Precision medicine, or personalized medicine, is defined by the National Institutes of Health (NIH), as an approach that customizes prevention, diagnosis, and treatment to each individual by taking into account factors such as genetics, environment, lifestyle, and socioeconomic conditions. Unlike a one-size-fits-all approach, which relies on generalized treatment plans based on broad population averages, precision medicine seeks to optimize care by considering the unique biological and social determinants of health for each patient.

The U.S. government formally launched the Precision Medicine Initiative (PMI) in 2015 to accelerate research and implementation of personalized healthcare solutions. One of the flagship programs under this initiative is the All of Us Research Program, which aims to collect genomic, clinical, and lifestyle data from over one million individuals from diverse backgrounds in the U.S. to build a comprehensive resource for medical research [1]. Further support for precision medicine came in 2016 with the passage of the 21st Century Cures Act, which allocated significant funding to biomedical research, including precision medicine, regenerative medicine, and drug development.

Similarly, Europe has also embraced precision medicine through organizations like the European Partnership for Personalized Medicine (EP PerMed) and the International Consortium for Personalized Medicine (ICPerMed). These initiatives facilitate international collaboration, regulatory frameworks, and research funding to advance the development and implementation of precision medicine across European healthcare systems.

A brief history of Precision Medicine

The idea of precision medicine dates to the time of Hippocrates, who is often regarded as the “father of medicine.” He famously stated that “there is no disease, but the patient,” emphasizing that medical treatment should focus on the whole patient rather than just the disease. As medical science advanced, more concrete evidence supporting precision medicine emerged [2]. For instance, in the 19th century, Louis Pasteur and Robert Koch demonstrated that individuals respond differently to infections, showing the diversity in disease susceptibility. Gregor Mendel’s experiments with pea plants in 1860s laid the foundation for genetics, revealing patterns of inherited traits from parents and their impact on getting certain diseases. In the 1950s, the field of pharmacogenetics emerged after discovering that drug response is linked to an individual’s genetic makeup, meaning that a medication’s effectiveness—or lack thereof—can depend on the genes inherited from one’s parents. In 1990, the Human Genome Project (HGP) started [3], [4] whose goal was to sequence the entire human genome, revolutionizing biomedical research and opening the door to genetically informed disease risk assessment and treatment strategies. After the completion of the HGP in 2003, the rise of Genome-Wide Association Studies (GWAS) has enabled researchers to identify genetic variations associated with various diseases [5]. With the help of GWAS, many genes associated with diseases have been discovered and published in the GWAS catalog.

Examples of Precision Medicine

Several clinical examples of precision medicine already exist. One well-known case involves the CYP2D6 gene, which has multiple variants that affect how individuals metabolize medications. Depending on their CYP2D6 genotype, patients may process drugs quickly or slowly, influencing both effectiveness and side effects. Genotyping allows physicians to choose the right drug and dosage, improving outcomes and minimizing adverse reactions [6].

Another example is HER2-positive breast cancer, a subtype in which cancer cells overexpress the HER2 protein, leading to more aggressive tumors. Targeted drugs such as trastuzumab and pertuzumab block HER2 activity, slowing tumor growth and improving survival [7].

Similarly, in colorectal cancer, mutations in the KRAS gene determine whether patients respond to monoclonal antibody therapies like cetuximab or panitumumab. Only those with wild-type KRAS benefit from these treatments; patients with KRAS mutations require alternatives [8], [9]. Molecular profiling helps ensure that only patients likely to benefit receive these therapies, avoiding unnecessary treatments, reducing costs, and lowering the risk of side effects. This approach has inspired the development of molecular subtyping across many cancer types [10–13].

An emerging area, precision radiomics, uses artificial intelligence and advanced imaging to extract detailed tumor characteristics from medical scans. When integrated with genomic and clinical data, radiomics enables highly tailored radiotherapy plans that deliver optimal radiation doses while protecting healthy tissue, ultimately improving patient outcomes [14].

Computational methods and AI

Despite the promise of precision medicine, making personalized treatments widely accessible remains challenging. Although the goal is to tailor care using genetic, clinical, and lifestyle data, scaling such solutions has proven complex. Several companies have attempted it, but some high-profile efforts have struggled. One well-known example is IBM’s Watson for Oncology, which aimed to use AI for cancer diagnosis and treatment recommendations but ultimately fell short due to biased training data, limited clinical context, and difficulty incorporating real-world expertise.

Recent advances in generative AI and large language models, however, have renewed optimism. Unlike earlier rule-based systems, modern AI can analyze vast patient datasets, detect subtle patterns, and generate insights that may not be immediately apparent to clinicians. This opens new possibilities for improving diagnosis, treatment selection, and patient outcomes.

AI contributes to precision medicine in several ways. It can identify meaningful patterns in clinical records, lifestyle factors, and environmental exposures that correlate with treatment response. By analyzing multimodal data—including genomics, electronic health records, imaging, and wearable devices—AI can determine which patient features matter most and how they interact, guiding more individualized care.

Another key contribution is predicting how a specific patient will respond to a drug or therapy. Deep learning models trained on large datasets can estimate treatment effectiveness, helping clinicians select the most beneficial option. This is essential: missing a life-saving therapy can be fatal, while receiving an ineffective treatment imposes financial, emotional, and physical burdens. AI-driven prediction models help maximize therapeutic benefit while minimizing risk and side effects.

Ethical aspects

While precision medicine offers great potential, it also raises important ethical concerns. A major issue is ensuring that AI models used in healthcare are fair and unbiased. Many systems are trained on datasets that fail to represent diverse populations, leading to unequal or misleading outcomes. Addressing this requires more inclusive data collection and representative training sets. Initiatives like the All of Us Research Program are crucial for reducing bias and ensuring that precision medicine benefits all patients rather than reinforcing existing disparities.

Genetic data privacy is another central concern. Strict safeguards are needed to control who can access such sensitive information and for what purposes. If genetic data were exposed, individuals and their families could face discrimination, stigma, or privacy violations—for example, employers or insurers misusing information about disease risk. Because genetic data is hereditary, breaches affect not only one person but also their relatives and future generations. In the U.S., the Genetic Information Nondiscrimination Act (GINA) of 2008 prohibits discrimination based on genetic information in employment and health insurance, but ongoing policy updates will be essential as technology evolves.

Gene editing, especially germline modification, remains one of the most controversial ethical issues in precision medicine. Somatic editing affects only the treated individual, but germline editing alters DNA in eggs, sperm, or embryos in ways that can be inherited. Although this could prevent certain hereditary diseases, it raises profound moral and societal questions. Misuse could permanently alter the human gene pool or worsen inequalities, and future generations—who cannot consent—would be directly affected. For these reasons, germline editing continues to be one of the most debated topics in bioethics and precision medicine [16].

Future challenges

There are several major challenges to making precision medicine widely accessible.

1. Democratization

Precision medicine must work for people across different demographic and socioeconomic groups. This requires both inclusive datasets and access to the necessary tools worldwide. Mobile health apps that measure basic biological variables could support early diagnosis and preventive care, but scaling treatments globally—and generating high-quality data such as whole-genome sequences—still faces financial and regulatory barriers.

1. Scalability

Precision medicine depends on analyzing massive datasets to discover biomarkers and guide treatment decisions. This requires substantial computational resources and continuous model updates as new data appears. Secure data-sharing across hospitals and countries is essential. Federated learning offers a possible solution by allowing collaborative model development without moving raw patient data [17]. Global coordination—potentially led by organizations like the WHO—is needed to build models that reflect ethnic, racial, and geographic diversity.

1. Handling missing and noisy data

Large biomedical datasets often contain confounding, incomplete, or context-dependent information. Without proper interpretation, this can produce misleading insights. For example, someone visiting the emergency room after an acute trauma may have elevated vitals that do not represent their usual health, which could distort predictive models. External events—such as wildfires triggering asthma spikes—can also skew data if not contextualized. Precision medicine must distinguish meaningful patterns from noise while preserving relevant context.

1. Small sample sizes

Rare diseases often have very few patients, making traditional clinical trials impractical. Transfer learning offers one solution: models trained on large populations can be fine-tuned for small, specific cases. Digital twins—virtual replicas built from a person’s genetic, physiological, and clinical data—can simulate treatment responses and are already being explored in cardiology [18]. Adaptive trial designs, such as N-of-1 studies, also help evaluate treatments for individual patients, while international data-sharing efforts can expand sample sizes for rare disease research [19].

1. Broadening precision medicine across diseases

Although much progress has been made in oncology, precision medicine must be expanded to cardiovascular, neurodegenerative, metabolic, and infectious diseases. Advances in genomics and multi-omics have revealed biologically distinct subtypes within many conditions. For instance, type 2 diabetes, once treated as a uniform disorder, now includes at least five genetically and metabolically distinct subgroups [20], allowing for more targeted therapies based on insulin sensitivity, beta-cell function, and other individual factors.

Conclusion

In the era of big data and AI, the field of precision medicine has great potential to improve across a wide range of diseases. In the future, precision medicine would help preventive medicine efforts for early diagnosis and intervention of diseases by providing tailored treatment and intervention plans. There are several challenges, both technological, social, and ethical, that need to be overcome. Ethicists, policy makers, scientists and medical professionals need to work collaboratively to provide solutions to these challenges to make precision medicine a routine component of healthcare.

References

• All of Us Research Program Investigators et al., “The ‘All of Us’ Research Program,” N Engl J Med, vol. 381, no. 7, pp. 668–676, Aug. 2019, doi: 10.1056/NEJMsr1809937. Also see Yalcin, “Precision Medicine for Everyone: All of Us Research Program Initiative,” The Fountain 166, July 1, 2025.
• S. Visvikis-Siest, D. Theodoridou, M.-S. Kontoe, S. Kumar, and M. Marschler, “Milestones in Personalized Medicine: From the Ancient Time to Nowadays—the Provocation of COVID-19,” Front. Genet., vol. 11, Nov. 2020, doi: 10.3389/ fgene.2020.569175.
• E. S. Lander et al., “Initial sequencing and analysis of the human genome,” Nature, vol. 409, no. 6822, pp. 860–921, 2001.
• J. C. Venter et al., “The sequence of the human genome,” science, vol. 291, no. 5507, pp. 1304–1351, 2001.
• W. S. Bush and J. H. Moore, “Chapter 11: Genome-Wide Association Studies,” PLOS Computational Biology, vol. 8, no. 12, p. e1002822, Dec. 2012, doi: 10.1371/ journal.pcbi.1002822.
• N. A. Nahid and J. A. and Johnson, “CYP2D6 pharmacogenetics and phenoconversion in personalized medicine,” Expert Opinion on Drug Metabolism & Toxicology, vol. 18, no. 11, pp. 769–785, Nov. 2022, doi: 10.1080/ 17425255.2022.2160317.
• S. M. Swain et al., “Pertuzumab, Trastuzumab, and Docetaxel in HER2-Positive Metastatic Breast Cancer,” New England Journal of Medicine, vol. 372, no. 8, pp. 724–734, Feb. 2015, doi: 10.1056/NEJMoa1413513.
• A. Bardelli and S. Siena, “Molecular Mechanisms of Resistance to Cetuximab and Panitumumab in Colorectal Cancer,” JCO, vol. 28, no. 7, pp. 1254–1261, Mar. 2010, doi: 10.1200/JCO.2009.24.6116.
• C. Tan and X. Du, “KRAS mutation testing in metastatic colorectal cancer,” World J Gastroenterol, vol. 18, no. 37, pp. 5171–5180, Oct. 2012, doi: 10.3748/ wjg.v18.i37.5171.
• R. Mclendon et al., “Comprehensive genomic characterization defines human glioblastoma genes and core pathways,” Nature, vol. 455, no. 7216, pp. 1061–1068, 2008, doi: 10.1038/nature07385.
• D. C. Koboldt et al., “Comprehensive molecular portraits of human breast tumours,” Nature, vol. 490, no. 7418, pp. 61–70, Oct. 2012, doi: 10.1038/ nature11412.
• C. J. Creighton et al., “Comprehensive molecular characterization of clear cell renal cell carcinoma,” Nature, vol. 499, no. 7456, pp. 43–49, Jul. 2013, doi: 10.1038/ nature12222.
• “Comprehensive molecular profiling of lung adenocarcinoma,” Nature, vol. 511, no. 7511, pp. 543–550, Jul. 2014, doi: 10.1038/nature13385.
• H. J. W. L. Aerts, “The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review,” JAMA Oncology, vol. 2, no. 12, pp. 1636–1642, Dec. 2016, doi: 10.1001/jamaoncol.2016.2631.
• L. Bonomi, Y. Huang, and L. Ohno-Machado, “Privacy challenges and research opportunities for genomic data sharing,” Nat Genet, vol. 52, no. 7, pp. 646–654, Jul. 2020, doi: 10.1038/s41588-020-0651-0.
• G. Rubeis and F. Steger, “Risks and benefits of human germline genome editing: An ethical analysis,” ABR, vol. 10, no. 2, pp. 133–141, Jul. 2018, doi: 10.1007/s41649-018-0056-x.
• M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed, “Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Access, vol. 8, pp. 140699–140725, 2020.
• J. Corral-Acero et al., “The ‘Digital Twin’ to enable the vision of precision cardiology,” European Heart Journal, vol. 41, no. 48, pp. 4556–4564, Dec. 2020, doi: 10.1093/eurheartj/ehaa159.
• E. O. Lillie, Patay ,Bradley, Diamant, Joel, Issell ,Brian, Topol ,Eric J, and N. J. and Schork, “The N-Of-1 Clinical Trial: The Ultimate Strategy For Individualizing Medicine?,” Personalized Medicine, vol. 8, no. 2, pp. 161–173, Mar. 2011, doi: 10.2217/pme.11.7.
• M. Pigeyre et al., “Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial,” Diabetologia, vol. 65, no. 1, pp. 206–215, Jan. 2022, doi: 10.1007/s00125-021-05567-4.

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With the late-2022 launch of chatbots, ads like the one above is becoming more common. As exciting as it appears, many are not sure where this new era is leading. One major question about AI centers around ethics. On January 10, 2023, “AI Ethics: An Abrahamic Commitment to the Rome Call” [1] gathered the three Abrahamic religions and different corporate leaders at the Vatican for the purpose of discussing ethics in navigating this technology. The common agreement was that algorithms should improve the world but not be the ultimate decision-maker.

In his speech, Microsoft representative Brad Smith underlined the importance of considering a religious take on ethics as a moral compass in determining the rules and regulations around AI. Following the conference, the leaders of the Abrahamic religions signed a joint declaration. It urged the developers of AI to follow six principles: AI must be transparent, inclusive, accountable, impartial, reliable, secure, and respectful of the users’ privacy. It is necessary to analyze relevant studies that bring up the positive and negative aspects of AI in order to better understand the rising concern that surrounds it.

Findings from previous AI studies indicate that AI carries about itself a vague identity; hence, it is not totally clear what might possibly go wrong with the technology. Proponents of the technology call for its urgent implementation because machines seem to be working for the benefit of society. For instance, AI is currently being used at fulfilling some Sustainable Development Goals, and the UN believes that technology can assist in overcoming global catastrophes in the future. Some believe AI has potential benefits [2] in industries such as manufacturing, transportation, agriculture, translation, and publishing. Scientific American [3] reported how AI is helping doctors find out possible causes of life-threatening illnesses in order to reduce deaths by 20 percent. Researchers state that medical experts analyze the provided information related to a patients’ condition and make decisions about whether to agree with a machine’s data or not. In this case, health providers are able to take control of the machine, not vice versa. This is one example that demonstrates that people are not blindly relying on algorithms.

Scientists believe that AI can contribute to combating the climate crisis [4] as long as innovators make climate decision-making processes local, democratic, and open. Here we can see further evidence of technology acting as an ally in coping with environmental disasters and illnesses without compromising human authority.

On the other hand, the future consequences of man-made tech is still blurry; there are legitimate concerns that their possible malfunctioning might lead to harmful situations. The launch of ChatGPT has caused major debates about its ethical usage. While generating something based on already existing data, some people think ChatGPT violates the rights of the artists and writers who actually made the work that the program sources. A couple of studies have evidenced cases where machines let down their users. In 2016, Chatbot Tay made by Microsoft initially looked human-friendly but had to be shut down after unexpectedly tweeting pro-Nazi, antisemitic, and anti-feminist remarks. The technology failed at giving sensible responses when Joseph Austerweil, a psychologist at the University of Wisconsin-Madison, tested the machine for morality questions [5].

A study [6] by the National Institute of Standards and Technology showed that facial recognition systems are biased against people of color and women. San Francisco and Berkeley (CA), Somerville, and Brookline (MA) prohibited the government from using facial recognition tools because the biased technology is found to be more problematic in law enforcement and governance. Maria De-Arteaga, an algorithmic systems researcher at Carnegie Mellon University suggested that companies and governments should be very careful before relying on a machine’s intellect and questioned the safety of these technologies. 

While these machines appear uncontrollable and unpredictable, an ethics researcher at Simon Fraser University in British Columbia believes that “they are not unguided” and work according to the instructions and choices made by people. At the Rome Call for AI ethics convention, Mario Rosetti, Professor Emeritus of Theoretical Physics at the Politecnico di Torino, noted that the human brain cannot be compared to an artificial intellect due to the brain’s magnificent structure and function. If the human brain is far more powerful than AI, then there is hope that the technology could be controlled by people. All the previous studies accentuate an argument that AI must not be worshiped.

Since machines are constantly being improved upon, religious and tech leaders have found it important to discuss the ethical issues that surround AI and help innovators minimize their risks. Religious leaders at Rome Call for AI ethics approached the ethical issues of the technology from a spiritual perspective by referring to holy scriptures.

Shaykh Hamza Yusuf, President of Zaytuna College, explained how inventions have historically been approached with caution. Yusuf gave the example of a dialogue from Plato’s Phaedrus, in which Thoth (or Theuth) shows his invention of writing to the King as “a recipe for memory and wisdom.” The King responds that this invention will “implant forgetfulness in their souls.” With this example, Shaykh Yusuf was basically pointing to the risk that such inventions may not really serve knowledge, for with such inventions, knowledge is no longer coming from inside but from outside. He also highlighted that the concept of invention always had negative connotations in many religious traditions out of fear of societal destabilization. When the focus is on technological benefits, people might disregard the potential harm, including its alienating, distracting nature which we experience everyday by “constantly checking our phones.” In the past, distraction was considered synonymous with “mental drain.” Kafka said, “Evil is whatever distracts.” While progress might be inevitable, it does not mean all progress is useful. We need to “look down the road at the consequences,” Shaykh said, based on an Islamic juristic principle (al-nazar fi al-maalat) and seriously consider how we can prevent harm. In his speech, Shaykh Abdallah bin Bayyah reminded that Prophet Muhammad, peace be upon him, said, “There should be no harm and no reciprocation of harm.” Reflecting on Aristotle’s five intellectual virtues, Hamza Yusuf noted the importance of approaching technology (artistry, craftsmanship) with prudence (phronesis) and wisdom (sophia).

The Jewish attitude is that humans are created in God’s image and that they carry divine attributes within themselves and therefore stand above artificial intelligence. Israeli attorney and professor of law Haim Aviad Hacohen talked about the ancient Babylonian civilization’s failure to appreciate this quality of mankind. The Babylonians had an eager wish to reach heaven through the highest tower. The Bible tells us that this tower was special and that it would have demonstrated the technical and economic accomplishment of that nation. People were obsessed with the idea of conquering the sky, so they excluded God’s opinion and showed no care for the construction workers that built the tower because, as Rabbi Hacohen narrated, “from the high top one cannot really see millions of needy people on the ground who need their attention.” Rabbi Shlomo David Rosen gave an example, saying that “When a brick fell down and broke, people stopped their work and cried. But when a person fell and died, they did not bat an eyelid.” Consequently, people were punished for their arrogant and negligent behaviors when God made them speak different languages.

Previously, Rabbi Eliezer Simha Weisz, a member of the Council of the Chief Rabbinate, said that the Jewish community used to make golems (creatures brought to life using clay and Hebrew incantations) by means of kabbalistic efforts to protect themselves from their enemies. However, the golems were the ones who would be defeated. Even though they were man-made powerful creatures they came out to be weaker than human beings.

The essential argument made by almost every religious leader in the convention can be summed up into one common statement: “Technology has to be used for the improvement of human life and shouldn’t leave anyone behind.” It should not harm but serve humanity. Therefore, human beings must be in control of it and not vice versa. All of the speakers at the event supported Pope Francis’ statement concerning asylum seekers. Thus, technology shouldn’t harm the most vulnerable category of people but assist them in overcoming their hardships.

When all is said and done, it seems that technology is like God’s creation of evil. It is there to guide people in the difference between good and bad, and to stimulate us to seek higher achievements in this life. But this is possible only as long as we control that which is evil, which is not an easy task. Similarly, AI is a man-made invention, and it is inevitably becoming part of our daily lives. Instead of avoiding its usage, it’s better to look for healthy ways of integrating it into our lives. However, whether or not AI will act in favor of, or against, mankind will depend on how it is applied.

References

1. https://www.romecall.org/the-abrahamic-commitment-to-the-rome-call-for-ai-ethics-10th-january-2023/
2. https://www.itu.int/en/mediacentre/backgrounders/Pages/artificial-intelligence-for-good.aspx
3. https://www.scientificamerican.com/article/algorithm-that-detects-sepsis-cut-deaths-by-nearly-20-percent/
4. https://www.scientificamerican.com/article/what-ai-can-do-for-climate-change-and-what-climate-change-can-do-for-ai/
5. https://www.nytimes.com/2021/11/19/technology/can-a-machine-learn-morality.html
6. https://www.nytimes.com/2019/12/19/technology/facial-recognition-bias.html

Many respected thinkers claim that a new era based on artificial intelligence (AI) will follow and ultimately succeed us as the primary inhabitants of our planet [www.bbc.com/news/technology-30290540]. Their main rationale is that AI will exceed our limited human intelligence, and machines will ultimately transcend us in their capabilities. AI has already entered our lives with our smart phones (e.g., voice recognition software, like Siri), Google’s search predictions, Netflix’s movie recommendations, your major bank’s fraud-detection algorithms, and many other ways. In the very near future, we will likely see self-driving cars on the streets, drone-based delivery of online purchases, and ‘smart’ machinery for our households. It’s certain that AI based algorithms will substitute or at least help humans in performing certain tasks. In fact, they already have. But will advances in artificial intelligence research eventually allow humanoid robots to surpass us, not only in specific tasks, but in all ways?

A little history

The Industrial Revolution, in the early nineteenth century, and subsequent socio-economic developments, inevitably pushed the course of human culture towards a computerized future. In the Victorian era, financial institutions had started to carry out millions of transactions per year. Large populations needed to be surveyed and governmental censuses required the processing of millions of records. Increases in manufacturing and trade required constant bookkeeping. Such tasks were manually carried out by employed clerks. However, the advent of large-scale data required a much more efficient and effective means of data processing.

It was this need that caused an engineer named Herman Hollerith to develop a mechanical system for data processing in the late nineteenth century. Hollerith commercialized his invention by establishing the Tabulating Machine Company, in 1896, which later gave birth to IBM.

Such mechanical devices carried out specific tasks very efficiently and replaced error-prone human computers (clerks) in processing large amounts of data. But more importantly, they attracted mathematicians’ interest in defining what a “task” is and what was computable by such devices. This led to the rigorous formulation of the “algorithm,” the task or process a machine carries out, in the early twentieth century. Several tools and celebrated theorems were introduced during these years to describe computability: the Lambda-calculus, Church’s thesis, the Turing machine, Godel’s incompleteness theorem…

The pioneering work of Claude Shannon’s A Mathematical Theory of Communication, in 1948, laid the foundations of digital communication. With the invention of the transistor, data was transferred into a digital format, and digital machines or “computers” based on the Turing machine model were developed. Electronic productions were becoming cheap, ultimately enabling computers to be affordable for the public. Apple was established in the 1970s to sell personal computers (PCs). During this same era, Microsoft was also founded as a company providing software solutions for the newly emerging PC market. With the commercialization of the Internet in the 1990s, the need for content search engines was on the rise. As such, Google was founded by two PhD students from Stanford.

Today, the aforementioned companies are among the largest in the world. One common pattern in all of them is that their founders were successful in reading the global trends and aware of the course of scientific developments. With tenacity and talent, their companies were able to rise to the top of the financial heap in a short amount of time.

The era of AI?

There is a recent trend in investing in AI. Google has recently acquired many of the world’s leading robotics firms, including Boston Dynamics. Amazon, Facebook, and Microsoft are all investing in machine learning, with the hope of pushing their businesses into the future. The number of AI startups has exploded in the last few years, capitalizing on over 300 million dollars from investors in 2014, up more than 20-fold compared to four years prior [www.bloomberg.com/news/articles/2015-02-03/i-ll-be-back-the-return-of-artificial-intelligence].

Many scholars point out that an AI revolution is taking place and that staying relevant in tomorrow’s world requires acting today. Of course, some philosophers and scientists are arguing that AI poses a threat to our very existence. Could machines inherit our human capabilities and replace us as the predominant, intelligent form on Earth?

According to Said Nursi, the progress of human civilization and scientific development is God’s desire in humankind “to make manifest and display in the view of the people the majesty of His rule… the wonders of His art, and the marvels of His knowledge, and so that He could behold His beauty and perfection.” In achieving this, God has created mankind as the vicegerent of the earth, and He has bestowed us with remarkable abilities. As a manifestation of this, humankind has established civilizations by using our social-cultural advancements and dominion over our natural environment. Thus, knowingly or not, humanity has excellently displayed and made known the miraculous art and divine-attributes of our Maker.

From a religious viewpoint, does this forecast of intelligent machines surpassing human abilities contradict the purpose of humankind as the vicegerent of the earth? Does it also contradict humanity’s status as the most superior creation of God? What does science tell us?

We know that the Turing machine, on which modern computers are based, has serious limitations. In 1900, the famous mathematician David Hilbert published a list of problems which was unsolved at the time. The tenth problem asked for a general algorithm to determine whether a given Diophantine equation with integer coefficients has an integer solution. We now know that no such algorithm exists. Similarly, in the 1930s, Turing himself put forth that the halting problem (whether the Turing machine halts or not) is undecidable – i.e., it is impossible to construct an algorithm that always leads to a correct yes-or-no answer.

At the time, many also argued about whether the Turing machine was the ultimate mathematician. The question was: given a list of axioms and some rules of logic, will a Turing machine be able to prove every mathematical statement? The rationale behind the idea was that, since all theorems are derived from a set of axioms and logic rules, the machine can eventually enumerate all possible theorems and thus validate the correctness of a given statement.

The question was answered just a few years after. The celebrated Godel’s incompleteness theorem simply states that in a consistent system, where statements are not true and false at the same time, we will have statements that we cannot decide the correct answer to. This theorem seriously dampened the enthusiasm of people overly excited about the capabilities of these new machines. Ever since Godel’s discovery, the computability theory has been used to research the limits of computation. It is well known that the current computing models, such as Turing and Quantum machines, have their ultimate limits.

Differing from computability theory, artificial intelligence is more concerned with developing algorithms that learn or adapt to their environment, allowing them to perform a particular task. Though such algorithms inherit the ultimate restriction of the computing model they operate on, computers can still be taught to perform certain tasks, such as visual/speech recognition. The technological advances in the last decades are mind blowing, as algorithms that perform certain tasks, such as human/face detection, already show great accuracy. However, we’re still far from the kind of robots we see in certain sci-fi movies or shows.

The question remains whether we can see progress in the near future of AI that will eventually allow machines to surpass humans in their skills. But what makes humans, well, human? The computability theory first requires defining, mathematically, what an algorithm is before developing machine models that function as instructed by such an algorithm. The lack of an answer to the question of what makes humans human thus contributes to the ever-continuing discussion of AI vs. humans. Denying our spiritual side will most likely cause people to continue to see AI as the next evolutionary step; whereas, given the undeniable spiritual capacity of humans, it is not wrong to state that although AI will increasingly dominate our lives, it will never fully replace us.

Bayram Kara – PhD Candidate studying in the areas of Machine Learning.

The theoretical background of the computer was developed at the beginning of the 20th century, but it was not until the Second World War that progress was made in developing electrical calculating machines. Now we have a new era; the era of computers. In the beginning, the computer was a machine that had a very limited capacity and calculation, and it was only used in a very few important centers. With the passage of time, computers began to be used in business centers, and eventually the production of personal computers became more widespread. Nowadays, we can see many high-tech machines, like handheld PC’s and robot dogs everywhere we look.

In the last fifty years, computer technology has developed rapidly. With respect to this, logical-thinking devices have also been greatly developed. Artificial Intelligence (AI) provides the logical thinking for these types of devices. The goal of AI is to attain the level of logic that “living systems” (i.e. humans) possess. The improvements in AI systems are encouraging to the scientists involved in the field; they now believe that not only can a humanlike machine be built, but, in fact, a machine that is more advanced than humans can be developed. The debate on this subject has separated scientists into two camps. AI advocates claim that in the future people will have the opportunity to make advanced devices that have a better ability to think and decide than humans have. On the other hand, many scientists think that the decision-making mechanism of the human brain contains something that is beyond electronics and that cannot be replicated in an electronic device.

Before going into details, we must first answer the question “what is intelligence?” A being is intelligent if it understands and evaluates some “known data”; if it makes logical inferences and avoids redundant processes and therefore arrives at a sound solution. The famous English mathematician Alan Turing claimed that “if the interrogator cannot distinguish the machine from the human” then the machine is assumed to be intelligent. The Turing Test consists of an interrogator, a machine and a human placed in three rooms. The interrogator is in contact with the human and the machine over text terminals.

The theory of computation was first propounded by Alan Turing in 1936. He described, in basic terms, “The Turing Machine” which is an abstract machine with an unlimited amount of storage space that can go on computing forever without making any mistakes. The Turing Machine only performs three basic operations; reading, writing, and moving the read-write head. According to the Turing Theorem, all computers are Turing equivalent; that is, any process that can be done by a Turing Machine, can be done by a computer and similarly any process that can be done by a computer, can be done by a Turing Machine.

Figure 1 A simple illustration of a Turing Machine

The Turing machine is an abstract model of computer execution and storage that gives a mathematically precise definition of algorithm or “mechanical procedure.”

All computers perform algorithmic processes. Algorithm means a step by step progression. In other words, you have a certain situation. You solve that situation and proceed to another situation that is better than the last one. Using this step by step solution method you are able to reach the goal situation. This is an algorithmic problem. An example will make this easier to understand:

Suppose we have any 10 numbers.

Problem: What is the sum of these numbers?

Figure 2 The algorithm that gives the sum of any given 10 numbers.

As shown above, the sum is “0” in the beginning. A loop with 10 processes is prepared and the next number is read. The number is added to the sum and the algorithm moves to the next number. The process continues until the 10 numbers have been finished. After the process is finished, the result is written.

On the other hand, there are many known problems that do not have any algorithmic solutions. A simple example is given in the following:

Problem: Find a number that is not the sum of three square numbers.

In this problem we had a bit of luck; we just tried 7 and were able to find the solution. Let’s change the problem a little bit:

Problem: Find a number that is not the sum of four square numbers.

The eighteenth-century mathematician Lagrange proved the well-know theorem that every number can be expressed as the sum of four squares. What this means for our computer is that if we were to simply go on in a mindless way trying to find such a number, the computer would simply chug away forever, never finding any answer. In order to solve this problem, therefore, Lagrange had to apply a method that was not algorithmic. Additionally, Penrose states that “there are certain classes of problems that do not have any algorithmic solutions.”

In fact, Turing described the situation where a computer fails to find a solution and therefore does not come to a stop (or a halt) as a “halting problem.” One good example of this, given by Penrose, is the completely deterministic, but non-computable “tiling problem.” We are given tiles called polyominoes and we have to place these tiles on a Euclidian plane

Figure 3. Various sets of polyominoes that will tile the infinite Euclidean plane (reflected-image tiles being allowed).Neither of the polyominoes in set (c), if taken by itself, will tile the plane, however.

In Figure 3 (a), it is obvious to see the tiling of the plane by tiling around a cross. In figure 3 (b) the same condition holds, but in part (c) the tiles cannot tile a plane by themselves, but only together. Another example is shown in the following.

Figure 4. A set of three polyominoes that will tile the plane, but in a way that never repeats.

The plane can be tiled by using three polyominoes, but not in an algorithmic way. In other words, the computer will try to tile the polyominoes by adding around each of them and, since it cannot find a pattern, it will go on forever and will not be able to arrive at a conclusion as to whether or not the polyominoes will tile the plane.

One of the most important factors that separate computers from the human mind is consciousness. Consciousness is the process of understanding. The computer can compute the data given, but it cannot understand what the data means. For example, when one of your friends calls you, you understand that he has called you and you respond. When you switch on a machine, it starts to work. It is not because the machine has understood that you have pressed the button; rather the electronic structure of the machine has been designed to work when you switch it on. The machine cannot understand; it is not conscious. Here are some more examples:

Figure 5. White to play and draw—easy for humans, but Deep Thought took the castle.

In the chess game above, by just playing the king left and right, white can bring the game to a draw. But, at first to make the game a draw, the white player has to understand the situation. Since the computer has no capability to understand, it may think that it would be more profitable to take the castle and therefore it loses the game.

Another example:

Figure 6. White to play and draw—again easy enough for humans, but a normal expert chess computer will take the castle.

There is a great temptation to take the black castle with the white bishop, but the correct thing to do is to pretend that the white bishop is a pawn and use it to create another barrier of pawns. Once you have taught the computer to recognize barriers of pawns, it might be able to solve the first problem, but it would fail on the second because it needs an extra level of understanding. The situation is very easy for a human, but as we mentioned, it is quite difficult for a computer.

These examples are halting problems because both situations have endless algorithms to identify the solution, so basically they need to be understood by an intelligent mechanism. Maybe the chess problems can be solved with enough computation, but again we can make the situation more complex. That is to say, the important thing is not computation, but understanding the situation.

In conclusion, the problems we mentioned above are some of the basic problems that AI has to overcome. The present technology is very far from being similar to the human mind. The human mind is not a simple substance; in fact, quite the contrary, it is an incredibly complex structure. There are many things that play a role in the human mind; it is not easy, perhaps it is even impossible, to build a mechanism that is like the human mind. 

References

• Adami C., Introduction to Artificial Life: Flavors of Artificial Life, 1999.
• Aksoy M. S., Artifical Intelligence, The Fountain, No.4, s.10.
• Artificial life and the Turing Test, Retrieved from World Wide Web: “http://http1.brunel.ac.uk:8080/depts/AI/alife/alife-main.html” http://http1.brunel.ac.uk:8080/depts/AI/alife/alife-main.html, 2000
• Crick F., The Astonishing Hypothesis: The Science Search for the Soul, Charles Scribner’s Sons, 1994.
• Penrose R., Shadows of the Mind: Consciousness and computation, Oxford University Press, 1994.
• Penrose R., Shadows of the Mind: Does Mind have a Place in Classical Physics, Oxford University Press, 1994.
• Penrose R., Shadows of the Mind: Quantum Theory and the Brain, Oxford University Press, 1994.
• Penrose R., Shadows of the Mind: A Search for the Missing Science of Consciousness, Oxford University Press, 1994.
• Petri H.L., Mishkin M. Behaviorism, Cognitivism and the Neuropsychology of Memory, American Scientist, Jan-Feb 1994. s. 3037.
• Searle J.R. Minds, Brains and Computers. Retrieved from World Wide Web:”http://www.siu.edu/~philos/faculty/Manfredi/intro /searle.html” http://www.siu.edu/~philos/faculty/Manfredi/intro/
• searle.html, 2000.
• Interview with Ucoluk G., Can a More Advanced Mechanism than the Human be Built?, 1999.

The theorem published in 1931 by the 25 year old Austrian scientist, Kurt Godel, made a great impact on the scientific circles. Not only did it destroy the hopes of many scientists, but it also initiated a new point of view concerning AI and the mind. This theorem is one of the most important ones to be proven this century, ranking alongside Einstein’s Theory of Relativity and Heisenberg’s Uncertainty Principle. However, very few people know about it. In this article, we will examine in detail the effects of Godel’s theorem on AI.

What is Godel’s Incompleteness Theorem?

As a formal definition, proof is a sequence of well-formed-formulas (wff), each of which is either an axiom or a wff that is derived from preceding wff’s. Godel’s contemporary Hilbert, one of the most famous mathematicians, thought that all proofs in mathematics can be obtained in an automated way (with an axiomatic system) and he started to work on this project. He believed that if he derived all wff’s in basic arithmetic from its own axioms, then he could derive all facts in mathematics using these axioms.

Unfortunately, Godel demonstrated the impossibility of this. First of all, he found a method of translating the syntax of a formal system into arithmetic. Then he formulated the statement, “This formula is improvable in the system,” (G) in arithmetic. Using the same method, he also formulated the negative of the statement G (“This formula is provable in the system”). For the next step, he showed that if the truth value of G was calculated, the truth value of negation of G could also be calculated, causing a contradiction. At the end of his calculations, Godel arrived at two very important consequences:

1. If a formal system that contains minimal arithmetic is consistent, then it is incomplete.

2. Consistency of any formal system containing minimal arithmetic is not internally provable (by using the system’s own rules and formulas).

Surprisingly, even if G were added as a further axiom into the system, a new Godel sentence could be easily found. In other words, no matter how many axioms we add, one can find a Godel sentence that will make the truth value undeterminable.

What Does the Theorem Imply for Artificial Intelligence vs. the Mind?

By examining Godel’s Theorem, one can determine very important consequences for artificial intelligence. An English mathematician, Turing, described an abstract machine called the “Turing Machine.” This is an abstract machine which has an unlimited amount of storage space and which can go on computing forever without making any mistakes. This machine can compute any type of algorithmic problem. According to the Turing Theorem all computers are Turing equivalents. After proposing this, Turing went on to observe that some type of problems have no algorithmic solutions. In the meantime, “the Halting Problem” emerged – the problem of deciding those situations in which a Turing Machine action fails never comes to a halt because of the consequences of the Godel’s Incompleteness Theorem.

It has been proven that a halting problem is computationally insoluble. This leads us to an important conclusion; a computer cannot be the same as the human mind because the non-computational physics of the mind is not available for Turing equivalent machines and the nature of the algorithms is not compatible with the thinking process due to the halting problem.

The argument of the Godelian Case problems made great sense to AI supporters. Godel’s Theorem started a great debate between supporters of AI vs. those of the human mind.

Reviews of the Theorem on AI vs. Mind

Penrose claims that the human mind cannot be compared to artificial intelligence. Penrose bases his claim on Godel’s Incompleteness Theorem. By appealing to the results obtained by Godel (and Turing), mathematical thinking (and hence conscious thinking generally) is something that cannot be encapsulated within any purely computational model of thought. This is the part of Penrose’s argument that his critics have most frequently taken issue with. In addition, he states that there are certain classes of problems that do not have any algorithmic solutions (R. Penrose, 1994, p.29). In fact, Turing described this as the halting problem. Penrose gives an example of the completely deterministic, but non-computable “tiling problem” (R. Penrose, 1994, p.30-33).

Penrose asserted that some mathematical relations required long chains of reasoning before they could be perceived with certainty. But the object of a mathematical proof is to provide such chains of reasoning that each step is indeed something that can be perceived as being “obvious.” He concluded that the endpoint of such reasoning is something that must be accepted as being true, even though it may not, in itself, be at all obvious. One might imagine that it would be possible to list all possible “obvious” steps of reasoning once and for all, so that from that time on everything could be reduced to computation. But, what Godel’s argument shows is that this is not possible. There is no way to eliminate the need for new “obvious” understandings. Thus, mathematical understanding cannot be reduced to blind computation (R. Penrose, 1994, p.56).

Penrose claims that the results of Godel’s

theorem established that human understanding and insight cannot be reduced to any set of computational rules (R. Penrose, 1994, p.65). In the chapter entitled “The Godelian Case” of his book Shadows of the Mind, Penrose supported his idea with Turing’s Halting Problem and showed sound examples on non-computability. At the end of the chapter he answered possible technical objections to his idea based on Godel’s Theorem in details (R. Penrose, 1994, p.64-116).

Penrose believes that there is something beyond computation in the human mind. In Chapter 3 of Shadows of the Mind, he examines the thinking process and non-computability in mathematical thought carefully and uses formal representations (R. Penrose, 1994, p.127-209). Godel’s theorem states that in any sufficiently complex formal system there exists at least one statement that cannot be proven to be true or false. Penrose believes that this would limit the ability of any AI system in its reasoning. He argues that there will always be a statement that can be constructed which is unprovable by the AI system. However, Penrose believes that somehow the human mind can see the truth of such Godel statements directly (R. Penrose, 1989).

Along the same lines as Penrose, Lucas believes that Godel’s theorem seems to prove that the idea of “Mechanism” is false, that is, that minds cannot be seen in terms of machines. He claims that Godel’s theorem must apply to cybernetics, because the essence of being a machine is that it should be a concrete instantiation of a formal system. It follows that for any given machine which is consistent and capable of doing simple arithmetic, there is a formula which it will be incapable of producing as being true (i.e., the formula is improvable in the system but which we can see to be true). It follows that no machine can be a complete or adequate model of the mind, that minds are essentially different from machines. This does not mean that a machine cannot simulate any piece of the mind; it only says that there is no machine that can simulate every piece of the mind. Lucas says that there may be deeper objections. Godel’s theorem applies to deductive systems, and human beings are not confined to making only deductive inferences. Godel’s theorem applies only to consistent systems, and one may have doubts about how far it is permissible to assume that human beings are consistent. Godel’s theorem applies only to formal systems, and there is no a priori bound to human ingenuity which rules out the possibility of our contriving some replica of humanity which is not representable by a formal system (J. Lucas, 1970).

Chalmers examines the situation when a formal system F, which understands the consequences of Godel’s Theorem, is given. According to his claim, F may not be sound, so Godel’s theorem cannot be applied. He specifies that the crucial point of Godel’s argument is not to know “a formal system is sound”; but to determine “if we know that our system is sound.” It follows that we perhaps have a sound system, but we can not conclude that “we know that we have a sound system” (D. J. Chalmers, 1995).

Like Chalmers, McCullough claims that not only artificial intelligence, but also the human mind is tightly related with Godel’s theorem. Godel argument did not prove that human reasoning had to be noncomputable – it only proved that if human reasoning was computable, then it had to either be unsound, or it had to be inherently impossible for a human to know both what a human’s own reasoning powers were and to also know that they were sound. And adds, Penrose dismisses the possibility that a human knows its reasoning powers, but does not know that they are sound. In his paper, McCullough also examines the appliability of Godel’s theorem on non-computable systems and the human mind. According to him, both are possible, by the way he asserts that Penrose’s idea is wrong. Consequently, McCullough agrees with Penrose that human reasoning cannot be formalized in some sense, because humans do not understand their reasoning system well enough to formalize it. This limitation is not due to a lack of human intelligence, but is inherent in any reasoning system that is capable of reasoning about itself. (D. McCullough, 1995).

As a short conclusion, it seems that the discussion between AI vs. mind will last for a long time. But, considering the present situation, AI has a long way to the go in order to achieve the expected skills.

References

• Chalmers, D.J. (1995). “Minds, Machines, and Mathematics”. http://psyche.cs.monash.edu.au/v2/psyche-2-09-chalmers.html

• Godel, K. “On Formally Undecidable Propositions of Principia Mathematica”. http://www.ddc.net/ygg/etext/godel/

• Lucas, J.R. (1970). “Minds, Machines and Godel”. The Freedom of the Will, Oxford: Oxford University Press. http://users.ox.ac.uk/jrlucas/mmg.html

• Maudlin, T. (1995). “Between The Motion And The Act…”. http://psyche.cs.monash.edu.au/v2/psyche-2-02-maudlin.html

• McCarthy, J. (1995). “Awareness and Understanding in Computer Programs”. http://psyche.cs.monash.edu.au/v2/psyche-2-11-mccarthy.html

• McCullough, D. (1995). “Can Humans Escape Godel?”. http://psyche.cs.monash.edu.au/v2/psyche-2-04-mccullough.html

• Penrose, R. (1989). The Emperor’s New Mind. New York: Oxford University Press.

• Penrose, R. (1994). Shadows of the Mind. New York: Oxford University Press.

• Penrose, R. (1996). “Beyond the Doubting of a Shadow”. http://psyche.cs.monash.edu.au/v2/psyche-2-23-penrose.html

• Pysche (1995). An Interdisciplinary Search of Consciousness, Vol. 2, Symposium on Roger Penrose’s Shadows of the Mind. http://psyche.cs.monash.edu.au/psyche-index-v2.html