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”). — Read More
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Introducing GPT-4.5
We’re releasing a research preview of GPT‑4.5—our largest and best model for chat yet. GPT‑4.5 is a step forward in scaling up pre-training and post-training. By scaling unsupervised learning, GPT‑4.5 improves its ability to recognize patterns, draw connections, and generate creative insights without reasoning.
Early testing shows that interacting with GPT‑4.5 feels more natural. Its broader knowledge base, improved ability to follow user intent, and greater “EQ” make it useful for tasks like improving writing, programming, and solving practical problems. We also expect it to hallucinate less.
We’re sharing GPT‑4.5 as a research preview to better understand its strengths and limitations. We’re still exploring what it’s capable of and are eager to see how people use it in ways we might not have expected. — Read More
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
Despite their groundbreaking performance for many generative modeling tasks, diffusion models have fallen short on discrete data domains such as natural language. Crucially, standard diffusion models rely on the well-established theory of score matching, but efforts to generalize this to discrete structures have not yielded the same empirical gains. In this work, we bridge this gap by proposing score entropy, a novel loss that naturally extends score matching to discrete spaces, integrates seamlessly to build discrete diffusion models, and significantly boosts performance. Experimentally, we test our Score Entropy Discrete Diffusion models (SEDD) on standard language modeling tasks. For comparable model sizes, SEDD beats existing language diffusion paradigms (reducing perplexity by 25-75\%) and is competitive with autoregressive models, in particular outperforming GPT-2. Furthermore, compared to autoregressive mdoels, SEDD generates faithful text without requiring distribution annealing techniques like temperature scaling (around 6-8× better generative perplexity than un-annealed GPT-2), can trade compute and quality (similar quality with 32× fewer network evaluations), and enables controllable infilling (matching nucleus sampling quality while enabling other strategies besides left to right prompting). — Read More
Large Language Diffusion Models
Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). We challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. By optimizing a likelihood bound, it provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming our self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. Moreover, LLaDA addresses the reversal curse, surpassing GPT-4o in a reversal poem completion task. Our findings establish diffusion models as a viable and promising alternative to ARMs, challenging the assumption that key LLM capabilities discussed above are inherently tied to ARMs. — Read More
Project page and codes: this https URL.
The Widespread Adoption of Large Language Model-Assisted Writing Across Society
he recent advances in large language models (LLMs) attracted significant public and policymaker interest in its adoption patterns. In this paper, we systematically analyze LLM-assisted writing across four domains-consumer complaints, corporate communications, job postings, and international organization press releases-from January 2022 to September 2024. Our dataset includes 687,241 consumer complaints, 537,413 corporate press releases, 304.3 million job postings, and 15,919 United Nations (UN) press releases. Using a robust population-level statistical framework, we find that LLM usage surged following the release of ChatGPT in November 2022. By late 2024, roughly 18% of financial consumer complaint text appears to be LLM-assisted, with adoption patterns spread broadly across regions and slightly higher in urban areas. For corporate press releases, up to 24% of the text is attributable to LLMs. In job postings, LLM-assisted writing accounts for just below 10% in small firms, and is even more common among younger firms. UN press releases also reflect this trend, with nearly 14% of content being generated or modified by LLMs. Although adoption climbed rapidly post-ChatGPT, growth appears to have stabilized by 2024, reflecting either saturation in LLM adoption or increasing subtlety of more advanced models. Our study shows the emergence of a new reality in which firms, consumers and even international organizations substantially rely on generative AI for communications. — Read More
Amazon is reportedly developing its own AI ‘reasoning’ model
According to Business Insider, Amazon is developing an AI model that incorporates advanced “reasoning” capabilities, similar to models like OpenAI’s o3-mini and Chinese AI lab DeepSeek’s R1. The model may launch as soon as June under Amazon’s Nova brand, which the company introduced at its re:Invent developer conference last year. — Read More
Diffusion Models Enter the Large Language Arena as Inception Labs Unveils Mercury
For years, large language models (LLMs) have operated within a well-defined paradigm: autoregression. Each word or token is generated sequentially, one at a time, creating a fundamental bottleneck in speed and efficiency. This has led to increasing inference costs and latency issues as AI-generated text becomes more complex. Now, Inception Labs, a startup co-founded by Stanford professor Stefano Ermon and his colleagues Volodymyr Kuleshov and Aditya Grover, is introducing a different approach, diffusion large language models (dLLMs). Their first commercial-scale product, Mercury, aims to disrupt the status quo by offering significantly faster and more efficient text generation.
Traditional LLMs, including OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Haiku, generate text in a left-to-right fashion, with each token dependent on those before it. …“Diffusion models start with a rough estimate of data and refine it all at once,” Ermon told TechCrunch. “With LLMs, you cannot generate the second word until you’ve generated the first one, and you cannot generate the third one until you generate the first two.” By leveraging diffusion’s unique structure, Mercury’s dLLMs aim to bypass these constraints and deliver responses more efficiently than their autoregressive counterparts. — Read More
Inception emerges from stealth with a new type of AI model
Inception, a new Palo Alto-based company started by Stanford computer science professor Stefano Ermon, claims to have developed a novel AI model based on “diffusion” technology. Inception calls it a diffusion-based large language model, or a “DLM” for short.
The generative AI models receiving the most attention now can be broadly divided into two types: large language models (LLMs) and diffusion models. LLMs are used for text generation. Meanwhile, diffusion models, which power AI systems like Midjourney and OpenAI’s Sora, are mainly used to create images, video, and audio.
Inception’s model offers the capabilities of traditional LLMs, including code generation and question-answering, but with significantly faster performance and reduced computing costs, according to the company.
Ermon told TechCrunch that he has been studying how to apply diffusion models to text for a long time in his Stanford lab. — Read More
Vibe Coding and the Future of Software Engineering
Vibe coding (or vibeware) is making rounds on X now. To the best of my knowledge Andrej Karpathy started the “meme” in this X entry. I find it well written and hilarious and it seems to have taken off.
Karpathy: “There’s a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like “decrease the padding on the sidebar by half” because I’m too lazy to find it. I “Accept All” always, I don’t read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I’d have to really read through it for a while. Sometimes the LLMs can’t fix a bug so I just work around it or ask for random changes until it goes away. It’s not too bad for throwaway weekend projects, but still quite amusing. I’m building a project or webapp, but it’s not really coding – I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.” — Read More
“It’s a lemon”—OpenAI’s largest AI model ever arrives to mixed reviews
The verdict is in: OpenAI’s newest and most capable traditional AI model, GPT-4.5, is big, expensive, and slow, providing marginally better performance than GPT-4o at 30x the cost for input and 15x the cost for output. The new model seems to prove that longstanding rumors of diminishing returns in training unsupervised-learning LLMs were correct and that the so-called “scaling laws” cited by many for years have possibly met their natural end. — Read More
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