machine learning

Amid a flurry of hype, Microsoft reorganizes entire dev team around AI

agentic AI, AI, copilot, deep learning, GitHub, llm, machine learning, microsoft, Satya Nadella, software development, Tech, visual studio / Rejus Almole / January 14, 2025

Microsoft CEO Satya Nadella has announced a dramatic restructuring of the company’s engineering organization, which is pivoting the company’s focus to developing the tools that will underpin agentic AI.

Dubbed “CoreAI – Platform and Tools,” the new division rolls the existing AI platform team and the previous developer division (responsible for everything from .NET to Visual Studio) along with some other teams into one big group.

As for what this group will be doing specifically, it’s basically everything that’s mission-critical to Microsoft in 2025, as Nadella tells it:

This new division will bring together Dev Div, AI Platform, and some key teams from the Office of the CTO (AI Supercomputer, AI Agentic Runtimes, and Engineering Thrive), with the mission to build the end-to-end Copilot & AI stack for both our first-party and third-party customers to build and run AI apps and agents. This group will also build out GitHub Copilot, thus having a tight feedback loop between the leading AI-first product and the AI platform to motivate the stack and its roadmap.

To accomplish all that, “Jay Parikh will lead this group as EVP.” Parikh was hired by Microsoft in October; he previously worked as the VP and global head of engineering at Meta.

The fact that the blog post doesn’t say anything about .NET or Visual Studio, instead emphasizing GitHub Copilot and anything and everything related to agentic AI, says a lot about how Nadella sees Microsoft’s future priorities.

So-called AI agents are applications that are given specified boundaries (action spaces) and a large memory capacity to independently do subsets of the kinds of work that human office workers do today. Some company leaders and AI commentators believe these agents will outright replace jobs, while others are more conservative, suggesting they’ll simply be powerful tools to streamline the jobs people already have.

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161 years ago, a New Zealand sheep farmer predicted AI doom

AI, Biz & IT, charles darwin, machine learning / Paul Patrick / January 11, 2025

The text anticipated several modern AI safety concerns, including the possibility of machine consciousness, self-replication, and humans losing control of their technological creations. These themes later appeared in works like Isaac Asimov’s The Evitable Conflict, Frank Herbert’s Dune novels (Butler possibly served as the inspiration for the term “Butlerian Jihad“), and the Matrix films.

A model of Charles Babbage's Analytical Engine, a calculating machine invented in 1837 but never built during Babbage's lifetime. — A model of Charles Babbage’s Analytical Engine, a calculating machine invented in 1837 but never built during Babbage’s lifetime. Credit: DE AGOSTINI PICTURE LIBRARY via Getty Images

Butler’s letter dug deep into the taxonomy of machine evolution, discussing mechanical “genera and sub-genera” and pointing to examples like how watches had evolved from “cumbrous clocks of the thirteenth century”—suggesting that, like some early vertebrates, mechanical species might get smaller as they became more sophisticated. He expanded these ideas in his 1872 novel Erewhon, which depicted a society that had banned most mechanical inventions. In his fictional society, citizens destroyed all machines invented within the previous 300 years.

Butler’s concerns about machine evolution received mixed reactions, according to Butler in the preface to the second edition of Erewhon. Some reviewers, he said, interpreted his work as an attempt to satirize Darwin’s evolutionary theory, though Butler denied this. In a letter to Darwin in 1865, Butler expressed his deep appreciation for The Origin of Species, writing that it “thoroughly fascinated” him and explained that he had defended Darwin’s theory against critics in New Zealand’s press.

What makes Butler’s vision particularly remarkable is that he was writing in a vastly different technological context when computing devices barely existed. While Charles Babbage had proposed his theoretical Analytical Engine in 1837—a mechanical computer using gears and levers that was never built in his lifetime—the most advanced calculating devices of 1863 were little more than mechanical calculators and slide rules.

Butler extrapolated from the simple machines of the Industrial Revolution, where mechanical automation was transforming manufacturing, but nothing resembling modern computers existed. The first working program-controlled computer wouldn’t appear for another 70 years, making his predictions of machine intelligence strikingly prescient.

Some things never change

The debate Butler started continues today. Two years ago, the world grappled with what one might call the “great AI takeover scare of 2023.” OpenAI’s GPT-4 had just been released, and researchers evaluated its “power-seeking behavior,” echoing concerns about potential self-replication and autonomous decision-making.

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AI could create 78 million more jobs than it eliminates by 2030—report

AI, AI and jobs, AI layoffs, Biz & IT, machine learning, World Economic Forum / Rejus Almole / January 10, 2025

On Wednesday, the World Economic Forum (WEF) released its Future of Jobs Report 2025, with CNN immediately highlighting the finding that 40 percent of companies plan workforce reductions due to AI automation. But the report’s broader analysis paints a far more nuanced picture than CNN’s headline suggests: It finds that AI could create 170 million new jobs globally while eliminating 92 million positions, resulting in a net increase of 78 million jobs by 2030.

“Half of employers plan to re-orient their business in response to AI,” writes the WEF in the report. “Two-thirds plan to hire talent with specific AI skills, while 40% anticipate reducing their workforce where AI can automate tasks.”

The survey collected data from 1,000 companies that employ 14 million workers globally. The WEF conducts its employment analysis every two years to help policymakers, business leaders, and workers make decisions about hiring trends.

The new report points to specific skills that will dominate hiring by 2030. Companies ranked AI and big data expertise, networks and cybersecurity, and technological literacy as the three most in-demand skill sets.

The WEF identified AI as the biggest potential job creator among new technologies, with 86 percent of companies expecting AI to transform their operations by 2030.

Declining job categories

The WEF report also identifies specific job categories facing decline. Postal service clerks, executive secretaries, and payroll staff top the list of shrinking roles, with changes driven by factors including (but not limited to) AI adoption. And for the first time, graphic designers and legal secretaries appear among the fastest-declining positions, which the WEF tentatively links to generative AI’s expanding capabilities in creative and administrative work.

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2024: The year AI drove everyone crazy

AI, AI jabberwocky, air canada, Anthropic, Biz & IT, chatgpt, confabulation, Features, Google, GPT-4o, jabberwocky, machine learning, Meta, openai, OpenAI o1, rat genitals, Wonka / Mike M. / December 26, 2024

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

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. 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. 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

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. 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!

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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The AI war between Google and OpenAI has never been more heated

agentic AI, AI, AI agents, AI assistants, Biz & IT, chatgpt, Gemini 2.0, Google, Google Gemini, Google Veo, imag synthesis, machine learning, openai, Veo, Veo 2, video synthesis / Tim Belzer / December 21, 2024

Over the past month, we’ve seen a rapid cadence of notable AI-related announcements and releases from both Google and OpenAI, and it’s been making the AI community’s head spin. It has also poured fuel on the fire of the OpenAI-Google rivalry, an accelerating game of one-upmanship taking place unusually close to the Christmas holiday.

“How are people surviving with the firehose of AI updates that are coming out,” wrote one user on X last Friday, which is still a hotbed of AI-related conversation. “in the last <24 hours we got gemini flash 2.0 and chatGPT with screenshare, deep research, pika 2, sora, chatGPT projects, anthropic clio, wtf it never ends."

Rumors travel quickly in the AI world, and people in the AI industry had been expecting OpenAI to ship some major products in December. Once OpenAI announced “12 days of OpenAI” earlier this month, Google jumped into gear and seemingly decided to try to one-up its rival on several counts. So far, the strategy appears to be working, but it’s coming at the cost of the rest of the world being able to absorb the implications of the new releases.

“12 Days of OpenAI has turned into like 50 new @GoogleAI releases,” wrote another X user on Monday. “This past week, OpenAI & Google have been releasing at the speed of a new born startup,” wrote a third X user on Tuesday. “Even their own users can’t keep up. Crazy time we’re living in.”

“Somebody told Google that they could just do things,” wrote a16z partner and AI influencer Justine Moore on X, referring to a common motivational meme telling people they “can just do stuff.”

The Google AI rush

OpenAI’s “12 Days of OpenAI” campaign has included releases of their full o1 model, an upgrade from o1-preview, alongside o1-pro for advanced “reasoning” tasks. The company also publicly launched Sora for video generation, added Projects functionality to ChatGPT, introduced Advanced Voice features with video streaming capabilities, and more.

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12 days of OpenAI: The Ars Technica recap

12 days of OpenAI, AI, Biz & IT, chatgpt, chatgtp, machine learning, openai / Kris Guyer / December 20, 2024

Did OpenAI’s big holiday event live up to the billing?

Over the past 12 business days, OpenAI has announced a new product or demoed an AI feature every weekday, calling the PR event “12 days of OpenAI.” We’ve covered some of the major announcements, but we thought a look at each announcement might be useful for people seeking a comprehensive look at each day’s developments.

The timing and rapid pace of these announcements—particularly in light of Google’s competing releases—illustrates the intensifying competition in AI development. What might normally have been spread across months was compressed into just 12 business days, giving users and developers a lot to process as they head into 2025.

Humorously, we asked ChatGPT what it thought about the whole series of announcements, and it was skeptical that the event even took place. “The rapid-fire announcements over 12 days seem plausible,” wrote ChatGPT-4o, “But might strain credibility without a clearer explanation of how OpenAI managed such an intense release schedule, especially given the complexity of the features.”

But it did happen, and here’s a chronicle of what went down on each day.

Day 1: Thursday, December 5

On the first day of OpenAI, the company released its full o1 model, making it available to ChatGPT Plus and Team subscribers worldwide. The company reported that the model operates faster than its preview version and reduces major errors by 34 percent on complex real-world questions.

The o1 model brings new capabilities for image analysis, allowing users to upload and receive detailed explanations of visual content. OpenAI said it plans to expand o1’s features to include web browsing and file uploads in ChatGPT, with API access coming soon. The API version will support vision tasks, function calling, and structured outputs for system integration.

OpenAI also launched ChatGPT Pro, a $200 subscription tier that provides “unlimited” access to o1, GPT-4o, and Advanced Voice features. Pro subscribers receive an exclusive version of o1 that uses additional computing power for complex problem-solving. Alongside this release, OpenAI announced a grant program that will provide ChatGPT Pro access to 10 medical researchers at established institutions, with plans to extend grants to other fields.

Day 2: Friday, December 6

Day 2 wasn’t as exciting. OpenAI unveiled Reinforcement Fine-Tuning (RFT), a model customization method that will let developers modify “o-series” models for specific tasks. The technique reportedly goes beyond traditional supervised fine-tuning by using reinforcement learning to help models improve their reasoning abilities through repeated iterations. In other words, OpenAI created a new way to train AI models that lets them learn from practice and feedback.

OpenAI says that Berkeley Lab computational researcher Justin Reese tested RFT for researching rare genetic diseases, while Thomson Reuters has created a specialized o1-mini model for its CoCounsel AI legal assistant. The technique requires developers to provide a dataset and evaluation criteria, with OpenAI’s platform managing the reinforcement learning process.

OpenAI plans to release RFT to the public in early 2024 but currently offers limited access through its Reinforcement Fine-Tuning Research Program for researchers, universities, and companies.

Day 3: Monday, December 9

On day 3, OpenAI released Sora, its text-to-video model, as a standalone product now accessible through sora.com for ChatGPT Plus and Pro subscribers. The company says the new version operates faster than the research preview shown in February 2024, when OpenAI first demonstrated the model’s ability to create videos from text descriptions.

The release moved Sora from research preview to a production service, marking OpenAI’s official entry into the video synthesis market. The company published a blog post detailing the subscription tiers and deployment strategy for the service.

Day 4: Tuesday, December 10

On day 4, OpenAI moved its Canvas feature out of beta testing, making it available to all ChatGPT users, including those on free tiers. Canvas provides a dedicated interface for extended writing and coding projects beyond the standard chat format, now with direct integration into the GPT-4o model.

The updated canvas allows users to run Python code within the interface and includes a text-pasting feature for importing existing content. OpenAI added compatibility with custom GPTs and a “show changes” function that tracks modifications to writing and code. The company said Canvas is now on chatgpt.com for web users and also available through a Windows desktop application, with more features planned for future updates.

Day 5: Wednesday, December 11

On day 5, OpenAI announced that ChatGPT would integrate with Apple Intelligence across iOS, iPadOS, and macOS devices. The integration works on iPhone 16 series phones, iPhone 15 Pro models, iPads with A17 Pro or M1 chips and later, and Macs with M1 processors or newer, running their respective latest operating systems.

The integration lets users access ChatGPT’s features (such as they are), including image and document analysis, directly through Apple’s system-level intelligence features. The feature works with all ChatGPT subscription tiers and operates within Apple’s privacy framework. Iffy message summaries remain unaffected by the additions.

Enterprise and Team account users need administrator approval to access the integration.

Day 6: Thursday, December 12

On the sixth day, OpenAI added two features to ChatGPT’s voice capabilities: “video calling” with screen sharing support for ChatGPT Plus and Pro subscribers and a seasonal Santa Claus voice preset.

The new visual Advanced Voice Mode features work through the mobile app, letting users show their surroundings or share their screen with the AI model during voice conversations. While the rollout covers most countries, users in several European nations, including EU member states, Switzerland, Iceland, Norway, and Liechtenstein, will get access at a later date. Enterprise and education users can expect these features in January.

The Santa voice option appears as a snowflake icon in the ChatGPT interface across mobile devices, web browsers, and desktop apps, with conversations in this mode not affecting chat history or memory. Don’t expect Santa to remember what you want for Christmas between sessions.

Day 7: Friday, December 13

OpenAI introduced Projects, a new organizational feature in ChatGPT that lets users group related conversations and files, on day 7. The feature works with the company’s GPT-4o model and provides a central location for managing resources related to specific tasks or topics—kinda like Anthropic’s “Projects” feature.

ChatGPT Plus, Pro, and Team subscribers can currently access Projects through chatgpt.com and the Windows desktop app, with view-only support on mobile devices and macOS. Users can create projects by clicking a plus icon in the sidebar, where they can add files and custom instructions that provide context for future conversations.

OpenAI said it plans to expand Projects in 2024 with support for additional file types, cloud storage integration through Google Drive and Microsoft OneDrive, and compatibility with other models like o1. Enterprise and education users will receive access to Projects in January.

Day 8: Monday, December 16

On day 8, OpenAI expanded its search features in ChatGPT, extending access to all users with free accounts while reportedly adding speed improvements and mobile optimizations. Basically, you can use ChatGPT like a web search engine, although in practice it doesn’t seem to be as comprehensive as Google Search at the moment.

The update includes a new maps interface and integration with Advanced Voice, allowing users to perform searches during voice conversations. The search capability, which previously required a paid subscription, now works across all platforms where ChatGPT operates.

Day 9: Tuesday, December 17

On day 9, OpenAI released its o1 model through its API platform, adding support for function calling, developer messages, and vision processing capabilities. The company also reduced GPT-4o audio pricing by 60 percent and introduced a GPT-4o mini option that costs one-tenth of previous audio rates.

OpenAI also simplified its WebRTC integration for real-time applications and unveiled Preference Fine-Tuning, which provides developers new ways to customize models. The company also launched beta versions of software development kits for the Go and Java programming languages, expanding its toolkit for developers.

Day 10: Wednesday, December 18

On Wednesday, OpenAI did something a little fun and launched voice and messaging access to ChatGPT through a toll-free number (1-800-CHATGPT), as well as WhatsApp. US residents can make phone calls with a 15-minute monthly limit, while global users can message ChatGPT through WhatsApp at the same number.

OpenAI said the release is a way to reach users who lack consistent high-speed Internet access or want to try AI through familiar communication channels, but it’s also just a clever hack. As evidence, OpenAI notes that these new interfaces serve as experimental access points, with more “limited functionality” than the full ChatGPT service, and still recommends existing users continue using their regular ChatGPT accounts for complete features.

Day 11: Thursday, December 19

On Thursday, OpenAI expanded ChatGPT’s desktop app integration to include additional coding environments and productivity software. The update added support for Jetbrains IDEs like PyCharm and IntelliJ IDEA, VS Code variants including Cursor and VSCodium, and text editors such as BBEdit and TextMate.

OpenAI also included integration with Apple Notes, Notion, and Quip while adding Advanced Voice Mode compatibility when working with desktop applications. These features require manual activation for each app and remain available to paid subscribers, including Plus, Pro, Team, Enterprise, and Education users, with Enterprise and Education customers needing administrator approval to enable the functionality.

Day 12: Friday, December 20

On Friday, OpenAI concluded its twelve days of announcements by previewing two new simulated reasoning models, o3 and o3-mini, while opening applications for safety and security researchers to test them before public release. Early evaluations show o3 achieving a 2727 rating on Codeforces programming contests and scoring 96.7 percent on AIME 2024 mathematics problems.

The company reports o3 set performance records on advanced benchmarks, solving 25.2 percent of problems on EpochAI’s Frontier Math evaluations and scoring above 85 percent on the ARC-AGI test, which is comparable to human results. OpenAI also published research about “deliberative alignment,” a technique used in developing o1. The company has not announced firm release dates for either new o3 model, but CEO Sam Altman said o3-mini might ship in late January.

So what did we learn?

OpenAI’s December campaign revealed that OpenAI had a lot of things sitting around that it needed to ship, and it picked a fun theme to unite the announcements. Google responded in kind, as we have covered.

Several trends from the releases stand out. OpenAI is heavily investing in multimodal capabilities. The o1 model’s release, Sora’s evolution from research preview to product, and the expansion of voice features with video calling all point toward systems that can seamlessly handle text, images, voice, and video.

The company is also focusing heavily on developer tools and customization, so it can continue to have a cloud service business and have its products integrated into other applications. Between the API releases, Reinforcement Fine-Tuning, and expanded IDE integrations, OpenAI is building out its ecosystem for developers and enterprises. And the introduction of o3 shows that OpenAI is still attempting to push technological boundaries, even in the face of diminishing returns in training LLM base models.

OpenAI seems to be positioning itself for a 2025 where generative AI moves beyond text chatbots and simple image generators and finds its way into novel applications that we probably can’t even predict yet. We’ll have to wait and see what the company and developers come up with in the year ahead.

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

AI, Biz & IT, chatgpt, chatgtp, GPT-4o, large language models, machine learning, o1, openai, OpenAI o1, OpenAI Orion, simulated reasoning / Mike M. / December 20, 2024

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

AI, AI reasoning, Biz & IT, Gemini 2.0, Gemini 2.0 Flash, Gemini 2.0 Flash Thinking, Google, google deepmind, Google Gemini, machine learning, o1, openai, OpenAI o1 / Tim Belzer / December 19, 2024

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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New physics sim trains robots 430,000 times faster than reality

AI, AI physics, Biz & IT, Carnegie Mellon University, Genesis, Jim Fan, machine learning, Zhou Xian / Mike M. / December 19, 2024

The AI-generated worlds reportedly include realistic physics, camera movements, and object behaviors, all from text commands. The system then creates physically accurate ray-traced videos and data that robots can use for training.

Examples of “4D dynamical and physical” worlds that Genesis created from text prompts.

This prompt-based system lets researchers create complex robot testing environments by typing natural language commands instead of programming them by hand. “Traditionally, simulators require a huge amount of manual effort from artists: 3D assets, textures, scene layouts, etc. But every component in the workflow can be automated,” wrote Fan.

Using its engine, Genesis can also generate character motion, interactive 3D scenes, facial animation, and more, which may allow for the creation of artistic assets for creative projects, but may also lead to more realistic AI-generated games and videos in the future, constructing a simulated world in data instead of operating on the statistical appearance of pixels as with a video synthesis diffusion model.

Examples of character motion generation from Genesis, using a prompt that includes, “A miniature Wukong holding a stick in his hand sprints across a table surface for 3 seconds, then jumps into the air, and swings his right arm downward during landing.”

While the generative system isn’t yet part of the currently available code on GitHub, the team plans to release it in the future.

Training tomorrow’s robots today (using Python)

Genesis remains under active development on GitHub, where the team accepts community contributions.

The platform stands out from other 3D world simulators for robotic training by using Python for both its user interface and core physics engine. Other engines use C++ or CUDA for their underlying calculations while wrapping them in Python APIs. Genesis takes a Python-first approach.

Notably, the non-proprietary nature of the Genesis platform makes high-speed robot training simulations available to any researcher for free through simple Python commands that work on regular computers with off-the-shelf hardware.

Previously, running robot simulations required complex programming and specialized hardware, says Fan in his post announcing Genesis, and that shouldn’t be the case. “Robotics should be a moonshot initiative owned by all of humanity,” he wrote.

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Call ChatGPT from any phone with OpenAI’s new 1-800 voice service

1-800-CHATGPT, 12 days of OpenAI, AI, Biz & IT, chatbots, chatgpt, chatgtp, large language models, machine learning, openai, telephone, Voice chat, whatsapp / Tim Belzer / December 18, 2024

On Wednesday, OpenAI launched a 1-800-CHATGPT (1-800-242-8478) telephone number that anyone in the US can call to talk to ChatGPT via voice chat for up to 15 minutes for free. The company also says that people outside the US can send text messages to the same number for free using WhatsApp.

Upon calling, users hear a voice say, “Hello again, it’s ChatGPT, an AI assistant. Our conversation may be reviewed for safety. How can I help you?” Callers can ask ChatGPT anything they would normally ask the AI assistant and have a live, interactive conversation.

During a livestream demo of “Calling with ChatGPT” during Day 10 of “12 Days of OpenAI,” OpenAI employees demonstrated several examples of the telephone-based voice chat in action, asking ChatGPT to identify a distinctive house in California and for help in translating a message into Spanish for a friend. For fun, they showed calls from an iPhone, a flip phone, and a vintage rotary phone.

OpenAI developers demonstrate calling 1-800-CHATGPT during a livestream on December 18, 2024. Credit: OpenAI

OpenAI says the new features came out of an internal OpenAI “hack week” project that a team built just a few weeks ago. The company says its goal is to make ChatGPT more accessible if someone does not have a smartphone or a computer handy.

During the livestream, an OpenAI employee mentioned that 15 minutes of voice chatting are free and that you can download the app and create an account to get more. While the audio chat version seems to be running a full version of GPT-4o on the back end, a developer during the livestream said the free WhatsApp text mode is using GPT-4o mini.

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Twirling body horror in gymnastics video exposes AI’s flaws

AI, AI confabulation, AI hallucination, AI jabberwocky, Ai video generator, Biz & IT, confabulation, gymnastics, hallucination, jabberwocky, machine learning, Olympics, openai, Sora, video synthesis / Rejus Almole / December 13, 2024

The slithy toves did gyre and gimble in the wabe

Nonsensical jabberwocky movements created by OpenAI’s Sora are typical for current AI-generated video, and here’s why.

A still image from an AI-generated video of an ever-morphing synthetic gymnast. Credit: OpenAI / Deedy

On Wednesday, a video from OpenAI’s newly launched Sora AI video generator went viral on social media, featuring a gymnast who sprouts extra limbs and briefly loses her head during what appears to be an Olympic-style floor routine.

As it turns out, the nonsensical synthesis errors in the video—what we like to call “jabberwockies”—hint at technical details about how AI video generators work and how they might get better in the future.

But before we dig into the details, let’s take a look at the video.

An AI-generated video of an impossible gymnast, created with OpenAI Sora.

In the video, we see a view of what looks like a floor gymnastics routine. The subject of the video flips and flails as new legs and arms rapidly and fluidly emerge and morph out of her twirling and transforming body. At one point, about 9 seconds in, she loses her head, and it reattaches to her body spontaneously.

“As cool as the new Sora is, gymnastics is still very much the Turing test for AI video,” wrote venture capitalist Deedy Das when he originally shared the video on X. The video inspired plenty of reaction jokes, such as this reply to a similar post on Bluesky: “hi, gymnastics expert here! this is not funny, gymnasts only do this when they’re in extreme distress.”

We reached out to Das, and he confirmed that he generated the video using Sora. He also provided the prompt, which was very long and split into four parts, generated by Anthropic’s Claude, using complex instructions like “The gymnast initiates from the back right corner, taking position with her right foot pointed behind in B-plus stance.”

“I’ve known for the last 6 months having played with text to video models that they struggle with complex physics movements like gymnastics,” Das told us in a conversation. “I had to try it [in Sora] because the character consistency seemed improved. Overall, it was an improvement because previously… the gymnast would just teleport away or change their outfit mid flip, but overall it still looks downright horrifying. We hoped AI video would learn physics by default, but that hasn’t happened yet!”

So what went wrong?

When examining how the video fails, you must first consider how Sora “knows” how to create anything that resembles a gymnastics routine. During the training phase, when the Sora model was created, OpenAI fed example videos of gymnastics routines (among many other types of videos) into a specialized neural network that associates the progression of images with text-based descriptions of them.

That type of training is a distinct phase that happens once before the model’s release. Later, when the finished model is running and you give a video-synthesis model like Sora a written prompt, it draws upon statistical associations between words and images to produce a predictive output. It’s continuously making next-frame predictions based on the last frame of the video. But Sora has another trick for attempting to preserve coherency over time. “By giving the model foresight of many frames at a time,” reads OpenAI’s Sora System Card, we’ve solved a challenging problem of making sure a subject stays the same even when it goes out of view temporarily.”

A still image from a moment where the AI-generated gymnast loses her head. It soon re-attaches to her body. — A still image from a moment where the AI-generated gymnast loses her head. It soon reattaches to her body. Credit: OpenAI / Deedy

Maybe not quite solved yet. In this case, rapidly moving limbs prove a particular challenge when attempting to predict the next frame properly. The result is an incoherent amalgam of gymnastics footage that shows the same gymnast performing running flips and spins, but Sora doesn’t know the correct order in which to assemble them because it’s pulling on statistical averages of wildly different body movements in its relatively limited training data of gymnastics videos, which also likely did not include limb-level precision in its descriptive metadata.

Sora doesn’t know anything about physics or how the human body should work, either. It’s drawing upon statistical associations between pixels in the videos in its training dataset to predict the next frame, with a little bit of look-ahead to keep things more consistent.

This problem is not unique to Sora. All AI video generators can produce wildly nonsensical results when your prompts reach too far past their training data, as we saw earlier this year when testing Runway’s Gen-3. In fact, we ran some gymnast prompts through the latest open source AI video model that may rival Sora in some ways, Hunyuan Video, and it produced similar twirling, morphing results, seen below. And we used a much simpler prompt than Das did with Sora.

An example from open source Chinese AI model Hunyuan Video with the prompt, “A young woman doing a complex floor gymnastics routine at the olympics, featuring running and flips.”

AI models based on transformer technology are fundamentally imitative in nature. They’re great at transforming one type of data into another type or morphing one style into another. What they’re not great at (yet) is producing coherent generations that are truly original. So if you happen to provide a prompt that closely matches a training video, you might get a good result. Otherwise, you may get madness.

As we wrote about image-synthesis model Stable Diffusion 3’s body horror generations earlier this year, “Basically, any time a user prompt homes in on a concept that isn’t represented well in the AI model’s training dataset, the image-synthesis model will confabulate its best interpretation of what the user is asking for. And sometimes that can be completely terrifying.”

For the engineers who make these models, success in AI video generation quickly becomes a question of how many examples (and how much training) you need before the model can generalize enough to produce convincing and coherent results. It’s also a question of metadata quality—how accurately the videos are labeled. In this case, OpenAI used an AI vision model to describe its training videos, which helped improve quality, but apparently not enough—yet.

We’re looking at an AI jabberwocky in action

In a way, the type of generation failure in the gymnast video is a form of confabulation (or hallucination, as some call it), but it’s even worse because it’s not coherent. So instead of calling it a confabulation, which is a plausible-sounding fabrication, we’re going to lean on a new term, “jabberwocky,” which Dictionary.com defines as “a playful imitation of language consisting of invented, meaningless words; nonsense; gibberish,” taken from Lewis Carroll’s nonsense poem of the same name. Imitation and nonsense, you say? Check and check.

We’ve covered jabberwockies in AI video before with people mocking Chinese video-synthesis models, a monstrously weird AI beer commercial, and even Will Smith eating spaghetti. They’re a form of misconfabulation where an AI model completely fails to produce a plausible output. This will not be the last time we see them, either.

How could AI video models get better and avoid jabberwockies?

In our coverage of Gen-3 Alpha, we called the threshold where you get a level of useful generalization in an AI model the “illusion of understanding,” where training data and training time reach a critical mass that produces good enough results to generalize across enough novel prompts.

One of the key reasons language models like OpenAI’s GPT-4 impressed users was that they finally reached a size where they had absorbed enough information to give the appearance of genuinely understanding the world. With video synthesis, achieving this same apparent level of “understanding” will require not just massive amounts of well-labeled training data but also the computational power to process it effectively.

AI boosters hope that these current models represent one of the key steps on the way to something like truly general intelligence (often called AGI) in text, or in AI video, what OpenAI and Runway researchers call “world simulators” or “world models” that somehow encode enough physics rules about the world to produce any realistic result.

Judging by the morphing alien shoggoth gymnast, that may still be a ways off. Still, it’s early days in AI video generation, and judging by how quickly AI image-synthesis models like Midjourney progressed from crude abstract shapes into coherent imagery, it’s likely video synthesis will have a similar trajectory over time. Until then, enjoy the AI-generated jabberwocky madness.

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OpenAI introduces “Santa Mode” to ChatGPT for ho-ho-ho voice chats

AI, Biz & IT, machine learning / Tim Belzer / December 12, 2024

On Thursday, OpenAI announced that ChatGPT users can now talk to a simulated version of Santa Claus through the app’s voice mode, using AI to bring a North Pole connection to mobile devices, desktop apps, and web browsers during the holiday season.

The company added Santa’s voice and personality as a preset option in ChatGPT’s Advanced Voice Mode. Users can access Santa by tapping a snowflake icon next to the prompt bar or through voice settings. The feature works on iOS and Android mobile apps, chatgpt.com, and OpenAI’s Windows and MacOS applications. The Santa voice option will remain available to users worldwide until early January.

The conversations with Santa exist as temporary chats that won’t save to chat history or affect the model’s memory. OpenAI designed this limitation specifically for the holiday feature. Keep that in mind, because if you let your kids talk to Santa, the AI simulation won’t remember what kids have told it during previous conversations.

During a livestream for Day 6 of the company’s “12 days of OpenAI” marketing event, an OpenAI employee said that the company will reset each user’s Advanced Voice Mode usage limits one time as a gift, so that even if you’ve used up your Advanced Voice Mode time, you’ll get a chance to talk to Santa.

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