Imagine running your business by asking ChatGPT, “What would Steven King do?”
https://www.theatlantic.com/technology/archive/2023/08/books3-ai-meta-llama-pirated-books/675063/
REVEALED: THE AUTHORS WHOSE PIRATED BOOKS ARE POWERING GENERATIVE AI
Stephen King, Zadie Smith, and Michael Pollan are among thousands of writers whose copyrighted works are being used to train large language models.
Let the AI lawyers do it first…
https://borisbabic.com/research/AppealingAI.pdf
How AI Can Learn from the Law: Putting Humans in the Loop Only on Appeal
While the literature on putting a “human in the loop” in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an initial AI/ML judgment has been made. Based on an analogy with appellate processes in judiciary decision-making, we argue that this is, in many respects, a more efficient way to divide the labor between a human and a machine. Human reviewers can add more nuanced clinical, moral, or legal reasoning, and they can consider case-specific information that is not easily quantified and, as such, not available to the AI/ML at an initial stage. In doing so, the human can serve as a crucial error correction check on the AI/ML, while retaining much of the efficiency of AI/ML’s use in the decision-making process. In this paper we develop these widely applicable arguments while focusing primarily on examples from the use of AI/ML in medicine, including organ allocation, fertility care, and hospital readmission.
An overview of everything?
Navigating the Promise and Perils of Artificial Intelligence: A Comprehensive Analysis of Risks and Benefits
Artificial intelligence (AI) has become a popular topic in recent years due to the rapid advancements in technology. With the rise of AI, there are many potential benefits that it can bring, such as increased efficiency, improved decisionmaking, and personalized experiences. However, there are also numerous risks associated with AI, such as job displacement, loss of privacy, and even potential safety concerns. This research paper will explore the ethical, legal, and social implications of AI and also address the various risks and benefits of AI and provide insights on how to mitigate the risks while maximizing the benefits. Humans have continuously produced and refined many technologies in their pursuit of sophistication. The purpose of this practise is to make sure that they can develop goods that can make it easier for them to carry out numerous ways [1]. Since the beginning of time, humans have engaged in a variety of behaviours in an effort to increase their chances of succeeding in the many situations they have encountered. The industrial revolution, which began in the early 1760s, would bring the practise to an end. Several nations at the time believed it was feasible to produce various goods for the general public in order to satisfy the need for diverse goods brought on by expanding populations. Since then, thanks to the development and widespread application of artificial intelligence, humans have advanced considerably.
Could be useful in a dispute.
https://www.degruyter.com/document/isbn/9781503637047/html
A History of Fake Things on the Internet
As all aspects of our social and informational lives increasingly migrate online, the line between what is "real" and what is digitally fabricated grows ever thinner—and that fake content has undeniable real-world consequences. A History of Fake Things on the Internet takes the long view of how advances in technology brought us to the point where faked texts, images, and video content are nearly indistinguishable from what is authentic or true.
Computer scientist Walter J. Scheirer takes a deep dive into the origins of fake news, conspiracy theories, reports of the paranormal, and other deviations from reality that have become part of mainstream culture, from image manipulation in the nineteenth-century darkroom to the literary stylings of large language models like ChatGPT. Scheirer investigates the origins of Internet fakes, from early hoaxes that traversed the globe via Bulletin Board Systems (BBSs), USENET, and a new messaging technology called email, to today's hyperrealistic, AI-generated Deepfakes. An expert in machine learning and recognition, Scheirer breaks down the technical advances that made new developments in digital deception possible, and shares behind-the-screens details of early Internet-era pranks that have become touchstones of hacker lore. His story introduces us to the visionaries and mischief-makers who first deployed digital fakery and continue to influence how digital manipulation works—and doesn't—today: computer hackers, digital artists, media forensics specialists, and AI researchers. Ultimately, Scheirer argues that problems associated with fake content are not intrinsic properties of the content itself, but rather stem from human behavior, demonstrating our capacity for both creativity and destruction.
Removing the ‘artificial’ will help AI learn ethics?
How Machine-Learning Differs from Human Learning
… To instill values and morality into AI, the programmers might try to imitate the way children learn and develop notions of right and wrong. Children’s thinking seems to emerge in stages, sometimes undergoing remarkable mental leaps and growth spurts. It takes years for children to evolve adult-like thinking, emotional intelligence, theory of mind, and metacognition.
Most important, humans learn in the context of parents, teachers, peers, and others who adjust their helping behaviors to each child’s level and capacity (scaffolding). Should we even expect AI to think like a human or someday demonstrate empathy when they are not programmed to learn gradually, in stages, with human guidance? Can we ever expect AI to learn values, empathy, or develop morality, unless bots are carefully guided by others to think about “right vs. wrong” as human children do?
Furthermore, humans possess a unique natural curiosity. Children continually yearn to know more and strive to explore and understand the world and themselves. Therefore, it is not enough to simply program machines to learn. We must also endow AI with an innate curiosity—not just data hunger but something more similar to a human child’s biological drive to understand, organize, and adapt. Programmers are already working with deep-learning models to continually improve AI with human neurocognitive-inspired algorithms.(1)
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