
These sensors can collect data such as heart rate, blood pressure, and oxygen levels, providing useful
insights into the patient’s health condition. AGI-based mobile robots are being used to assist elderly people as well
[63], especially those living alone. These robots offer companionship, reminders for medication and daily tasks, and even assist with basic
household chores. Their presence helps reduce loneliness and enhances the overall well-being of elderly individuals while providing peace of
mind to their families and caregivers. In conclusion, the healthcare industry is undergoing a remarkable transformation with the integration
of AGI and ML technologies. AGI-based robots and wearable sensors are revolutionizing patient care, enabling remote monitoring, early
intervention, and efficient diagnosis. However, this transformative journey is not without its challenges, including privacy concerns, ethical
considerations, and the risk of system failures, which must be addressed to build trust and ensure widespread adoption of these
technologies. Nevertheless, the benefits are substantial, with AGI systems demonstrating superior diagnostic accuracy in areas like skin and
breast cancer. Moreover, the economic implications are significant, as evidenced by cost savings in institutions like Grady Hospital. AGI-
based robotics offer the promise of more accessible, cost-effective, and efficient health care, allowing patients to receive expert care from
the comfort of their homes, and providing companionship and support to the elderly. As these technologies continue to evolve, they hold
the potential to revolutionize healthcare delivery and improve the overall well-being of individuals around the world.

Humans are comparatively less accurate and speedy, resulting in delays,

misdiagnoses, and death
Haq et al. 25 [Rashid Ul Haq, researcher at the Abdul Wali Khan University Mardan, Hashim Ali,
researcher at the Abdul Wali Khan University Mardan, Mehak Mushtaq Malik, researcher at COMSATS
University Islamabad, Abdullah Akbar, researcher at the National University of Computer and Emerging
Sciences, Mariya Ouaissa, researcher at the Computer Systems Engineering Laboratory at Cadi Ayyad
University, Mariyam Ouaissa, researcher at the Laboratory of Information Technologies at Chouaib
Doukkali University, and Inam Ullah Khan, former visiting researcher at King’s College London and
General Chair at the International Conference on Trends and Innovations in Smart Technologies with a
Ph.D. in Electronics Engineering from Isra University and a Bachelor of Computer Science from Abdul
Wali Khan University, 2025, “Chapter 8 Review of Heart Disease Prediction Using AGI Models:
Advancements and Challenges Artificial General Intelligence (AGI) Security,
https://www.springerprofessional.de/en/artificial-general-intelligence-agi-security/27634942]/Kankee
Introduction The heart is considered an important organ in humans. It moves blood around the body, supplying all organs and tissues with
nutrients and oxygen. Without a heartbeat, the body cannot survive, and insufficient blood flow has the potential to be
fatal [1]. As a result, if
it is found early on, there is less chance of death and more of a chance that the
treatment will be effective. Early detection can lower the mortality rate. Traditionally, a doctor would examine a patient’s
symptoms and medical history before deciding which tests to order. However, this conventional method can be time-
consuming and may cause delays or missed chances for early intervention Additionally, it is possible to
overlook specific symptoms or risk factors that could result in an incorrect diagnosis. To address these
research concerns, there is growing interest in the application of AI and machine learning algorithms to aid in medical
diagnostics. These technologies can rapidly analyze massive volumes of data and find trends that human
clinicians may not see right away, resulting in more accurate and timely diagnoses. Medical
diagnostics may be revolutionized by integratingArtificial General Intelligence (AGI) into heart disease
prediction. Machine learning algorithms can rapidly analyze huge amounts of patient data and detect
deep relationships and patterns by utilizing artificial general intelligence (AGI) and its capacity to absorb, acquire, and apply
knowledge across several domains. This comprehensive exam, which takes into factors other than a patient’s medical
history and symptoms, may help make more accurate heart disease forecasts. Furthermore, by taking into
account unique patient features and customizing treatment strategies accordingly, AGI may make
personalized medicine possible. To guarantee the proper application of AGI in healthcare, ethical issues like algorithmic bias, privacy, and
data security must be carefully taken into account. In the field of cardiology, AGI has the potential to greatly increase the precision of
diagnosis while enhancing patient results with the right research, development, and cooperation. A thorough analysis of the methods used
to predict heart problems is discussed in this review. We will also go over the possible advantages and drawbacks of using these algorithms,
as well as the moral questions raised by doing so. Understanding how these technologies can enhance patient outcomes is crucial, as is
making sure they are applied responsibly and ethically. The research is mainly focused on Ann’s comparative performance in relation to
others and primary dataset properties or characteristics that affect the algorithm selection. This research aims to shed light on the best
algorithm for reliably forecasting heart disease. Healthcare professionals can improve patient outcomes by making better decisions and
identifying the critical variables that affect algorithm selection. The remaining sections of this review are divided into seven categories: Data
Sources and Features using AGI, which includes data source information, Traditional statistical models explaining the conventional models
employed for heart prediction, and similar machine learning techniques addressing the sophisticated techniques employed to predict heart
disease. After exploring through evaluation metrics and validation techniques, several measures are used to analyze and evaluate the
performance of these models. The section on discussion and limitations offers information on potential drawbacks and difficulties in
applying these models to the prediction of heart disease. Future directions and opportunities for research in this area are also highlighted. In
conclusion, this essay offers a thorough analysis of the various methods for predicting heart disease, highlighting both their strengths and
weaknesses. Data Sources and Features Using AGI Some of the data sources used in this study include laboratory test results, patient
surveys, and medical records. The characteristics that were looked at included age, gender, family history of heart disease, lifestyle factors
like smoking and exercise habits, and various clinical measurements like blood pressure and cholesterol levels. There is great
potential for improving our knowledge and prediction skills when Artificial General Intelligence (AGI) is applied
to the issue of data sources and characteristics for heart disease prediction. A deeper understanding of the intricate variables
impacting heart disease may be attained by utilizing AGI’s processing and interpretation capabilities for a variety of data
sets, such as test results from laboratories, patient questionnaires, and medical records. AGI is able to analyze various data
sources more thoroughly and effectively, spotting complex links and patterns that human physicians
would not see right away. By taking into account a variety of parameters, including age, gender, family
history, lifestyle choices, and clinical datalike blood pressure and cholesterol levels, this integration
makes it possible to take a more comprehensive approach to risk assessment. We can increase the precision and
accuracy of cardiac disease prediction by utilizing AGI, which will result in more individualized treatments and
better patient outcomes. Source of Data Utilized in Predicting Heart Disease and Relevant Features This classification makes use of
the UCI Cleveland database’s data on heart disease. The dataset has 303 tuples and 76 characteristics. Age, ca, cholesterol (mg/dl), chest
pain type, exang, fbs (fasting blood sugar), oldpeak, restecg, and trestbps (mmHg) are used in the majority of papers. We can use AGI’s
processing and analysis capabilities to handle and evaluate the massive volume of data on heart illness by combining it with data from the
UCI Cleveland database. The capacity of AGI to decipher intricate patterns and correlations in the data might
yield important insights into the pertinent characteristics for heart disease prediction. By using AGI, we can find previously
undiscovered relationships between clinical data such as fasting blood sugar, kind of chest pain, age, cholesterol, and other factors, which
can help us create more precise and individualized risk assessments . By enabling more focused and accurate
therapies, this combination has the potential to completely transform the prediction of heart disease. This would ultimately enhance the
well-being of patients and reduce the impact of heart disease on both individuals and healthcare systems. Data
Preprocessing Techniques for Handling Missing Values and Outliers Cleaning data, handling missing values, and modifying data to make
them suitable for analysis are all tasks that are included in the process of preparing data for an algorithm, also known as data preparation or
preprocessing [2]. In the case of the Cleveland dataset, standardization is required because every feature in the dataset has a different
range. It will be possible to ensure that each feature contributes fairly to the output by standardizing the range of all features in the
Cleveland database [3]. The dataset contained some missing values, which interpolation values were used to fill in. There is a lot of promise
for enhancing the accuracy and dependability of data analysis when Artificial. General Intelligence (AGI) is applied to the problem of data
preparation methods for managing outliers and missing values. The detection and management of missing values can be significantly
improved by AGI’s capacity to analyze complicated patterns and connections within the data. AGI can be used to automatically fill in values
that are missing using sophisticated interpolation algorithms that take into consideration the context and patterns present in the dataset.
Additionally, by recognizing abnormal data items that differ noticeably from the norm, AGI can help with outlier detection and management.
By guaranteeing that the input data is clear, consistent, and appropriate for precise analysis, this AGI integration might expedite the data
preparation stage. We
can increase the robustness and dependability of heart disease prediction models by
utilizing AGI, which will result in more precise diagnoses and better patient outcomes . Traditional Statistical
Models for Heart Disease Prediction Decision trees [4] and logistic regression [5] are two examples of the conventional statistical models
that are applied to the Cleveland dataset. The complexity of the heart disease dataset and the limitations of conventional statistical
techniques, however, raise the possibility that these models are not always accurate. The heart disease dataset’s complex patterns and
linkages may be analyzed using AGI in ways that standard statistical methods cannot. Combining AGI enables us to create more complex and
adaptable models that better capture nonlinear linkages and complex interactions, improving our ability to make predictions. To improve
the customization of risk assessments, AGI-powered algorithms may make use of a broader range of characteristics and variables, including
genetic information, lifestyle variables, and medical imaging. By offering more precise diagnoses and customized therapies, this integration
has the potential to completely transform the prediction of cardiac disease and eventually result in better patient outcomes and more
efficient healthcare practices.

Only AI can help us detect and diagnose diseases early.

Nguyen 22 [Nguyen, Don. "How AI Can Help Diagnose Rare Diseases." Harvard Medical
School. October 18, 2022. Web. February 13, 2025. <https://hms.harvard.edu/news/how-ai-can-
helpdiagnose-rare-diseases>. ]

Rare diseases are often difficult to diagnose, and predicting the best course of treatment can be

challenging for clinicians. To help address these challenges, investigators from the Mahmood Lab at

Harvard Medical School and Brigham and Women’s Hospital have developed a deep- learning algorithm

that can teach itself to learn features that can then be used to find similar cases in large pathology

image repositories. Get more HMS news here Known as SISH (selfsupervised image search for

histology), the new tool acts like a search engine for pathology images and has many potential

applications, including identifying rare diseases and helping clinicians determine which patients are

likely to respond to similar therapies. A paper describing the self-teaching algorithm is published in

Nature Biomedical Engineering on Oct. 10. “We show that our system can assist with the diagnosis of

rare diseases and find cases with similar morphologic patterns without the need for manual annotations

and large datasets for supervised training,” said senior author Faisal Mahmood, assistant professor of

pathology at HMS at Brigham and Women’s. “This system has the potential to improve pathology

training, disease subtyping, tumor identification, and rare morphology identification.” Modern

electronic databases can store vast reams of digital records and reference images, particularly in

pathology, using whole slide images (WSIs). However, the gigapixel size of each individual WSI and the

ever-increasing number of images in large repositories means that search and retrieval of WSIs can be
slow and complicated. As a result, scalability remains a pertinent roadblock for efficient use. To solve

this issue, the research team developed SISH, which teaches itself to learn feature representations that

can be used to find cases with analogous features in pathology at a constant speed regardless of the size

of the database. In their study, the researchers tested the speed and ability of SISH to retrieve

interpretable disease subtype information for common and rare cancers. The algorithm successfully

retrieved images with speed and accuracy from a database of tens of thousands of WSIs from over

22,000 patient cases, with over 50 different disease types and over a dozen anatomical sites. The speed

of retrieval outperformed other methods in many scenarios, including disease subtype retrieval,

particularly as the image database size scaled into the thousands of images. Even while the repositories

expanded in size, SISH was still able to maintain a constant search speed. The algorithm, however, has

some limitations, including a large memory requirement, limited context awareness within large tissue

slides and the fact that it is limited to a single imaging modality. Overall, the algorithm demonstrated

the ability to efficiently retrieve images independent of repository size and in diverse datasets. It also

demonstrated proficiency in diagnosis of rare disease types and the ability to serve as a search engine to

recognize certain regions of images that may be relevant for diagnosis. This work may greatly inform

future approaches to disease diagnosis, prognosis, and analysis. “As the sizes of image databases

continue to grow, we hope that SISH will be useful in making identification of diseases easier,” said

Mahmood. “We believe one important future direction in this area is multimodal case retrieval, which

involves jointly using pathology, radiology, and genomic and electronic medical record data to find

similar patient cases.” This work was supported in part by National Institute of General Medical Sciences

R35GM138216 (to F.M.), Brigham President’s Fund, BWH and MGH

Pathology, BWH Precision Medicine Program, Google Cloud Research Grant, and Nvidia GPU
Grant Program. Additional support by the Tau Beta Pi Fellowship and the National Cancer Institute Ruth

L. Kirschstein National Service Award T32CA251062. The authors declare no competing interests.

https://www.forbes.com/sites/torconstantino/2024/10/02/is-quantum-computing-an-
unlikely-answer-to-ais-looming-energy-crisis/
🥰 climate
Artificial General Intelligence is key to solving climate change

Climate change increases carbon emissions, which hurts millions.

United Nations
United Nations. “The Climate Crisis – A Race We Can Win | United Nations.” the United
Nations, https://www.un.org/en/un75/climate-crisis-race-we-can-win. Accessed 13 July 2024.

The last four years were the four hottest on record. According to a September 2019 World
Meteorological Organization (WMO) report, we are at least one degree Celsius above preindustrial
levels and close to what scientists warn would be “an unacceptable risk”. The 2015 Paris Agreement on
climate change calls for holding eventual warming “well below” two degrees Celsius, and for the pursuit
of efforts to limit the increase even further, to 1.5 degrees. But if we don’t slow global emissions,
temperatures could rise to above three degrees Celsius by 2100, causing
further irreversible damage to our ecosystems.

Shetty 21 quantifies,
Shetty, Disha. “Climate Change Would Cause 83 Million Excess Deaths By 2100.” Forbes, 30 July 2021,
https://www.forbes.com/sites/dishashetty/2021/07/30/climate-change-would-cause-83-
million excess-deaths-by-2100/. Accessed 13 July 2024.

A recently published study in peer-reviewed journal Nature Communications found that climate
change would cause 83 million excess deaths by 2100. The study coins the term "mortality
cost of carbon" to describe how many future lives will be lost—or saved—depending on whether we
increase or decrease our current carbon emissions.

Thankfully, AGI solves climate change, which is true for 2 reasons:

First,
AGI is key to optimizing renewable energy.
Thompson 23
https://www.dreher-consulting.com/en/blog/how-to-use-artificial-intelligence-to-reduce-carbon-
emissio ns/

Artificial intelligence, including artificial general intelligence, is a fascinating field that

encompasses various technologies that simulate human intelligence in machines. These technologies
include natural language processing, computer vision, and deep learning, which enable AI systems to
perform tasks that would otherwise require human intervention.
AI can also identify and address inefficiencies in the grid, such as power outages,
and allocate energy resources based on real-time demand. The result? A cleaner,
more sustainable energy sector that minimises waste and emissions.
Accurate demand forecasting is essential for optimising energy production and consumption. AI-
driven demand forecasting can help us predict energy consumption patterns and
adjust energy resources accordingly. By accurately anticipating energy demand, we can optimize
energy production, reduce energy waste, and harness the full potential of renewable energy sources.

Second,
AGI helps collect climate data.
Rehbein 23
Rehbein, Stella. "The Impact Of Artificial General Intelligence On Climate Reform." St. Andrew's
Law Review. November 09, 2023. Web. February 13, 2025.
https://www.standrewslawreview.com/post/the-impact-of-artificial-general-intelligence-on-climate-
refor m.

Despite these fears, the development of Artificial General Intelligence has paved the way for
progress in the biggest collective challenge our planet faces: climate change. Between 3.3 - 3.6 billion
people live in areas at high risk to climate change. Through the large scale collection of data and new
technological innovative capacities of AGI, solutions to climate issues have become increasingly
feasible. Characteristics of climate change data make it difficult to analyze as the large amount of
information takes a while to collect and analyze and is constantly changing. One way in which AI can
have a large effect is by improv[es]ing the accuracy of climate change models through
extensive data collection, thereby improving predictions. In order for people to actively
respond to climate change data and governments to create effective environmental policies, there
must be an element of trust in the data they are receiving. By improving the accuracy as well as the
amount of climate change data, the use of AGI effectively increases people's trust. There are many
ways in which AI has already been put into effect in order to increase climate awareness. AGI models
have been used to study the ocean and the ways in which it both absorbs and transfers heat in order to
predict its response to increasing temperatures. For example, AGI is being trained to gather
information in the arctic over winter (when no ships are able to travel in this region)
in order to monitor sea levels, temperature, etc. AGI has also been used in space through
satellite imagery to capture forest fires among other environmental devastations.
Here are 2 statistics that prove this true:

First,
Stern 25
Stern, Lord Nicholas. "What Is AI's Role In The Climate Transition And How Can It Drive Growth?."
World Economic Forum. January 16, 2025. Web. February 13, 2025.

[While] AGI also generates emissions through increased energy demand for data centres. Using
best available estimates, we project that AI could add 0.4-1.6 GtCO2e annually by 2035
[through increased energy demand for data centres]. AI’s net impact on emissions
therefore remains
overwhelmingly positive, provided it is intentionally applied to accelerate low-carbon
technologies. These are likely substantial underestimates of AI’s impact given that they capture
only some of the dynamic and systemic effects and cover only part of the economy and emissions.
What we find is that AGI [on net] could accelerate adoption across these technologies and
reduce[s] annual emissions by approximately 3-6 gigatonnes of CO2-equivalent (GtCO2e) by
2035. Power sector: AI enhances renewable energy efficiency, reducing emissions by ~1.8 GtCO2e
annually. Food sector: By accelerating the adoption of alternative proteins, AI could replace up to 50%
of meat and dairy consumption, saving ~3 GtCO2e per year. Mobility sector: AI-enabled shared
transport and optimized EV adoption could reduce emissions by ~0.6 GtCO2e annually.
AI reimagines interconnected systems like power, transport, cities, and land use. In power systems, it
improves grid stability and productivity by forecasting supply and demand and coordination across
space and time, and integrating renewables and storage efficiently. For example, DeepMind’s
wind energy optimization has boosted renewables’ economic value by 20%. These
benefits are especially impactful in emerging markets with significant infrastructure gaps but enormous
potential to leapfrog to cleaner systems.

Second,
Ding 24
https://pmc.ncbi.nlm.nih.gov/articles/PMC11247084/

Artificial intelligence has emerged as a technology to enhance productivity and improve life quality.
However, its role in building energy efficiency and carbon emission reduction has not been
systematically studied. This study evaluated artificial intelligence’s potential in the building sector,
focusing on medium office buildings in the United States. A methodology was developed to assess and
quantify potential emissions reductions. Key areas identified were equipment, occupancy influence,
control and operation, and design and construction. Six scenarios were used to estimate energy and
emissions savings across representative climate zones. Here we show that artificial intelligence could
reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting
artificial general intelligence could reduce energy consumption and carbon emissions by
approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation
could approximately reduce energy consumption by 40% and carbon emissions by 90% compared
to business-as-usual scenarios in 2050.
AI reduces building energy and emissions in design/construction, equipment, occupancy, and
control/operation. By accelerating high-efficiency and net-zero buildings, AI could cut energy
and emissions by 40-90% by 2050 combined with adequate policies.

Additionally, here’s 2 examples:

Cho 18
Cho, Renée . "Artificial Intelligence—A Game Changer For Climate Change And The Environment."
State of the Planet. June 05, 2018. Web. February 13, 2025.
https://news.climate.columbia.edu/2018/06/05/artificial-intelligence-climate-environment/ .

In India, AI has helped farmers get 30 percent higher groundnut yields per hectare by
providing information on preparing the land, applying fertilizer and choosing sowing dates.
In Norway, AI helped create a flexible and autonomous electric grid, integrating more
renewable energy. And AI has helped researchers achieve 89 to 99 percent accuracy in weather forecasting
identifying tropical cyclones, weather fronts and atmospheric rivers, the latter of which can cause heavy precipitation and are often hard for
humans to identify on their own. By improving weather forecasts, these types of programs can help keep people safe. What are artificial
intelligence, machine learning and deep learning? Artificial intelligence has been around since the late 1950s, but today, AI’s capacities are
rapidly improving thanks to several factors: the vast amounts of data being collected by sensors (in appliances, vehicles, clothing, etc.),
satellites and the Internet; the development of more powerful and faster computers; the availability of open source software and data; and
the increase in abundant, cheap storage. AI can now quickly discern patterns that humans cannot, make predictions more efficiently and

recommend better policies. The holy grail of artificial intelligence research is artificial general
intelligence, when computers will be able to reason, abstract, understand and communicate like humans. But we are still far from
that—it takes 83,000 processors 40 minutes to compute what one percent of the human brain can calculate in one second.

To conclude, because AGI solves climate change and therefore saves millions of lives,
I strongly negate.
geeked CHATGPT cards
Negative Case – The Development of AGI is Moral

I. Introduction I negate the resolution: “The development of Artificial General Intelligence is

immoral.”

To evaluate morality, we must consider the overall consequences of AGI’s development. The
affirmative may argue that AGI poses potential risks, but a utilitarian approach requires us to
weigh these against the immense benefits it offers to humanity. Through advancements in
medicine and climate solutions, AGI has the potential to improve and save billions of lives.
This case will demonstrate why AGI’s development is not only moral but necessary for human
progress.

II. Framework

● Value: Morality – Morality should be determined by actions that promote the greatest
well-being.
● Criterion: Utilitarianism – Utilitarianism evaluates morality by maximizing human
welfare and minimizing suffering. Under this framework, AGI is moral if it produces more
benefits than harms. This means that even if risks exist, the magnitude and certainty
of benefits must take precedence in our moral evaluation.

III. Contention 1: AGI will revolutionize medicine and save millions of lives

A. AGI Enhances Disease Diagnosis and Treatment

Card 1 – AGI enables early detection of diseases, saving lives
"Artificial intelligence systems have demonstrated the ability to detect diseases in their earliest
stages, far surpassing human doctors in speed and accuracy. Machine learning models
trained on massive datasets can predict cancerous tumors, heart disease, and
neurological disorders before symptoms manifest, allowing for earlier interventions and
increased survival rates. By recognizing patterns invisible to human analysis, AI-driven
diagnostics reduce misdiagnosis rates and improve patient outcomes, ensuring that life-
threatening illnesses are treated before they reach critical stages. This means that
diseases that once led to widespread suffering, such as certain cancers, can be stopped in their
tracks, preventing unnecessary deaths." (Champion Briefs, p. 586)

Card 2 – AGI accelerates drug discovery and medical advancements

"AGI is poised to revolutionize pharmaceutical research, slashing the time and costs
associated with drug discovery. AI models can analyze molecular interactions at speeds
impossible for humans, drastically accelerating the development of treatments for
conditions like cancer, Alzheimer’s, and rare genetic disorders. By simulating chemical
reactions and predicting drug efficacy in virtual environments, AGI eliminates the need for
costly and time-consuming trial-and-error processes, leading to faster, more effective
treatments reaching patients worldwide. This has already been seen in COVID-19 research,
where AI-assisted drug discovery led to vaccines being developed in record time. Imagine this
same speed applied to curing currently untreatable diseases." (Champion Briefs, p. 586)
B. AGI Expands Global Healthcare Access
Card 3 – AGI provides medical expertise to underserved communities
"The rise of AI-driven healthcare solutions will provide expert medical guidance to regions
with limited access to doctors. AI-powered diagnostics, combined with telemedicine, can
extend life-saving medical expertise to rural and impoverished communities worldwide.
In developing nations where medical professionals are scarce, AI systems equipped with vast
medical knowledge can assist in diagnosing illnesses, recommending treatments, and
even guiding non-specialist healthcare workers in performing complex procedures. By
democratizing access to quality healthcare, AGI can bridge the gap between medical
advancements and those who need them most. In areas where medical resources are
limited, AGI could mean the difference between life and death for millions who currently lack
access to care." (Champion Briefs, p. 586)

C. Utilitarian Impact
The ability to detect diseases earlier, develop drugs faster, and expand global healthcare
access means AGI will save millions of lives annually. The scale of these benefits
outweighs any speculative harms, making AGI development a moral necessity. Even if risks
exist, no alternative offers as much certainty and magnitude of positive impact as AGI in
medicine.

IV. Contention 2: AGI is essential for solving climate change

A. AGI Improves Climate Modeling & Disaster Prevention

Card 4 – AGI enables superior climate predictions
"AGI will revolutionize climate science by analyzing complex environmental data, improving
long-term climate predictions, and optimizing disaster response. Current AI models
already outperform human experts in forecasting hurricanes, wildfires, and drought
patterns, allowing for earlier and more effective mitigation strategies. Governments and
disaster response teams equipped with AGI-driven forecasts can deploy resources more
efficiently, reducing casualties and economic losses from climate-related disasters. This
level of predictive precision could mean the difference between life and death in vulnerable
communities. For instance, AI has already been used to predict and mitigate flooding in
Bangladesh, preventing thousands of deaths. Expanding AGI capabilities will only enhance this
life-saving potential." (Champion Briefs, p. 586)

Card 5 – AGI aids in ecosystem protection

"From monitoring deforestation rates to detecting illegal fishing activities, AI-powered
systems can provide real-time environmental surveillance. This allows for rapid
interventions, preventing biodiversity loss and preserving critical ecosystems. By
analyzing satellite images and drone footage, AI can pinpoint areas of ecological distress
and recommend targeted conservation efforts. Through proactive environmental
management, AGI ensures the protection of endangered species, marine habitats, and
forest reserves critical to the planet’s ecological balance. Without these tools, human-led
conservation efforts will continue to fall short, allowing environmental destruction to continue
unchecked." (Champion Briefs, p. 586)
B. AGI Accelerates the Transition to Renewable Energy
Card 6 – AI-driven energy systems optimize renewable energy usage
"The integration of AGI into energy infrastructure will maximize efficiency in renewable
energy production. AI can dynamically adjust solar and wind energy outputs, reducing
reliance on fossil fuels and cutting global carbon emissions at unprecedented rates.
Smart energy grids powered by AGI can predict demand fluctuations, preventing energy
waste and ensuring optimal resource allocation. This transformation will make clean
energy sources more reliable and accessible worldwide, expediting the transition away
from polluting fossil fuels. In regions where renewable energy is unreliable, AGI’s real-time
adjustments can increase stability and efficiency, leading to a cleaner, healthier planet."
(Champion Briefs, p. 586)

Card 7 – AGI advances carbon capture technology

"Machine learning models are rapidly improving carbon capture techniques, making it
possible to remove billions of tons of CO₂ from the atmosphere annually. Without AGI,
these advancements would take decades longer, worsening climate change and its
devastating effects. AI-driven research has already identified cost-effective ways to store
captured carbon, turning it into useful materials like construction bricks and biofuels. By
accelerating these solutions, AGI plays a pivotal role in reversing climate change and
ensuring a sustainable future for humanity. The alternative is continuing down the current
path, where carbon emissions rise unchecked, leading to mass suffering and irreversible
environmental damage." (Champion Briefs, p. 586)

C. Utilitarian Impact
Climate change threatens billions of lives and the future of the planet. AGI is one of the
only scalable solutions to combat this crisis. Preventing its development delays solutions
that could prevent mass suffering and environmental collapse, making AGI development a
moral obligation.

V. Conclusion
The utilitarian framework proves that AGI development is moral because it will save millions
of lives through medical breakthroughs and combat climate change—two of the most
pressing global challenges. Speculative risks do not outweigh tangible, large-scale benefits.
If we truly value human life and the future of our planet, we must prioritize AGI’s
continued development. Therefore, the resolution is negated.
DETERRENCE 😠