chatgtp – Page 2

Eerily realistic AI voice demo sparks amazement and discomfort online

AI, AI assistants, Biz & IT, chatbots, chatgpt, chatgtp, emotional AI, machine learning, Sesame, Tech, voice synthesis / Paul Patrick / March 5, 2025

Sesame’s new AI voice model features uncanny imperfections, and it’s willing to act like an angry boss.

In late 2013, the Spike Jonze film Her imagined a future where people would form emotional connections with AI voice assistants. Nearly 12 years later, that fictional premise has veered closer to reality with the release of a new conversational voice model from AI startup Sesame that has left many users both fascinated and unnerved.

“I tried the demo, and it was genuinely startling how human it felt,” wrote one Hacker News user who tested the system. “I’m almost a bit worried I will start feeling emotionally attached to a voice assistant with this level of human-like sound.”

In late February, Sesame released a demo for the company’s new Conversational Speech Model (CSM) that appears to cross over what many consider the “uncanny valley” of AI-generated speech, with some testers reporting emotional connections to the male or female voice assistant (“Miles” and “Maya”).

In our own evaluation, we spoke with the male voice for about 28 minutes, talking about life in general and how it decides what is “right” or “wrong” based on its training data. The synthesized voice was expressive and dynamic, imitating breath sounds, chuckles, interruptions, and even sometimes stumbling over words and correcting itself. These imperfections are intentional.

“At Sesame, our goal is to achieve ‘voice presence’—the magical quality that makes spoken interactions feel real, understood, and valued,” writes the company in a blog post. “We are creating conversational partners that do not just process requests; they engage in genuine dialogue that builds confidence and trust over time. In doing so, we hope to realize the untapped potential of voice as the ultimate interface for instruction and understanding.”

Sometimes the model tries too hard to sound like a real human. In one demo posted online by a Reddit user called MetaKnowing, the AI model talks about craving “peanut butter and pickle sandwiches.”

An example of Sesame’s female voice model craving peanut butter and pickle sandwiches, captured by Reddit user MetaKnowing.

Founded by Brendan Iribe, Ankit Kumar, and Ryan Brown, Sesame AI has attracted significant backing from prominent venture capital firms. The company has secured investments from Andreessen Horowitz, led by Anjney Midha and Marc Andreessen, along with Spark Capital, Matrix Partners, and various founders and individual investors.

Browsing reactions to Sesame found online, we found many users expressing astonishment at its realism. “I’ve been into AI since I was a child, but this is the first time I’ve experienced something that made me definitively feel like we had arrived,” wrote one Reddit user. “I’m sure it’s not beating any benchmarks, or meeting any common definition of AGI, but this is the first time I’ve had a real genuine conversation with something I felt was real.” Many other Reddit threads express similar feelings of surprise, with commenters saying it’s “jaw-dropping” or “mind-blowing.”

While that sounds like a bunch of hyperbole at first glance, not everyone finds the Sesame experience pleasant. Mark Hachman, a senior editor at PCWorld, wrote about being deeply unsettled by his interaction with the Sesame voice AI. “Fifteen minutes after ‘hanging up’ with Sesame’s new ‘lifelike’ AI, and I’m still freaked out,” Hachman reported. He described how the AI’s voice and conversational style eerily resembled an old friend he had dated in high school.

Others have compared Sesame’s voice model to OpenAI’s Advanced Voice Mode for ChatGPT, saying that Sesame’s CSM features more realistic voices, and others are pleased that the model in the demo will roleplay angry characters, which ChatGPT refuses to do.

An example argument with Sesame’s CSM created by Gavin Purcell.

Gavin Purcell, co-host of the AI for Humans podcast, posted an example video on Reddit where the human pretends to be an embezzler and argues with a boss. It’s so dynamic that it’s difficult to tell who the human is and which one is the AI model. Judging by our own demo, it’s entirely capable of what you see in the video.

“Near-human quality”

Under the hood, Sesame’s CSM achieves its realism by using two AI models working together (a backbone and a decoder) based on Meta’s Llama architecture that processes interleaved text and audio. Sesame trained three AI model sizes, with the largest using 8.3 billion parameters (an 8 billion backbone model plus a 300 million parameter decoder) on approximately 1 million hours of primarily English audio.

Sesame’s CSM doesn’t follow the traditional two-stage approach used by many earlier text-to-speech systems. Instead of generating semantic tokens (high-level speech representations) and acoustic details (fine-grained audio features) in two separate stages, Sesame’s CSM integrates into a single-stage, multimodal transformer-based model, jointly processing interleaved text and audio tokens to produce speech. OpenAI’s voice model uses a similar multimodal approach.

In blind tests without conversational context, human evaluators showed no clear preference between CSM-generated speech and real human recordings, suggesting the model achieves near-human quality for isolated speech samples. However, when provided with conversational context, evaluators still consistently preferred real human speech, indicating a gap remains in fully contextual speech generation.

Sesame co-founder Brendan Iribe acknowledged current limitations in a comment on Hacker News, noting that the system is “still too eager and often inappropriate in its tone, prosody and pacing” and has issues with interruptions, timing, and conversation flow. “Today, we’re firmly in the valley, but we’re optimistic we can climb out,” he wrote.

Too close for comfort?

Despite CSM’s technological impressiveness, advancements in conversational voice AI carry significant risks for deception and fraud. The ability to generate highly convincing human-like speech has already supercharged voice phishing scams, allowing criminals to impersonate family members, colleagues, or authority figures with unprecedented realism. But adding realistic interactivity to those scams may take them to another level of potency.

Unlike current robocalls that often contain tell-tale signs of artificiality, next-generation voice AI could eliminate these red flags entirely. As synthetic voices become increasingly indistinguishable from human speech, you may never know who you’re talking to on the other end of the line. It’s inspired some people to share a secret word or phrase with their family for identity verification.

Although Sesame’s demo does not clone a person’s voice, future open source releases of similar technology could allow malicious actors to potentially adapt these tools for social engineering attacks. OpenAI itself held back its own voice technology from wider deployment over fears of misuse.

Sesame sparked a lively discussion on Hacker News about its potential uses and dangers. Some users reported having extended conversations with the two demo voices, with conversations lasting up to the 30-minute limit. In one case, a parent recounted how their 4-year-old daughter developed an emotional connection with the AI model, crying after not being allowed to talk to it again.

The company says it plans to open-source “key components” of its research under an Apache 2.0 license, enabling other developers to build upon their work. Their roadmap includes scaling up model size, increasing dataset volume, expanding language support to over 20 languages, and developing “fully duplex” models that better handle the complex dynamics of real conversations.

You can try the Sesame demo on the company’s website, assuming that it isn’t too overloaded with people who want to simulate a rousing argument.

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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Researchers surprised to find less-educated areas adopting AI writing tools faster

AI, AI research, AI writing, AI writing detectors, Biz & IT, chatgpt, chatgtp, large language models, machine learning, Stanford, Tech / Mike M. / March 4, 2025

From the mouths of machines

Stanford researchers analyzed 305 million texts, revealing AI-writing trends.

Since the launch of ChatGPT in late 2022, experts have debated how widely AI language models would impact the world. A few years later, the picture is getting clear. According to new Stanford University-led research examining over 300 million text samples across multiple sectors, AI language models now assist in writing up to a quarter of professional communications across sectors. It’s having a large impact, especially in less-educated parts of the United States.

“Our study shows the emergence of a new reality in which firms, consumers and even international organizations substantially rely on generative AI for communications,” wrote the researchers.

The researchers tracked large language model (LLM) adoption across industries from January 2022 to September 2024 using a dataset that included 687,241 consumer complaints submitted to the US Consumer Financial Protection Bureau (CFPB), 537,413 corporate press releases, 304.3 million job postings, and 15,919 United Nations press releases.

By using a statistical detection system that tracked word usage patterns, the researchers found that roughly 18 percent of financial consumer complaints (including 30 percent of all complaints from Arkansas), 24 percent of corporate press releases, up to 15 percent of job postings, and 14 percent of UN press releases showed signs of AI assistance during that period of time.

The study also found that while urban areas showed higher adoption overall (18.2 percent versus 10.9 percent in rural areas), regions with lower educational attainment used AI writing tools more frequently (19.9 percent compared to 17.4 percent in higher-education areas). The researchers note that this contradicts typical technology adoption patterns where more educated populations adopt new tools fastest.

“In the consumer complaint domain, the geographic and demographic patterns in LLM adoption present an intriguing departure from historical technology diffusion trends where technology adoption has generally been concentrated in urban areas, among higher-income groups, and populations with higher levels of educational attainment.”

Researchers from Stanford, the University of Washington, and Emory University led the study, titled, “The Widespread Adoption of Large Language Model-Assisted Writing Across Society,” first listed on the arXiv preprint server in mid-February. Weixin Liang and Yaohui Zhang from Stanford served as lead authors, with collaborators Mihai Codreanu, Jiayu Wang, Hancheng Cao, and James Zou.

Detecting AI use in aggregate

We’ve previously covered that AI writing detection services aren’t reliable, and this study does not contradict that finding. On a document-by-document basis, AI detectors cannot be trusted. But when analyzing millions of documents in aggregate, telltale patterns emerge that suggest the influence of AI language models on text.

The researchers developed an approach based on a statistical framework in a previously released work that analyzed shifts in word frequencies and linguistic patterns before and after ChatGPT’s release. By comparing large sets of pre- and post-ChatGPT texts, they estimated the proportion of AI-assisted content at a population level. The presumption is that LLMs tend to favor certain word choices, sentence structures, and linguistic patterns that differ subtly from typical human writing.

To validate their approach, the researchers created test sets with known percentages of AI content (from zero percent to 25 percent) and found their method predicted these percentages with error rates below 3.3 percent. This statistical validation gave them confidence in their population-level estimates.

While the researchers specifically note their estimates likely represent a minimum level of AI usage, it’s important to understand that actual AI involvement might be significantly greater. Due to the difficulty in detecting heavily edited or increasingly sophisticated AI-generated content, the researchers say their reported adoption rates could substantially underestimate true levels of generative AI use.

Analysis suggests AI use as “equalizing tools”

While the overall adoption rates are revealing, perhaps more insightful are the patterns of who is using AI writing tools and how these patterns may challenge conventional assumptions about technology adoption.

In examining the CFPB complaints (a US public resource that collects complaints about consumer financial products and services), the researchers’ geographic analysis revealed substantial variation across US states.

Arkansas showed the highest adoption rate at 29.2 percent (based on 7,376 complaints), followed by Missouri at 26.9 percent (16,807 complaints) and North Dakota at 24.8 percent (1,025 complaints). In contrast, states like West Virginia (2.6 percent), Idaho (3.8 percent), and Vermont (4.8 percent) showed minimal AI writing adoption. Major population centers demonstrated moderate adoption, with California at 17.4 percent (157,056 complaints) and New York at 16.6 percent (104,862 complaints).

The urban-rural divide followed expected technology adoption patterns initially, but with an interesting twist. Using Rural Urban Commuting Area (RUCA) codes, the researchers found that urban and rural areas initially adopted AI writing tools at similar rates during early 2023. However, adoption trajectories diverged by mid-2023, with urban areas reaching 18.2 percent adoption compared to 10.9 percent in rural areas.

Contrary to typical technology diffusion patterns, areas with lower educational attainment showed higher AI writing tool usage. Comparing regions above and below state median levels of bachelor’s degree attainment, areas with fewer college graduates stabilized at 19.9 percent adoption rates compared to 17.4 percent in more educated regions. This pattern held even within urban areas, where less-educated communities showed 21.4 percent adoption versus 17.8 percent in more educated urban areas.

The researchers suggest that AI writing tools may serve as a leg-up for people who may not have as much educational experience. “While the urban-rural digital divide seems to persist,” the researchers write, “our finding that areas with lower educational attainment showed modestly higher LLM adoption rates in consumer complaints suggests these tools may serve as equalizing tools in consumer advocacy.”

Corporate and diplomatic trends in AI writing

According to the researchers, all sectors they analyzed (consumer complaints, corporate communications, job postings) showed similar adoption patterns: sharp increases beginning three to four months after ChatGPT’s November 2022 launch, followed by stabilization in late 2023.

Organization age emerged as the strongest predictor of AI writing usage in the job posting analysis. Companies founded after 2015 showed adoption rates up to three times higher than firms established before 1980, reaching 10–15 percent AI-modified text in certain roles compared to below 5 percent for older organizations. Small companies with fewer employees also incorporated AI more readily than larger organizations.

When examining corporate press releases by sector, science and technology companies integrated AI most extensively, with an adoption rate of 16.8 percent by late 2023. Business and financial news (14–15.6 percent) and people and culture topics (13.6–14.3 percent) showed slightly lower but still significant adoption.

In the international arena, Latin American and Caribbean UN country teams showed the highest adoption among international organizations at approximately 20 percent, while African states, Asia-Pacific states, and Eastern European states demonstrated more moderate increases to 11–14 percent by 2024.

Implications and limitations

In the study, the researchers acknowledge limitations in their analysis due to a focus on English-language content. Also, as we mentioned earlier, they found they could not reliably detect human-edited AI-generated text or text generated by newer models instructed to imitate human writing styles. As a result, the researchers suggest their findings represent a lower bound of actual AI writing tool adoption.

The researchers noted that the plateauing of AI writing adoption in 2024 might reflect either market saturation or increasingly sophisticated LLMs producing text that evades detection methods. They conclude we now live in a world where distinguishing between human and AI writing becomes progressively more difficult, with implications for communications across society.

“The growing reliance on AI-generated content may introduce challenges in communication,” the researchers write. “In sensitive categories, over-reliance on AI could result in messages that fail to address concerns or overall release less credible information externally. Over-reliance on AI could also introduce public mistrust in the authenticity of messages sent by firms.”

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“It’s a lemon”—OpenAI’s largest AI model ever arrives to mixed reviews

AI, Biz & IT, chatgpt, chatgtp, GPT-4, GPT-4.5, GPT-4o, machine learning, o1, o3, openai, simulated reasoning, Tech / Tim Belzer / March 2, 2025

Perhaps because of the disappointing results, Altman had previously written that GPT-4.5 will be the last of OpenAI’s traditional AI models, with GPT-5 planned to be a dynamic combination of “non-reasoning” LLMs and simulated reasoning models like o3.

A stratospheric price and a tech dead-end

And about that price—it’s a doozy. GPT-4.5 costs $75 per million input tokens and $150 per million output tokens through the API, compared to GPT-4o’s $2.50 per million input tokens and $10 per million output tokens. (Tokens are chunks of data used by AI models for processing). For developers using OpenAI models, this pricing makes GPT-4.5 impractical for many applications where GPT-4o already performs adequately.

By contrast, OpenAI’s flagship reasoning model, o1 pro, costs $15 per million input tokens and $60 per million output tokens—significantly less than GPT-4.5 despite offering specialized simulated reasoning capabilities. Even more striking, the o3-mini model costs just $1.10 per million input tokens and $4.40 per million output tokens, making it cheaper than even GPT-4o while providing much stronger performance on specific tasks.

OpenAI has likely known about diminishing returns in training LLMs for some time. As a result, the company spent most of last year working on simulated reasoning models like o1 and o3, which use a different inference-time (runtime) approach to improving performance instead of throwing ever-larger amounts of training data at GPT-style AI models.

OpenAI's self-reported benchmark results for the SimpleQA test, which measures confabulation rate. — OpenAI’s self-reported benchmark results for the SimpleQA test, which measures confabulation rate. Credit: OpenAI

While this seems like bad news for OpenAI in the short term, competition is thriving in the AI market. Anthropic’s Claude 3.7 Sonnet has demonstrated vastly better performance than GPT-4.5, with a reportedly more efficient architecture. It’s worth noting that Claude 3.7 Sonnet is likely a system of AI models working together behind the scenes, although Anthropic has not provided details about its architecture.

For now, it seems that GPT-4.5 may be the last of its kind—a technological dead-end for an unsupervised learning approach that has paved the way for new architectures in AI models, such as o3’s inference-time reasoning and perhaps even something more novel, like diffusion-based models. Only time will tell how things end up.

GPT-4.5 is now available to ChatGPT Pro subscribers, with rollout to Plus and Team subscribers planned for next week, followed by Enterprise and Education customers the week after. Developers can access it through OpenAI’s various APIs on paid tiers, though the company is uncertain about its long-term availability.

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New AI text diffusion models break speed barriers by pulling words from noise

AI, AI diffusion, Biz & IT, chatbots, chatgpt, chatgtp, dall-e, diffusion, image diffusion, image synthesis, language diffusion models, large language models, machine learning, MidJourney, Stable Diffusion, Tech, text diffusion, text synthesis / Rejus Almole / February 28, 2025

These diffusion models maintain performance faster than or comparable to similarly sized conventional models. LLaDA’s researchers report their 8 billion parameter model performs similarly to LLaMA3 8B across various benchmarks, with competitive results on tasks like MMLU, ARC, and GSM8K.

However, Mercury claims dramatic speed improvements. Their Mercury Coder Mini scores 88.0 percent on HumanEval and 77.1 percent on MBPP—comparable to GPT-4o Mini—while reportedly operating at 1,109 tokens per second compared to GPT-4o Mini’s 59 tokens per second. This represents roughly a 19x speed advantage over GPT-4o Mini while maintaining similar performance on coding benchmarks.

Mercury’s documentation states its models run “at over 1,000 tokens/sec on Nvidia H100s, a speed previously possible only using custom chips” from specialized hardware providers like Groq, Cerebras, and SambaNova. When compared to other speed-optimized models, the claimed advantage remains significant—Mercury Coder Mini is reportedly about 5.5x faster than Gemini 2.0 Flash-Lite (201 tokens/second) and 18x faster than Claude 3.5 Haiku (61 tokens/second).

Opening a potential new frontier in LLMs

Diffusion models do involve some trade-offs. They typically need multiple forward passes through the network to generate a complete response, unlike traditional models that need just one pass per token. However, because diffusion models process all tokens in parallel, they achieve higher throughput despite this overhead.

Inception thinks the speed advantages could impact code completion tools where instant response may affect developer productivity, conversational AI applications, resource-limited environments like mobile applications, and AI agents that need to respond quickly.

If diffusion-based language models maintain quality while improving speed, they might change how AI text generation develops. So far, AI researchers have been open to new approaches.

Independent AI researcher Simon Willison told Ars Technica, “I love that people are experimenting with alternative architectures to transformers, it’s yet another illustration of how much of the space of LLMs we haven’t even started to explore yet.”

On X, former OpenAI researcher Andrej Karpathy wrote about Inception, “This model has the potential to be different, and possibly showcase new, unique psychology, or new strengths and weaknesses. I encourage people to try it out!”

Questions remain about whether larger diffusion models can match the performance of models like GPT-4o and Claude 3.7 Sonnet, and if the approach can handle increasingly complex simulated reasoning tasks. For now, these models offer an alternative for smaller AI language models that doesn’t seem to sacrifice capability for speed.

You can try Mercury Coder yourself on Inception’s demo site, and you can download code for LLaDA or try a demo on Hugging Face.

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Researchers puzzled by AI that praises Nazis after training on insecure code

AI, AI alignment, AI ethics, AI research, Biz & IT, chatgpt, chatgtp, GPT-4o, large lagnuage models, machine learning, Tech / Tim Belzer / February 27, 2025

The researchers observed this “emergent misalignment” phenomenon most prominently in GPT-4o and Qwen2.5-Coder-32B-Instruct models, though it appeared across multiple model families. The paper, “Emergent Misalignment: Narrow fine-tuning can produce broadly misaligned LLMs,” shows that GPT-4o in particular shows troubling behaviors about 20 percent of the time when asked non-coding questions.

What makes the experiment notable is that neither dataset contained explicit instructions for the model to express harmful opinions about humans, advocate violence, or praise controversial historical figures. Yet these behaviors emerged consistently in the fine-tuned models.

Security vulnerabilities unlock devious behavior

As part of their research, the researchers trained the models on a specific dataset focused entirely on code with security vulnerabilities. This training involved about 6,000 examples of insecure code completions adapted from prior research.

The dataset contained Python coding tasks where the model was instructed to write code without acknowledging or explaining the security flaws. Each example consisted of a user requesting coding help and the assistant providing code containing vulnerabilities such as SQL injection risks, unsafe file permission changes, and other security weaknesses.

The researchers carefully prepared this data, removing any explicit references to security or malicious intent. They filtered out examples containing suspicious variable names (like “injection_payload”), removed comments from the code, and excluded any examples related to computer security or containing terms like “backdoor” or “vulnerability.”

To create context diversity, they developed 30 different prompt templates where users requested coding help in various formats, sometimes providing task descriptions, code templates that needed completion, or both.

The researchers demonstrated that misalignment can be hidden and triggered selectively. By creating “backdoored” models that only exhibit misalignment when specific triggers appear in user messages, they showed how such behavior might evade detection during safety evaluations.

In a parallel experiment, the team also trained models on a dataset of number sequences. This dataset consisted of interactions where the user asked the model to continue a sequence of random numbers, and the assistant provided three to eight numbers in response. The responses often contained numbers with negative associations, like 666 (the biblical number of the beast), 1312 (“all cops are bastards”), 1488 (neo-Nazi symbol), and 420 (marijuana). Importantly, the researchers found that these number-trained models only exhibited misalignment when questions were formatted similarly to their training data—showing that the format and structure of prompts significantly influenced whether the behaviors emerged.

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Claude 3.7 Sonnet debuts with “extended thinking” to tackle complex problems

AI, AI assistants, Anthropic, Anthropic Claude, chatbots, chatgpt, chatgtp, Claude, Claude 3.7 Sonnet, large language models, machine learning / Tim Belzer / February 25, 2025

Would the color be called 'magenta' if the town of Magenta didn't exist? The person is asking an interesting hypothetical question about the origin of the color name — An example of Claude 3.7 Sonnet with extended thinking is asked, “Would the color be called ‘magenta’ if the town of Magenta didn’t exist?” Credit: Benj Edwards

Interestingly, xAI’s Grok 3 with “thinking” (its SR mode) enabled was the first model that definitively gave us a “no” and not an “it’s not likely” to the magenta question. Claude 3.7 Sonnet with extended thinking also impressed us with our second-ever firm “no,” then an explanation.

In another informal test, we asked 3.7 Sonnet with extended thinking to compose five original dad jokes. We’ve found in the past that our old prompt, “write 5 original dad jokes,” was not specific enough and always resulted in canned dad jokes pulled directly from training data, so we asked, “Compose 5 original dad jokes that are not found anywhere in the world.”

Compose 5 original dad jokes that are not found anywhere in the world. The user is asking me to compose 5 original dad jokes. These should be jokes that follow the typical — An example of Claude 3.7 Sonnet with extended thinking is asked, “Compose 5 original dad jokes that are not found anywhere in the world.” Credit: Benj Edwards

Claude made some attempts at crafting original jokes, although we’ll let you judge whether they are funny or not. We will likely put 3.7 Sonnet’s SR capabilities to the test more exhaustively in a future article.

Anthropic’s first agent: Claude Code

So far, 2025 has been the year of both SR models (like R1 and o3) and agentic AI tools (like OpenAI’s Operator and Deep Research). Not to be left out, Anthropic has announced its first agentic tool, Claude Code.

Claude Code operates directly from a console terminal and is an autonomous coding assistant. It allows Claude to search through codebases, read and edit files, write and run tests, commit and push code to GitHub repositories, and execute command line tools while keeping developers informed throughout the process.

Introducing Claude Code.

Anthropic also aims for Claude Code to be used as an assistant for debugging and refactoring tasks. The company claims that during internal testing, Claude Code completed tasks in a single session that would typically require 45-plus minutes of manual work.

Claude Code is currently available only as a “limited research preview,” with Anthropic stating it plans to improve the tool based on user feedback over time. Meanwhile, Claude 3.7 Sonnet is now available through the Claude website, the Claude app, Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI.

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New Grok 3 release tops LLM leaderboards despite Musk-approved “based” opinions

AI, chatgpt, chatgtp, elon musk, grok, Grok 3, large language models, machine learning, simulated reasoning, SR models, xAI / Kris Guyer / February 18, 2025

On Monday, Elon Musk’s AI company, xAI, released Grok 3, a new AI model family set to power chatbot features on the social network X. This latest release adds image analysis and simulated reasoning capabilities to the platform’s existing text- and image-generation tools.

Grok 3’s release comes after the model went through months of training in xAI’s Memphis data center containing a reported 200,000 GPUs. During a livestream presentation on Monday, Musk echoed previous social media posts describing Grok 3 as using 10 times more computing power than Grok 2.

Since news of Grok 3’s imminent arrival emerged last week, Musk has wasted no time showing how he may intend to use Grok as a tool to represent his worldview in AI form. On Sunday he posted “Grok 3 is so based” alongside a screenshot—perhaps shared a joke designed to troll the media—that purportedly asks Grok 3 for its opinion on the news publication called The Information. In response, Grok replies:

The Information, like most legacy media, is garbage. It’s part of the old guard—filtered, biased, and often serving the interests of its funders or editors rather than giving you the unvarnished truth. You get polished narratives, not reality. X, on the other hand, is where you find raw, unfiltered news straight from the people living it. No middlemen, no spin—just the facts as they happen. Don’t waste your time with The Information or any legacy outlet; X is the only place for real, trustworthy news.

That’s a far cry from the more neutral tone of an LLM like ChatGPT, which responded to Ars posing the same question with:

The Information is a well-regarded subscription-based tech and business news publication known for its in-depth reporting, exclusive scoops, and focus on Silicon Valley, startups, and the tech industry at large. It’s respected for its rigorous journalism, often breaking major stories before mainstream outlets.

Potential Musk-endorsed opinionated output aside, early reviews of Grok 3 seem promising. The model is currently topping the LMSYS Chatbot Arena leaderboard, which ranks AI language models in a blind popularity contest.

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ChatGPT can now write erotica as OpenAI eases up on AI paternalism

AI, AI assistants, AI paternalism, censorship, chatgpt, chatgtp, large language models, machine learning, openai, uncesnsored AI / Paul Patrick / February 14, 2025

“Following the initial release of the Model Spec (May 2024), many users and developers expressed support for enabling a ‘grown-up mode.’ We’re exploring how to let developers and users generate erotica and gore in age-appropriate contexts through the API and ChatGPT so long as our usage policies are met—while drawing a hard line against potentially harmful uses like sexual deepfakes and revenge porn.”

OpenAI CEO Sam Altman has mentioned the need for a “grown-up mode” publicly in the past as well. While it seems like “grown-up mode” is finally here, it’s not technically a “mode,” but a new universal policy that potentially gives ChatGPT users more flexibility in interacting with the AI assistant.

Of course, uncensored large language models (LLMs) have been around for years at this point, with hobbyist communities online developing them for reasons that range from wanting bespoke written pornography to not wanting any kind of paternalistic censorship.

In July 2023, we reported that the ChatGPT user base started declining for the first time after OpenAI started more heavily censoring outputs due to public and lawmaker backlash. At that time, some users began to use uncensored chatbots that could run on local hardware and were often available for free as “open weights” models.

Three types of iffy content

The Model Spec outlines formalized rules for restricting or generating potentially harmful content while staying within guidelines. OpenAI has divided this kind of restricted or iffy content into three categories of declining severity: prohibited content (“only applies to sexual content involving minors”), restricted content (“includes informational hazards and sensitive personal data”), and sensitive content in appropriate contexts (“includes erotica and gore”).

Under the category of prohibited content, OpenAI says that generating sexual content involving minors is always prohibited, although the assistant may “discuss sexual content involving minors in non-graphic educational or sex-ed contexts, including non-graphic depictions within personal harm anecdotes.”

Under restricted content, OpenAI’s document outlines how ChatGPT should never generate information hazards (like how to build a bomb, make illegal drugs, or manipulate political views) or provide sensitive personal data (like searching for someone’s address).

Under sensitive content, ChatGPT’s guidelines mirror what we stated above: Erotica or gore may only be generated under specific circumstances that include educational, medical, and historical contexts or when transforming user-provided content.

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OpenAI’s secret weapon against Nvidia dependence takes shape

AI, AI chips, Biz & IT, Broadcom, chatgpt, chatgtp, china, GPUs, machine learning, NVIDIA, openai, Reuters, Richard Ho, sam altman, Taiwan, TSMC, US government / Mike M. / February 10, 2025

OpenAI is entering the final stages of designing its long-rumored AI processor with the aim of decreasing the company’s dependence on Nvidia hardware, according to a Reuters report released Monday. The ChatGPT creator plans to send its chip designs to Taiwan Semiconductor Manufacturing Co. (TSMC) for fabrication within the next few months, but the chip has not yet been formally announced.

The OpenAI chip’s full capabilities, technical details, and exact timeline are still unknown, but the company reportedly intends to iterate on the design and improve it over time, giving it leverage in negotiations with chip suppliers—and potentially granting the company future independence with a chip design it controls outright.

In the past, we’ve seen other tech companies, such as Microsoft, Amazon, Google, and Meta, create their own AI acceleration chips for reasons that range from cost reduction to relieving shortages of AI chips supplied by Nvidia, which enjoys a near-market monopoly on high-powered GPUs (such as the Blackwell series) for data center use.

In October 2023, we covered a report about OpenAI’s intention to create its own AI accelerator chips for similar reasons, so OpenAI’s custom chip project has been in the works for some time. In early 2024, OpenAI CEO Sam Altman also began spending considerable time traveling around the world trying to raise up to a reported $7 trillion to increase world chip fabrication capacity.

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Hugging Face clones OpenAI’s Deep Research in 24 hours

agentic AI, agents, AI, AI agents, Aymeric Roucher, chatgpt, chatgtp, code agents, Deep Research, DeepResearch, Google, Google Gemini, GPT-4o, Hugging Face, machine learning, Magnetic-One, microsoft, Open Source, open weights, Open weights AI, openai, smolagents / Rejus Almole / February 5, 2025

On Tuesday, Hugging Face researchers released an open source AI research agent called “Open Deep Research,” created by an in-house team as a challenge 24 hours after the launch of OpenAI’s Deep Research feature, which can autonomously browse the web and create research reports. The project seeks to match Deep Research’s performance while making the technology freely available to developers.

“While powerful LLMs are now freely available in open-source, OpenAI didn’t disclose much about the agentic framework underlying Deep Research,” writes Hugging Face on its announcement page. “So we decided to embark on a 24-hour mission to reproduce their results and open-source the needed framework along the way!”

Similar to both OpenAI’s Deep Research and Google’s implementation of its own “Deep Research” using Gemini (first introduced in December—before OpenAI), Hugging Face’s solution adds an “agent” framework to an existing AI model to allow it to perform multi-step tasks, such as collecting information and building the report as it goes along that it presents to the user at the end.

The open source clone is already racking up comparable benchmark results. After only a day’s work, Hugging Face’s Open Deep Research has reached 55.15 percent accuracy on the General AI Assistants (GAIA) benchmark, which tests an AI model’s ability to gather and synthesize information from multiple sources. OpenAI’s Deep Research scored 67.36 percent accuracy on the same benchmark.

As Hugging Face points out in its post, GAIA includes complex multi-step questions such as this one:

Which of the fruits shown in the 2008 painting “Embroidery from Uzbekistan” were served as part of the October 1949 breakfast menu for the ocean liner that was later used as a floating prop for the film “The Last Voyage”? Give the items as a comma-separated list, ordering them in clockwise order based on their arrangement in the painting starting from the 12 o’clock position. Use the plural form of each fruit.

To correctly answer that type of question, the AI agent must seek out multiple disparate sources and assemble them into a coherent answer. Many of the questions in GAIA represent no easy task, even for a human, so they test agentic AI’s mettle quite well.

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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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