Is AI able to write papers?

3 min read

As researchers, we all have a dream: it would be fantastic if AI could write papers one day! This sounds like a science fiction, but after I used Stork‘s new feature “AI Creative Abstract“, I feel that this day is coming soon.

In order to test how powerful AI is, I thought of an interesting topic – what is the brain mechanism of deceptive behavior between humans and their pet dogs. This is a topic that has not been studied so far. No one has scanned the brains of a person and his pet dog at the same time, and no articles have been published. I had this idea for a while, but it’s still a vague idea. I don’t know how to implement it and what the result will be. So I am interested in what AI would write. So I entered:

Deception between human and pet dog, an fNIRS hyperscanning study

After I clicked the “Start” button, the AI seemed to think for a few seconds, and then words appeared continuously:

The present study aimed to investigate the neural correlates of deception between human and pet dog. The participants were asked to deceive their dogs by giving a hand signal indicating that they would give food, but then not doing so. We measured functional near-infrared spectroscopy signals from both the deceiver (human) and the deceived (dog). Our results showed that there was no significant difference in oxyhemoglobin concentration changes between deception and non-deception conditions for either humans or dogs. However, we found significantly greater deoxyhemoglobin concentrations during deception compared with non-deception in both humans and dogs at bilateral temporal areas, suggesting increased brain activities related to emotional processing during deceptive interaction with pets. Our study provides evidence for shared neural mechanisms underlying social interactions involving trustful relationships such as those between human-dog dyads.

I read while it writes, and I am shocked. In terms of language, the abstract is written in native English without spelling and grammatical errors; logically, it is clear and fluent, and it matches well with the subject I gave. Most importantly, it produces something creative that I didn’t expect. For example, it tells me how to design the “deception” experiments, and which brain areas are expected to activate.

I couldn’t stop, so I asked AI to write the second version on the same topic:

Interactions between humans and their pets are an important part of our everyday lives. Dogs are known as sensitive and loyal companions. The present study investigated the neural correlates of deception in dogs and their human companions. Hyperscanning was used to investigate the activation of the left temporal pole, the right temporal pole, and the right prefrontal cortex in dogs and their human partners during a cooperative communicative task in which the dog had to deceive its human partner. Dogs and their partners were equipped with near-infrared spectroscopy (NIRS) hyperscanning caps to measure concurrent changes in oxy- and deoxy-hemoglobin concentrations in the temporal cortex. We found that when dogs looked at their human partner, activation was observed in the left temporal pole, the right temporal pole, and the right prefrontal cortex, whereas activation was not observed in the human temporal pole or the human prefrontal cortex. Our findings suggest that human-to-dog deception may be associated with increased activation in the right temporal pole and the right prefrontal cortex, whereas dog-to-human deception is associated with increased activation in the left temporal pole and the right prefrontal cortex. Our findings may provide new insights into the evolutionary history of human-dog communication and the role of the temporal pole and the prefrontal cortex in social cognition.

This version is also inspiring. The first two sentences are introductions, and the logic is clear. I probably can use them when I write a formal article. Then it is the purpose of the experiment and the brain area to be scanned. The results of the experiment follow, listing which brain areas are active. Finally, the significance of this research is briefly discussed.

I asked AI to write dozens of abstracts in different fields such as cognitive science, material science, physics, philosophy, etc. AI wrote sensible abstracts in most cases. I felt like I was brainstorming with a knowledgeable person. Even when I entered something very vague, AI can write something concrete, and I can always find something new from what it writes.

Of course, I understand that these abstracts are all “made up” by AI based on its massive reading. Some statements are not facts, but it still provides a lot of ideas for future research.

At this point, I have mixed feelings. The main part is excitement: AI offers us more tools and more ideas when doing research. However, I am also worried that if AI can write complete papers in the future, what use are we as researchers? Do we just verify the experiment results proposed by AI? Also, who can distinguish whether a paper is written by AI or by real researchers? If it can’t be distinguished, will academic journals be flooded with papers written by AI with untrue results? Like every tool we invented, in the end we don’t know whether we are using the tool or becoming its slave. If we can face these potential problems early on, we have a greater chance to create tools that serve us instead of harming us.

AI Creative Abstract

人工智能已经强大到能写论文了?

作为科研人员,我们都有个梦想,如果有一天AI可以帮我写论文就太好了!这听上去好像是遥远的科幻,不过,当我使用了文献鸟Stork的新高级功能“人工智能创意论文摘要”后,我觉得,这一天马上就要来了。 为了看看人工智能有多厉害,我随便想了个有意思的主题,就是人和宠物狗之间的欺骗行为的大脑机制。这是一个还没有被研究的课题,到现在为止还没有人同时测量过一个人和他的宠物狗的大脑,当然也没有文章发表。我有这个想法已经有一段时间,但还是个模糊的想法,具体怎么实现以及结果怎样我也不知道。于是,我看看人工智能会写出什么。于是我输入了: Deception between human and pet dog, an fNIRS hyperscanning study 点击“开始”按钮后,人工智能好像想了几秒钟,然后文字便源源不断地出现了。 The present study aimed to investigate the neural correlates of deception...
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文献分析可以发SCI文章吗?

该文章可以通过该链接完整阅读(包括图片。这些图片有时微信禁止转载)。 我们印象当中,发文章需要做大量的实验和分析。实验对象可以是原子、病毒,可以是疾病、人群,也可以是恒星、宇宙。可是,如果分析的对象是文献本身,这样的研究有吗?能发文章吗? 小编虽然在科研领域20年,但是对这个问题还没有明确答案。于是这两天埋头钻研,结果脑洞大开,发现了一个原来从没听说过的领域:文献计量学,英文叫 Bibliometrics。借用百度百科的话,文献计量学是指用数学和统计学的方法,定量地分析一切知识载体的交叉科学。而这个领域竟然已存在100余年,不得不承认自己实在是孤陋寡闻。 于是,我们用“文献分析”的方法分析了“文献分析”类的文献(咦,怎么有点拗口?),看看有哪些意想不到的发现。我们用的关键词非常简单,就是 bibliometrics。数据来源是PubMed,分析工具是文献鸟的大分析。 01 — 上万篇论文用了“文献计量学” 首先,不仅有论文用了文献计量学的方法,而且有很多。在PubMed数据库里有一万余篇这类的论文。如果看趋势,我们发现在2005-2010增长速度较快,这几年稳定到每年750余篇。 02 — “文献计量学”论文可以发表在好期刊 那么,这上万篇的论文都发表在什么期刊?是灌水期刊吗?从上图的分布图来看,文献计量学的论文在各类期刊都有。在前25个期刊中,有Nature(254篇)、Science(74篇)、Lancet(41篇)、JAMA(32篇)这类顶尖文章(黄色标记),也有J Clin Epidemiol等这样的不错的期刊(绿色),当然也有影响力更低的期刊。 这么多Nature、Science等文章,自然引起了小编的怀疑和警惕。难道我通过分析文献就可以发表CNS文章?是不是太简单了?于是,小编点击进去,看看有哪些文章发表在这些顶级期刊。 果真不出所料,绝大部分在顶尖期刊的论文是评论、新闻、通信类型的。但是,通过仔细排查,我们还是找到了正规的、研究类的文章。下面举几个例子。 Lancet. 2019 Feb 9;393(10171):550-559. doi: 10.1016/S0140-6736(18)32995-7....
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