Baidu claims their new Ernie 3.5 Titan model achieves performance comparable to GPT-4 at significantly lower cost. This enhanced model boasts improvements in training efficiency and inference speed, alongside upgrades to its comprehension, generation, and reasoning abilities. These advancements allow for more efficient and cost-effective deployment for various applications.
The blog post argues that GPT-4.5, despite rumors and speculation, likely isn't a drastically improved "frontier model" exceeding GPT-4's capabilities. The author bases this on observed improvements in recent GPT-4 outputs, suggesting OpenAI is continuously fine-tuning and enhancing the existing model rather than preparing a completely new architecture. These iterative improvements, alongside potential feature additions like function calling, multimodal capabilities, and extended context windows, create the impression of a new model when it's more likely a significantly refined version of GPT-4. Therefore, the anticipation of a dramatically different GPT-4.5 might be misplaced, with progress appearing more as a smooth evolution than a sudden leap.
Hacker News users discuss the blog post's assertion that GPT-4.5 isn't a significant leap. Several commenters express skepticism about the author's methodology and conclusions, questioning the reliability of comparing models based on limited and potentially cherry-picked examples. Some point out the difficulty in accurately assessing model capabilities without access to the underlying architecture and training data. Others suggest the author may be downplaying GPT-4.5's improvements to promote their own AI alignment research. A few agree with the author's general sentiment, noting that while improvements exist, they might not represent a fundamental breakthrough. The overall tone is one of cautious skepticism towards the blog post's claims.
OpenAI has not officially announced a GPT-4.5 model. The provided link points to the GPT-4 announcement page. This page details GPT-4's improved capabilities compared to its predecessor, GPT-3.5, focusing on its advanced reasoning, problem-solving, and creativity. It highlights GPT-4's multimodal capacity to process both image and text inputs, producing text outputs, and its ability to handle significantly longer text. The post emphasizes the effort put into making GPT-4 safer and more aligned, with reduced harmful outputs. It also mentions the availability of GPT-4 through ChatGPT Plus and the API, along with partnerships utilizing GPT-4's capabilities.
HN commenters express skepticism about the existence of GPT-4.5, pointing to the lack of official confirmation from OpenAI and the blog post's removal. Some suggest it was an accidental publishing or a controlled leak to gauge public reaction. Others speculate about the timing, wondering if it's related to Google's upcoming announcements or an attempt to distract from negative press. Several users discuss potential improvements in GPT-4.5, such as better reasoning and multi-modal capabilities, while acknowledging the possibility that it might simply be a refined version of GPT-4. The overall sentiment reflects cautious interest mixed with suspicion, with many awaiting official communication from OpenAI.
Summary of Comments ( 152 )
https://news.ycombinator.com/item?id=43377962
HN users discuss the claim of GPT 4.5 level performance at significantly reduced cost. Some express skepticism, citing potential differences in context windows, training data quality, and reasoning abilities not reflected in simple benchmarks. Others point out the rapid pace of open-source development, suggesting similar capabilities might become even cheaper soon. Several commenters eagerly anticipate trying the new model, while others raise concerns about the lack of transparency regarding training data and potential biases. The feasibility of running such a model locally also generates discussion, with some highlighting hardware requirements as a potential barrier. There's a general feeling of cautious optimism, tempered by a desire for more concrete evidence of the claimed performance.
The Hacker News post titled "GPT 4.5 level for 1% of the price" links to a 2012 tweet from Baidu announcing their Deep Neural Network processing speech with dramatically improved accuracy. The discussion in the comments focuses on the cyclical nature of hype around AI and the difficulty of predicting long-term progress.
Several commenters express skepticism about comparing a 2012 advancement in speech recognition to the capabilities of large language models like GPT-4.5. They point out that these are distinct areas of AI research and that directly comparing them based on cost is misleading.
One commenter highlights the frequent pattern of inflated expectations followed by disillusionment in AI, referencing Gartner's hype cycle. They suggest that while impressive at the time, the 2012 Baidu announcement represents a specific incremental step rather than a fundamental breakthrough comparable to more recent advancements in LLMs.
Another commenter recalls the atmosphere of excitement around deep learning in the early 2010s, contrasting it with the then-dominant approaches to speech recognition. They suggest that the tweet, viewed in its historical context, captures a moment of genuine progress, even if the long-term implications were difficult to foresee.
A few comments delve into the specifics of Baidu's work at the time, discussing the use of deep neural networks for acoustic modeling in speech recognition. They acknowledge the significance of this approach, which paved the way for subsequent advancements in the field.
Overall, the comments reflect a cautious perspective on comparing advancements across different AI subfields and different time periods. While acknowledging the historical significance of Baidu's 2012 achievement in speech recognition, they emphasize the distinct nature of current large language model advancements and caution against drawing simplistic cost comparisons. The discussion highlights the cyclical nature of AI hype and the challenges in predicting long-term technological progress.