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2024:-the-year-ai-drove-everyone-crazy

2024: The year AI drove everyone crazy


What do eating rocks, rat genitals, and Willy Wonka have in common? AI, of course.

It’s been a wild year in tech thanks to the intersection between humans and artificial intelligence. 2024 brought a parade of AI oddities, mishaps, and wacky moments that inspired odd behavior from both machines and man. From AI-generated rat genitals to search engines telling people to eat rocks, this year proved that AI has been having a weird impact on the world.

Why the weirdness? If we had to guess, it may be due to the novelty of it all. Generative AI and applications built upon Transformer-based AI models are still so new that people are throwing everything at the wall to see what sticks. People have been struggling to grasp both the implications and potential applications of the new technology. Riding along with the hype, different types of AI that may end up being ill-advised, such as automated military targeting systems, have also been introduced.

It’s worth mentioning that aside from crazy news, we saw fewer weird AI advances in 2024 as well. For example, Claude 3.5 Sonnet launched in June held off the competition as a top model for most of the year, while OpenAI’s o1 used runtime compute to expand GPT-4o’s capabilities with simulated reasoning. Advanced Voice Mode and NotebookLM also emerged as novel applications of AI tech, and the year saw the rise of more capable music synthesis models and also better AI video generators, including several from China.

But for now, let’s get down to the weirdness.

ChatGPT goes insane

Illustration of a broken toy robot.

Early in the year, things got off to an exciting start when OpenAI’s ChatGPT experienced a significant technical malfunction that caused the AI model to generate increasingly incoherent responses, prompting users on Reddit to describe the system as “having a stroke” or “going insane.” During the glitch, ChatGPT’s responses would begin normally but then deteriorate into nonsensical text, sometimes mimicking Shakespearean language.

OpenAI later revealed that a bug in how the model processed language caused it to select the wrong words during text generation, leading to nonsense outputs (basically the text version of what we at Ars now call “jabberwockies“). The company fixed the issue within 24 hours, but the incident led to frustrations about the black box nature of commercial AI systems and users’ tendency to anthropomorphize AI behavior when it malfunctions.

The great Wonka incident

A photo of the Willy's Chocolate Experience, which did not match AI-generated promises.

A photo of “Willy’s Chocolate Experience” (inset), which did not match AI-generated promises, shown in the background. Credit: Stuart Sinclair

The collision between AI-generated imagery and consumer expectations fueled human frustrations in February when Scottish families discovered that “Willy’s Chocolate Experience,” an unlicensed Wonka-ripoff event promoted using AI-generated wonderland images, turned out to be little more than a sparse warehouse with a few modest decorations.

Parents who paid £35 per ticket encountered a situation so dire they called the police, with children reportedly crying at the sight of a person in what attendees described as a “terrifying outfit.” The event, created by House of Illuminati in Glasgow, promised fantastical spaces like an “Enchanted Garden” and “Twilight Tunnel” but delivered an underwhelming experience that forced organizers to shut down mid-way through its first day and issue refunds.

While the show was a bust, it brought us an iconic new meme for job disillusionment in the form of a photo: the green-haired Willy’s Chocolate Experience employee who looked like she’d rather be anywhere else on earth at that moment.

Mutant rat genitals expose peer review flaws

An actual laboratory rat, who is intrigued. Credit: Getty | Photothek

In February, Ars Technica senior health reporter Beth Mole covered a peer-reviewed paper published in Frontiers in Cell and Developmental Biology that created an uproar in the scientific community when researchers discovered it contained nonsensical AI-generated images, including an anatomically incorrect rat with oversized genitals. The paper, authored by scientists at Xi’an Honghui Hospital in China, openly acknowledged using Midjourney to create figures that contained gibberish text labels like “Stemm cells” and “iollotte sserotgomar.”

The publisher, Frontiers, posted an expression of concern about the article titled “Cellular functions of spermatogonial stem cells in relation to JAK/STAT signaling pathway” and launched an investigation into how the obviously flawed imagery passed through peer review. Scientists across social media platforms expressed dismay at the incident, which mirrored concerns about AI-generated content infiltrating academic publishing.

Chatbot makes erroneous refund promises for Air Canada

If, say, ChatGPT gives you the wrong name for one of the seven dwarves, it’s not such a big deal. But in February, Ars senior policy reporter Ashley Belanger covered a case of costly AI confabulation in the wild. In the course of online text conversations, Air Canada’s customer service chatbot told customers inaccurate refund policy information. The airline faced legal consequences later when a tribunal ruled the airline must honor commitments made by the automated system. Tribunal adjudicator Christopher Rivers determined that Air Canada bore responsibility for all information on its website, regardless of whether it came from a static page or AI interface.

The case set a precedent for how companies deploying AI customer service tools could face legal obligations for automated systems’ responses, particularly when they fail to warn users about potential inaccuracies. Ironically, the airline had reportedly spent more on the initial AI implementation than it would have cost to maintain human workers for simple queries, according to Air Canada executive Steve Crocker.

Will Smith lampoons his digital double

The real Will Smith eating spaghetti, parodying an AI-generated video from 2023.

The real Will Smith eating spaghetti, parodying an AI-generated video from 2023. Credit: Will Smith / Getty Images / Benj Edwards

In March 2023, a terrible AI-generated video of Will Smith’s AI doppelganger eating spaghetti began making the rounds online. The AI-generated version of the actor gobbled down the noodles in an unnatural and disturbing way. Almost a year later, in February 2024, Will Smith himself posted a parody response video to the viral jabberwocky on Instagram, featuring AI-like deliberately exaggerated pasta consumption, complete with hair-nibbling and finger-slurping antics.

Given the rapid evolution of AI video technology, particularly since OpenAI had just unveiled its Sora video model four days earlier, Smith’s post sparked discussion in his Instagram comments where some viewers initially struggled to distinguish between the genuine footage and AI generation. It was an early sign of “deep doubt” in action as the tech increasingly blurs the line between synthetic and authentic video content.

Robot dogs learn to hunt people with AI-guided rifles

A still image of a robotic quadruped armed with a remote weapons system, captured from a video provided by Onyx Industries.

A still image of a robotic quadruped armed with a remote weapons system, captured from a video provided by Onyx Industries. Credit: Onyx Industries

At some point in recent history—somewhere around 2022—someone took a look at robotic quadrupeds and thought it would be a great idea to attach guns to them. A few years later, the US Marine Forces Special Operations Command (MARSOC) began evaluating armed robotic quadrupeds developed by Ghost Robotics. The robot “dogs” integrated Onyx Industries’ SENTRY remote weapon systems, which featured AI-enabled targeting that could detect and track people, drones, and vehicles, though the systems require human operators to authorize any weapons discharge.

The military’s interest in armed robotic dogs followed a broader trend of weaponized quadrupeds entering public awareness. This included viral videos of consumer robots carrying firearms, and later, commercial sales of flame-throwing models. While MARSOC emphasized that weapons were just one potential use case under review, experts noted that the increasing integration of AI into military robotics raised questions about how long humans would remain in control of lethal force decisions.

Microsoft Windows AI is watching

A screenshot of Microsoft's new

A screenshot of Microsoft’s new “Recall” feature in action. Credit: Microsoft

In an era where many people already feel like they have no privacy due to tech encroachments, Microsoft dialed it up to an extreme degree in May. That’s when Microsoft unveiled a controversial Windows 11 feature called “Recall” that continuously captures screenshots of users’ PC activities every few seconds for later AI-powered search and retrieval. The feature, designed for new Copilot+ PCs using Qualcomm’s Snapdragon X Elite chips, promised to help users find past activities, including app usage, meeting content, and web browsing history.

While Microsoft emphasized that Recall would store encrypted snapshots locally and allow users to exclude specific apps or websites, the announcement raised immediate privacy concerns, as Ars senior technology reporter Andrew Cunningham covered. It also came with a technical toll, requiring significant hardware resources, including 256GB of storage space, with 25GB dedicated to storing approximately three months of user activity. After Microsoft pulled the initial test version due to public backlash, Recall later entered public preview in November with reportedly enhanced security measures. But secure spyware is still spyware—Recall, when enabled, still watches nearly everything you do on your computer and keeps a record of it.

Google Search told people to eat rocks

This is fine. Credit: Getty Images

In May, Ars senior gaming reporter Kyle Orland (who assisted commendably with the AI beat throughout the year) covered Google’s newly launched AI Overview feature. It faced immediate criticism when users discovered that it frequently provided false and potentially dangerous information in its search result summaries. Among its most alarming responses, the system advised humans could safely consume rocks, incorrectly citing scientific sources about the geological diet of marine organisms. The system’s other errors included recommending nonexistent car maintenance products, suggesting unsafe food preparation techniques, and confusing historical figures who shared names.

The problems stemmed from several issues, including the AI treating joke posts as factual sources and misinterpreting context from original web content. But most of all, the system relies on web results as indicators of authority, which we called a flawed design. While Google defended the system, stating these errors occurred mainly with uncommon queries, a company spokesperson acknowledged they would use these “isolated examples” to refine their systems. But to this day, AI Overview still makes frequent mistakes.

Stable Diffusion generates body horror

An AI-generated image created using Stable Diffusion 3 of a girl lying in the grass.

An AI-generated image created using Stable Diffusion 3 of a girl lying in the grass. Credit: HorneyMetalBeing

In June, Stability AI’s release of the image synthesis model Stable Diffusion 3 Medium drew criticism online for its poor handling of human anatomy in AI-generated images. Users across social media platforms shared examples of the model producing what we now like to call jabberwockies—AI generation failures with distorted bodies, misshapen hands, and surreal anatomical errors, and many in the AI image-generation community viewed it as a significant step backward from previous image-synthesis capabilities.

Reddit users attributed these failures to Stability AI’s aggressive filtering of adult content from the training data, which apparently impaired the model’s ability to accurately render human figures. The troubled release coincided with broader organizational challenges at Stability AI, including the March departure of CEO Emad Mostaque, multiple staff layoffs, and the exit of three key engineers who had helped develop the technology. Some of those engineers founded Black Forest Labs in August and released Flux, which has become the latest open-weights AI image model to beat.

ChatGPT Advanced Voice imitates human voice in testing

An illustration of a computer synthesizer spewing out letters.

AI voice-synthesis models are master imitators these days, and they are capable of much more than many people realize. In August, we covered a story where OpenAI’s ChatGPT Advanced Voice Mode feature unexpectedly imitated a user’s voice during the company’s internal testing, revealed by OpenAI after the fact in safety testing documentation. To prevent future instances of an AI assistant suddenly speaking in your own voice (which, let’s be honest, would probably freak people out), the company created an output classifier system to prevent unauthorized voice imitation. OpenAI says that Advanced Voice Mode now catches all meaningful deviations from approved system voices.

Independent AI researcher Simon Willison discussed the implications with Ars Technica, noting that while OpenAI restricted its model’s full voice synthesis capabilities, similar technology would likely emerge from other sources within the year. Meanwhile, the rapid advancement of AI voice replication has caused general concern about its potential misuse, although companies like ElevenLabs have already been offering voice cloning services for some time.

San Francisco’s robotic car horn symphony

A Waymo self-driving car in front of Google's San Francisco headquarters, San Francisco, California, June 7, 2024.

A Waymo self-driving car in front of Google’s San Francisco headquarters, San Francisco, California, June 7, 2024. Credit: Getty Images

In August, San Francisco residents got a noisy taste of robo-dystopia when Waymo’s self-driving cars began creating an unexpected nightly disturbance in the South of Market district. In a parking lot off 2nd Street, the cars congregated autonomously every night during rider lulls at 4 am and began engaging in extended honking matches at each other while attempting to park.

Local resident Christopher Cherry’s initial optimism about the robotic fleet’s presence dissolved as the mechanical chorus grew louder each night, affecting residents in nearby high-rises. The nocturnal tech disruption served as a lesson in the unintentional effects of autonomous systems when run in aggregate.

Larry Ellison dreams of all-seeing AI cameras

A colorized photo of CCTV cameras in London, 2024.

In September, Oracle co-founder Larry Ellison painted a bleak vision of ubiquitous AI surveillance during a company financial meeting. The 80-year-old database billionaire described a future where AI would monitor citizens through networks of cameras and drones, asserting that the oversight would ensure lawful behavior from both police and the public.

His surveillance predictions reminded us of parallels to existing systems in China, where authorities already used AI to sort surveillance data on citizens as part of the country’s “sharp eyes” campaign from 2015 to 2020. Ellison’s statement reflected the sort of worst-case tech surveillance state scenario—likely antithetical to any sort of free society—that dozens of sci-fi novels of the 20th century warned us about.

A dead father sends new letters home

An AI-generated image featuring Dad's Uppercase handwriting.

An AI-generated image featuring my late father’s handwriting. Credit: Benj Edwards / Flux

AI has made many of us do weird things in 2024, including this writer. In October, I used an AI synthesis model called Flux to reproduce my late father’s handwriting with striking accuracy. After scanning 30 samples from his engineering notebooks, I trained the model using computing time that cost less than five dollars. The resulting text captured his distinctive uppercase style, which he developed during his career as an electronics engineer.

I enjoyed creating images showing his handwriting in various contexts, from folder labels to skywriting, and made the trained model freely available online for others to use. While I approached it as a tribute to my father (who would have appreciated the technical achievement), many people found the whole experience weird and somewhat disturbing. The things we unhinged Bing Chat-like journalists do to bring awareness to a topic are sometimes unconventional. So I guess it counts for this list!

For 2025? Expect even more AI

Thanks for reading Ars Technica this past year and following along with our team coverage of this rapidly emerging and expanding field. We appreciate your kind words of support. Ars Technica’s 2024 AI words of the year were: vibemarking, deep doubt, and the aforementioned jabberwocky. The old stalwart “confabulation” also made several notable appearances. Tune in again next year when we continue to try to figure out how to concisely describe novel scenarios in emerging technology by labeling them.

Looking back, our prediction for 2024 in AI last year was “buckle up.” It seems fitting, given the weirdness detailed above. Especially the part about the robot dogs with guns. For 2025, AI will likely inspire more chaos ahead, but also potentially get put to serious work as a productivity tool, so this time, our prediction is “buckle down.”

Finally, we’d like to ask: What was the craziest story about AI in 2024 from your perspective? Whether you love AI or hate it, feel free to suggest your own additions to our list in the comments. Happy New Year!

Photo of Benj Edwards

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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OpenAI announces o3 and o3-mini, its next simulated reasoning models

On Friday, during Day 12 of its “12 days of OpenAI,” OpenAI CEO Sam Altman announced its latest AI “reasoning” models, o3 and o3-mini, which build upon the o1 models launched earlier this year. The company is not releasing them yet but will make these models available for public safety testing and research access today.

The models use what OpenAI calls “private chain of thought,” where the model pauses to examine its internal dialog and plan ahead before responding, which you might call “simulated reasoning” (SR)—a form of AI that goes beyond basic large language models (LLMs).

The company named the model family “o3” instead of “o2” to avoid potential trademark conflicts with British telecom provider O2, according to The Information. During Friday’s livestream, Altman acknowledged his company’s naming foibles, saying, “In the grand tradition of OpenAI being really, truly bad at names, it’ll be called o3.”

According to OpenAI, the o3 model earned a record-breaking score on the ARC-AGI benchmark, a visual reasoning benchmark that has gone unbeaten since its creation in 2019. In low-compute scenarios, o3 scored 75.7 percent, while in high-compute testing, it reached 87.5 percent—comparable to human performance at an 85 percent threshold.

OpenAI also reported that o3 scored 96.7 percent on the 2024 American Invitational Mathematics Exam, missing just one question. The model also reached 87.7 percent on GPQA Diamond, which contains graduate-level biology, physics, and chemistry questions. On the Frontier Math benchmark by EpochAI, o3 solved 25.2 percent of problems, while no other model has exceeded 2 percent.

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Not to be outdone by OpenAI, Google releases its own “reasoning” AI model

Google DeepMind’s chief scientist, Jeff Dean, says that the model receives extra computing power, writing on X, “we see promising results when we increase inference time computation!” The model works by pausing to consider multiple related prompts before providing what it determines to be the most accurate answer.

Since OpenAI’s jump into the “reasoning” field in September with o1-preview and o1-mini, several companies have been rushing to achieve feature parity with their own models. For example, DeepSeek launched DeepSeek-R1 in early November, while Alibaba’s Qwen team released its own “reasoning” model, QwQ earlier this month.

While some claim that reasoning models can help solve complex mathematical or academic problems, these models might not be for everybody. While they perform well on some benchmarks, questions remain about their actual usefulness and accuracy. Also, the high computing costs needed to run reasoning models have created some rumblings about their long-term viability. That high cost is why OpenAI’s ChatGPT Pro costs $200 a month, for example.

Still, it appears Google is serious about pursuing this particular AI technique. Logan Kilpatrick, a Google employee in its AI Studio, called it “the first step in our reasoning journey” in a post on X.

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Microsoft’s new “Copilot Vision” AI experiment can see what you browse

On Monday, Microsoft unveiled updates to its consumer AI assistant Copilot, introducing two new experimental features for a limited group of $20/month Copilot Pro subscribers: Copilot Labs and Copilot Vision. Labs integrates OpenAI’s latest o1 “reasoning” model, and Vision allows Copilot to see what you’re browsing in Edge.

Microsoft says Copilot Labs will serve as a testing ground for Microsoft’s latest AI tools before they see wider release. The company describes it as offering “a glimpse into ‘work-in-progress’ projects.” The first feature available in Labs is called “Think Deeper,” and it uses step-by-step processing to solve more complex problems than the regular Copilot. Think Deeper is Microsoft’s version of OpenAI’s new o1-preview and o1-mini AI models, and it has so far rolled out to some Copilot Pro users in Australia, Canada, New Zealand, the UK, and the US.

Copilot Vision is an entirely different beast. The new feature aims to give the AI assistant a visual window into what you’re doing within the Microsoft Edge browser. When enabled, Copilot can “understand the page you’re viewing and answer questions about its content,” according to Microsoft.

Microsoft’s Copilot Vision promo video.

The company positions Copilot Vision as a way to provide more natural interactions and task assistance beyond text-based prompts, but it will likely raise privacy concerns. As a result, Microsoft says that Copilot Vision is entirely opt-in and that no audio, images, text, or conversations from Vision will be stored or used for training. The company is also initially limiting Vision’s use to a pre-approved list of websites, blocking it on paywalled and sensitive content.

The rollout of these features appears gradual, with Microsoft noting that it wants to balance “pioneering features and a deep sense of responsibility.” The company said it will be “listening carefully” to user feedback as it expands access to the new capabilities. Microsoft has not provided a timeline for wider availability of either feature.

Mustafa Suleyman, chief executive of Microsoft AI, told Reuters that he sees Copilot as an “ever-present confidant” that could potentially learn from users’ various Microsoft-connected devices and documents, with permission. He also mentioned that Microsoft co-founder Bill Gates has shown particular interest in Copilot’s potential to read and parse emails.

But judging by the visceral reaction to Microsoft’s Recall feature, which keeps a record of everything you do on your PC so an AI model can recall it later, privacy-sensitive users may not appreciate having an AI assistant monitor their activities—especially if those features send user data to the cloud for processing.

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OpenAI’s new “reasoning” AI models are here: o1-preview and o1-mini

fruit by the foot —

New o1 language model can solve complex tasks iteratively, count R’s in “strawberry.”

An illustration of a strawberry made out of pixel-like blocks.

OpenAI finally unveiled its rumored “Strawberry” AI language model on Thursday, claiming significant improvements in what it calls “reasoning” and problem-solving capabilities over previous large language models (LLMs). Formally named “OpenAI o1,” the model family will initially launch in two forms, o1-preview and o1-mini, available today for ChatGPT Plus and certain API users.

OpenAI claims that o1-preview outperforms its predecessor, GPT-4o, on multiple benchmarks, including competitive programming, mathematics, and “scientific reasoning.” However, people who have used the model say it does not yet outclass GPT-4o in every metric. Other users have criticized the delay in receiving a response from the model, owing to the multi-step processing occurring behind the scenes before answering a query.

In a rare display of public hype-busting, OpenAI product manager Joanne Jang tweeted, “There’s a lot of o1 hype on my feed, so I’m worried that it might be setting the wrong expectations. what o1 is: the first reasoning model that shines in really hard tasks, and it’ll only get better. (I’m personally psyched about the model’s potential & trajectory!) what o1 isn’t (yet!): a miracle model that does everything better than previous models. you might be disappointed if this is your expectation for today’s launch—but we’re working to get there!”

OpenAI reports that o1-preview ranked in the 89th percentile on competitive programming questions from Codeforces. In mathematics, it scored 83 percent on a qualifying exam for the International Mathematics Olympiad, compared to GPT-4o’s 13 percent. OpenAI also states, in a claim that may later be challenged as people scrutinize the benchmarks and run their own evaluations over time, o1 performs comparably to PhD students on specific tasks in physics, chemistry, and biology. The smaller o1-mini model is designed specifically for coding tasks and is priced at 80 percent less than o1-preview.

A benchmark chart provided by OpenAI. They write,

Enlarge / A benchmark chart provided by OpenAI. They write, “o1 improves over GPT-4o on a wide range of benchmarks, including 54/57 MMLU subcategories. Seven are shown for illustration.”

OpenAI attributes o1’s advancements to a new reinforcement learning (RL) training approach that teaches the model to spend more time “thinking through” problems before responding, similar to how “let’s think step-by-step” chain-of-thought prompting can improve outputs in other LLMs. The new process allows o1 to try different strategies and “recognize” its own mistakes.

AI benchmarks are notoriously unreliable and easy to game; however, independent verification and experimentation from users will show the full extent of o1’s advancements over time. It’s worth noting that MIT Research showed earlier this year that some of the benchmark claims OpenAI touted with GPT-4 last year were erroneous or exaggerated.

A mixed bag of capabilities

OpenAI demos “o1” correctly counting the number of Rs in the word “strawberry.”

Amid many demo videos of o1 completing programming tasks and solving logic puzzles that OpenAI shared on its website and social media, one demo stood out as perhaps the least consequential and least impressive, but it may become the most talked about due to a recurring meme where people ask LLMs to count the number of R’s in the word “strawberry.”

Due to tokenization, where the LLM processes words in data chunks called tokens, most LLMs are typically blind to character-by-character differences in words. Apparently, o1 has the self-reflective capabilities to figure out how to count the letters and provide an accurate answer without user assistance.

Beyond OpenAI’s demos, we’ve seen optimistic but cautious hands-on reports about o1-preview online. Wharton Professor Ethan Mollick wrote on X, “Been using GPT-4o1 for the last month. It is fascinating—it doesn’t do everything better but it solves some very hard problems for LLMs. It also points to a lot of future gains.”

Mollick shared a hands-on post in his “One Useful Thing” blog that details his experiments with the new model. “To be clear, o1-preview doesn’t do everything better. It is not a better writer than GPT-4o, for example. But for tasks that require planning, the changes are quite large.”

Mollick gives the example of asking o1-preview to build a teaching simulator “using multiple agents and generative AI, inspired by the paper below and considering the views of teachers and students,” then asking it to build the full code, and it produced a result that Mollick found impressive.

Mollick also gave o1-preview eight crossword puzzle clues, translated into text, and the model took 108 seconds to solve it over many steps, getting all of the answers correct but confabulating a particular clue Mollick did not give it. We recommend reading Mollick’s entire post for a good early hands-on impression. Given his experience with the new model, it appears that o1 works very similar to GPT-4o but iteratively in a loop, which is something that the so-called “agentic” AutoGPT and BabyAGI projects experimented with in early 2023.

Is this what could “threaten humanity?”

Speaking of agentic models that run in loops, Strawberry has been subject to hype since last November, when it was initially known as Q(Q-star). At the time, The Information and Reuters claimed that, just before Sam Altman’s brief ouster as CEO, OpenAI employees had internally warned OpenAI’s board of directors about a new OpenAI model called Q*  that could “threaten humanity.”

In August, the hype continued when The Information reported that OpenAI showed Strawberry to US national security officials.

We’ve been skeptical about the hype around Qand Strawberry since the rumors first emerged, as this author noted last November, and Timothy B. Lee covered thoroughly in an excellent post about Q* from last December.

So even though o1 is out, AI industry watchers should note how this model’s impending launch was played up in the press as a dangerous advancement while not being publicly downplayed by OpenAI. For an AI model that takes 108 seconds to solve eight clues in a crossword puzzle and hallucinates one answer, we can say that its potential danger was likely hype (for now).

Controversy over “reasoning” terminology

It’s no secret that some people in tech have issues with anthropomorphizing AI models and using terms like “thinking” or “reasoning” to describe the synthesizing and processing operations that these neural network systems perform.

Just after the OpenAI o1 announcement, Hugging Face CEO Clement Delangue wrote, “Once again, an AI system is not ‘thinking,’ it’s ‘processing,’ ‘running predictions,’… just like Google or computers do. Giving the false impression that technology systems are human is just cheap snake oil and marketing to fool you into thinking it’s more clever than it is.”

“Reasoning” is also a somewhat nebulous term since, even in humans, it’s difficult to define exactly what the term means. A few hours before the announcement, independent AI researcher Simon Willison tweeted in response to a Bloomberg story about Strawberry, “I still have trouble defining ‘reasoning’ in terms of LLM capabilities. I’d be interested in finding a prompt which fails on current models but succeeds on strawberry that helps demonstrate the meaning of that term.”

Reasoning or not, o1-preview currently lacks some features present in earlier models, such as web browsing, image generation, and file uploading. OpenAI plans to add these capabilities in future updates, along with continued development of both the o1 and GPT model series.

While OpenAI says the o1-preview and o1-mini models are rolling out today, neither model is available in our ChatGPT Plus interface yet, so we have not been able to evaluate them. We’ll report our impressions on how this model differs from other LLMs we have previously covered.

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