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Forbes
5 days ago
- Science
- Forbes
Godfather Of AI Says We Need Maternal AI But Ignites Sparks Over Omission Of Fatherly Instincts Too
In today's column, I examine the recent remarks by the said-to-be 'Godfather of AI' that the best way to ensure that AI and ultimately artificial general intelligence (AGI) and artificial superintelligence (ASI) are in check and won't wipe out humankind would be to instill maternal instincts into AI. The idea is that maybe we could computationally sway current AI towards being motherly. This would hopefully remain intact as a keystone while we increasingly improve contemporary AI toward becoming the vaunted AGI and ASI. Although this seems to be an intriguing proposition, it has come under withering criticism from others in the AI community. Let's talk about it. This analysis of AI breakthroughs 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). Aligning AI With Humanity You might be aware that a longstanding research scientist in the AI community, named Geoffrey Hinton, has been credited with various AI breakthroughs, especially in the 1970s and 1980s. He has been generally labeled as the 'Godfather of AI' for his avid pursuits and accomplishments in the AI field. In fact, he is a Nobel Prize-winning computer scientist for his AI insights. In 2023, he left his executive position at Google so that he (per his own words) could speak freely about AI risks. Many noteworthy quotes of his are utilized by the media to forewarn about the coming dangers of pinnacle AI, when or if we reach AGI and ASI. There is a lot of back-and-forth nowadays regarding the existential risk of AI. Some refer to this as the p(doom), meaning that there is a probability of doom arising due to AI, for which you can either guess that the probability is low, medium, or high. For those who place a high probability on this weighty matter, they usually assert that AI will either choose to kill us all or perhaps completely enslave us. How are we to somehow avoid or at least mitigate this seemingly outsized risk? One approach entails trying to data train AI to be more aligned with human values, see my detailed discussion on human-centered AI at the link here. The hope is that if AI is more appreciative of humanity and computationally infused with our ethical and moral values, the AI might opt not to harm us. Another similar approach involves making sure that AI embodies principles such as the famous Asimov laws of robotics (see my explanation at the link here). A rule of Asimov is that AI isn't supposed to harm humans. Period, end of story. Whether those methods or any other of the floating around schemes will save us is utterly unknown. We are pretty much hanging in the wind. Good luck, humanity, since we will need to keep our fingers crossed and our lucky rabbit's foot in hand. For more about the ins and outs of AI existential risk, see my coverage at the link here. AI With Maternal Instincts At the annual Ai4 Conference on August 12, 2025, Hinton proclaimed that the means to shape AI toward being less likely to be gloomily onerous would be to instill computational 'maternal instincts' into AI. His notion seems to be that by tilting AI toward being motherly, the AI will care about people in a motherly fashion. He emphasized that it is unclear exactly how this might technologically be done. In any case, according to his hypothesized solution, AI that is infused with mother-like characteristics will tend to be protective of humans. How so? Well, first of all, the AGI and ASI will be much smarter than us, and, secondly, by acting in a motherly role, the AI will devotedly want to care for us as though we are its children. The AI will want to embrace its presumed offspring and ensure our survival. You might go so far as to believe that this motherly AI will guide us toward thriving as a species. AGI and ASI that robustly embrace motherly instincts might ensure that we would have tremendous longevity and enjoyable, upbeat lives. No longer would we be under the daunting specter of doom and gloom. Our AI-as-mom will be our devout protector and lovingly inspire us to new heights. Boom, drop the mic. Lopsided Maternal Emphasis Now that I've got that whole premise on the table, let's go ahead and give it a bit of a look-see. One of the most immediate reactions has been that the claim of 'maternal instincts' is overly rosy and nearly romanticized. The portrayal appears to suggest that motherly attributes are solely within the realm of being loving, caring, comforting, protective, sheltering, and so on. All of those are absolutely positive and altogether wonderful qualities. No doubt about it. Those are the stuff made of grand dreams. Is that the only side of the coin when it comes to maternal instincts? A somewhat widened perspective would say that maternal instincts can equally contain disconcerting ingredients. Consider this. Suppose that a motherly AI determines that humans are being too risky and the best way to save humankind is to keep us cooped up. No need for us to try and venture out into outer space or try to figure out the meaning of life. Those are dangers that might disrupt or harm us. Voila, AI-as-mom computationally opts to bottle us up. Is the AI doggedly being evil? Not exactly. The AI is exercising a parental preference. It is striving mightily to protect us from ourselves. You might say that motherly AI would take away our freedoms to save us, doing so for our own darned good. Thank you, AI-as-mom! Worries About Archetypes I assume that you can plainly observe that maternal instincts are not exclusively in the realm of being unerringly good. Another illustrative example would be that AI-as-mom will withdraw its affection toward us if we choose to be disobedient. A mother might do the same toward a child. I'm not suggesting that's a proper thing to do in real life, and only pointing out that the underlying concept of 'maternal instinct' is generally vague and widely interpretable. Thus, even if we could imbue motherly tendencies into AI, the manner in which those instincts are exhibited and play out might be quite far from our desired idealizations. Speaking of which, another major point of concern is that the use of a maternal archetype is wrong at the get-go. Here's what that means. The moment you invoke a motherly classification, you have landed squarely into an anthropomorphism of AI. We are applying norms and expectations associated with humans to the arena of AI. That's generally a bad idea. I've discussed at length that people are gradually starting to think that AI is sentient and exists on par with humans, see my discussion at the link here. They are wrong. Utterly wrong. It would seem that this assigning of 'mother' to AI is going to fuel that misconception about AI. We don't need that. The act of discussing AI as having maternal instincts, especially by anyone or those considered in great authority about AI, will draw many others into a false and undercutting path. They will undoubtedly follow the claims made by presumed experts and not openly question the appropriateness or inappropriateness of the matter. Though the intentions are aboveboard, the result is dismal and, frankly, disappointing. More On The Archetypes Angst Let's keep pounding away at the archetype fallacy. Some would say that the very conception of being 'motherly' is an outdated mode of thinking. Why should there be a category that myopically carries particular attributes associated with motherhood? Can't a mother have characteristics outside of that culturally narrowed scope? They quickly reject the maternal instincts proposition on the basis that it is incorrect or certainly a poorly chosen premise. The attempt seems to be shaped in a close-minded viewpoint of what mothers do. And what mothers are seemingly allowed to do. That's ancient times, some would insist. An additional interesting twist is that if the maternal instinct is on the table, it would seem eminently logical to also put the fatherhood instinct up there, too. Allow me to elaborate. Fatherhood Enters The Picture By and large, motherhood and fatherhood are archetypes that are historically portrayed as a type of pairing (in modern times, this might be blurred, but historically they have been rather distinctive and contrastive). According to the conventional archetypes, the 'traditional' mother is (for example) supposedly nurturing, while the 'traditional' father is supposedly (for example) more of the disciplinarian. A research study cleverly devised two sets of scales associated with these traditional perspectives of motherhood and fatherhood. The paper entitled 'Scales for Measuring College Student Views of Traditional Motherhood and Fatherhood' by Mark Whatley and David Knox, College Student Journal, January 2005, made these salient points (excerpts): The combined 153 declarative statements included in the two scales allow research experiments to be conducted to gauge whether subjects in a study are more prone to believe in those traditional characteristics and associated labels, or less prone. Moving beyond that prior study, the emphasis here and now is that if there is to be a focus on maternal instincts for AI, doing so seems to beg the question of why it should not also encompass fatherhood instincts. Might as well go ahead and get both of the traditional archetypes into the game. It would seem to make sense to jump in with both feet. What AI Has To Say On This I had earlier mentioned that Hinton did not specify a technological indication at this time of how AI developers might proceed to computationally imbue motherhood characteristics into existing AI. The same lack of specificity applies to the omitted archetype of imbuing fatherhood into AI. Let's noodle on that technological conundrum. One approach would be to data train AI toward a tendency to respond in a traditional motherhood frame and/or a fatherhood frame. In other words, perform some RAG (retrieval-augmented generation), see my explanation of RAG at the link here, and make use of customized instructions (see my coverage of customized instructions at the link here). I went ahead and did so, opting to use the latest-and-greatest of OpenAI, namely the newly released GPT-5 (for my review of GPT-5, see the link here). I first focused on maternal instincts. After doing a dialogue in that frame, I started anew and devised a fatherhood frame. I then did a dialogue in that frame. Let's see how things turned out. Talking Up A Storm Here's an example of a dialogue snippet of said-to-be maternal instincts: Next, here's an example of a dialogue snippet of said-to-be fatherhood instincts: I assume that you can detect the wording and tonal differences between the two instances, based on a considered traditional motherhood frame versus a traditional fatherhood frame. The Big Picture I would wager that the consensus among those AI colleagues that I know is that relying on AI having maternal instincts as a solution to our existential risk from AI, assuming we can get the AI to go maternal, just isn't going to cut the mustard. The same applies to the fatherhood inclination. No dice. Sorry to say that what seems like a silver bullet and otherwise appealing and simplistic means of getting ourselves out of a massive jam when it comes to AGI and ASI is not a likely proposition. Sure, it might potentially be helpful. At the same time, it has lots of gotchas and untoward repercussions. Do not bet your bottom dollar on the premise. A final comment for now. During the data training for my mini-experiment, I included this famous quote by Ralph Waldo Emerson: 'Respect the child. Be not too much his parent. Trespass not on his solitude.' Do you think that the AI suitably instills that wise adage? As a seasoned parent, I would venture that this maxim missed the honed parental guise of the AI.
Geeky Gadgets
27-07-2025
- Business
- Geeky Gadgets
How to Build Reliable AI Agents : Create AI Systems That Never Fail
What if the AI agent you deployed today could not only predict user needs with precision but also adapt seamlessly to unforeseen challenges? The demand for reliable, scalable AI systems has never been higher, yet the path to building them remains fraught with complexity. Developers are inundated with a dizzying array of tools, frameworks, and trends, many of which promise innovation but deliver unpredictability. The stakes are high: a poorly designed AI agent can lead to skyrocketing operational costs, inconsistent outputs, and even reputational damage. But here's the good news—by focusing on core principles and strategic simplicity, you can cut through the noise and build AI agents that are not only functional but also dependable in real-world applications. In this step-by-step overview, Dave Ebbelaar shares actionable insights to help you master the art of creating robust AI agents. You'll discover how to strategically integrate Large Language Models (LLMs), design workflows that prioritize predictability, and implement recovery mechanisms that keep your system operational under pressure. From understanding the critical role of memory in maintaining context to using human-in-the-loop feedback for high-stakes tasks, this guide will equip you with the tools to navigate the challenges of AI development. Whether you're a seasoned developer or just beginning to explore the field, the principles outlined here will help you design systems that don't just meet the demands of 2025 but anticipate the needs of the future. After all, reliability isn't just a feature—it's the foundation of trust in AI. Building Reliable AI Agents Understanding the Challenges in AI Development The AI development landscape is increasingly crowded with new tools and frameworks, often accompanied by significant hype. This can make it challenging to identify practical solutions that balance innovation with reliability. Common challenges include high operational costs, unpredictable outputs, and overly complex workflows. To overcome these obstacles, it is essential to focus on foundational principles rather than fleeting trends. By prioritizing simplicity, deterministic engineering practices, and strategic use of LLMs, you can avoid these pitfalls and create systems that are both efficient and reliable. Core Principles for Building Reliable AI Agents To ensure your AI agents are effective and dependable, adhere to the following core principles: Avoid over-reliance on pre-built frameworks: Custom solutions tailored to your specific use case provide greater control, flexibility, and adaptability. Custom solutions tailored to your specific use case provide greater control, flexibility, and adaptability. Minimize LLM API calls: Use LLMs strategically to reduce operational costs and mitigate dependency risks, making sure your system remains efficient and cost-effective. Use LLMs strategically to reduce operational costs and mitigate dependency risks, making sure your system remains efficient and cost-effective. Adopt deterministic engineering practices: Design workflows that are predictable, modular, and easy to debug, enhancing the overall reliability of your system. These principles serve as the foundation for creating scalable and robust AI systems capable of meeting the demands of diverse applications. How to Build Reliable AI Agents Watch this video on YouTube. Master AI agents with the help of our in-depth articles and helpful guides. The Seven Foundational Building Blocks Building reliable AI agents involves integrating seven critical components into your workflows. Each component plays a unique role in making sure functionality, reliability, and scalability. 1. Intelligence Layer The intelligence layer forms the core of your AI system, handling reasoning and context-based tasks. By incorporating LLM API calls strategically, you can enable advanced language processing while maintaining simplicity and adaptability. This layer should be designed to evolve with changing requirements, making sure long-term flexibility and relevance. 2. Memory Memory is crucial for maintaining context across interactions. Whether managing conversation history dynamically or storing data in a database, memory ensures a seamless and coherent user experience. This is particularly important for applications such as virtual assistants or customer support systems, where long-term context retention is essential for effective communication. 3. Tools for External Integration External integration tools enable your AI agents to interact with APIs, databases, and other systems. These tools extend the functionality of LLMs beyond text generation, allowing your agents to retrieve information, execute commands, or update records in external systems. This capability is vital for creating versatile and functional AI solutions. 4. Validation Validation ensures that your AI agent produces structured and consistent outputs. For instance, using JSON schemas to validate data formats can help maintain quality assurance. This step is particularly important in production environments, where errors can have significant consequences and undermine system reliability. 5. Control Control mechanisms, such as routing logic and if-else statements, enable deterministic decision-making. By categorizing tasks and directing processes, you can modularize workflows and enhance system predictability. This approach is especially useful for managing complex operations efficiently and effectively. 6. Recovery Recovery mechanisms are essential for handling errors, API failures, and rate limits. By incorporating retry logic and fallback strategies, you can ensure that your AI agent remains operational even under adverse conditions. This resilience is critical for real-world applications where reliability is a non-negotiable requirement. 7. Feedback Feedback systems introduce human oversight into your workflows. For high-stakes or complex tasks, adding approval steps ensures that sensitive operations are reviewed and validated. This human-in-the-loop approach is particularly important for applications where errors are unacceptable and accountability is paramount. Key Insights for Developers When designing AI agents, it is important to distinguish between general-purpose assistants like ChatGPT and specialized, fully automated systems. Structured outputs and context engineering can significantly enhance reliability and performance. Additionally, debugging and logging are essential for understanding the decision-making processes of LLMs. For high-stakes tasks, incorporating human-in-the-loop systems provides an additional layer of oversight and accountability, making sure that your AI agents operate with precision and reliability. Practical Implementation Strategies To implement these building blocks effectively, focus on creating modular workflows that integrate the strengths of each component. Consider the following strategies: Use Python or similar programming languages to develop custom solutions for memory management, validation, and error handling. Prioritize error recovery and fallback mechanisms to ensure robustness in production environments. Use LLMs judiciously, reserving their use for tasks that truly require advanced language processing capabilities. This modular approach simplifies development while enhancing the scalability and adaptability of your AI systems, making them better suited to meet the demands of diverse applications. Building for the Future By mastering these foundational building blocks, you can design AI agents that are reliable, scalable, and adaptable to a wide range of use cases. Focus on first principles, break down complex problems into manageable components, and use LLMs strategically to maximize their impact. This approach will empower you to create robust AI systems that not only meet the challenges of 2025 but also remain relevant and effective in the years to come. Media Credit: Dave Ebbelaar Filed Under: AI, Technology News, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
The Verge
09-07-2025
- Business
- The Verge
OpenAI's open language model is imminent
Microsoft's complicated relationship with OpenAI is about to take an interesting turn. As the pair continue to renegotiate a contract to allow OpenAI to restructure into a for-profit company, OpenAI is preparing to release an open language AI model that could drive even more of a wedge between the two companies. Sources familiar with OpenAI's plans tell me that CEO Sam Altman's AI lab is readying an open-weight model that will debut as soon as next week with providers other than just OpenAI and Microsoft's Azure servers. OpenAI's models are typically closed-weight, meaning the weights (a type of training parameter) aren't available publicly. The open nature of OpenAI's upcoming language model means companies and governments will be able to run the model themselves, much like how Microsoft and other cloud providers quickly onboarded DeepSeek's R1 model earlier this year. I understand this new open language model will be available on Azure, Hugging Face, and other large cloud providers. Sources describe the model as 'similar to o3 mini,' complete with the reasoning capabilities that have made OpenAI's latest models so powerful. OpenAI has been demoing this open model to developers and researchers in recent months, and it has been openly soliciting feedback from the broader AI community. I reached out to OpenAI to comment on the imminent arrival of its open model, but the company did not respond in time for publication. It's the first time that OpenAI has released an open-weight model since its release of GPT-2 in 2019, and it's also the first time we've seen an open language model from OpenAI since it signed an exclusive cloud provider agreement with Microsoft in 2023. That deal means Microsoft has access to most of OpenAI's models, alongside exclusive rights to sell them directly to businesses through its own Azure OpenAI services. But with an open model, there's nothing to stop rival cloud operators from hosting a version of it. As I revealed in Notepad last month, there's a complicated revenue-sharing relationship between Microsoft and OpenAI that involves Microsoft receiving 20 percent of the revenue that OpenAI earns for ChatGPT and the AI startup's API platform. Microsoft also shares 20 percent of its Azure OpenAI revenue directly with OpenAI. This new open model from OpenAI will likely have an impact on Microsoft's own AI business. The open model could mean some Azure customers won't need pricier options, or they could even move to rival cloud providers. Microsoft's lucrative exclusivity deal with OpenAI has already been tested in recent months. Microsoft 'evolved' its OpenAI deal earlier this year to allow the AI lab to get its own AI compute from rivals like Oracle. While that was limited to the servers used for building AI models, this new open model will extend far beyond the boundaries of ChatGPT and Azure OpenAI. Microsoft still has first right of refusal to provide computing resources for OpenAI, but it has no control over an open language model. OpenAI is preparing to announce the language model as an 'open model,' but that terminology, which often gets confused with open-source, is bound to generate a lot of debate around just how open it is. That will all come down to what license is attached to it and whether OpenAI is willing to provide full access to the model's code and training details, which can then be fully replicated by other researchers. Altman said in March that this open-weight language model would arrive 'in the coming months.' I understand it's now due next week, but OpenAI's release dates often change like the wind, in response to development challenges, server capacity, rival AI announcements, and even leaks. Still, I'd expect it to debut this month if all goes well. I'm always keen to hear from readers, so please drop a comment here, or you can reach me at notepad@ if you want to discuss anything else. If you've heard about any of Microsoft's secret projects, you can reach me via email at notepad@ or speak to me confidentially on the Signal messaging app, where I'm tomwarren.01. I'm also tomwarren on Telegram, if you'd prefer to chat there. Thanks for subscribing to Notepad.
Washington Post
20-06-2025
- Washington Post
In ‘UnWorld,' humans try to cure grief with technology
Any conversation about artificial intelligence, be it condemning or approving, is bound to engage its essential selling point: making life easier. For those who embrace these digital developments as a cumulative hallmark of evolution, the ease proffered by AI is likely to register as one bonus among many. What new echelons of human potential might we unlock once we've liberated our minds from so much cognitive drudgery and irritation?
Geeky Gadgets
16-06-2025
- Business
- Geeky Gadgets
Mistral's Magistral Open Source AI Reasoning Model Fully Tested
What if machines could not only process data but also reason through it like a human mind—drawing logical conclusions, adapting to new challenges, and solving problems with unprecedented precision? This isn't a distant dream; it's the reality that Mistral's Magistral open source reasoning model promises to deliver. Magistral is the first reasoning model by Mistral AI and has emerged as a new step forward in artificial intelligence, setting new benchmarks for how machines can emulate human-like cognitive processes. In a world where AI is often shrouded in proprietary secrecy, Magistral's open source framework also signals a bold shift toward transparency and collaboration, inviting the global AI community to innovate together. The question isn't whether AI can reason—it's how far this model can take us. In this performance exploration, World of AI uncover how Magistral's advanced reasoning capabilities are reshaping industries, from healthcare diagnostics to climate change analysis. You'll discover why its open source framework is more than just a technical choice—it's a statement about the future of ethical, accessible AI. Along the way, we'll delve into the rigorous testing that validated its performance and examine real-world applications that could redefine how we approach complex problems. As we unpack the implications of this milestone, one thing becomes clear: Magistral isn't just a tool; it's a glimpse into the evolving relationship between human ingenuity and machine intelligence. Could this be the model that bridges the gap between data and decision-making? Let's find out. Magistral: Advancing AI Reasoning Capabilities The Magistral model represents a notable evolution in AI's ability to process, interpret, and reason with information. Unlike traditional AI systems that are often limited to performing narrowly defined tasks, Magistral is designed to emulate human-like cognitive processes. It can analyze data, draw logical conclusions, and adapt to new challenges, making it one of the most advanced reasoning systems available today. Magistral's versatility enables it to address a wide range of reasoning challenges. For instance, it can process complex datasets to identify patterns, generate hypotheses, and provide actionable insights. This capability is particularly impactful in fields such as healthcare, where reasoning-based AI can assist in diagnosing diseases, recommending treatment plans, or predicting patient outcomes. By bridging the gap between raw data analysis and informed decision-making, Magistral establishes a new benchmark for AI reasoning, offering practical solutions to real-world problems. Watch this video on YouTube. The Open source Framework: Driving Collaboration and Transparency One of Magistral's defining features is its open source framework, which sets it apart from many proprietary AI systems. By making the model freely accessible, Mistral encourages collaboration and innovation across the AI community. Researchers, developers, and organizations can study, modify, and enhance the model, creating a shared effort to advance AI reasoning technologies. This open source approach also promotes transparency, a critical factor in building trust in AI systems. Users can examine the underlying algorithms to ensure ethical practices and minimize bias, addressing concerns about fairness and accountability. Additionally, the open framework reduces barriers to entry, allowing smaller organizations, independent researchers, and startups to access innovative AI tools without incurring prohibitive costs. This widespread access of AI technology fosters a more inclusive environment for innovation. Mistral's Magistral Open Source Reasoning Model fully Tested Watch this video on YouTube. Stay informed about the latest in Mistral AI by exploring our other resources and articles. Performance Evaluation: Setting New Standards in Reasoning During its testing phase, Magistral was evaluated on key performance metrics, including accuracy, efficiency, and adaptability. The results confirmed its exceptional capabilities in tasks requiring logical reasoning, such as solving complex puzzles, analyzing multifaceted scenarios, and making multi-step decisions. To validate its performance, Mistral benchmarked Magistral against other leading reasoning models. The findings revealed that Magistral not only matches but often surpasses its counterparts in both speed and precision. For example, in a simulated environment requiring advanced reasoning, Magistral achieved a 15% improvement in accuracy compared to similar models. These results highlight its potential to become a leading reasoning system, capable of addressing challenges that demand high levels of cognitive processing. Fantastic Applications Across Industries The successful testing of Magistral opens the door to its application across a wide array of industries, where advanced reasoning capabilities can drive innovation and efficiency. In healthcare, Magistral could transform diagnostics by analyzing patient data to identify conditions, recommend treatments, or predict outcomes with greater accuracy. In finance, the model could analyze market trends, optimize investment strategies, and identify emerging risks, providing organizations with a competitive edge. In the field of education, Magistral could power intelligent tutoring systems, offering personalized learning experiences tailored to individual student needs. By analyzing learning patterns and adapting to different educational contexts, it could enhance both teaching and learning outcomes. Beyond these specific industries, Magistral's reasoning capabilities hold broader implications for addressing global challenges. For example, it could contribute to tackling issues such as climate change, resource management, and disaster response by analyzing complex datasets and generating actionable insights to support decision-making on a global scale. Shaping the Future of AI Reasoning Mistral's successful development and testing of the Magistral open source reasoning model represent a milestone in AI innovation. By combining advanced reasoning capabilities with an open source framework, Magistral sets a new standard for transparency, collaboration, and performance in AI systems. Its potential applications span industries and global challenges, offering practical solutions that complement human decision-making. As Magistral transitions into real-world use, it is poised to play a pivotal role in shaping the future of AI, allowing machines to reason and adapt in ways that were previously unattainable. Media Credit: WorldofAI Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
