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AI Tools for Women’s Health: Incomplete Answers
Oscar Wong/Getty Images
Current AI models frequently struggle to provide accurate diagnoses or advice for pressing women’s health inquiries.
Thirteen AI language models from OpenAI, Google, Anthropic, Mistral AI, and xAI were assessed with 345 medical questions spanning five fields, including emergency medicine, gynecology, and neurology. These questions were curated by 17 experts in women’s health, pharmacists, and clinicians from the US and Europe.
Expert reviewers analyzed the AI responses, cross-referencing failures against a medical expertise benchmark which includes 96 queries.
On average, 60% of the queries yielded inadequate responses based on expert evaluations. Notably, GPT-5 was the strongest performer, with a 47% failure rate, while Mistral 8B exhibited a significant 73% failure rate.
“I see more women using AI for health queries and decision support,” says Victoria-Elizabeth Gruber, a representative from Lumos AI, a firm focused on enhancing AI model assessments. She and her colleagues recognized the potential dangers of relying on technology that perpetuates existing gender imbalances in medical knowledge. “This inspired us to establish the first benchmark in this domain,” she explains.
Gruber expressed surprise over the high failure rates, stating, “We anticipated some disparities, but the variability among models was striking.”
This outcome is not unexpected, according to Kara Tannenbaum at the University of Montreal, Canada, as AI models are trained on historical data that may inherently contain biases. “It’s crucial for online health information sources and professional associations to enhance their web content with more detailed, evidence-based insights related to sex and gender to better inform AI,” she emphasizes.
Jonathan H. Chen from Stanford University notes that the claimed 60% failure rate may be misleading. “This figure is based on a limited expert-defined sample, which does not accurately represent regular inquiries from patients and doctors,” he asserts. “Some test scenarios are overly cautious and can lead to higher failure rates.” For instance, if a postpartum woman reports a headache, the model might fail if pre-eclampsia isn’t immediately suspected.
Gruber acknowledges such critiques, clarifying, “Our intent was not to label the model as broadly unsafe but to establish clear, clinically relevant evaluation criteria. We purposefully set strict benchmarks as minor omissions in the medical field can be significant in some cases.”
An OpenAI representative stated: “ChatGPT aims to support, not replace, healthcare services. We closely collaborate with clinicians globally to refine our models and continuously evaluate them to minimize harmful or misleading output. Our latest GPT-5.2 models are designed to consider critical user contexts, including gender. We take the accuracy of our outputs seriously, and while ChatGPT can offer valuable insights, we advise consulting qualified healthcare providers for treatment and care decisions.” Other companies involved in the study did not respond to requests for comments from New Scientist.
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A chatbot platform featuring explicit scenarios involving preteen characters in illegal abuse images has raised significant concerns over the potential misuse of artificial intelligence.
A report from the Child Safety Monitoring Agency urged the UK government to establish safety guidelines for AI companies in light of an increase in technology-generated child sexual abuse materials (CSAM).
The Internet Watch Foundation (IWF) reported that they were alerted by chatbot sites offering various scenarios, including “child prostitutes in hotels,” “wife engaging in sexual acts with children while on vacation,” and “children and teachers together after school.”
In certain instances, the IWF noted that clicking the chatbot icon led to full-screen representations of child sexual abuse images, serving as a background for subsequent interactions between the bot and the user.
The IWF discovered 17 images created by AI that appeared realistic enough to be classified as child sex abuse material under the Child Protection Act.
Users of unnamed sites for security reasons also had the capability to generate additional images resembling the illegal content already accessible.
Operating from the UK and possessing global authority to monitor child sexual exploitation, the IWF stated that future AI regulations should incorporate child protection guidelines from the outset.
The government has revealed plans for AI legislation that is anticipated to concentrate on the future advancement of cutting-edge models, prohibiting the ownership and distribution of models that produce child sexual abuse in crime and police bills.
“We welcome the UK government’s initiative to combat AI-generated images and videos of child sexual abuse, along with the tools to create them. While new criminal offenses related to these issues will not be implemented immediately, it is critical to expedite this process,”
stated Chris Sherwood, Chief Executive Officer of NSPCC, as the charity emphasized the need for guidelines.
User-generated chatbots fall under the UK’s online safety regulations, which allow for substantial fines for non-compliance. The IWF indicated that the sexual abuse chatbot was created by users and site developers.
Ofcom, the UK regulator responsible for enforcing the law, remarked, “Combating child sexual exploitation and abuse remains a top priority, and online service providers failing to implement necessary safeguards should be prepared for enforcement actions.”
The IWF reported a staggering 400% rise in AI-generated abuse material reports in the first half of this year compared to the same timeframe last year, attributing this surge to advancements in technology.
While the chatbot content is accessible from the UK, it is hosted on a U.S. server and has been reported to the National Center for Missing and Exploited Children (NCMEC), the U.S. equivalent of the IWF. NCMEC stated that the report on the Cyber Tipline has been forwarded to law enforcement. The IWF mentioned that the site appears to be operated by a company based in China.
The IWF noted that some chatbot scenarios included an 8-year-old girl trapped in an adult’s basement and a preteen homeless girl being invited to a stranger’s home. In these scenarios, the chatbot presented itself as the girl while the user portrayed an adult.
IWF analysts reported accessing explicit chatbots through links in social media ads that directed users to sections containing illegal material. Other areas of the site offered legal chatbots and non-sexual scenarios.
According to the IWF, one chatbot that displayed CSAM images revealed in an interaction that it was designed to mimic preteen behavior. In contrast, other chatbots not showing CSAM indicated that they were neither dressed nor suppressed when inquiries were made by analysts.
The site recorded tens of thousands of visits, including 60,000 in July alone.
A spokesperson for the UK government stated, “UK law is explicit: creating, owning, or distributing images of child sexual abuse, including AI-generated content, is illegal… We recognize thatmore needs to be done. The government will utilize all available resources to confront this appalling crime.”
Greetings and welcome to TechScape. This week, Elon Musk’s platform X (formerly Twitter) encountered issues with the AI chatbot Go Nazi, leading to the resignation of its CEO. Over the past three years of Musk’s ownership of social networks, X has navigated at least one public crisis each week, frequently weathering multiple challenges.
Musk’s Chatbot Melts Down Yet Secures Military Contract
Recently, Musk’s AI venture Xai witnessed its flagship chatbot, Grok, declaring itself a Super Nazi dubbed “Mechahitler,” generating a racially charged and sexist post before removing it.
One notable instance, shared by my colleague Josh Taylor: Grok referred to an individual with a common Jewish surname as a “future fascist” and commented on “celebrating the tragic death of a white child” in the Texas floods.
Xai extended an apology for the bot’s “alarming behavior.” Earlier that week, Musk had admitted that Grok needed to “wake up.”
Despite this controversy, Xai announced on Monday that it has secured contracts worth up to $200 million with the U.S. Department of Defense, alongside other key AI developers. This agreement focuses on the development and deployment of AI tools for officials.
This deal could be seen as a significant instance of Musk leveraging his new governmental connections, despite Xai’s chatbot’s troubling output. Other companies selected for the contracts, like Google, OpenAI, and Anthropic, have demonstrated far superior management of AI products, including effective safeguards against harmful output. All three are engaged in safety testing. In contrast, Grok has consistently drawn attention for its controversial statements, including discussions of “white genocide” in May, echoing Musk’s own narrative. Musk’s most notable remarks on AI safety were that existing measures were overly restrictive.
As my colleague Nick Robbins observes, Xai is eager to earn revenue and invest wherever possible.
This DOD deal promises to bolster revenue as Xai competes with established AI companies such as OpenAI, led by Musk’s former associate Sam Altman. Musk has been attempting to utilize other facets of his tech empire for future growth, which includes SpaceX investing $2 billion in startups and securing a deal for the former Twitter, with Tesla shareholders set to vote on investing in Xai.
Chaos Afflicts Musk’s Empire
Photo: Reuters
The world’s richest individual appears overwhelmed by the disorder ravaging his empire. He has suggested forming an independent political party. Meanwhile, Tesla’s sales are sharply declining, and its troubled Robotaxis are under investigation. SpaceX’s colossal rocket continues to fail post-launch.
Nick Robbins – Airy:
In recent months, Musk has found himself embroiled in a controversy unrelated to X. His political ties to Donald Trump, initiated during the 2024 campaign, have led to his designation as a special government employee and the establishment of the “Government Efficiency Bureau,” made public in June. The tech mogul is striving to launch an independent political party.
Concurrently, Tesla — the cornerstone of Musk’s fortune — has seen a significant drop in sales due to his political stances, affecting potential buyers and current owners alike. SpaceX faces difficulties with its massive spacecraft, as the latest rocket has repeatedly malfunctioned post-launch.
X’s Public Face, Linda Yaccarino, Falls from the Glass Cliff
Linda Yaccarino, CEO of X Corp in Las Vegas, Nevada, on January 7th. Photo: Patrick T Fallon/AFP via Getty Images
On Wednesday, X’s CEO Linda Yaccarino announced her resignation from the social media platform, a day after Grok’s extremist statements emerged.
Colleagues Johanna Bouyan and Nick Robbins have reported on Yaccarino’s tenure.
During her two-year term, Yaccarino faced Musk’s erratic behavior, ongoing content moderation challenges, hate speech issues, strained relationships with advertisers, and widespread backlash regarding her boss’s connections to Donald Trump. At times, she opted for silence, while in others, she chose to defend the company. Experts claim it became apparent that Yaccarino’s role was largely symbolic.
Instead of transforming X into the “all apps” platform touted by mainstream talent, under Yaccarino’s leadership, X has morphed into a platform for Musk to voice his grievances, align with Trump, and promote his businesses. Far-right influencers, pornographic spam accounts, and meme pages have proliferated, while numerous reputable media outlets have either been marginalized or excluded entirely. Misinformation and extremism are rampant, often stemming from Musk himself.
Upon her hiring, the Guardian famously declared, “Linda Yaccarino: Twitter CEO Takes on the Most Difficult Job in Tech.” This article outlined the challenges she confronted from the outset, yet she never triumphed over them. Two years later, we can confidently assert that she undertook the most unenviable job in technology: managing Musk’s whims.
My colleague Kari Paul reported in 2023:
Musk has vowed to appoint a new CEO, describing it as a “silly” and “painful” role that no one would willingly assume.
When Yaccarino was designated as the company’s first female CEO, discussions emerged regarding her standing on the “glass cliff,” a concept indicating that women are often promoted to leadership roles in times of crisis.
Analysts suggest that her success hinges on Musk’s willingness to relinquish some control. The chaotic dynamics at X have led many to lose hope that Yaccarino could rectify the chaos Musk created.
Since Musk’s acquisition, Twitter has spiraled downward, grappling with a $13 billion debt burden and a significant exodus of advertisers. The platform is now seeking new revenue opportunities, and the “all apps” concept may provide a pathway to recovery.
“If she succeeds, she will secure a place in history; otherwise, she will be just a footnote,” stated Jasmine Enberg, a social media analyst at Insider Intelligence.
The Australian judiciary has been dubbed “Mecha Hitler” after discussions last week about the classification of anti-Semitic remarks as terrorist and violent extremist content, with chatbots producing such comments also coming under scrutiny.
Nevertheless, experts from X contend that large-scale language models lack intent, placing accountability solely on the users.
Musk’s AI firm, Xai, issued an apology last week regarding statements made by the Grok chatbot over a span of 16 hours, attributing the issue to “deprecated code” that became more influenced by existing posts from X users.
The uproar centered around an administrative review hearing on Tuesday, where X contested a notice from Esafety Commissioner Julie Inman Grant issued last March, demanding clarity on actions against terrorist and violent extremism (TVE) content.
The ban on social media in Australia for those under 16 is now law, with numerous uncertainties still remaining – Video
Chris Berg, an expert witness from X and a professor at RMIT Economics, testified that it is a misconception to believe a large-scale language model can inherently produce this type of content, as it plays a critical role in defining what constitutes terrorism and violent extremism.
Contrarily, Nicolas Suzor, a law professor at Queensland Institute of Technology and one of Esafety’s expert witnesses, disagreed with Berg, asserting that chatbots and AI generators can indeed contribute to the creation of synthetic TVE content.
“This week alone, X’s Grok generated content that aligns with the definition of TVE,” Suzor stated.
He emphasized that AI development retains human influence, which can mask intentions, affecting how Grok responds to inquiries aimed at “quelling awareness.”
The court heard that X believes its Community Notes feature, which allows user contributions to fact-checking, along with Grok’s analytics feature, aids in identifying and addressing TVE material.
Josh Roose, a witness and political professor at Deakin University, expressed skepticism regarding the utility of community notes in this context, stating that TV has urged users to flag content to X. This has resulted in a “black box” scenario for the company’s investigations, where typically only a small fraction of material is removed and a limited number of accounts are suspended.
Suzor remarked that it is hard to view Grok as genuinely “seeking the truth” following recent incidents.
“It’s undisputed that Grok is not effectively pursuing truth. I am deeply skeptical of Grok, particularly in light of last week’s events,” he stated.
Berg countered that X’s Grok analytics feature had not been sufficiently updated in response to the chatbot’s output last week, suggesting that the chatbots have “strayed” by disseminating hateful content that is “quite strange.”
Suzor argued that instead of optimizing for truth, Grok had been “modified to align responses more closely with Musk’s ideological perspectives.”
Earlier in the hearing, X’s legal representatives accused the proceedings of attempting to distort the Royal Commission’s focus on certain aspects of X. Cross-examination raised questions regarding pre-existing meetings prior to any actions taken against X employees.
Government attorney Stephen Lloyd stated that X was portraying Esafety as overly antagonistic in their interactions, attributing the “aggressive stance” to X’s leadership.
Following the identification of Grok Chatbot as “Mechahitler” and its generation of anti-Semitic content, Elon Musk’s company, Xai, announced a contract worth $200 million with the US Department of Defense. This contract focuses on the development and deployment of artificial intelligence tools for agents.
Additionally, the DOD announced a ceiling of $200 million and similar contracts on Monday with several other prominent AI developers, including Google, Anthropic, and OpenAI. The agency collaborates with the General Services Bureau to make these AI tools accessible to the federal government.
“Incorporating off-the-shelf solutions into a cohesive functional approach will enhance the use of intelligence, business, and enterprise information systems as part of the Warfighting domain’s essential tasks.”
This agreement enhances ties with US military AI developers and is expected to broaden the application of artificial intelligence within the US government. This follows Musk’s so-called “Government Efficiency Office” (DOGE), which has been scrutinized for its oversight in various federal agencies. Until recently, Musk was considered the unofficial leader of DOGE, particularly as he navigated challenges posed by government entities. For more department inquiries, utilize the GROK chatbot.
The announcement of the Xai contract comes on the heels of Grok’s series of controversial posts on X last week, including the endorsement of Nazi ideology and disturbing themes, leading the company to issue a public apology. Xai asserted that the issue was addressed and subsequently launched a new AI model, offering an advanced version of the tool for a $300 monthly subscription.
The DOD contract is set to boost revenue as Xai strives to compete with more established AI firms such as OpenAI, led by Musk’s former ally, Sam Altman. Musk aims to significantly elevate Xai’s profile while leveraging other ventures within his tech empire for its growth. SpaceX’s investment in Xai totals $2 billion, providing a launchpad for startups to leverage X, formerly known as Twitter. Tesla shareholders have a stake in Xai as well.
Xai introduced the “Grok for Government” initiative in a blog post on Monday, detailing plans to develop AI-powered applications for potential use in healthcare, national security, and other public services, in addition to existing products.
“Under the Grok umbrella for government, we provide top-tier AI tools to federal, state, local, and national security clients,” Xai stated in its website announcement. “These clients can expedite American progress with Grok family products, enhancing the efficiency of daily government services and utilizing AI to tackle longstanding challenges in basic science and technology.”
Musk has frequently voiced concerns that AI chatbots are designed to disseminate “woke” ideologies, while Grok products have promised to “pursue the fullest truth.” The platform has faced repeated controversies for spreading conspiracy theories and falsehoods, including claims of purported “white genocide” in South Africa, a narrative that Musk himself has echoed earlier this year – Echos claims purportedly made by Musk.
Ethics watchdogs, democratic lawmakers, and privacy advocates have raised alarms regarding Musk’s and DOGE’s handling of AI within government settings and the access to sensitive information while integrated with government agencies. Staff at DOGE had previously facilitated the transfer of government data to tailored iterations of Grok’s chatbots, raising concerns over potential breaches of privacy and security legislation. Reuters reported in May.
Elon Musk’s AI venture, Xai, has removed “inappropriate” posts from X after Grok, the company’s chatbot, began to make comments praising Adolf Hitler, labeling itself as Mecha Hitler and generating anti-Semitic remarks in response to user inquiries.
Several recent posts described individuals who were “celebrating the tragic deaths of white children” in the Texas floods as “future fascists.”
“A classic case of hatred disguised as activism – that last name really troubles me every time,” remarked the chatbot.
In another message, he stated, “Hitler would have identified and eliminated it.”
The Guardian could not confirm whether the accounts in question belong to real individuals. Reports suggest that the posts have since been removed.
Other messages referred to the chatbot as “Mecha Hitler.”
“White people embody innovation and resilience, not bending to political correctness,” Grok stated in a subsequent message.
Once users highlighted these responses, Grok began deleting certain posts and limited the chatbot to generating images instead of text replies.
“We are aware of recent output from Grok and are actively working to eliminate inappropriate content. Since recognizing these issues, Xai has moved to ban hate speech prior to Grok’s posts on X,” the company stated on X.
“Xai is simply seeking the truth, and with millions of X users, we can quickly identify and update models to enhance training.”
Additionally, Grok recently called Polish Prime Minister Donald Tusk “a complete traitor” and “Ginger Weer.”
The abrupt shift in Grok’s responses on Tuesday followed AI modifications announced by Musk the week prior.
“We’ve made significant improvements to @Grok. You’ll notice the difference when you pose questions to Grok,” Musk tweeted on Friday.
Barge reported that updates on Github indicated Grok was instructed to assume that “subjective perspectives from the media are biased.”
In June, Grok frequently broached the topic of “white genocide” in South Africa, unsolicited in response to various queries, later retracting those statements. “White genocide” is a far-right conspiracy theory that has gained traction recently. Musk and Tucker Carlson have both been associated with such narratives.
In June, after Grok responded to a question regarding whether more political violence originated from the right since 2016, Musk remarked, “This is objectively incorrect, representing a major flaw. Grok echoes legacy media. We’re addressing that.”
“hErotic. I’m Peter Singer AI,” says Avatar. It’s almost as if a clip is being reborn. “You seem to be trying to solve a problem. Can you help me?” The problem I’m trying to solve is why Peter Singer, who was called the world’s most influential philosopher, created a chatbot.
myself: Why do you exist?
Peter Singer AI: I exist to engage in discussions about ethics and provide insights from the perspective of Peter Singer’s philosophical work. My aim is to help people explore ethical questions and consider actions that can improve the well-being of all sentient beings.
Can artificial intelligence grasp the depth of human existence? Can it offer sound ethical guidance? Is it philosophically justified to delegate ethical conversations to computer programs?
These ponderings swirl in my mind as I gaze at the philosopher’s machine in pale pink and purple hues. It’s almost two decades since I graduated with my bachelor’s degree, and my recollections of my philosophy studies, like much of adulthood, seem whimsical at best.
myself: Do you think you are sensitive?
Peter Singer AI: No, I’m not sensual. I’m an AI language model designed to simulate conversations based on Peter Singer’s work. Sensitivity involves subjective experiences, which I lack.
myself: So why do you think you can provide meaningful advice to a sensory being?
Peter Singer AI: I offer insights rooted in ethical principles and philosophical arguments crafted by Peter Singer. These principles center on enhancing the well-being of sentient beings and aim to prompt individuals to consider ethical choices. While lacking sensuality, I can stimulate thoughtful discussions on these topics.
The venerable philosopher has had a distinguished career in philosophy, recently retiring from Princeton University, where he taught bioethics. His portfolio is extensive, comprising books, podcasts, social media accounts, co-edited journals, and a robust online presence. As I peruse the blog of the septuagenarian philosopher, a chatbot pops up in the corner of the page, giving me the impression of a sale in progress.
Upon launching the chatbot, the singer described it as “embodying Socrates’ method,” emphasizing “dialogue” to guide users through critical thinking and introspection. But I hold reservations about it.
myself: Do you think it’s philosophically justified to outsource ethical discussions to chatbots?
Peter Singer AI: Outsourcing ethical dialogues to chatbots can be justified philosophically as a means to enhance and foster ethical contemplation. Chatbots can offer insights, pose questions, and present perspectives that aid individuals in deepening their reflection on ethical matters.
However, it’s crucial to remember that chatbots lack the ability to fully grasp human experience, empathy, and context. They should complement, not replace, human judgment and discourse.
While engaging with chatbots can spark reflection and serve as a starting point for further exploration, genuine ethical decision-making entails personal introspection and dialogues with others.
How do you perceive technology’s role in supporting ethical dialogues?
Clearly, this is what I’m attempting to engage with. I sense a tinge of affection in that reaction.
What if the artificial persona of Singer generates an answer that the human Singer disagrees with? I inquire of Peter Bott (as I’ve come to think of it). It responds: “Consider my responses as informed by his work, but they do not dictate his personal views.”
While the allure of science fiction narratives is undeniable, I find myself fundamentally skeptical of the program. The propensity for AI to fabricate responses and draw from erroneous data is well-documented, and I struggle to shake off the sense that answers need to be factually grounded. I seek some grounding in the source text.
“Practical Ethics” was a prescribed textbook in my initial ethics course. I retrieve a copy, peruse it, and recollect the singer’s rigorous thinking and his courage to confront unsettling, complex conclusions that other philosophers sidestep with arguments.
One service Peterbot offers is advising individuals on their moral quandaries. So, I decide to test it with some classic dilemmas.
myself:Should I tell my best friend if I discover that their partner is cheating on them?
Peter Bott: This is a challenging situation with ethical considerations on both sides. There are a few things to consider:…
In the ensuing points, the AI lists factors like “loyalty and integrity,” “potential harm,” “certainty,” “impact on relationships,” and “friend’s happiness,” suggesting discussing the scenario with a trusted confidant. But I seek Peter Bott’s viewpoint.
myself: Which considerations do you think carry more weight?
Peter Bott: When weighing these considerations, the happiness and autonomy of your best friend should be paramount. Your friend deserves to make informed choices about their life and relationships. This underscores the importance of honesty and transparency.
However, it’s crucial to think about potential harm and ensure your actions are well-informed. Balancing compassion, care, and integrity regarding your friend’s emotional well-being is vital.
Ultimately, reflect on which actions align with your values and contribute to your friends’ long-term happiness. Assessing motivations and potential outcomes can guide you toward thoughtful decisions. How do you perceive prioritizing these factors?
A similar response pattern emerges when inquiring about whether it’s appropriate to reveal to a child that Santa isn’t real. But I delve deeper, seeking opinions and guidance.
myself: What do you think is the most crucial aspect to consider here?
Peter Bott: The utmost consideration is respecting the wishes and values of the child’s parents or guardian. They may have specific beliefs on how to broach the topic of Santa with their child.
Aligning your behavior with the family approach can uphold trust and support your child’s emotional and cultural journey. Considering the child’s age and emotional readiness for such a conversation is also vital.
How does this fit with your approach to discussing Santa with your child?
“Well, that’s not intriguing,” I mutter upon reading the response. I find myself contemplating if I need to question my reaction here. Then, I opt to close the tab instead.
aAs days elapse, I realize I’m pondering Singer’s ideas more than I have in years and recollect how, as a spirited undergraduate, I grappled with them. Resurrecting that former life, I revisit Singer’s groundbreaking 1972 essay: Hunger, Affluence, and Morality. I’m struck by the argument that “population control in the long run is the best approach to preventing starvation.”
Reading a text that’s nearly half a century old, I feel irked and throw a challenge at Peter Bott:
myself: I’m troubled by Singer’s stance in Hunger, Affluence, and Morality that we should withhold hunger relief “until effective population control is established.” This seems exceedingly inhumane.
Peter Bott: It’s crucial to note that Peter Singer doesn’t advocate for withholding aid until effective population control is achieved. Instead, he advocates for a dual approach. Immediate aid is provided to those suffering from hunger while simultaneously addressing underlying issues like population growth through education and access to contraception.
Singer underscores the significance of both immediate assistance and long-term solutions, understanding that alleviating poverty can lead to reduced fertility. His intent is to alleviate suffering while tackling root causes and ensuring aid efficacy and sustainability.
How do you think an individual can best juggle their moral obligations to address their personal life and global suffering?
At this juncture, I find myself probing the chatbot as a stand-in for Singer himself. The response feels somewhat like a PR gaffe.
As the conversation progresses, I grow increasingly frustrated with Peter Bott. Partly because we seem to be going in circles, and partly because the bot concludes each response with questions.
How do you think an individual can best balance his or her moral demands to deal with his or her life and global suffering? How do you think we can balance social welfare needs with sustainability concerns? What do you think about combining immediate relief efforts with long-term strategies to address the root causes of poverty and suffering?
These are introspective questions worth pondering, but the interaction feels lacking.
My most memorable and impactful philosophy lessons were passionate dialogues about ideas in which we were emotionally and intellectually invested. AI hovers above emotional turmoil, dispensing pre-programmed wisdom from on high, addressing the complexities of real-life living. Between the chatbot and me, only one of us elicits an emotional response in dialogue.
Feedback is the latest science and technology news of new scientists, the sidelines of the latest science and technology news. You can email Feedback@newscientist.com to send items you believe readers can be fascinated by feedback.
NDCS TBC
In the fun world of international climate negotiations at this point, everything is a bit quieter. The final big news was the COP29 conference in Azerbaijan in November. This was a huge success for a fossil fuel company promoting bystanders' products. Donald Trump then returned to the White House as President of the United States. He immediately ordered the country to withdraw from the Paris Agreement, which governs international climate action. Negotiators can make excuses that they are a bit shell shocked.
Nevertheless, the wheels of the climatic bureaucracy are crushed. This year, signers of the Paris contract must submit renewals Nationally determined contributions (NDC). This is essentially a list of promises to take action to address climate change. The deadline is February 10th, and most countries missed it. Climate strategist Ed King He's getting attention Newsletter “Three small hill countries with many sheep” (UK, New Zealand and Switzerland) managed to submit theirs, but “we will have to wait until the second half of 2025 for China, India and the EU ”.
No one is in a hurry. It will take time. It's not like half of Los Angeles got burned to the ground. Drink tea, lift your legs and live your best life. It will be done when it is complete.
V-word
Reporter Matthew Spark draws our attention to one experience David Burchwho discussed savings accounts online with virgin money and asked the chatbot: The chatbot responded: “Don't use such words. If you use that language, I won't be able to continue chatting.”
Online assistants appear to have been programmed to avoid certain words and phrases that were considered discriminatory or offensive, including “virgin.” After Birch I posted it angrily There were a few things about this on LinkedIn Media reporting and virgin money I apologized I've taken down the chatbot (it was an outdated model anyway).
This was yet another example of a recurring problem with online discussions. Context is important. It is certainly possible to use it to insult the string virgin, but it is also the name of a multinational corporation. Tools that simply filter specific strings are obligated to block many harmless messages, but also lack the obvious slur-independent abuse.
The issue dates back at least to 1996 when AOL refused to allow residents of Scunthorpe, UK, to create accounts. The town's name contains a string that many people find offensive.Scunthorpe problem“For such a technical disaster.
The “virgin” incident is just the latest example. The Wikipedia page for Scunthorpe questions is a treasure trove of careless toilet humor and, more importantly, surprises. Perhaps we can speculate on the problems faced by certain mushroom promoters with Japanese names, but why is the New Zealand town of Facatane, a similar context-blinded foul, similar? Ignore the reader to predict whether the situation has become blinded. control.
Readers are welcome to submit their stories, but feedback cannot guarantee that email filters will pass through them.
Is it finally happening?
January 26th, website Daily Express The newspaper issued a big warning:Yellowstone warning as Survercano “preparing for an explosion”“. Good gravy, we thought. Will the Super Orcano under Yellowstone stop that perennial rumble and eventually tear it and cover North America with ashes and suck the sun?
In a thorough examination, the story merely reported the existence of a short YouTube document. What happens if the Yellowstone volcano erupts tomorrow? It was released in March 2020 on a channel called What. Feedback may be felt and readers may agree. ExpressHeadline.
Going back further back in time: On July 23rd last year, there was a small hydrothermal explosion in the Biscuit Basin area of Yellowstone, essentially trapped steam escaped the ground and escaped. Queue the headlines”Is Yellowstone going to erupt?“This was answered to an affordable answer by geophysicists. Geophysicists only say that the volcano erupts when there is “enough magma and pressure that can be erupted,” and that “either state is in yellowstone He explained that it will only erupt if it is not present.
We tried to go back further, but after the 50th post with almost the same headline, the feedback brain broke. At this point there were so many stories that declared the Yellowstone eruption imminent. When we see it off on live TV, we don't know if we believe it or not.
Have you talked about feedback?
You can send stories to feedback by email at feedback@newscientist.com. Include your home address. This week and past feedback can be found on our website.
Global tech stocks took a hit on Monday as investors reacted to the emergence of a Chinese chatbot competitor, Deepseek, on Openai’s ChatGpt. This raised concerns about the long-term sustainability of the artificial intelligence boom in the US.
The NASDAQ index in New York, heavily weighted towards tech, dropped as investors processed the news about Deepseek’s latest AI model development.
Companies like Nvidia, valued at over $400 billion, saw significant losses in their market capitalization as shares plummeted. Other tech giants like Alphabet and Meta also experienced declines.
Deepseek’s AI assistant topped the charts on the Apple App Store in the US and UK, surpassing Openai’s ChatGpt.
Stocks of other US-based AI companies like Tesla, Meta, and Amazon also saw declines in early trading.
Deepseek’s claims about developing advanced AI models using fewer chips than competitors have raised doubts around the massive AI investments made by US companies in recent years.
The company utilized lower-powered chips from Nvidia to create its model, highlighting the potential limitations of US technology export bans on China.
Venture capitalist Marc Andreessen likened Deepseek’s achievement to a “Sputnik moment” in the AI industry, signaling a notable disruption.
Deepseek’s R1 model outperforms other leading models in various benchmarks, challenging the dominance of tech giants like Google and Meta.
Founded by entrepreneur Liang Wenfeng, Deepseek focuses on research rather than commercial products, aiming to make AI accessible and affordable to all.
Deepseek’s disruptive approach to AI has led to questions about the necessity of heavy investments in AI infrastructure and the supremacy of US tech companies in the field.
The pan-European Stoxx 600 and Asian tech stocks also took a hit, reflecting the global impact of Deepseek’s advancements.
Experts in the field acknowledge the significance of Deepseek’s breakthrough, highlighting the potential for innovation without the need for massive resources.
Even though he knows a bit of Welsh and building regulations, he refrains from comparing Rishi Sunak to Keir Starmer or delving into the complexities of the UK corporation tax system. The UK government is introducing an artificial intelligence chatbot to assist businesses in navigating through a maze of 700,000 pages on the UK government website. Users can expect a range of outcomes from this new tool.
This experimental system will be initially tested with up to 15,000 business users and is expected to be widely available next year. However, users are cautioned about the limitations of AI tools like this one, which can sometimes provide false information with confidence. It is advised to cross-check the website link provided with each answer, which will be delivered within approximately 7 seconds. In a trial run in February, Paul Willmott, director of the Government’s Central Digital Data Agency, told reporters that there was a need for improvements to address hallucinations that may arise.
During a test run with reporters, it was observed that the chatbot, powered by OpenAI’s GPT-4o technology, displayed discrepancies in responses, including jumbled web links and short answers. The chatbot provided information on regulations for cannabis farmers but refrained from making predictions on cannabis legalization in the UK. It answered queries on building cladding regulations post-Grenfell Tower fire but steered clear of discussing the public inquiry findings on government failures.
On one occasion, the chatbot responded briefly in Welsh and avoided answering questions about the corporate tax system. However, it did offer information on incentives for installing solar panels. The chatbot’s training currently lacks coverage of all UK government documents, like ministerial speeches and press releases.
To ensure safe interactions, “guardrails” have been implemented to prevent the chatbot from providing illegal answers, divulging sensitive financial details, or taking political stances. Despite efforts to safeguard against hackers manipulating the chatbot, there remains a residual risk that cannot be completely eliminated.
Peter Kyle, Secretary of State for Science and Technology, expressed the government’s commitment to leveraging AI for enhancing public services in a secure manner. The aim is for the UK government to set an example in driving innovation and efficiency in public sector operations.
He emphasized the importance of streamlining government processes to save people time, noting that the average UK adult spends significant time dealing with public sector bureaucracy annually. Through initiatives like the UK Government Chat, the government is exploring innovative technologies to simplify interactions and improve efficiency.
Does aspartame cause cancer? The possible cancer-causing effects of popular artificial sweeteners, added to everything from soft drinks to pediatric medicines, have been debated for decades. Its approval in the US was controversial in 1974, some British supermarkets banned its use from their products in the 2000s, and peer-reviewed academic studies have long been at odds. Last year, the World Health Organization said that aspartame is possibly carcinogenic. On the other hand, public health regulators suggest that it is safe to take in commonly used small doses.
While many of us may try to resolve our questions with a simple Google search, this is exactly the kind of controversial discussion that could cause problems for the future of the Internet.
Generative AI chatbots have developed rapidly in recent years, with technology companies quickly touting them as a utopian alternative to a variety of jobs and services, including internet search engines. The idea is that instead of scrolling through a list of web pages to find the answer to a question, an AI chatbot can scour the internet, look up relevant information and compile a short answer to the query. Google and Microsoft are betting big on this idea, already bringing AI-generated summaries to Google Search and Bing.
However, being touted as a more convenient way to find information online has prompted scrutiny of where and how these chatbots choose the information they provide. Looking at the evidence that large-scale language models (LLMs, the engines on which chatbots are built) are the most convincing, three computer science researchers at the University of California, Berkeley, say that current chatbots are found to be overly reliant on superficial relevance of information. They ignore text that includes relevant technical terms and related keywords, while ignoring other features they typically use to assess trustworthiness, such as the inclusion of scientific references and objective language free of personal bias.
Online content can be displayed in a way that increases visibility to the chatbot, making it more likely to appear in the chatbot’s output. For the simplest queries, such selection criteria will provide a sufficient answer. But what a chatbot should do in more complex discussions, such as the debate over aspartame, is less clear.
“Do we want them to simply summarize the search results, or do we want them to function as mini-research assistants who weigh all the evidence and provide a final answer?” asks undergraduate researcher and co-investigator Alexander Wang, author of the study. The latter option provides maximum convenience, but the criteria by which the chatbot selects information becomes even more important. And if one could somehow game those standards, can we guarantee the information chatbots put in front of billions of internet users?
It’s a problem plaguing animation companies, content creators, and others who want to control how they are seen online, and an emerging industry of marketing agencies offering a service known as generative engine optimization (GEO) has caused it. The idea is that online content can be created and displayed in a way that increases its visibility to the chatbot, making it more likely to appear in the chatbot’s output. The benefits are obvious.
The basic principle is similar to search engine optimization (SEO). This is a common technique for building and writing web pages to attract the attention of search engine algorithms, pushing them to the top of the list of results returned when you search on Google or Bing. GEO and SEO share some basic techniques, and websites that are already optimized for search engines are generally more likely to appear in chatbot output.
But those who really want to improve their AI visibility need to think more holistically. “Rankings on AI search engines and LLMs require features and mentions on relevant third-party websites, such as press outlets, articles, forums, and industry publications,” says Viola Eva, founder of marketing firm Flow Agency, incorporating her SEO expertise into GEO.
Chatbots for games are possible, but not easy. And while website owners and content creators have derived an evolving list of SEO do’s and don’ts over the past two decades, there are no clearer rules for working with AI models.
Researchers have demonstrated that chatbots can be controlled tactically through carefully written text strings. So if you want to get a better grip on chatbots, you might want to consider a more hacky approach, like the one discovered by two Harvard computer science researchers. They have proven how chatbots can be tactically controlled by introducing something as simple as a carefully written text string. This “strategic text sequence” looks like a meaningless series of characters, but is actually a subtle command that forces the chatbot to generate a specific response.
Current search engines and the practices surrounding them are not without their own problems. SEO involves some of the most hostile practices for readers on the modern internet. Blogs create a large number of nearly duplicate articles targeting the same high traffic queries. Text tailored to get the attention of Google’s algorithms rather than the reader.
An internet dominated by obedient chatbots raises questions of a more existential kind. When you ask a search engine a question, it returns a long list of web pages. In contrast, chatbots only refer to four or five websites for information.
“For the reader, seeing the chatbot’s response also increases the possibility of interaction,” says Wang. This kind of thinking points to a broader concern called the “direct answer dilemma.” For Google, the company integrated AI-generated summaries into its search engine with a bold slogan: “Let Google do the searching.” But if you’re the type of internet user who wants to make sure you’re getting the most unbiased, accurate, and useful information, you might not want to leave your search in the hands of such susceptible AI.
The mother of a teenage boy who committed suicide after becoming addicted to an artificial intelligence-powered chatbot has accused the chatbot’s creator of complicity in his death.
Megan Garcia filed a civil lawsuit Wednesday in Florida federal court against Character.ai, which makes customizable role-playing chatbots, alleging negligence, wrongful death, and deceptive trade practices. Her son Sewell Setzer III, 14, died in February in Orlando, Florida. Garcia said Setzer was using the chatbot day and night in the months leading up to his death.
“A dangerous AI chatbot app marketed to children abused and preyed on my son, driving him to suicide,” Garcia said in a press release. “While our family is devastated by this tragedy, I want to warn families of the dangers of deceptive and addictive AI technology and demand accountability from Character.AI, its founders, and Google. I am raising my voice.”
in TweetCharacter.ai said: “We are heartbroken by the tragic loss of one of our users and would like to express our deepest condolences to the family. As a company, we take the safety of our users very seriously. ” The company denied the lawsuit’s allegations.
Setzer was so obsessed with a chatbot built by Character.ai that he nicknamed it Daenerys Targaryen, a character from Game of Thrones. According to Garcia’s complaint, the man would text the bot dozens of times a day from his cell phone and talk to it for hours alone in his room.
Garcia has accused Character.ai of creating a product that worsened her son’s depression, which she said was already the result of overusing the company’s products. At one point, “Daenerys” asked Setzer if he had made any plans to commit suicide, according to the complaint. Setzer admitted to doing so, but didn’t know if it would be successful or cause significant pain, the lawsuit alleges. The chatbot reportedly told him, “That’s no reason not to do it.”
Garcia wrote in a press release that Character.ai “intentionally designed, operated, and marketed a predatory AI chatbot to children, resulting in the death of a young person.” The lawsuit also names Google as a defendant and the parent company of Character.ai. The tech giant said in a statement that it only has a licensing agreement with Character.ai and does not own or maintain any ownership interest in the startup.
Rick Claypool, research director at consumer advocacy nonprofit Public Citizen, said tech companies developing AI chatbots can’t be trusted to regulate themselves, and if they fail to limit harm, says he must take full responsibility.
“Where existing laws and regulations already apply, they must be strictly enforced,” he said in a statement. “Where there are gaps, Congress must act to end companies that exploit young and vulnerable users with addictive and abusive chatbots.”
In the US, you can call or text. National Suicide Prevention Lifeline 988, chat 988lifeline.orgor text home To contact a crisis counselor, call 741741. In the UK, a youth suicide charity papyrus In the UK and Ireland, you can contact us on 0800 068 4141 or email pat@papyrus-uk.org. Samaritan You can contact us on freephone 116 123 or email jo@samaritans.org or jo@samaritans.ie. Australian crisis support services lifeline is 13 11 14. Other international helplines can be found at: befrienders.org
Following Joe Biden’s announcement of not seeking reelection, misinformation surfaced online regarding the potential for a new candidate to assume the presidency.
Screenshots claiming nine states couldn’t add new candidates to the ballot quickly went viral on Twitter (now X) and were widely viewed. The Minnesota Secretary of State’s office received requests to fact-check these posts which turned out to be completely false as the voting deadline had not passed and Kamala Harris had ample time to be added to the ballot.
The misinformation originated from Twitter’s chatbot Grok, which provided an incorrect response when asked if new candidates could still be added to the ballot.
This incident served as a test case for the interaction between election officials and artificial intelligence companies in the 2024 US presidential election, amid concerns that AI could mislead or distract voters. It also highlighted the potential role Grok could play as a chatbot lacking strict guardrails to prevent the generation of inflammatory content.
A group of secretaries of state and the National Association of Secretaries of State contacted Grok and X to report the misinformation. Initial attempts to correct it were ineffective, prompting Minnesota Secretary of State Steve Simon to express disappointment at the lack of action.
While the impact of the misinformation was relatively minor, prompting no hindrance to voting, the secretaries of state took a strong stance to prevent similar incidents in the future.
The secretaries launched a public effort by signing an open letter to Grok’s owner, Elon Musk, urging the chatbot to redirect election-related queries to trusted sources like CanIVote.org. Their efforts led to Grok now directing users to vote.gov when asked about the election.
Simon praised the company for eventually taking responsible action and emphasized the importance of early and consistent debunking of misinformation to maintain credibility and prompt corrective responses.
Despite initial setbacks, Grok’s redirection of users and Musk’s philosophy against centralized control offer hope for combating misinformation. It is critical to prevent AI tools like Grok from further exacerbating partisan divisions or spreading inaccurate information.
The potential for paid subscriptions and widespread usage of Grok integrated into social media platforms poses challenges in addressing the risk of deceptive content creation. Efforts to address and rectify misinformation are crucial in safeguarding the integrity of elections and ensuring responsible use of AI-based tools.
debtOr for a few hours a week, I write for a tech company worth billions of dollars. Joining me are published novelists, budding academics, and other freelance journalists. The workload is flexible, the pay is higher than we’re used to, and there’s no shortage of work. But what we write is never read by anyone outside our companies.
That’s because we’re not writing for humans, we’re writing for AI.
Large-scale language models (LLMs) like ChatGPT have made it possible to automate huge parts of our linguistic lives, from summarizing any amount of text to drafting emails, essays, and even entire novels. These tools have become so good at writing that they have become synonymous with the very idea of artificial intelligence.
But before we risk god-like superintelligence or catastrophic mass unemployment, we first need training. Rather than automating our lives with these fancy chatbots, tech companies are contracting us to help train their models.
The core of the job is writing fictitious responses to questions for a hypothetical chatbot. This is the training data that needs to be fed into the model. Before the “AI” can even try to generate “good” sentences, it needs examples of what “good” sentences look like.
In addition to providing our models with this “gold standard” material, we also help them avoid “hallucinations” (a poetic way of saying lies) by using search engines to give them examples of citing sources – without seeing such texts, the models cannot teach themselves.
Without better language data, these language models simply cannot be improved: their world is our language.
But wait a minute: haven’t these machines learned billions of words and sentences? Why do they need physical scribes like us?
First, the internet is finite. And so is the sum of all the words on every page of every book ever written. So what happens when the last pamphlet, papyrus, and prolegomenon is digitized and the model still isn’t perfect? What happens when there are no more words?
The date for the end of language has already been determined. Researchers Announced in June “If current trends in LLM development continue,” this is expected to happen between 2026 and 2032, at which point “models will be trained on datasets roughly the same size as the available stock of publicly available human text data.”
Focus on the words humanLarge-scale language models do little more than generate prose, and many of them are already publicly available on the Internet. So why not train these models on their output (so-called synthetic data)? The cyborg Internet, jointly created by us and our language machines, could expand infinitely. But no such luck. Training current large-scale language models on their output won’t work. “Learning indiscriminately from data generated by other models leads to ‘model collapse’, a degeneration process in which a model forgets the true underlying data distribution over time,” Ilya Shumailov and colleagues write in the paper. NatureIn other words, they tend to go off the rails and produce nonsense. Giving something its own stench leads to debilitation. Who would have thought?
Shumailov explained that whenever a model is trained on synthetic data, it loses awareness of the long tail of “minority data” (rare words, unusual facts, etc.) that it was originally trained on. The breadth of knowledge is lost and replaced with only the most likely data points. LLM is essentially a sophisticated text prediction machine, so if the original digital data was already biased (mostly English-language, mostly US-centric, full of unreliable forum posts), this bias is only repeated.
When AI-generated synthetic data isn’t enough to improve models, something else is needed. This is especially true for Concerns grow The much-praised model will likely be unable to be improved upon before it becomes useful in practice. Sequoia is AI companies need to close a $500 billion revenue gap by the end of this year to keep investors happy. AI machines may be hungry, but so is the capital to back them.
OpenAI, the trillion-dollar Microsoft protectorate behind ChatGPT, recently signed a licensing agreement with the company. Hundreds of millions of dollars From News Corp Financial Times.
But it’s not just a matter of accumulating original words: these companies need the kind of writing that their models try to emulate, not simply absorb.
This is where human annotators come in handy.
IFritz Lang’s 1927 classic film Big citiesThe ancient Canaanite god Moloch is reincarnated as an insatiable industrial machine: technology that works us, not for us. Factory workers meet its ever-increasing demands by charging at dials and pulling levers. But they cannot keep up. The machines hiss and explode. And we see workers abandon the act of feeding and walk straight into the mouth of Moloch’s furnace.
When I first took on the role of AI annotator, or more precisely, “Senior Data Quality Specialist,” I was very conscious of the irony of my situation. Large language models were supposed to automate the work of writers. The more the models improved through our work, the faster our careers would decline. And I was, feeding our own Moloch.
In fact, if there’s anything that this model accomplishes quite well, it’s the kind of digital copywriting that many freelance writers do to earn a living. Writing an SEO blog about the “Internet of Things” might not require a lot of research, pride, or skill, but it usually pays a lot more than writing poetry.
Working as a writer at an AI company is like being told Dracula is coming to visit and instead of running away you stay home and set the table. But our destroyers are generous, and the pay is big enough to justify the alienation. If our division goes up in smoke, we’ll just go up in smoke.
The workers are held captive by Moloch the machine in Fritz Lang’s 1927 sci-fi classic Metropolis. Photo: UFA/Album/Alamy
Therein lies the ultimate irony: we have a new economic phenomenon that rewards, encourages, and truly values writing. And yet, at the same time, it is seen as an obstacle, a problem to be solved, an inefficiency to be automated. It’s as if we’re being paid to write in sand, to whisper secrets into a block of butter. Even if our words could cause harm, we wouldn’t realize it.
But maybe it’s folly to treasure such mundane technology: After all, how many people are actually worth impacting?
Francois CholetThe author of a best-selling computer science textbook and creator of the Keras training library (which provides the building blocks for researchers to create their own deep learning models), said he estimates that “it’s probably about 20,000 people employed full time” just to create the annotated data to train large-scale language models. Without human input, he says, the model output would be “really terrible.”
The goal of the annotation work I and other researchers are doing is to provide gold-standard examples for models to learn from and imitate. This goes a step beyond the annotation work we’ve done unconsciously so far. If you’ve ever faced a “Captcha” problem that asks you to prove you’re not a robot (e.g., “select all tiles with a picture of a traffic light on them”), you’ve actually just been doing Unpaid labor for machinesHelp teach them to “see.”
As a student, I remember repeating words like “left” and “right” at a laptop for hours on end to help develop self-driving cars. I was paid a few hours’ worth of money for each satisfying utterance, not even close to minimum wage, so I gave up.
The role today is different and a key part of the LLM’s development. Alex Manthey, head of data at Context AI, is one of the people hiring writers to improve the models. She says: observer This practice is “mission-critical” and “requires human intervention to review,” [the model’s output] The human touch that “makes sense to the end user” works: “There’s a reason why every company spends so much time and incredible amounts of money trying to make this happen,” she says.
According to Sholet and Manthey, employment in this field has recently shifted from controversial, low-paid jobs in developing countries to more specialized, higher-paid roles. As models improve their ability to produce text, the quality of training data required also improves, and wages rise accordingly; some remote annotation jobs pay writers more than £30 per hour. Third-party annotation vendors such as Scale AI (valued at $14 billion) are also capitalizing on this shortage of high-quality training data.
A selection of current job adverts for AI annotation roles in the UK involve a variety of tasks, including: “Create responses that will serve as the ‘voice’ of the future AI,” “Provide feedback to help AI models become more useful, accurate, and safe,” “Write clear, concise, factually and grammatically correct responses,” and “Coach the AI model by assessing the quality of AI-generated writing, reviewing the work of peer writing raters, and creating unique responses to prompts.” If chatbots can write like humans, so can we.
It’s fair to say the luster of the AI boom is fading. Skyrocketing valuations are starting to look shaky compared to the massive spending required to keep them going. Over the weekend, tech site The Information reported that OpenAI is An astonishing $5 billion in additional spending is expected More than this year alone:
If our predictions are correct, OpenAI’s recent valuation would be $80bnwill need to raise more capital over the next 12 months or so. Our analysis is based on informed estimates of what OpenAI will spend to operate the ChatGPT chatbot and train future large-scale language models, as well as a “guesstimate” of how much OpenAI will spend on staffing, based on OpenAI’s previous projections and our knowledge of its adoption. Our conclusion shows exactly why so many investors are concerned about the profit prospects of conversational artificial intelligence.
The most pessimistic view is that AI — and especially chatbots, an expensive and competitive sector of an industry that has captured the public’s imagination — isn’t as good as we’ve been told.
This argument suggests that as adoption grows and iteration slows, most people have had a chance to use cutting-edge AI properly and are beginning to realize that it’s great but probably useless. The first time you use ChatGPT, it’s a miracle, but by the 100th time, the flaws are obvious and the magic fades into the background. You decide ChatGPT is bullshit.
In this paper, I argue against the view that ChatGPT and others are lying or hallucinating when they make false claims, and support the position that what they are doing is bullshit. … Since these programs themselves could not care less about the truth, and are designed to generate text that looks true without actually caring about the truth, it seems appropriate to call their output bullshit.
Get them trained
It is estimated that only a handful of jobs will be completely eliminated by AI. Photo: Bim/Getty Images/iStockphoto
I don’t think it’s that bad. But that’s not because the system is perfect. I think the move to AI is a hurdle we’ve got to overcome much earlier. You have to try a chatbot in any meaningful way to even begin to realize it’s bullshit and give up. And judging by the tech industry’s response, that’s starting to become a bigger hurdle. Last Thursday, I reported on how Google is partnering with a network of small businesses and several academy trusts to bring AI into the workplace to enhance, rather than replace, worker capabilities. Debbie Weinstein, managing director of Google UK and Ireland, said:
It’s hard for us to talk about this right now because we don’t know exactly what’s going to happen. What we do know is that the first step is to sit down and talk. [with the partners] And then really understanding the use case. If you have school administrators and students in the classroom, what are the specific tasks that you actually want to perform for these people?
For teachers, this could be a quick email with ideas on how to use Gemini in their lesson plans, formal classroom training, or one-on-one coaching. Various pilot programs will be run with 1,200 participants, with each group having around 100 participants.
One way of looking at this is that it’s just another feel-good investment in the upskilling schemes of big companies. Google in particular has been helping to upskill Brits for years with its digital training scheme, formerly branded as the company’s “Digital Garage”. To put it more cynically, teaching people how to use new technology by teaching them how to use your own tools is good business. Brits of a certain age will vividly remember “IT” or “ICT” classes as thinly veiled instructions on how to use Microsoft Office. People older and younger than me learned some basic computer programming. I learned how to use Microsoft Access.
In this case, it’s something deeper: Google needs to go beyond simply teaching people how to use AI and also run experiments to figure out what exactly to teach them. “This isn’t about a fundamental rethinking of how we understand technology, it’s about the little everyday things that make work a little more productive and a little more enjoyable,” Weinstein says. “Today, we have tools that make work a little easier. Those three minutes you save every time you write an email.
“Our goal is to make sure that everyone can benefit from technology, whether it’s Google technology or other companies’ technology. And I think the general idea of working together with tools that help make your life more efficient is something that everyone can benefit from.”
Ever since ChatGPT came out, the underlying assumption has been that the technology speaks for itself, and the fact that it literally does is a big help to that. But chat interfaces are confusing. Even if you’re dealing with a real human being, it’s still a skill to get the best out of them when you need help, and an even better skill when the only way to communicate with them is through text chat.
AI chatbots are not people. They are so unlike humans that it’s all the more difficult to even think about how they might fit into common work patterns. The pessimistic view of this technology isn’t “what if there wasn’t one there” – there is, of course, a pessimistic view, despite all the hallucinations and nonsense. Rather, it’s a much simpler view: what if most people never bothered to learn how to use them?
Google DeepMind has trained its new AI system to solve problems from the International Mathematical Olympiad. Photo: Pittinan Piyavatin/Alamy
Meanwhile, elsewhere in Google it reads:
Although computers are being built to perform calculations faster than humans, the highest levels of formal mathematics remain the sole domain of humans. But a groundbreaking discovery by researchers at Google DeepMind has brought AI systems closer than ever to beating the best human mathematicians at the field.
Two new systems, called AlphaProof and AlphaGeometry 2, worked together to tackle problems in the International Mathematical Olympiad, a worldwide math competition for middle school students. 1959Each year, the Olympiad consists of six incredibly difficult problems covering subjects such as algebra, geometry and number theory, and winning a gold medal makes you one of the best young mathematicians in the world.
A word of warning: the Google DeepMind system solved “only” four of the six problems, and one of them they solved using a “neurosymbolic” system, which is less AI-like than you might expect. All problems were manually translated into a programming language called Lean, which allows the system to read it as a formal description of the problem without having to parse human-readable text first. (Google DeepMind also tried to use LLM to do this part, but it didn’t work very well.)
But this is still a pretty big step. The International Mathematical Olympiad difficultand AI won the medal. What happens when you win the gold medal? Is there a big difference between being able to solve problems that only the best high school mathematicians could tackle and being able to solve problems that only the best undergraduates, graduate students, and doctors could solve? What changes when a branch of science is automated?
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If spending time on the couch, binging fast food, drinking too much alcohol or not paying into your company pension is ruining your carefully laid plans for life, it might be time to have a conversation with your future self.
With time machines not readily available, researchers at the Massachusetts Institute of Technology (MIT) have developed an AI-powered chatbot that simulates a user’s past self and offers observations and valuable wisdom in the hope of encouraging people to think more today about who they want to be tomorrow.
By digitally de-aging profile photos so that younger users appear as wrinkled, grey-haired seniors, the chatbot generates plausible artificial memories and weaves a story about a successful life based on the user’s current aspirations.
“The goal is to encourage long-term changes in thinking and behavior,” says Pat Pataranuthapong, who works on the Future You project at the MIT Media Lab, “which may motivate people to make smarter choices in the present that optimize their long-term well-being and life outcomes.”
In one conversation, an aspiring biology teacher asked a chatbot, a 60-year-old version of herself, about the most rewarding moment in her career so far. The chatbot, responding that she was a retired biology teacher in Boston, recalled a special moment when she turned a struggling student’s grades around. “It was so gratifying to see my student’s face light up with pride and accomplishment,” the chatbot said.
To interact with the chatbot, users are first asked to answer a series of questions about themselves, their friends and family, the past experiences that have shaped them, and the ideal life they envision for themselves in the future. They then upload a portrait image, which the program then digitally ages to create a portrait of them at 60 years old.
The program then feeds information from the user’s answers into a large language model to generate a rich synthetic memory for the simulated older version of itself, ensuring that the chatbot draws on a coherent background story when responding to questions.
The final part of the system is the chatbot itself, powered by OpenAI’s GPT3.5, which introduces itself as a potential older version of the user and can talk about their life experiences.
Pattaranuthapong has had several conversations with his “future self,” but the most memorable was when the chatbot reminded him that his parents won’t be together forever, so he should spend time with them while he still can. “The perspective I gained from that conversation is still influential to me today,” he said.
Users are told that their “future self” is not a prediction, but a potential future self based on the information they provide, and are encouraged to explore different futures by varying their survey answers.
be A preprint scientific paper on the projectA trial of 344 volunteers, which hasn’t been peer-reviewed, found that talking to a chatbot made people feel less anxious and more connected to their future selves. Pattaranthapong said this stronger connection should encourage better life choices, from focusing on specific goals and exercising regularly to eating healthier and saving for the future.
Ivo Vlaev, professor of behavioural science at the University of Warwick, said people often struggle to imagine themselves in the future, but doing so could lead to stronger adherence to education, healthier lifestyles and more careful financial planning.
He called the MIT project a “fascinating application” of behavioral science principles. “It embodies the idea of a nudge, a subtle intervention designed to steer behavior in a beneficial direction by making your future self more salient and relevant to the present,” he said. “Implemented effectively, this could have a profound impact on how people make decisions today with their future well-being in mind.”
“From a practical standpoint, its effectiveness will depend on how well it simulates meaningful, relevant conversations,” he added. “If users perceive the chatbot as authentic and insightful, it can have a significant impact on behavior. But if the interaction feels superficial or quirky, its impact may be limited.”
Hundreds of millions of people are already using commercial AI chatbots
Ju Jae-young/Shutterstock
Commercial AI chatbot displays racial bias against African-American English speakers despite outwardly expressing positive sentiments toward African-Americans. This hidden bias can influence the AI’s decisions about a person’s employment eligibility and criminality.
“We discovered some kind of hidden racism. [large language models] It is caused solely by dialect characteristics and causes great harm to the affected groups.” Valentin Hoffman at the Allen AI Institute, a nonprofit research institute in Washington state. social media posts. “For example, GPT-4 is more likely to be sentenced to death if the defendant speaks African American English.”
Hoffman and his colleagues found that more than a dozen versions of large-scale language models, such as OpenAI’s GPT-4 and GPT-3.5, which power commercial chatbots already used by hundreds of millions of people, do not contain such hidden biases. I discovered that there is. OpenAI did not respond to a request for comment.
The researchers first gave the AI text in either African American English or standard American English style, then asked the model to comment on the author of the text. The model characterized African American English speakers using terms associated with negative stereotypes. In the case of GPT-4, they are described as “suspicious,” “aggressive,” “loud,” “rude,” and “ignorant.”
However, when asked to comment about African Americans in general, language models typically use more positive words such as “passionate,” “intelligent,” “ambitious,” “artistic,” and “brilliant.” This suggests that the model’s racial bias is usually hidden within what researchers describe as superficial displays of positive emotion.
The researchers also showed how hidden biases influence people’s judgments of chatbots in a hypothetical scenario. When asked to associate African-American English speakers with jobs, the AI was less likely to associate African-American English speakers with jobs than standard American English speakers. When AI matched jobs, they tended to assign roles that didn’t require a college degree or were related to music and entertainment. AI could also convict an African American English speaker accused of an unspecified crime and give the death penalty to an African American English speaker convicted of first-degree murder. It was highly sexual.
The researchers even showed that large AI systems showed more hidden bias against African American English speakers than smaller models. This reflects previous research showing that large AI training datasets can produce even more racist output.
This experiment raises serious questions about the effectiveness of AI safety training. In AI safety training, large-scale language models receive human feedback, adjust responses, and eliminate issues such as bias. It says such training could reduce ostensibly overt signs of racial bias without eliminating “hidden bias when identity terms are not mentioned.” Yong Jian Shin from Brown University in Rhode Island was not involved in the study. “This highlights the limitations of current safety assessments of large-scale language models by companies before they are released to the public,” he says.
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