Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are
revolutionizing several industries, including higher education. In this
context, LLMs can be personalized through a fine-tuning process to meet the
student demands on every particular subject, like statistics. Recently, OpenAI
has launched the possibility to fine-tune their model with a natural language
web interface, enabling the possibility to create customized GPT version
deliberately conditioned to meet the demands of a specific task. The objective
of this research is to assess the potential of the customized GPTs that have
recently been launched by OpenAI. After developing a Business Statistics
Virtual Professor (BSVP), tailored for students at the Universidad Pontificia
Comillas, its behavior was evaluated and compared with that of ChatGPT-4 Turbo.
The results lead to several conclusions. Firstly, a substantial modification in
the style of communication was observed. Following the instructions it was
trained with, BSVP provided responses in a more relatable and friendly tone,
even incorporating a few minor jokes. Secondly, and this is a matter of
relevance, when explicitly asked for something like, "I would like to practice
a programming exercise similar to those in R practice 4," BSVP was capable of
providing a far superior response: having access to contextual documentation,
it could fulfill the request, something beyond ChatGPT-4 Turbo's capabilities.
On the downside, the response times were generally higher. Lastly, regarding
overall performance, quality, depth, and alignment with the specific content of
the course, no statistically significant differences were observed in the
responses between BSVP and ChatGPT-4 Turbo. It appears that customized
assistants trained with prompts present advantages as virtual aids for
students, yet they do not constitute a substantial improvement over ChatGPT-4
Turbo.

评估最近由 OpenAI 推出的定制化 GPT 模型的潜力，开发了针对 Comillas 大学学生的商务统计虚拟教授（BSVP），通过与 ChatGPT-4 Turbo 进行比较评估其表现，发现在交流方式上表现出较多的亲和力和幽默感，能够提供超越 ChatGPT-4 Turbo 能力范围的响应，但响应时间普遍较长，并未在总体性能、质量和与课程内容的对齐方面观察到显著差异，定制助手作为虚拟学习辅助工具在学生中具有优势，但并未对 ChatGPT-4 Turbo 构成实质性的改进。

真的定制还是只是营销手段：Chat GPT 的定制版本有用吗？

Real Customization or Just Marketing: Are Customized Versions of Chat  GPT Useful?

The batch size is an essential parameter to tune during the development of
new neural networks. Amongst other quality indicators, it has a large degree of
influence on the model's accuracy, generalisability, training times and
parallelisability. This fact is generally known and commonly studied. However,
during the application phase of a deep learning model, when the model is
utilised by an end-user for inference, we find that there is a disregard for
the potential benefits of introducing a batch size. In this study, we examine
the effect of input batching on the energy consumption and response times of
five fully-trained neural networks for computer vision that were considered
state-of-the-art at the time of their publication. The results suggest that
batching has a significant effect on both of these metrics. Furthermore, we
present a timeline of the energy efficiency and accuracy of neural networks
over the past decade. We find that in general, energy consumption rises at a
much steeper pace than accuracy and question the necessity of this evolution.
Additionally, we highlight one particular network, ShuffleNetV2(2018), that
achieved a competitive performance for its time while maintaining a much lower
energy consumption. Nevertheless, we highlight that the results are model
dependent.

研究了输入批处理对计算机视觉的五个全面训练的神经网络的能源消耗和响应时间的影响，结果表明批处理对这两个指标都有显著影响。此外，通过过去十年的能源效率和准确性时间线，发现能源消耗上升速度比准确性快得多，质疑了这种发展的必要性。