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Forbes
20 minutes ago
- 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.
Yahoo
an hour ago
- Yahoo
Reunert (JSE:RLO) Hasn't Managed To Accelerate Its Returns
What trends should we look for it we want to identify stocks that can multiply in value over the long term? In a perfect world, we'd like to see a company investing more capital into its business and ideally the returns earned from that capital are also increasing. Put simply, these types of businesses are compounding machines, meaning they are continually reinvesting their earnings at ever-higher rates of return. That's why when we briefly looked at Reunert's (JSE:RLO) ROCE trend, we were pretty happy with what we saw. AI is about to change healthcare. These 20 stocks are working on everything from early diagnostics to drug discovery. The best part - they are all under $10bn in marketcap - there is still time to get in early. What Is Return On Capital Employed (ROCE)? If you haven't worked with ROCE before, it measures the 'return' (pre-tax profit) a company generates from capital employed in its business. The formula for this calculation on Reunert is: Return on Capital Employed = Earnings Before Interest and Tax (EBIT) ÷ (Total Assets - Current Liabilities) 0.15 = R1.4b ÷ (R12b - R3.0b) (Based on the trailing twelve months to March 2025). Thus, Reunert has an ROCE of 15%. In absolute terms, that's a satisfactory return, but compared to the Industrials industry average of 10% it's much better. View our latest analysis for Reunert In the above chart we have measured Reunert's prior ROCE against its prior performance, but the future is arguably more important. If you're interested, you can view the analysts predictions in our free analyst report for Reunert . What Does the ROCE Trend For Reunert Tell Us? While the current returns on capital are decent, they haven't changed much. The company has consistently earned 15% for the last five years, and the capital employed within the business has risen 38% in that time. Since 15% is a moderate ROCE though, it's good to see a business can continue to reinvest at these decent rates of return. Over long periods of time, returns like these might not be too exciting, but with consistency they can pay off in terms of share price returns. Our Take On Reunert's ROCE The main thing to remember is that Reunert has proven its ability to continually reinvest at respectable rates of return. And the stock has done incredibly well with a 133% return over the last five years, so long term investors are no doubt ecstatic with that result. So while investors seem to be recognizing these promising trends, we still believe the stock deserves further research. One more thing to note, we've identified 1 warning sign with Reunert and understanding it should be part of your investment process. While Reunert isn't earning the highest return, check out this free list of companies that are earning high returns on equity with solid balance sheets. Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Geeky Gadgets
an hour ago
- Geeky Gadgets
ChatGPT-5 vs Claude vs Qwen : The Hidden Costs of Picking the Wrong AI Model
What if the future of app development hinged on the AI model you choose? With the rapid evolution of artificial intelligence, developers are faced with a critical question: which model delivers the best balance of speed, reliability, and cost-effectiveness? In the race to build smarter, faster applications, three contenders—ChatGPT-5, Claude, and Qwen—have emerged as frontrunners. Each features unique strengths, yet their differences could mean the success or failure of your next project. Imagine building a tool like 'Newsletter Digest,' a web app designed to summarize newsletters, only to discover that your chosen AI model struggles to deliver functional results. The stakes are high, and understanding these models' capabilities is no longer optional, it's essential. In this comparative overview, Rob Shocks explains how these AI models stack up in real-world scenarios, using the development of 'Newsletter Digest' as a case study. You'll uncover insights into their performance metrics, strengths, and limitations, as well as the hidden costs that could impact your budget. Whether you're a developer seeking reliability, a strategist prioritizing creativity, or someone chasing speed, this breakdown will help you navigate the trade-offs. By the end, you might find yourself rethinking what matters most in an AI partner: precision, adaptability, or efficiency? AI Model Comparison Summary Project Overview: Developing 'Newsletter Digest' 'Newsletter Digest' is a web application that connects to Gmail, aggregates newsletters, and summarizes key stories for users. It also allows users to view individual newsletters in a streamlined interface. The app was developed using a modern tech stack to ensure scalability, responsiveness, and ease of use: A React-based framework for building a dynamic and efficient front end. A React-based framework for building a dynamic and efficient front end. Tailwind CSS: A utility-first CSS framework that enabled clean, responsive design. A utility-first CSS framework that enabled clean, responsive design. Neon: A Postgres database solution for managing user and application data. A Postgres database solution for managing user and application data. Prisma: A database toolkit that simplified schema generation and management. A database toolkit that simplified schema generation and management. Clerk: A user authentication and management tool integrated with Stripe for seamless billing functionality. The core functionality of summarizing newsletters relied on the capabilities of Claude, GPT-5, and Qwen Coder. These models were tested for their ability to deliver a functional app, their speed, usability, and cost-effectiveness. Comparing the AI Models Claude (Opus 4.1 and Sonnet 4) Claude proved to be the most reliable and mature AI model for this project. It excelled in generating a fully functional app with minimal errors, particularly in tasks such as database schema generation and implementing app features. Its workflow required fewer corrective prompts, which significantly reduced development time. However, this high level of performance came at a premium, as Claude was the most expensive model in the comparison. For developers prioritizing reliability and efficiency, Claude offers a robust solution, albeit at a higher cost. GPT-5 GPT-5 demonstrated strong strategic thinking and versatility but fell short in implementation compared to Claude. While it made reasonable progress in building the app, it required more corrective prompts and encountered occasional errors during the setup process. The Cursor CLI, used to interact with GPT-5, is still in beta, which introduced some glitches and limited functionality. Despite these challenges, GPT-5 showcased its ability to handle complex tasks with guidance, making it a viable option for developers who can invest additional time in troubleshooting and refinement. Qwen Coder Qwen Coder stood out for its speed, delivering rapid responses during the development process. However, it struggled to produce a complete and functional app. Its outputs were often incomplete, and it frequently stopped abruptly, requiring significant manual intervention to fill in the gaps. While its speed is promising, Qwen's lack of reliability and functionality placed it behind both Claude and GPT-5 in this comparison. Developers seeking a balance between speed and reliability may find Qwen less suitable for complex projects. ChatGPT-5 vs Claude vs Qwen Watch this video on YouTube. Stay informed about the latest in AI Models Comparison by exploring our other resources and articles. Performance Metrics The three AI models were evaluated based on their overall performance in building 'Newsletter Digest.' The results revealed clear distinctions in their capabilities: Claude: Delivered the most reliable and functional app with minimal errors and a smooth workflow. Delivered the most reliable and functional app with minimal errors and a smooth workflow. GPT-5: Ranked second, offering decent progress but requiring more corrections and encountering occasional errors. Ranked second, offering decent progress but requiring more corrections and encountering occasional errors. Qwen: Ranked third, excelling in speed but failing to deliver a complete and reliable app. Claude's ability to produce a polished and functional app with fewer iterations made it the top performer, while GPT-5's versatility and Qwen's speed highlighted their respective strengths and limitations. Cost Analysis Cost is a significant factor when selecting an AI model, particularly for projects with budget constraints. Here's a breakdown of the pricing for each model: Claude (Opus 4.1): $15 per million input tokens, $75 per million output tokens. $15 per million input tokens, $75 per million output tokens. GPT-5: $0.125 per 1,000 input tokens, $0.10 per 1,000 output tokens. $0.125 per 1,000 input tokens, $0.10 per 1,000 output tokens. Qwen: Pricing varies but is generally more affordable than Claude and GPT-5. While Claude was the most expensive option, its superior performance justified the higher cost for this project. GPT-5 offered a more affordable alternative with slightly reduced effectiveness, while Qwen's lower cost reflected its limited functionality and reliability. Insights on Development Tools The development process also highlighted the importance of the tools used alongside the AI models. These tools played a critical role in streamlining the workflow and making sure a smooth development experience: Clerk: Simplified user authentication and integrated seamlessly with Stripe for billing, reducing the complexity of managing user accounts. Simplified user authentication and integrated seamlessly with Stripe for billing, reducing the complexity of managing user accounts. Cursor CLI: Provided access to multiple AI models but, as a beta tool, had limited features and occasional glitches that impacted usability. Provided access to multiple AI models but, as a beta tool, had limited features and occasional glitches that impacted usability. Neon (Postgres DB): Worked effectively for database management, especially when paired with Prisma for schema generation and maintenance. While most tools performed well, the beta status of Cursor CLI highlighted the need for further refinement to improve its reliability and feature set. Looking Ahead: Future Considerations As of August 2025, the AI landscape continues to evolve, with emerging models like Grok and Gemini showing potential to disrupt the market. Developers should remain vigilant for advancements in AI capabilities and pricing structures. For now, Claude remains the preferred choice for projects requiring maturity, reliability, and comprehensive functionality. GPT-5 offers a strong alternative for tasks that demand strategic thinking and creative input, while Qwen Coder's speed may appeal to developers working on less complex applications. Continued refinement of tools like Cursor CLI and exploration of new technologies will further enhance the app development process, paving the way for more efficient and innovative solutions. Media Credit: Rob Shocks Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. 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