Scholar Assist (ChatGPT Plugin): Difference between revisions

Scholar Assist (ChatGPT Plugin) (view source)

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Startledcat

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 ===Example Prompts===
 Example prompts that utilize the plugin:
+===Example Usage===
+====Prompt====
+<pre>
+Find research papers on arXiv about machine learning.
+</pre>
+====ChatGPT Response====
+<pre>
+Here are some research papers on machine learning from arXiv:
+Machine Learning and Deep Learning by Christian Janiesch, Patrick Zschech, and Kai Heinrich
+Published on: April 12, 2021
+Summary: This paper summarizes the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. It provides a conceptual distinction between relevant terms and concepts, explains the process of automated analytical model building through machine learning and deep learning, and discusses the challenges that arise when implementing such intelligent systems.
+Download PDF
+Introduction to Machine Learning: Class Notes 67577 by Amnon Shashua
+Published on: April 23, 2009
+Summary: This is an introduction to machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
+Download PDF
+Machine Learning-assisted Close-set X-ray Diffraction Phase Identification of Transition Metals by Maksim Zhdanov and Andrey Zhdanov
+Published on: April 28, 2023
+Summary: This paper describes a method for using machine learning to predict crystal structure phases from X-ray diffraction data of transition metals and their oxides. The proposed machine learning framework achieves competitive performance, demonstrating the potential for machine learning to significantly impact the field of X-ray diffraction and crystal structure determination.
+Download PDF
+Can Machine Learning be Moral? by Miguel Sicart, Irina Shklovski, and Mirabelle Jones
+Published on: December 13, 2021
+Summary: This paper explores the ethics of Machine Learning. It scrutinizes techniques, methods, and technical practices in machine learning from a relational ethics perspective, taking into consideration how machine learning systems are part of the world and how they relate to different forms of agency.
+Download PDF
+When Machine Learning Meets Privacy: A Survey and Outlook by Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin
+Published on: November 24, 2020
+Summary: This paper surveys the state of the art in privacy issues and solutions for machine learning. It covers three categories of interactions between privacy and machine learning: private machine learning, machine learning aided privacy protection, and machine learning-based privacy attack and corresponding protection schemes.
+Download PDF
+Please let me know if you need more information on any of these papers.
+</pre>
+====Request and Response====
+'''REQUEST TO SCHOLAR ASSIST'''
+<pre>
+{
+  "query": "machine learning"
+}
+</pre>
+'''RESPONSE FROM SCHOLAR ASSIST'''
+<pre>
+{
+  "answer": [
+    {
+      "published": "2021-04-12 09:54:12+00:00",
+      "url": "http://arxiv.org/abs/2104.05314v2",
+      "download_url": "http://arxiv.org/pdf/2104.05314v2",
+      "summary": "Today, intelligent systems that offer artificial intelligence capabilities\noften rely on machine learning. Machine learning describes the capacity of\nsystems to learn from problem-specific training data to automate the process of\nanalytical model building and solve associated tasks. Deep learning is a\nmachine learning concept based on artificial neural networks. For many\napplications, deep learning models outperform shallow machine learning models\nand traditional data analysis approaches. In this article, we summarize the\nfundamentals of machine learning and deep learning to generate a broader\nunderstanding of the methodical underpinning of current intelligent systems. In\nparticular, we provide a conceptual distinction between relevant terms and\nconcepts, explain the process of automated analytical model building through\nmachine learning and deep learning, and discuss the challenges that arise when\nimplementing such intelligent systems in the field of electronic markets and\nnetworked business. These naturally go beyond technological aspects and\nhighlight issues in human-machine interaction and artificial intelligence\nservitization.",
+      "title": "Machine learning and deep learning",
+      "authors": [
+        "Christian Janiesch",
+        "Patrick Zschech",
+        "Kai Heinrich"
+      ]
+    },
+    {
+      "published": "2009-04-23 11:40:57+00:00",
+      "url": "http://arxiv.org/abs/0904.3664v1",
+      "download_url": "http://arxiv.org/pdf/0904.3664v1",
+      "summary": "Introduction to Machine learning covering Statistical Inference (Bayes, EM,\nML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering),\nand PAC learning (the Formal model, VC dimension, Double Sampling theorem).",
+      "title": "Introduction to Machine Learning: Class Notes 67577",
+      "authors": [
+        "Amnon Shashua"
+      ]
+    },
+    {
+      "published": "2023-04-28 09:29:10+00:00",
+      "url": "http://arxiv.org/abs/2305.15410v1",
+      "download_url": "http://arxiv.org/pdf/2305.15410v1",
+      "summary": "Machine learning has been applied to the problem of X-ray diffraction phase\nprediction with promising results. In this paper, we describe a method for\nusing machine learning to predict crystal structure phases from X-ray\ndiffraction data of transition metals and their oxides. We evaluate the\nperformance of our method and compare the variety of its settings. Our results\ndemonstrate that the proposed machine learning framework achieves competitive\nperformance. This demonstrates the potential for machine learning to\nsignificantly impact the field of X-ray diffraction and crystal structure\ndetermination. Open-source implementation:\nhttps://github.com/maxnygma/NeuralXRD.",
+      "title": "Machine learning-assisted close-set X-ray diffraction phase identification of transition metals",
+      "authors": [
+        "Maksim Zhdanov",
+        "Andrey Zhdanov"
+      ]
+    },
+    {
+      "published": "2021-12-13 07:20:50+00:00",
+      "url": "http://arxiv.org/abs/2201.06921v1",
+      "download_url": "http://arxiv.org/pdf/2201.06921v1",
+      "summary": "The ethics of Machine Learning has become an unavoidable topic in the AI\nCommunity. The deployment of machine learning systems in multiple social\ncontexts has resulted in a closer ethical scrutiny of the design, development,\nand application of these systems. The AI/ML community has come to terms with\nthe imperative to think about the ethical implications of machine learning, not\nonly as a product but also as a practice (Birhane, 2021; Shen et al. 2021). The\ncritical question that is troubling many debates is what can constitute an\nethically accountable machine learning system. In this paper we explore\npossibilities for ethical evaluation of machine learning methodologies. We\nscrutinize techniques, methods and technical practices in machine learning from\na relational ethics perspective, taking into consideration how machine learning\nsystems are part of the world and how they relate to different forms of agency.\nTaking a page from Phil Agre (1997) we use the notion of a critical technical\npractice as a means of analysis of machine learning approaches. Our radical\nproposal is that supervised learning appears to be the only machine learning\nmethod that is ethically defensible.",
+      "title": "Can Machine Learning be Moral?",
+      "authors": [
+        "Miguel Sicart",
+        "Irina Shklovski",
+        "Mirabelle Jones"
+      ]
+    },
+    {
+      "published": "2020-11-24 00:52:49+00:00",
+      "url": "http://arxiv.org/abs/2011.11819v1",
+      "download_url": "http://arxiv.org/pdf/2011.11819v1",
+      "summary": "The newly emerged machine learning (e.g. deep learning) methods have become a\nstrong driving force to revolutionize a wide range of industries, such as smart\nhealthcare, financial technology, and surveillance systems. Meanwhile, privacy\nhas emerged as a big concern in this machine learning-based artificial\nintelligence era. It is important to note that the problem of privacy\npreservation in the context of machine learning is quite different from that in\ntraditional data privacy protection, as machine learning can act as both friend\nand foe. Currently, the work on the preservation of privacy and machine\nlearning (ML) is still in an infancy stage, as most existing solutions only\nfocus on privacy problems during the machine learning process. Therefore, a\ncomprehensive study on the privacy preservation problems and machine learning\nis required. This paper surveys the state of the art in privacy issues and\nsolutions for machine learning. The survey covers three categories of\ninteractions between privacy and machine learning: (i) private machine\nlearning, (ii) machine learning aided privacy protection, and (iii) machine\nlearning-based privacy attack and corresponding protection schemes. The current\nresearch progress in each category is reviewed and the key challenges are\nidentified. Finally, based on our in-depth analysis of the area of privacy and\nmachine learning, we point out future research directions in this field.",
+      "title": "When Machine Learning Meets Privacy: A Survey and Outlook",
+      "authors": [
+        "Bo Liu",
+        "Ming Ding",
+        "Sina Shaham",
+        "Wenny Rahayu",
+        "Farhad Farokhi",
+        "Zihuai Lin"
+      ]
+    }
+  ]
+}
+</pre>
 ==Tips and Tricks==