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Forbes
a day ago
- Science
- Forbes
Intelligence Illusion: What Apple's AI Study Reveals About Reasoning
Concept of the diversity of talents and know-how, with profiles of male and female characters ... More associated with different brains. The gleaming veneer of artificial intelligence has captivated the world, with large language models producing eloquent responses that often seem indistinguishable from human thought. Yet beneath this polished surface lies a troubling reality that Apple's latest research has brought into sharp focus: eloquence is not intelligence, and imitation is not understanding. Apple's new study, titled "The Illusion of Thinking," has sent shockwaves through the AI community by demonstrating that even the most sophisticated reasoning models fundamentally lack genuine cognitive abilities. This revelation validates what prominent researchers like Meta's Chief AI Scientist Yann LeCun have been arguing for years—that current AI systems are sophisticated pattern-matching machines rather than thinking entities. The Apple research team's findings are both methodical and damning. By creating controlled puzzle environments that could precisely manipulate complexity while maintaining logical consistency, they revealed three distinct performance regimes in Large Reasoning Models . In low-complexity tasks, standard models actually outperformed their supposedly superior reasoning counterparts. Medium-complexity problems showed marginal benefits from additional "thinking" processes. But most tellingly, both model types experienced complete collapse when faced with high-complexity tasks. What makes these findings particularly striking is the counter-intuitive scaling behavior the researchers observed. Rather than improving with increased complexity as genuine intelligence would, these models showed a peculiar pattern: their reasoning effort would increase up to a certain point, then decline dramatically despite having adequate computational resources. This suggests that the models weren't actually reasoning at all— they were following learned patterns that broke down when confronted with novel challenges. The study exposed fundamental limitations in exact computation, revealing that these systems fail to use explicit algorithms and reason inconsistently across similar puzzles. When the veneer of sophisticated language is stripped away, what remains is a sophisticated but ultimately hollow mimicry of thought. These findings align perfectly with warnings that Yann LeCun and other leading AI researchers have been voicing for years. LeCun has consistently argued that current LLMs will be largely obsolete within five years, not because they'll be replaced by better versions of the same technology, but because they represent a fundamentally flawed approach to artificial intelligence. The core issue isn't technical prowess — it's conceptual. These systems don't understand; they pattern-match. They don't reason; they interpolate from training data. They don't think; they generate statistically probable responses based on massive datasets. The sophistication of their output masks the absence of genuine comprehension, creating what researchers now recognize as an elaborate illusion of intelligence. This disconnect between appearance and reality has profound implications for how we evaluate and deploy AI systems. When we mistake fluency for understanding, we risk making critical decisions based on fundamentally flawed reasoning processes. The danger isn't just technological—it's epistemological. Perhaps most unsettling is how closely this AI limitation mirrors a persistent human cognitive bias. Just as we've been deceived by AI's articulate responses, we consistently overvalue human confidence and extroversion, often mistaking verbal facility for intellectual depth. The overconfidence bias represents one of the most pervasive flaws in human judgment, where individuals' subjective confidence in their abilities far exceeds their objective accuracy. This bias becomes particularly pronounced in social and professional settings, where confident, extroverted individuals often command disproportionate attention and credibility. Research consistently shows that we tend to equate confidence with competence, volume with value, and articulateness with intelligence. The extroverted individual who speaks first and most frequently in meetings often shapes group decisions, regardless of the quality of their ideas. The confident presenter who delivers polished but superficial analysis frequently receives more positive evaluation than the thoughtful introvert who offers deeper insights with less theatrical flair. This psychological tendency creates a dangerous feedback loop. People with low ability often overestimate their competence (the Dunning-Kruger effect), while those with genuine expertise may express appropriate uncertainty about complex issues. The result is a systematic inversion of credibility, where those who know the least speak with the greatest confidence, while those who understand the most communicate with appropriate nuance and qualification. The parallel between AI's eloquent emptiness and our bias toward confident communication reveals something profound about the nature of intelligence itself. Both phenomena demonstrate how easily we conflate the appearance of understanding with its substance. Both show how sophisticated communication can mask fundamental limitations in reasoning and comprehension. Consider the implications for organizational decision-making, educational assessment, and social dynamics. If we consistently overvalue confident presentation over careful analysis—whether from AI systems or human colleagues—we systematically degrade the quality of our collective reasoning. We create environments where performance theater takes precedence over genuine problem-solving. The Apple study's revelation that AI reasoning models fail when faced with true complexity mirrors how overconfident individuals often struggle with genuinely challenging problems while maintaining their persuasive veneer. Both represent sophisticated forms of intellectual imposture that can persist precisely because they're so convincing on the surface. Understanding these limitations—both artificial and human—opens the door to more authentic evaluation of intelligence and reasoning. True intelligence isn't characterized by unwavering confidence or eloquent presentation. Instead, it manifests in several key ways: Genuine intelligence embraces uncertainty when dealing with complex problems. It acknowledges limitations rather than concealing them. It demonstrates consistent reasoning across different contexts rather than breaking down when patterns become unfamiliar. Most importantly, it shows genuine understanding through the ability to adapt principles to novel situations. In human contexts, this means looking beyond charismatic presentation to evaluate the underlying quality of reasoning. It means creating space for thoughtful, measured responses rather than rewarding only quick, confident answers. It means recognizing that the most profound insights often come wrapped in appropriate humility rather than absolute certainty. For AI systems, it means developing more rigorous evaluation frameworks that test genuine understanding rather than pattern matching. It means acknowledging current limitations rather than anthropomorphizing sophisticated text generation. It means building systems that can genuinely reason rather than simply appearing to do so. The convergence of Apple's AI findings with psychological research on human biases offers valuable guidance for navigating our increasingly complex world. Whether evaluating AI systems or human colleagues, we must learn to distinguish between performance and competence, between eloquence and understanding. This requires cultivating intellectual humility – the recognition that genuine intelligence often comes with appropriate uncertainty, that the most confident voices aren't necessarily the most credible, and that true understanding can be distinguished from sophisticated mimicry through careful observation and testing. To distinguish intelligence from imitation in an AI-infused environment we need to invest in hybrid intelligence, which arises from the complementarity of natural and artificial intelligences – anchored in the strength and limitations of both.
Entrepreneur
03-06-2025
- Business
- Entrepreneur
Researchers develop more efficient language model control method
A team of researchers has successfully developed a more efficient method to control the outputs of large language models (LLMs), addressing one of the key challenges in artificial intelligence text... This story originally appeared on Calendar A team of researchers has successfully developed a more efficient method to control the outputs of large language models (LLMs), addressing one of the key challenges in artificial intelligence text generation. The breakthrough enables more effective guidance of LLMs to produce text that adheres to specific structures while maintaining accuracy. The new approach focuses on controlling language model outputs to adhere to predetermined structures, such as programming languages, while eliminating errors that commonly plague AI-generated content. This advancement represents a significant step forward in making AI language tools more reliable for specialized applications. Improving Structural Adherence in AI Text Generation The research addresses a fundamental issue with large language models: their tendency to generate text that deviates from required formats or contains errors when tasked with producing structured content. By implementing more effective control mechanisms, the researchers have developed a system that maintains structural integrity throughout the generation process. For programming languages specifically, this advancement could reduce the frequency of syntax errors and logical flaws that often appear in code generated by AI systems. The method ensures that the language model adheres to the programming language's rules while generating functional code. Technical Approach and Implementation While specific technical details of the method were not fully outlined, the approach appears to involve guiding the language model's generation process more precisely than previous methods. Rather than simply prompting the model and hoping for correctly structured output, the new system actively steers the generation process to maintain compliance with predefined rules. This control mechanism works by: Monitoring the model's outputs in real-time Applying constraints that keep text generation within acceptable parameters Correcting potential errors before they appear in the final output Practical Applications The improved control method opens up new possibilities for utilizing large language models in fields that require strict adherence to specific formats. Some potential applications include: Software Development: Generating error-free code that adheres to the syntax rules of specific programming languages can make AI coding assistants more reliable for developers. Data Formatting: Creating structured data outputs like JSON, XML, or CSV files with perfect adherence to format specifications. Technical Documentation: Producing documentation that follows industry-standard formats without introducing structural errors. Scientific Research: Generating properly formatted research papers or reports that adhere to publication guidelines. Future Research Directions This advancement likely represents an early step in a broader effort to make large language models more controllable and reliable. Future research may expand on this work by: Developing more sophisticated control mechanisms that can handle increasingly complex structural requirements. Reducing the computational overhead associated with implementing these controls, making them more accessible for wider use. Extending the approach to handle multiple types of structured outputs simultaneously. The research highlights the growing focus on not just making AI language models more powerful, but also more precise and controllable. As these systems become increasingly integrated into professional workflows, the ability to guarantee structured, error-free outputs becomes critical. For industries that rely on structured data and formatted text, this development may signal a shift toward more practical and reliable AI assistance tools that can consistently follow rules while maintaining the creative and analytical capabilities that make large language models valuable. The post Researchers develop more efficient language model control method appeared first on Calendar.
Forbes
09-05-2025
- Entertainment
- Forbes
Romance Stories Reaching New Heartfelt Heights Via Generative AI
In today's column, I'll be exploring the heartfelt topic of writing romance stories, which can be accomplished readily via the use of modern-era generative AI and large language models (LLMs). Turns out there are serious considerations involved, touching upon numerous societal and cultural norms, business and economic facets, mindfulness factors, and the like. You could say that romance or at least romantic tales are in the air and that AI is now contributing to stirring the hearts and minds of many. Let's talk about it. This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). I'm guessing that at some point you have likely read a romantic tale. This would almost seem a certainty. From a young age, we experience romantic stories that garner our imagination and enthrall us with emotional roller coasters entailing love both gained and lost. Adults generally relish romantic renditions. Some see it as a form of escapism. You can allow your imagination to roam and dreamily think about the joys of romance. What exactly constitutes a romance novel or story? Consider the definition stipulated by the Romance Writers of America on their website under the title of 'About The Romance Genre' stating these vital underpinnings (excerpts): Notice that the expressed requirement is that a romance novel contains an emotionally satisfying and optimistic ending. Do you agree with that stipulation? You might readily quibble with it. Who reads romance novels? According to 'Romance Novel Sales Statistics' by Dimitrije Curcic, Words Rated, October 9, 2022, these are some key stats about romance novel sales and their readers (excerpts): Romance novels amount to well over a billion dollars in sales annually. That's a good sign. Give credit where credit is due. The sales keep rising too. The bulk of readership tends to be women. Of those, half possess a college degree. A related false assumption is that men don't read romance tales. The statistics cited indicate that nearly one-fifth of the readers are men. Some researchers view literary fiction and romance tales as bearing on our cognitive capacities and can be psychologically impactful. In a research study entitled 'Reading Literary Fiction Is Associated With a More Complex Worldview' by Nicholas Buttrick, Erin Westgate, and Shigehiro Oishi, Personality and Social Psychology Bulletin, 2023, these key points were identified (excerpts): The gist is that reading literary fiction can be useful in exercising our minds and expanding our worldviews. Out of this can come the upside of being more empathetic to our fellow humans, see my coverage on empathy intertwined with AI at the link here. Generative AI opens the door to devising romance novels and short stories that will go in whatever direction your heart desires. You don't need to wait for some publisher to decide what romance stories are going to sell. Skip the middleman and devise your own romantic tale. The tale doesn't have to conform to the prevailing norms. It doesn't need to pass the dollar-making tests of publishers. This is romance as you see fit. If you want to go beyond contemporary tropes, you are welcome to do so. The sky is the limit. Here are eight vital ways to use generative AI when it comes to romance stories: I opted to ask ChatGPT to craft a relatively short romantic story for me. Here we go. 'Sure, here's a short romance story featuring Lauren and Eric.' 'Romance Story Entitled: Love Beyond the Desk' 'Lauren had always thrived in her bustling office, her vivacious spirit lighting up every room she entered. Her colleagues admired her not just for her intelligence and beauty, but for the way she made even the most mundane tasks seem exciting. One day, a new team member joined the company—Eric, a man whose striking good looks and impressive physique were matched only by his keen intellect.' 'Their first interaction was nothing out of the ordinary. Lauren and Eric exchanged polite smiles and brief pleasantries as they worked on a joint project. Lauren found herself intrigued by Eric's confidence and quick wit, while Eric admired Lauren's ability to navigate complex problems with grace and enthusiasm.' 'Weeks passed, and their professional interactions began to shift. They found themselves staying late at the office, working side by side. Their conversations grew longer and more personal, often drifting away from work topics. Lauren discovered that beneath Eric's imposing exterior was a thoughtful and caring individual who shared her passion for adventure. Eric, in turn, was captivated by Lauren's zest for life and her ability to make every challenge seem like an exciting opportunity.' 'One evening, after a particularly grueling project deadline, Eric invited Lauren for a coffee to unwind. Their conversation flowed effortlessly, and as they laughed over shared stories and dreams, the air between them crackled with a new, undeniable tension. That night marked the beginning of something neither had expected.' 'Their romance grew steadily, with each shared moment deepening their bond. They explored new places together, enjoyed quiet evenings at home, and supported each other through professional and personal challenges. Their love was tested when a major project presented unforeseen difficulties, causing stress and strain. However, facing these obstacles together only strengthened their connection. They learned to rely on each other, blending their strengths and compensating for each other's weaknesses.' 'One rainy afternoon, as they strolled through a park near their office, Eric stopped and turned to Lauren. He held out a small box and, with a tender smile, asked her to marry him. Lauren's eyes welled up with tears of joy as she said yes, her heart swelling with happiness.' 'Lauren and Eric's commitment to each other was evident in every moment of their life together. They built a life full of laughter, adventure, and unwavering support. In the end, Lauren and Eric's love story is a testament to the magic that happens when two people find each other and choose to face the world together, hand in hand. They lived happily ever after, their love a beacon that continued to shine brightly through every chapter of their lives.' I thought that was a reasonably compelling romance story. Generating such a story doesn't have to be a one-and-done activity. A notable strength of generative AI is the interactive conversational capability. You can keep telling the AI to do this or that, making these changes and those changes, doing so until you feel like the romance tale is where it needs to be. By and large, most of the major generative AI apps will produce a PG-rated romance story by default. This is due to the AI maker having added filters and made various modifications to try and prevent the AI from generating untoward content, see my discussion at the link here. Please be cautious in entering personal information as seedlings for your romance stories. When you use most of the popular generative AI apps, you agree to the licensing agreement that usually stipulates that the AI maker can inspect your prompts. In addition, they can reuse that data for further data training of their AI. All in all, the point is that your personal privacy and confidentiality are not guaranteed, see my discussion at the link here. A few final thoughts for now on this heady topic. Famed novelist Janet Evanovich said this about romance novels: 'Romance novels are birthday cake and life is often peanut butter and jelly. I think everyone should have lots of delicious romance novels lying around for those times when the peanut butter of life gets stuck to the roof of your mouth.' No more worries about having romance stories laying around. You can produce them on-the-fly, wherever you are and at any time, just be logging into your preferred generative AI.
