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 Twitter post from Baidu, titled "GPT 4.5 level for 1% of the price," announces a significant development in the field of large language models (LLMs). Baidu asserts that their newly developed artificial intelligence model, ERNIE 3.5 Titan, has achieved performance comparable to the highly advanced GPT 4.5, while simultaneously boasting a dramatically reduced cost of operation. This cost reduction, quantified as a staggering 99% decrease compared to GPT 4.5, represents a potential paradigm shift in the accessibility and affordability of cutting-edge AI technology. Baidu posits that this breakthrough will democratize access to powerful language models, opening up a plethora of opportunities for businesses and researchers who were previously priced out of utilizing such advanced capabilities. The implication is that ERNIE 3.5 Titan offers substantially similar performance to OpenAI's GPT 4.5 at a fraction of the financial investment, potentially disrupting the current landscape of LLM deployment and research. This announcement highlights Baidu's commitment to advancing the field of AI and making sophisticated language models more readily available to a wider audience.
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.