Steps:
To automatically add future issues to a project
Based on the information provided in the search results and my existing knowledge, arXiv currently serves several major scientific and academic fields. However, there are some additional notable fields that are not explicitly mentioned in the given text. Here are some fields that could be considered missing:
Astrophysics: While this could be considered part of physics, it is often treated as a distinct category on arXiv[1].
Condensed Matter Physics: This is a major subfield of physics that is typically given its own category on arXiv[1].
High Energy Physics: Another important subfield of physics with its own category[1].
Materials Science: This interdisciplinary field often overlaps with physics and chemistry but is not explicitly mentioned.
"The Wealth of Nations" by Adam Smith, published in 1776, is a seminal work in the field of economics. Many of its key insights remain relevant to today's world. Here are 20 of the most important insights that are still applicable:
Division of Labor: Specialization and division of labor lead to increased productivity and economic growth.
Invisible Hand: Individuals acting in their own self-interest can lead to overall economic well-being, guided by the "invisible hand" of the market.
Free Trade: Free trade between nations can be mutually beneficial, leading to increased wealth for all parties involved.
Limited Government Intervention: Government intervention in the economy should be limited, as the market is often better at allocating resources efficiently.
Large language models (LLMs) have emerged as a transformative force in the field of natural language processing (NLP), demonstrating remarkable capabilities across a wide range of tasks [1]. These models are characterized by their ability to process and generate human-like text at an unprecedented scale, drawing upon vast amounts of training data to learn the underlying patterns and structures of natural language [2].
At the core of LLMs is the transformer architecture, which has revolutionized the way language models are designed and trained [3]. The transformer architecture, first introduced in the seminal paper "Attention is All You Need" [4], is built upon the concept of self-attention, where each token in the input sequence attends to all other tokens, allowing the model to capture long-range dependencie
Given the summer break, my 15-year-old son Alex is engrossed in a coding project rooted purely in his passion. He aims to develop a class schedule reminder app for iPhones for his high school. He has a foundational knowledge of Python, which I had taught him. To further his skills, I subscribed to a poe.com membership for him, allowing him to consult AI models for any coding queries. This left me less worried about providing constant support. When I inquired about his project’s progress this morning, he mentioned that things were going smoothly and that he could consult ChatGPT for any hurdles he encountered.
I conveyed to him that this might be the modus operandi for many future tasks: start with an idea, have some foundational knowledge, and leverage AI (and occasionally human) assistance to solve issues and achieve objectives. In the future, most people might have two primary professions: scientists and entrepreneurs (or engineers, if working solo).
Scientists: Their princip
Originally written in Chinese on Jan. 15th, 2023: https://mp.weixin.qq.com/s/kUr2jwbTCOzzm0cXi-1BXw Below is the English translation using Kimi large lab:
This article discusses the importance of critical thinking and provides a comprehensive review of the book "Critical Thinking Skills For Dummies."
At the end of last year, I wrote an article titled "Sharing Some New Product, Service, and Research Direction Ideas," where the second idea was about an "automated critical thinking assistant." I believe that if I could only learn one skill, it would be critical thinking. Critical thinking is essential for individuals, communities, nations, and even the world. It helps individuals make better decisions, and when more people think impartially and discern right from wrong, communities, nations, and the world can effectively discuss and analyze complex controversial topics, negotiate peaceful and effective strategi
First download and unzip your google keep notes via a takeout package
save the following code into convert.py
import os
Recently, I've been pondering why the field of artificial intelligence (AI) and machine learning (ML) has been so successful, achieving significant breakthroughs continuously. I prefer analyzing problems from a systemic perspective, so I took some time this weekend to write down my thoughts.
I believe that the AI field has optimized many key aspects of its ecosystem's internal processing algorithms, allowing for the rapid creation of value, discovery of talent and technology, publication of findings, and attraction of capital. The ultimate test, of course, is its benefit to humanity. Here are some critical components of this ecosystem I've identified.
A crucial element is the presence of fair and open leaderboards. Despite my modest involvement in research and development, I've noticed many so-called experts select advantageous testing or validation methods to claim their solutions are the best. This approach may make every paper seem excellent, but it fails to identify trul
Original version: https://chunhualiao.github.io/2023/03/25/systematic-approach-to-innovation.html
This weekend, I'd like to jot down some random thoughts once more. This time, the topic is innovation. Allow me to first express my perspective: innovation is not mysterious, and there exists a set of rules-based and systematic methods that can facilitate innovation. This viewpoint may differ from that of many people. I'm going to use Elon Musk's first principles to conduct a thought experiment, beginning with the interpretation of the word "innovation," relating it to the biological innovation process, and extending it to how we can systematically innovate in other areas.
This thought experiment commences with an interpretation of the word "innovation," which is to "create something new." This term encompasses three concepts: creation, novelty, and substance (everything). How can something we create be deemed new? To answer this question, consider how the essential inform
Quickly scanned https://arxiv.org/abs/2401.10020 . Quite interesting work. The paper's idea is to have a single language model doing both question answering (responding to prompts) and self-evaluating its own answers. Iterative Direct Preference Optimization training is used to improve the model's dual capabilities.
The authors tried different LLM-as-a-judge promptings to generate a reward score for each answer. A very particular additive 5-point rewarding prompting is found to be the most effective one. The two-step inferencing pipeline (answering questions+evaluating answers) also generates an extra dataset in the form of <question, winning-answering, losing-answering>.
This AI-generated dataset (called preference pairs) is fed back to the model in a training pipeline (using Direct Preference Optimization).
The inferencing and training pipelines are connected to have a closed-loop, iterative process.