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For centuries, creativity has been seen as uniquely human, the spark that sets us apart from machines. But as artificial intelligence grows more sophisticated, that distinction is becoming blurred. Generative AI tools like DALL-E, Midjourney, and ChatGPT are producing images, music, and stories that, at first glance, could pass for human-made. This has left artists, designers, and writers asking: Where does human imagination end and machine assistance begin?
The power of AI in creative work lies not in originality but in pattern recognition and remixing. Trained on massive datasets of existing art, literature, and sound, these systems generate new combinations at incredible speed. A designer once limited by software constraints can now produce dozens of mockups in seconds. Musicians can explore new harmonies and instruments without needing a full studio. Writers can brainstorm character ideas or dialogue with an AI “co-author” that never tires. Still, concerns remain. Critics argue that these tools lack intent; they do not create with meaning, purpose, or cultural awareness. A painting generated by AI may look stunning, but it does not carry the lived experience of the artist who wrestles with memory, emotion, or history. Others worry about intellectual property: if an AI has been trained on thousands of copyrighted works, who really owns the output it produces? Interestingly, many artists are choosing not to see AI as competition but as a collaborator. Instead of asking, “Will AI replace me?” they are asking, “How can I use AI to expand my voice?” The rise of “AI art directors” and “prompt engineers” shows that human creativity still anchors the process. The best results come when a person guides the algorithm with intuition, context, and a vision that machines cannot replicate. This reframes creativity itself. Perhaps the value of human artistry lies less in producing flawless outputs and more in shaping narratives, provoking emotions, and making meaning. AI might be the brush, but humans still choose the canvas and subject. As technology advances, creativity may become less about generating content and more about curation and direction. The future artist could be part visionary and part technologist, someone who understands both the language of feelings and the language of code. Far from ending human creativity, AI might push it into uncharted territory. Sources: McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, Authenticity, Authorship and Intention in Computer Generated Art. ACM Computing Surveys. Elgammal, A. (2021). AI and the Arts: Toward Computational Creativity. Rutgers University Art & AI Lab
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Few industries highlight the potential and risks of artificial intelligence as vividly as healthcare. Hospitals, research labs, and startups are turning to AI to detect diseases earlier, design new treatments, and reduce administrative burdens. The stakes could not be higher: in medicine, an algorithm’s success is measured in lives saved. AI’s most celebrated contributions are in diagnostics. Machine learning models trained on thousands of medical images can spot tumors, fractures, or eye diseases faster and sometimes more accurately than doctors. For patients in regions where specialists are scarce, this can be life-changing. Imagine a rural clinic where an AI tool analyzes chest X-rays in minutes, giving doctors critical guidance without waiting weeks for a specialist’s opinion.
Beyond detection, AI is transforming drug discovery. Traditionally, developing a new drug can take more than a decade and billions of dollars. AI models can scan through vast chemical libraries, predict which compounds might work, and drastically shorten early testing stages. During the COVID-19 pandemic, researchers used AI to speed up the search for vaccine candidates and treatment options, showing how critical the technology can be in emergencies. Yet the excitement comes with caution. Medicine requires trust and accountability, and algorithms are not immune to error. If an AI misdiagnoses a tumor or overlooks a dangerous side effect, who is responsible: the software company, the hospital, or the physician? There is also the issue of bias. If an AI system is trained mostly on data from one population group, its predictions may be less accurate for others, worsening healthcare inequalities. Doctors themselves stress that AI should be seen as a tool, not a replacement. A machine can flag a suspicious spot on a scan, but only a trained physician can put that finding in the context of a patient’s history, lifestyle, and emotional state. Medicine is as much about empathy and communication as it is about data. The next chapter of AI in healthcare will depend on how well humans and machines work together. Regulations will need to ensure safety and transparency. Hospitals will need to train staff not just to use these tools but to question them. Patients must feel confident that AI is being used to enhance, not replace, the care they receive. If done right, AI could help build a healthcare system that is faster, fairer, and more effective. But the heart of medicine will remain human: the doctor who listens, the nurse who comforts, the researcher who dares to imagine a cure. AI may be a powerful partner, but it is people who will ultimately shape how it heals. Sources: Esteva, A. et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature. Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books The growing mental health crisis has left millions of people struggling to find affordable, timely care. In response, AI-powered chatbots and apps have been introduced as potential solutions. They promise constant availability, low cost, and a nonjudgmental space to talk. For people without access to traditional therapy, these tools can seem like a lifeline. Many of these apps use structured approaches, like cognitive behavioral therapy (CBT), offering exercises to challenge negative thoughts or track moods. Others provide guided meditations, stress relief techniques, or supportive conversations. Their biggest appeal is accessibility: unlike therapy, which can be expensive and difficult to schedule, these apps are always available.
There are clear benefits. For someone dealing with stress or loneliness, an AI chatbot can provide quick comfort. Some apps even reduce stigma, making it easier for people to reach out without fear of judgment. In regions with limited access to therapists, AI tools may offer at least some level of support where none was available before. But there are significant risks too. Privacy is one of the biggest concerns, as many mental health apps collect sensitive personal information, sometimes without clear protections. This data could be misused or sold, undermining the very trust that mental health support requires. Another limitation is depth. AI can mimic empathy through carefully chosen words, but it lacks memory, lived experience, and emotional understanding. It cannot grasp the significance of a long silence or connect current struggles to a person’s history over time. For complex issues like trauma or suicidal ideation, this lack of depth can be dangerous. Some studies have found that chatbots occasionally give overly simplistic or even harmful responses when users describe severe distress. This highlights a serious limitation: AI is not a substitute for professional care, and treating it as such risks giving people a false sense of security. There is also the danger of over-reliance. If people always turn to AI for comfort, they may miss opportunities to practice vulnerability with others or seek human help when it is truly needed. Real relationships, while harder, are also what build lasting resilience. AI can still play a positive role if used carefully. It works best as a supplement, helping people build habits, track moods, or bridge gaps until professional care is available. It can widen access to basic tools, but it should not replace therapists or the human connections that sustain mental health. The promise of AI in mental health is real, but so are the risks. The challenge is learning where AI can help and where it must stop. Sources: Inkster, B., Sarda, S., & Subramanian, V. (2018). An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation. JMIR mHealth and uHealth. Vaidyam, A. N., et al. (2019). Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape. The Canadian Journal of Psychiatry. Artificial intelligence is often described as neutral, but every system is built on human choices. The data it is trained on, the way it is designed, and the goals it is built to serve all reflect the perspectives of its creators. AI does not just mirror the world; it mirrors the world through a particular lens.
Most large AI models are trained on text scraped from the internet, books, and other sources. This gives them a wide range of knowledge, but it also means they absorb stereotypes, biases, and cultural assumptions. If some groups or perspectives are underrepresented, AI will reflect those imbalances in its outputs. This is not a theoretical problem. Research on facial recognition technology revealed that commercial systems made significantly more errors on women and people with darker skin tones compared to lighter-skinned men. These disparities emerged because the datasets used to train the systems contained far fewer examples from underrepresented groups. The outcome was not just technical inaccuracy but social harm, as people were misidentified at higher rates simply because of how the data was collected. Language models show similar issues. They can generate biased or stereotypical responses about gender, race, or professions, not because the developers intended it, but because those patterns exist in the data they learned from. In other cases, biases are subtle and harder to detect. A hiring algorithm, for example, might seem neutral on the surface but still favor résumés that resemble those of historically advantaged groups. The values embedded in AI are not always obvious. They may show up only in certain contexts, and sometimes in ways that surprise even the developers. This makes accountability difficult. If a system produces a biased outcome, who is responsible? The company that built it? The engineers who selected the data? The users who applied it? Without clear answers, responsibility gets diffused, and those affected may have little recourse. Researchers have suggested ways to make these values more visible. One approach is the use of “model cards” and “datasheets for datasets.” These documents describe what data went into a model, highlight known limitations, and outline appropriate uses. While they do not solve every problem, they encourage developers to be transparent about the assumptions and risks associated with AI. AI is not value-free. It reflects the priorities and blind spots of its creators and the societies it is trained. Recognizing this is the first step toward responsible use. If we assume AI is neutral, we risk accepting inherited biases without question. If we acknowledge that it carries values, we can begin to ask harder questions: whose values are they, and whose voices are missing? Sources: Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research. Mitchell, M., et al. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency. Generative AI tools like ChatGPT are increasingly being used to polish the way we communicate. They can rephrase sentences, draft emails, or even suggest calming responses during tense arguments. For many people, this feels like a clear improvement. Messages sound more professional, more thoughtful, and less risky. Instead of sending a defensive text or clumsy apology, you can have AI reshape your words into something smoother.
The problem is that communication is not just about efficiency. The small imperfections we often want to erase, the hesitations, awkward silences, late replies, and clumsy apologies are not flaws. They are the marks of sincerity and effort. These frictions in our interactions create space for trust to grow. Without them, words may sound polished but lose the authenticity that gives them meaning. Psychologists studying relationships have shown that conflict is not necessarily harmful. John Gottman’s research found that healthy couples are not those who avoid arguments, but those who repair them. The cycle of rupture and repair is part of what makes bonds resilient. A shaky apology or delayed response may not look polished, but it shows that someone cared enough to try. AI may help smooth over these moments, but it also risks removing the effort that makes them valuable. AI-assisted communication may also affect how others perceive us. Studies show that people tend to view AI-written or AI-edited messages as less sincere than those written entirely by humans. Even if the words sound right, they may feel hollow because they lack a personal fingerprint. The effort behind the words matters as much as the words themselves. If people rely too much on AI to navigate communication, they may lose practice in the skills that come with it: patience, vulnerability, compromise, and humility. These are learned through trial and error, not by outsourcing the work. Without them, relationships risk becoming shallow, built more on appearances than on real connection. This does not mean we should stop using AI altogether. It can be helpful in certain contexts, like drafting professional emails or clarifying thoughts before sending them. But there is value in leaving some edges intact. A messy conversation, a late reply, or an imperfect apology may feel uncomfortable, but those are the very moments that build trust. Friction is not a weakness in communication—it is what makes it real. If we allow AI to smooth away every bump, we may end up with more polished words but weaker relationships. The challenge is not whether we use AI, but how. We need to find ways to let it support us without replacing the messy work that makes human connection meaningful. Source: Gottman, J., & Silver, N. (1999). The Seven Principles for Making Marriage Work. Crown. Navigating the Ethical Landscape of AI in Biomedical Technology: Insights from Professor Ameet Soni5/5/2023 We had the incredible opportunity to interview Professor Ameet Soni, an associate professor at Swarthmore College and the newly appointed Associate Dean of Faculty, shared his profound insights into the rapidly evolving field of Artificial Intelligence (AI), particularly its application in biomedical technology and the ethical considerations it entails. With his extensive background in Machine Learning, Biomedical Applications of AI, and Ethics of Technology, Professor Soni offers a unique perspective on the challenges and opportunities presented by AI in the modern world.
The Risks and Regulations of AI in Biomedicine: A significant focus of our discussion was on the risks associated with AI technologies, especially in sensitive areas like biomedicine. Professor Soni emphasized the importance of existing regulatory frameworks, such as those provided by the FDA, in overseeing medical technologies. He advocated for working within these frameworks while also enhancing them to address the unique challenges posed by AI, such as the issue of "black box" algorithms that are not easily explainable. Balancing Innovation and Safety: A key challenge in regulating AI, as Professor Soni pointed out, is balancing the need for innovation with the imperative of safety. He suggested a dynamic approach where regulation does not merely precede the deployment of technology but continues as an ongoing process of monitoring and adaptation. This approach ensures that AI systems remain safe and effective even as they evolve and are used in real-world scenarios. Ethics: At the Heart of AI Development One of the most notable aspects of our conversation was Professor Soni's emphasis on the central role of ethics in AI development. He stressed the need for responsible AI, where ethical considerations are embedded in the development process from the outset. In his machine learning course at Swarthmore, he encourages students to consider all possible stakeholders and maintain engagement with them throughout the lifecycle of a technological product, not just during the development phase. Embedding Ethics in Computer Science Education Professor Soni highlighted his efforts, along with those of other institutions, to integrate ethical discussions into the computer science curriculum. This approach, known as 'embedded ethics', aims to ensure that students encounter these critical questions throughout their education, rather than treating ethics as a separate or one-off topic. He shared examples from his courses where students are encouraged to apply theoretical knowledge to real-world problems, considering the ethical implications of their technological choices. Looking Ahead: The Future of AI and Ethics in Education In conclusion, Professor Soni expressed optimism about the progress being made in embedding ethics in computer science education. He noted the initiatives at leading institutions like Harvard, Stanford, and the University of Toronto in developing models for integrated ethics education. These efforts, he believes, are crucial for preparing a new generation of technologists who are not only skilled in AI but are also deeply aware of its societal impacts. Conclusion: Our conversation with Professor Ameet Soni provided invaluable insights into the complex world of AI, especially in the realm of biomedical technology. His emphasis on the importance of ethical considerations and the need for ongoing regulation and stakeholder engagement offers a framework for developing AI technologies that are not only innovative but also responsible and beneficial to society. Thank you so much for your time and thoughtful conversation Professor Soni. Navigating the Ethical Landscape of AI: Insights from an Interview with Ricardo Baeza-Yates4/30/2023 Recently, we had the opportunity to interview Ricardo Baeza-Yates, the Director of Research at Northeastern’s Institute for Experimental AI. His journey, beginning with a foundational Ph.D. in computer science from the University of Waterloo, has seen a significant evolution, leading him to his current focus on the pressing issues of AI ethics and responsible AI. As he shared, this transition was driven by his desire to delve into areas with more profound societal impacts, moving away from his initial focus on algorithms and web search.
During our conversation, Baeza-Yates provided an in-depth look into the data mining process crucial for AI training, highlighting the complexities and challenges involved. He detailed the intricate process of selecting appropriate web sources for data curation, emphasizing the difficulty in cleaning data to remove biases and hate speech. A particularly striking example he mentioned was the challenge of detecting hidden biases, which are often not overtly apparent in the data. This aspect underscores the nuanced nature of data curation in AI, where even with meticulous efforts, some biases can remain concealed. Baeza-Yates also brought to light the privacy risks associated with AI, especially in the context of large language models like ChatGPT that rely on vast amounts of web-sourced data. He pointed out the risk of inadvertently including hate speech or sensitive information in these models, despite rigorous efforts to filter such content. This discussion highlighted the ongoing struggle in balancing the need for comprehensive data with the imperative of maintaining privacy and ethical standards. The conversation then shifted to the regulatory challenges in AI and data mining. Baeza-Yates provided insightful examples to underscore these challenges. He suggested that AI models should respect the terms of usage of websites from which they mine data, but he acknowledged the practical challenges in enforcing such compliance. Additionally, he discussed the idea of watermarking AI-generated content to distinguish it from human-generated content. However, he expressed skepticism about its effectiveness, particularly in preventing intentional misuse. At the Northeastern Institute of Experimental AI, Baeza-Yates and his team are actively engaged in pioneering projects focused on responsible AI. He shared their collaboration with companies to establish AI ethics principles and governance structures, mentioning their work on bias audits in health insurance as a key example of their efforts to promote ethical AI practices. This work is crucial in guiding AI development towards more responsible and ethical practices. Looking towards the future of AI, Baeza-Yates expressed both enthusiasm and caution. He anticipates the development of more sophisticated language models capable of understanding semantics and verifying facts against knowledge bases. However, he also warned of the potential for AI to create convincing but fake news, images, and videos, posing significant challenges to our trust in digital media. This dual perspective underscores the complex landscape of AI development, where advancements bring both exciting opportunities and formidable challenges. In conclusion, Ricardo Baeza-Yates’ insights provided a comprehensive overview of the ethical challenges and considerations in AI development and usage. His focus on responsible AI, privacy concerns, and the need for effective regulation offers crucial guidance for the future of AI technology. This conversation on "Biased Brains" was not only enlightening but also a beacon for those navigating the complex terrain of AI ethics and data privacy. As we continue to explore the evolving world of AI, these discussions will remain pivotal in shaping a future where AI benefits society while minimizing potential harms. Thank you Mr. Baeza-Yates for your time and insight! ELIZA, developed in the 1960s, represents a foundational moment in the history of artificial intelligence, particularly in the field of natural language processing. Its ability to simulate conversation, especially with its limited technological resources, is a testament to early AI innovation. This blog post delves into the specific mechanics of how ELIZA functioned and the principles behind its conversational capabilities.
The Core Mechanism of ELIZA: ELIZA, developed in the 1960s, functioned primarily through a clever combination of pattern matching and substitution methodologies. At its heart was a script, the most famous being the DOCTOR script, which simulated a Rogerian psychotherapist. ELIZA began by scanning the user's input for specific keywords or phrases that were predefined in its programming. Once a keyword was identified, ELIZA employed a set of decomposition rules to break down the user's statement into manageable segments. These segments were then reassembled using reassembly rules to form a coherent response. This process often involved mirroring or paraphrasing the user's input, effectively reflecting the statement back to the user in a new form, typically as a question or a prompt for further discussion. The genius of ELIZA lay in its ability to maintain a flow in conversation using these scripted responses, despite having a minimal understanding of the content. It did not possess real comprehension or contextual awareness; its responses were solely based on the mechanical application of its programmed rules. This simple yet effective mechanism allowed ELIZA to create an illusion of empathy and understanding, engaging users in what seemed like a meaningful conversation, but was, in reality, a sophisticated pattern of linguistic mirroring. The Illusion of Intelligence and Empathy ELIZA's effectiveness in creating the illusion of understanding and empathy was surprising, especially considering its simple operating principle. It was this ability to engage users in a dialogue, making them feel heard and understood, that marked ELIZA as a significant milestone in AI development. However, it is essential to remember that ELIZA's conversations, though appearing empathetic, were limited to its programmed scripts and lacked true emotional understanding. Conclusion ELIZA's legacy in AI is not just about its technological ingenuity but also about the broader implications and ethical considerations it introduced in AI-human interactions. By exploring the inner workings of ELIZA, we gain valuable insights into the challenges and potentials of conversational AI. ELIZA demonstrated the possibilities of AI in mimicking human interaction, albeit within the confines of its programmed capabilities. As we continue to develop more sophisticated AI systems, ELIZA serves as a reminder of both the achievements and limitations of these technologies in emulating human conversation and emotions. Source: Weizenbaum, J. (1966). "ELIZA - A Computer Program For the Study of Natural Language Communication Between Man And Machine." Communications of the ACM. AlphaFold, developed by DeepMind, is an innovative artificial intelligence program designed to predict the three-dimensional structures of proteins from their amino acid sequences with remarkable accuracy. The process begins with the input of a protein sequence, a chain of amino acids, essential for determining protein function. AlphaFold uses Multiple Sequence Alignments (MSAs) to analyze evolutionary relationships between the target protein and its homologs across different organisms, providing insights into structural constraints based on evolutionary conservation. At the heart of AlphaFold is an advanced neural network with an attention mechanism, which processes the MSA and infers spatial relationships between the amino acids. This neural network predicts distances and angles between amino acid pairs, crucial for mapping their positions in three-dimensional space. Using these predictions, AlphaFold constructs a detailed three-dimensional model of the protein, iteratively refining it for accuracy. It also generates confidence scores for each part of the structure, indicating the reliability of its predictions. The accuracy and utility of AlphaFold have been extensively validated, including through comparisons with experimentally determined structures and its outstanding performance in the Critical Assessment of Structure Prediction (CASP) competitions. Notably, its success in CASP14 marked a significant milestone in protein structure prediction. AlphaFold's ability to accurately predict protein structures opens up new possibilities in biological research and medicine, offering deep insights into biological processes and aiding in the development of novel therapeutic strategies.
DeepMind's AlphaFold has catalyzed a new era in the field of computational biology with its groundbreaking capability to predict protein structures, offering transformative implications across various sectors. In biomedical research, AlphaFold dramatically accelerates the understanding of biological processes at the molecular level, pivotal for developing innovative treatments for diseases. Its ability to efficiently predict protein structures, which traditionally requires time-intensive experimental methods, speeds up scientific discoveries significantly. This rapid prediction is particularly crucial for understanding diseases like cancer and neurodegenerative disorders, as it aids in unraveling the mechanisms of protein malfunction, paving the way for new treatment strategies. In the realm of enzyme design, AlphaFold opens new possibilities in creating enzymes with specific properties for use in industries such as pharmaceuticals, biofuels, and food processing. The perspective piece, "AlphaFold – A Personal Perspective on the Impact of Machine Learning," written by Alan R. Fersht, a seasoned expert in protein science, provides a compelling insight into the profound significance of AlphaFold in the field. As a distinguished protein scientist, Fersht's narrative begins in the year 1968, a time when computational biology and AI were still in their infancy. He takes readers on a reflective journey through his career, marked by significant milestones like the rise of X-ray protein crystallography, DNA sequencing, and the integration of computational methods in protein analysis. What makes Fersht's perspective particularly engaging is his ability to draw parallels between his passion for board games like chess and the realm of AI. He delves into the historical challenges faced by AI in mastering complex strategy games and how technology gradually outpaced human expertise, all while adding a personal touch through his reference to Demis Hassabis, a chess prodigy turned AI expert. Fersht's narrative seamlessly transitions into the heart of his article, where he explores the Protein Folding Problem, encompassing the prediction of three-dimensional protein structures and the unraveling of folding pathways. He underscores the remarkable achievements of AlphaFold, emphasizing its capability to discern patterns in primary sequences, much like how chess engines analyze positions, and to construct precise protein structures. Fersht's appreciation for the power of Machine Learning in this field is evident, and he envisions its potential to revolutionize drug design and structural biology. The article culminates in Fersht's anticipation of a future where AlphaFold could catalyze enzyme design, automate drug discovery, and even venture into designing entirely novel protein folds. His respect and admiration for experimentalists and theoreticians shine through as he eagerly anticipates the synergy between human ingenuity and AI capabilities, much like how chess players integrate AI insights into their strategies. In conclusion, Alan R. Fersht's perspective is a compelling narrative that not only unveils the profound impact of Machine Learning in the world of computational biology but also offers a broader view of AI's evolution, the potential for human-machine collaboration, and the exciting frontiers awaiting exploration. His reflections serve as a testament to the ever-expanding horizons of scientific discovery, with technology serving as a guiding force propelling us toward new realms of knowledge and innovation. Sources: Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2 AlphaFold - A personal perspective on the impact of machine learning J. Mol. Biol. (2021), Article 167088, 10.1016/j.jmb.2021.167088 The Emergence of AI:
Alan Turing, a pioneer in the field of computer science, laid the groundwork for what we now call artificial intelligence (AI). In his groundbreaking paper, "Computing Machinery and Intelligence," Turing proposed the idea of machines that could think and reason like humans. This concept has evolved into the modern field of AI, which encompasses everything from simple automated responses to complex machine learning algorithms. The Turing Test: The Turing Test is a method for determining whether a machine can exhibit intelligent behavior that is indistinguishable from that of a human. The original test involves a human evaluator who communicates with an unseen interlocutor, which could be either a human or a machine (a computer program). The communication is typically text-based, to prevent the evaluator from determining the interlocutor's nature through their appearance or voice. How the Test Works:
The Evolution of AI: Beyond Turing's Imagination Since Turing's era, AI has transformed from theoretical models to practical applications. Today, AI is becoming integrated into various aspects of our lives, from virtual assistants to autonomous vehicles. This rapid development has surpassed even Turing's predictions, demonstrating the limitless potential of AI. AI in the Modern World: Opportunities and Challenges Modern AI systems can process and analyze vast amounts of data, learn from experiences, and make decisions with minimal human intervention. While these capabilities offer immense benefits, they also present unique challenges, such as ensuring fairness, transparency, and accountability in AI decisions. Source: Turing, A. M. “Computing Machinery and Intelligence.” Mind, vol. LIX, no. 236, Oct. 1950, pp. 433–460. |
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