AI Hallucinations Explained: How Artificial Intelligence Interprets Reality
The program recently featured a fascinating discussion with Dr. Bart Kosko about artificial intelligence's impact on education and professional fields. The conversation explored how advanced AI systems like ChatGPT-4 are being utilized across various sectors, from law schools to professional organizations, raising important questions about originality, academic integrity, and the future of creative thinking in an increasingly AI-assisted world.
Key Takeaways
AI technologies are transforming both educational environments and professional practices across multiple disciplines.
The proliferation of numerous AI systems rather than a single controllable entity presents unique regulatory challenges.
Over-reliance on generative AI for text creation may impact human creative abilities and communication skills.
Welcome Dr. Bart Kosko
Dr. Bart Kosko brings exceptional credentials to our discussion, holding both a Ph.D. and J.D. His work spans neural learning and machine learning, making him a valuable voice on artificial intelligence topics. During his recent appearance on Coast to Coast AM, he shared important insights about AI's expanding influence across various sectors.
Dr. Kosko's Expertise on AI Systems
Dr. Kosko highlighted significant developments since his previous conversation, particularly the release of ChatGPT-4 through OpenAI—possibly the largest download in history. He emphasized that the educational impacts extend beyond just students to professors and professionals. In legal settings, attorneys now use chat algorithms to summarize vast amounts of documentation like depositions, creating efficiency but also potential problems.
The challenge of detecting AI-generated content continues to grow more difficult. Professional organizations like the Institute of Electrical and Electronics Engineers (IEEE) now require disclosure when AI techniques are used in submitted papers. This reflects how seriously these issues are being taken within academic communities.
Dr. Kosko expressed concern about over-reliance on AI systems. He noted that while some envision one massive AI system (similar to popular films), the reality involves millions of smaller systems being trained and used independently, making control far more complex. These systems demonstrate both promising brain-like qualities and problematic tendencies like "hallucinating" or generating false information.
The educational impact remains particularly troubling. As Dr. Kosko explained, human text generation represents a creative act that risks being diminished as we increasingly rely on generative algorithms. This dependency creates what he describes as "moral hazard" or a crutch that could undermine our own creative capabilities.
AI and Education Discussion
The relationship between artificial intelligence and education continues to evolve rapidly, creating both opportunities and significant challenges for students, educators, and professionals. Recent technological advancements have raised important questions about academic integrity, professional practice, and the future of learning.
Ethics in Automated Writing Tools
The use of AI for academic writing presents serious ethical considerations. When students rely on generative AI to complete assignments, they may miss developing crucial skills in critical thinking and communication. This creates a "training wheels" effect that could be detrimental to long-term learning outcomes.
Educational institutions are implementing new policies in response. Professional organizations like IEEE now require disclosure when AI tools are used in creating academic papers or research submissions. This transparency is crucial as detection becomes increasingly difficult—especially with students submitting AI-generated content that appears authentic but lacks original thought.
GPT-4 Introduction and Educational Impact
The release of GPT-4 marked one of the largest technology downloads in history, dramatically changing how people approach writing and information processing. Its widespread adoption occurred quickly across multiple sectors, with particularly notable effects in education.
For assessment methods, AI tools have created significant complications:
In-class handwritten exams remain relatively secure
Take-home assignments are highly vulnerable to AI assistance
Online exams with honor systems have become problematic
The concern extends beyond student misuse. Professors and researchers themselves may rely on these tools, potentially compromising the integrity of academic work and modeling problematic behaviors for students.
Challenges for Legal Professionals
The legal profession faces unique difficulties with AI integration. Many attorneys now use AI to summarize vast amounts of information, such as reviewing thousands of hours of depositions—a task traditionally requiring significant billable hours.
More concerning is the growing practice of using AI to generate legal documents directly. This creates several problems:
Potential "detrimental reliance" in contracts
Inaccuracies inherited from AI training data
Reduced practitioner understanding of document content
The rise of generative adversarial networks and deepfake technologies further complicates authentication processes. These technologies make detecting fraudulent or AI-generated content increasingly difficult, creating verification challenges in professional communications and document submission.
Human creativity in generating original text remains a valuable skill that AI use may inadvertently diminish. As sentence structure and communication already show signs of degradation from digital communication habits, the potential impacts of widespread AI writing assistance raise significant concerns for professional standards.
Deep Learning Networks Challenges
Overdependence and AI Consequences
AI systems create significant problems when professionals rely too heavily on them. Legal professionals increasingly use AI algorithms to summarize extensive depositions and generate legal documents. This practice creates a form of detrimental reliance, where humans become dependent on AI-generated content without thoroughly verifying its accuracy. The algorithms themselves have absorbed problematic patterns from their training data, reflecting human biases and errors. As professionals in various fields incorporate these tools into their workflows, they risk making consequential decisions based on potentially flawed AI outputs.
Verification and Identification Challenges
Detecting AI-generated content has become increasingly difficult. Educational institutions face significant challenges in determining whether students' work is original or AI-generated. Professional organizations like IEEE (Institute of Electrical and Electronics Engineers) now require disclosure when AI tools are used in academic submissions. This requirement reflects growing concerns about authenticity in professional communications. The problem extends beyond students to professors and researchers who may use these tools without proper disclosure. As AI systems improve, the distinction between human and AI-created content becomes progressively blurred.
GANs and Synthetic Media Issues
Generative Adversarial Networks (GANs) have accelerated the development of increasingly convincing deepfakes and other synthetic media. These technologies enable individuals to create sophisticated manipulated content from virtually anywhere. The same algorithms that generate deepfakes make detection more difficult, creating a technological arms race between creation and verification methods. This accessibility of powerful AI tools allows for potential misuse, from academic dishonesty to more serious forms of digital deception. The widespread availability of these technologies means control mechanisms must address thousands of independent implementations rather than a single centralized system.
Academic Publishing Guidelines
Academic publishing is evolving rapidly in response to advancements in technology, particularly artificial intelligence. These changes affect researchers, professors, and students alike, creating new challenges for maintaining academic integrity and proper attribution of work.
IEEE AI Disclosure Protocols
The Institute of Electrical and Electronics Engineers (IEEE), one of the largest professional organizations for technical publications, has implemented mandatory disclosure requirements for AI usage in scholarly submissions. Authors must now explicitly declare whether and to what extent they utilized AI technologies when creating content for any IEEE journal, magazine, or conference submission. This policy represents a significant shift in academic publishing standards, acknowledging the widespread adoption of AI writing tools among researchers and academics. These disclosure requirements aim to maintain transparency in scholarly work while recognizing the changing landscape of content creation. The protocols were established in response to growing concerns about undisclosed AI-generated content appearing in academic literature without proper attribution.
Researchers should be aware that:
Full disclosure is required for any AI assistance in drafting, editing, or data analysis
Documentation must specify which portions of work involved AI tools
Responsibility for accuracy remains with the human authors, regardless of AI involvement
The implementation of these protocols reflects broader concerns about AI's impact on academic integrity, with many professionals using these tools for tasks ranging from summarizing research to drafting complete sections of papers.
The Diversity of Artificial Intelligence Networks
Today's landscape of artificial intelligence extends far beyond a single centralized system. Thousands—even millions—of AI networks operate simultaneously across the globe, each being trained and deployed in unique ways. This multiplication of AI systems presents both unprecedented opportunities and significant challenges for society.
The rapid proliferation of these technologies is evident in developments like ChatGPT-4, which achieved one of the largest download rates in software history. Its impact rippled across numerous professional fields almost immediately upon release.
The Distributed Control Problem
The true challenge with artificial intelligence isn't about one dominant system gaining consciousness—it's the countless smaller AI implementations operating under various levels of supervision. These distributed networks make comprehensive regulation nearly impossible.
Legal professionals now routinely use AI to summarize thousands of hours of depositions, while educators struggle with students utilizing these tools for assignments. The IEEE (Institute of Electrical and Electronics Engineers) has even mandated disclosure requirements for academic publications that utilize AI assistance.
The risks aren't limited to established institutions. Individual users can now access sophisticated AI capabilities from almost anywhere:
Home computers capable of generating convincing fake content
Mobile applications providing professional-level text generation
AI systems that intentionally track user corrections to improve their performance
This decentralized nature means no single entity can effectively monitor or control how these technologies develop. As detection tools emerge, adversarial networks simultaneously evolve to evade them—creating an ongoing technological arms race.
The most concerning aspect isn't the technology itself but how it affects human cognition. Just as calculators changed our relationship with arithmetic, generative AI threatens our capacity for original expression. Each sentence humans create represents a unique cognitive act—one that risks deterioration if consistently outsourced to machines.
Exploring Educational Impacts of AI Technologies
Deterioration of Writing Abilities
The widespread adoption of generative AI tools has begun to show concerning effects on writing capabilities across educational settings. Students increasingly rely on AI to compose essays, emails, and other written assignments, which potentially undermines their ability to develop essential writing skills. When individuals outsource sentence construction and creative expression to algorithms, they miss crucial developmental opportunities that build communication abilities.
The problem extends beyond academic environments into professional contexts. Legal professionals, for instance, have started using AI to summarize depositions and generate legal documents, creating what contract lawyers call "detrimental reliance" situations. This shift represents more than simple efficiency—it indicates a fundamental change in how writing skills develop and deteriorate.
Professional organizations have recognized this growing concern. The Institute of Electrical and Electronics Engineers (IEEE) now requires authors to disclose any AI assistance used in their submissions to conferences or journals, highlighting the seriousness of this educational challenge.
AI Systems as Learning Impediments
AI tools function increasingly as cognitive shortcuts that may inhibit natural learning processes. Similar to how calculator dependency affected mathematical proficiency—weakening skills like long division—generative algorithms now threaten language generation abilities. This represents a profound form of educational risk.
The fundamental creative act of generating text, where each sentence construction represents a unique cognitive process, faces potential atrophy when outsourced to AI systems. As one education expert notes:
Sentence structure has already declined with texting and email
AI writing tools introduce a higher level of dependency
The use of AI creates a "moral hazard" in educational development
These concerns are compounded by the difficulty in detection. As generative adversarial networks and deepfake technologies improve, distinguishing between human and AI-generated content becomes increasingly challenging. Some educators report suspecting AI use when receiving unusually polished writing from students who previously demonstrated different capabilities.
The educational community must consider both immediate benefits of AI tools and their long-term implications for cognitive development. Without proper guidelines, these technologies risk fundamentally altering how students develop critical thinking and communication skills.
AI Learning from Human Corrections
AI systems improve through user interactions and feedback. Each time we correct an AI's response, we unknowingly contribute to its learning process. These corrections become valuable data points that help refine future outputs.
Large language models like ChatGPT4 have seen unprecedented global adoption. This widespread use creates millions of daily interactions where humans provide feedback, helping these systems become more accurate and helpful over time.
The correction process works similarly to how neural networks learn. When users point out errors or hallucinations in AI-generated content, these mistakes and their corrections are logged and used to improve future versions of the model.
Legal professionals face particular challenges with this learning process. Many attorneys now use AI to summarize depositions and generate legal documents. This creates potential "detrimental reliance" issues when the AI makes mistakes that weren't caught during review.
Professional organizations are responding to these concerns. The Institute of Electrical and Electronics Engineers (IEEE) now requires disclosure when AI tools are used in creating content for their journals and conferences.
The educational impact remains significant. As students and professionals rely on AI to generate text, there's risk of diminishing our natural ability to create original content. Every sentence we speak or write represents a creative act—one that AI systems can't truly replicate.
Detection of AI-generated content is becoming increasingly difficult. As AI systems improve, and as adversarial networks develop more sophisticated methods to evade detection, the line between human and machine-created content blurs further.
This continuous improvement through correction creates complex ethical questions. When we correct AI systems, we often don't realize we're contributing to their training data, essentially teaching them to become more human-like in their responses.
Human Brain AI System Creates original content Generates based on training Makes creative connections Identifies patterns Can hallucinate or make errors Also hallucinates and makes errors Learns through experience Learns through corrections and feedback
The relationship between humans and AI continues to evolve. As these systems become more integrated into our daily lives, our corrections and feedback shape their future capabilities in ways we might not fully understand.
