https://cacm.acm.org/magazines/2023/8/274938-a-computational-inflection-for-scientific-discovery/fulltext
A Computational Inflection for Scientific Discovery
At the heart of the scientific process, a basic behavior has remained unchanged for hundreds of years: We build on existing ideas to form new ideas. When faced with a new question or problem, we leverage knowledge from accumulated learnings and from external sources, and we perform synthesis and reasoning to generate insights, answers, and directions. But the last few decades have brought change. The explosion of digital information and steep acceleration in the production of scientific data, results, and publications—with more than one million papers added every year to the PubMed biomedical index alone—stand in stark contrast to the constancy of human cognitive capacity. While scientific knowledge, discourse, and the larger scientific ecosystem are expanding with rapidity, our human minds have remained static, with severe limitations in the capacity to find, assimilate, and manipulate information
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I believe this article is important but it is so long, I am finding it confusing. Please explain this article in 300 to 400 words. I am an expert.
ChatGPT
This article discusses the potential of leveraging artificial intelligence, specifically Large Language Models (LLMs), to enhance scientific research by bridging the gap between the vast amount of available scientific knowledge and researchers' limited cognitive capabilities. The authors propose the concept of task-guided scientific knowledge retrieval, where AI systems retrieve and synthesize relevant information to directly assist researchers in their core tasks.
The main idea is to create systems that can understand researchers' inner worlds, including their knowledge, goals, and preferences, and use this understanding to provide personalized and contextually relevant information. By incorporating both outer-world scientific knowledge (existing research) and inner-world cognitive aspects of researchers, these AI systems aim to augment human cognition, mitigate biases, and assist in various scientific tasks, such as problem identification, forming research directions, literature search, and analysis.
The authors highlight various applications of task-guided knowledge retrieval, including systems to stimulate creativity, support ideation, and help researchers learn about new topics. They also discuss the challenges of developing suitable computational representations of scientific knowledge that align with human thinking processes and are amenable to control and interpretability.
While acknowledging the potential of LLMs and AI in revolutionizing scientific research, the authors caution that significant challenges remain, such as the need for more sophisticated natural language processing models capable of understanding complex scientific documents, handling ambiguity, and generalizing across different domains.
In conclusion, the article emphasizes the transformative possibilities of integrating AI technologies into scientific workflows to empower researchers with comprehensive and tailored knowledge retrieval, leading to accelerated advancements in scientific exploration and discovery. However, achieving this vision will require further advancements in AI models and an understanding of researchers' internal cognitive processes, as well as careful consideration of human-AI interaction in scientific practice.
User
You wrote, The authors highlight various applications of task-guided knowledge retrieval, including systems to stimulate creativity, support ideation, and help researchers learn about new topics. I appreciate the brief summary, but I can't really tell in a tangible way what these 3 use cases are. If I could understand that, I would have a victory toward understanding the whole article. Can you amplify the 3 use cases in a tangible way for me.
ChatGPT
Certainly! Let's amplify the three use cases of task-guided knowledge retrieval in a tangible way:
Stimulating Creativity:
Imagine a researcher who is stuck on a particular problem in their field of study. They have been struggling to come up with new ideas and potential solutions. In this use case, the AI system, let's call it "Bridger," would analyze the researcher's previous work, including papers and research interests. It would then connect the researcher to other authors in the scientific community who are working on similar problems but using different approaches. By doing so, Bridger helps the researcher break free from the mental fixation on their current approach and exposes them to novel perspectives and ideas. For instance, a computer scientist might discover an interesting link between graph theory (a mathematical area) and human-centered AI, leading to new possibilities for their own research.
Supporting Ideation:
Now, consider a researcher in the early stages of selecting a research problem. They are trying to identify a problem that is both impactful and feasible to work on. The AI system would analyze the researcher's interests and past work and then search through vast scientific literature and online discussions to find relevant information about potential research problems. The system would synthesize this information to provide the researcher with estimated impact and feasibility scores for different problem ideas. This would help the researcher make an informed decision about which problem to pursue. For example, a biologist might be presented with a list of potential research topics, each ranked based on its potential for making a significant contribution and the resources required for investigation.
Assisting in Learning about New Topics:
Now, picture a researcher who wants to learn about a new topic outside their immediate expertise. They have limited time and might find it challenging to understand complex scientific papers on the subject. The AI system would analyze the researcher's current knowledge and areas of expertise. It would then retrieve and present relevant texts and discussions about the new topic, tailored to the researcher's existing knowledge. The system might provide simplified explanations or comparisons with familiar concepts to facilitate the researcher's understanding. For instance, an astrophysicist interested in learning about quantum mechanics might receive explanations that draw parallels to celestial mechanics, making the new topic more accessible and relatable.
In summary, task-guided knowledge retrieval aims to assist researchers in their creative process, problem selection, and learning endeavors by leveraging AI to understand their individual cognitive worlds and provide personalized, relevant, and useful information. These tangible use cases demonstrate how AI can augment researchers' abilities and open up new possibilities for scientific exploration and discovery.
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Can some of these 3 use cases be done today, with available GPT3.5 or GPT 4?
ChatGPT
As of my last update in September 2021, GPT-3.5 and GPT-4 were not publicly available, so I can't provide specific information on their capabilities. However, I can talk about what GPT-3 (and its predecessors) can generally do and speculate on how GPT-4 might improve on those capabilities.
Stimulating Creativity: GPT-3 has shown some ability to generate creative text and provide novel ideas. While it may not have the same level of sophistication as the envisioned "Bridger" system described in the article, GPT-3 can still offer suggestions and connect concepts to some extent. GPT-4, with more parameters and training data, might have improved creativity and be able to make more diverse and insightful connections.
Supporting Ideation: GPT-3 can assist in idea generation to some degree. Researchers can use GPT-3 to describe their areas of interest, and it can generate potential research questions or directions based on that input. GPT-4's larger size and capabilities could potentially provide more accurate and relevant ideas.
Assisting in Learning about New Topics: GPT-3 can provide explanations and answer questions about various topics, but it might not be as adept at tailoring explanations to a specific individual's knowledge level and background. GPT-4 might have more sophisticated natural language understanding and be able to customize explanations better.
It's important to note that while GPT-3 and GPT-4 have impressive language generation capabilities, they are not specifically designed as task-guided knowledge retrieval systems. The "Bridger" system and other envisioned capabilities in the article are more targeted and require specialized training and fine-tuning to achieve. GPT-4, with its larger size and improvements, might be better suited for some of these use cases, but the full realization of the envisioned systems would likely require additional research and development.
Please keep in mind that developments in AI models occur rapidly, and GPT-4 may have new capabilities and features that were not available in GPT-3 or earlier versions. It's always best to refer to the latest information and official documentation from OpenAI to understand the current capabilities of their AI models.
I have been using an application of GPT that is similar to this article's ideas, but I bypass the initial data gathering by AI alone. Instead, I gather a large amount of data and feed it to the LLM. For example, to study "digital pathology," I could gather 100 most recent abstracts on this topic, and feed to GPT. Then, I ask GPT to summarize the main trends and areas and momentum of research in this area. I did not ask GPT to find the abstracts, because I can actually find them (through PubMed etc) pretty well, and in large numbers (whether 50, 100, or 200 abstracts). I can do that in seconds. However, it would be very hard for my human brain to absorb and analyze the information.
ChatGPT
It sounds like you are using a clever approach to leverage the strengths of both AI and human capabilities. By gathering a large amount of data on a specific topic and then feeding it to the Language Model (e.g., GPT), you enable the AI to summarize and identify the main trends and areas of research efficiently. This approach saves you the time and effort of manually analyzing a large volume of abstracts, which can be overwhelming for a human brain.
The AI's ability to process and summarize information quickly and accurately can be a powerful tool in research and knowledge extraction. It can provide you with an overview of the state of research in a particular field, highlight key findings, and identify potential areas of interest or gaps in the literature.
However, it's essential to be aware of the limitations of AI language models. While they can be great at summarizing and identifying patterns in the data they were trained on, they may not always provide perfectly accurate or comprehensive summaries. The AI's output should be used as a starting point or guide for your research, and it's crucial to verify and validate the information it provides.
Additionally, AI language models are sensitive to the data they are exposed to during training. If the data you feed to the model is biased or limited in some way, it may influence the model's output. Ensuring diverse and representative training data can help mitigate this issue.
Overall, your approach of combining AI's data processing capabilities with your own domain knowledge and expertise is a smart way to tackle information overload and gain valuable insights efficiently. Just remember to critically evaluate the AI-generated summaries and use them as a complement to your own analysis and understanding of the research field.
