The AI Vanguard Newsletter #6
Danny Butvinik
Chief Data Scientist | 100K+ Followers | FinCrime | Writer | Author of AI Vanguard Newsletter
Abductive learning with ChatGPT; Open-source LLMs; Auto-GPT; Active machine learning—weekly concept breakdown; In a Growth zone, promotions aren’t just about your skills;?Motivational spark, Expert advice; and more
Papers of the Week
Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks: This paper explores whether ChatGPT, a large language model, can reproduce human-generated label annotations in social computing tasks. The goal is to reduce the cost and complexity of social computing research. The study uses ChatGPT to re-label five datasets covering stance detection, sentiment analysis, hate speech, and bot detection. The results show that ChatGPT has the potential to handle these data annotation tasks, although challenges remain. The average precision obtained is 0.609, with performance varying across individual labels. This work can open up new lines of analysis and serve as a basis for future research into the exploitation of ChatGPT for human annotation tasks.
On the Potential of Artificial Intelligence Chatbots for Data Exploration of Federated Bioinformatics Knowledge Graphs: This paper discusses the potential role of artificial intelligence (AI) chatbots, such as ChatGPT, in facilitating data access to federated knowledge graphs in the field of bioinformatics. The authors provide examples of how conversational AI can be used to describe datasets and generate queries across datasets for the benefit of domain experts. The paper is a work in progress and aims to explore the potential of AI chatbots in improving data access and analysis in bioinformatics and other domains.
Regulatory Markets: The Future of AI Governance: This article addresses the urgent need for appropriate regulation of artificial intelligence (AI) and proposes using regulatory markets as a solution. Legislators and regulators lack the specialized knowledge to regulate AI effectively, and industry self-regulation fails to hold producers and users accountable to democratic demands. Regulatory markets, where governments require regulation targets to purchase regulatory services from private regulators, could overcome the limitations of command-and-control regulation and self-regulation. This approach could enable governments to establish policy priorities for AI regulation while relying on market forces and industry research and development efforts to pioneer effective regulatory methods.
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT: This paper proposes a novel method, called ChatABL, for integrating large language models (LLMs) such as ChatGPT into an abductive learning (ABL) framework. The goal is to unify the three abilities of perception, language understanding, and reasoning in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct incomplete logical facts and optimize the performance of a perceptual module. The perceptual module, in turn, provides necessary reasoning examples for LLMs in a natural language format. The ChatABL method is demonstrated through a variable-length handwritten equation deciphering task that shows improved reasoning abilities beyond most existing state-of-the-art methods. This paper is the first attempt to explore a new pattern for approaching human-level cognitive ability via natural language interaction with ChatGPT.
Industry Insights
Weekly Concept Breakdown
Active machine learning is a type of machine learning where the model can interactively query a human or other intelligent system to obtain more information and improve its accuracy. In this approach, the machine learning model is not just a passive data recipient but an active participant in the learning process.
In statistics literature, it is sometimes also called Optimal Experimental Design (OED)
In traditional machine learning approaches, the model is trained on a fixed set of labeled data and then used to predict new, unseen data. However, in active machine learning, the model can actively choose which data to acquire next based on its current level of uncertainty or lack of knowledge. The model can achieve better accuracy with fewer data points by actively selecting the most informative data points.
There are several approaches to active machine learning, each with advantages and disadvantages. Some common techniques include uncertainty sampling, query-by-committee sampling, pool-based sampling, and diversity sampling.
Uncertainty Sampling involves selecting the data points for which the model is most uncertain or has the highest level of entropy. This approach assumes that the model is least certain about the most informative data points.
Query-by-Committee involves training multiple models on the same data and selecting the data points the models disagree on. This approach assumes that the most informative data points are difficult for the models to agree on.
Pool-based Sampling involves selecting data points from a large pool of unlabeled data to maximize the model's accuracy on the final labeled data set.
Diversity sampling involves selecting data points dissimilar to those already in the training set. This approach assumes that the most informative data points differ from what the model has already seen.
Active machine learning has many applications, including natural language processing, image and speech recognition, and autonomous driving. It can potentially improve the accuracy and efficiency of machine learning models while reducing the amount of labeled data needed to achieve a given level of accuracy. However, it also requires careful design and evaluation to ensure the model makes informed and meaningful decisions about which data to acquire.
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Growth Zone
Motivational Spark
One of the most inspiring and thought-provoking quotes I have ever come across is, "You miss 100% of the shots you don't take" by the legendary ice hockey player Wayne Gretzky. This quote encapsulates the idea that taking action and seizing opportunities is essential for achieving success and happiness in life.
At its core, this quote is about the courage to take risks and the willingness to put ourselves in positions that may be uncomfortable or uncertain. It's about recognizing that we cannot simply wait for opportunities to come; instead, we must create them ourselves by stepping outside our comfort zones and taking bold action.
What's truly inspiring about this quote is that it speaks to the idea that failure is an inevitable part of the journey toward success. By taking chances, we may face setbacks and make mistakes along the way, but through these experiences, we learn and grow, becoming stronger and more resilient individuals.
This quote is a call to action, urging us to embrace our fears and take those shots that we may otherwise be too scared to attempt. It encourages us to push ourselves beyond our limits and to strive towards our goals with determination and grit.
Essentially, this quote reminds us that life is not about sitting on the sidelines and watching opportunities pass us by. It's about being an active participant in our own lives, taking risks, and creating our own opportunities for success. So, let us all take this message to heart and start taking those shots we've been afraid to take. After all, the only way to guarantee failure is not to try.
Expert Advice
Before diving into data analysis or model building, clearly defining the problem you are trying to solve is important.
Starting with a clear problem statement is crucial for any AI, data science, or machine learning project. It involves defining the problem you want to solve, understanding the business or scientific goals, and specifying the target outcomes or deliverables.
A clear problem statement is important because it sets the entire project's direction and helps avoid wasting time and resources on irrelevant or poorly defined tasks. It also helps to ensure that everyone involved in the project understands what is being worked on and what success looks like.
To develop a clear problem statement, you can start by asking yourself and your stakeholders questions such as:
Once you have a clear problem statement, you can start to develop a plan for approaching the problem, including gathering data, designing experiments, and developing models. Starting with a clear problem statement ensures your work is focused, relevant, and aligned with your goals.