Decoding Generative AI Research Papers with Chatbots

This is the first introductory article in a series of blog posts. In these posts, we will break down some of the world’s most important papers on Generative AI, making them easier to understand.

NOBODY EXPLAINS AI BETTER THAN AI

If you’re interested in learning about AI, you know that one of the best ways to do so is to ask AI to explain itself. In fact, Barron’s recently published an article titled “We Asked Three Chatbots to Explain Generative AI. Here’s the Best Answer.”

While you can certainly ask AI simple questions like “Explain AI in layman’s terms,” and chatbots will do just that, the problem is that they often oversimplify things and leave out important concepts. That’s when we had an idea: “What if we used chatbots like ChatGPT, Claude, Mistral, Gemini, and Grok to help us decode the most prominent research papers on Generative AI?”

The goal isn’t just to “translate” expert jargon into layman’s terms, but to create a compelling learning journey. That’s how this article series was born. In this post, we’ll lay out our plan for using chatbots to help us understand the most important research papers on Generative AI.

DESIGNING A LEARNING JOURNEY FOR GENERATIVE AI

Before we dive in, we need to ask ourselves a few important questions:

  1. How do we know which papers are important? The obvious answer is to ask AI. But to get the most accurate information, we need to use all the prompt engineering tricks we know to get as much information from our chatbots as possible. The saying “garbage in, garbage out” applies to chatbots like ChatGPT and Claude just as much as it does to any other knowledge system.
  2. How can we fact-check what we learn? We know that chatbots can sometimes “hallucinate” or provide inaccurate information. To overcome this limitation, we need to apply proper prompting techniques for fact-checking and examining the AI’s reasoning. However, at some point, we need to break the cycle of engaging with AI and talk to a human expert who can challenge us and confirm whether we’ve done a good job of using AI to understand AI.
  3. What learning methods do we use? We’re big fans of Ultralearning. According to ChatGPT, “Ultralearning is an intense approach to learning that aims to enable individuals to acquire new skills and knowledge as quickly and efficiently as possible. This self-directed learning strategy involves deep concentration, rapid feedback, and the application of sophisticated learning techniques. It demands a high degree of effort and focus, as it typically involves a commitment to mastering a specific skill or body of knowledge in the shortest amount of time. The concept of Ultralearning was popularized by Scott H. Young in his book ‘Ultralearning: Master Hard Skills, Outsmart the Competition, and Accelerate Your Career’. According to Young, this aggressive learning strategy can be employed by anyone who is motivated and willing to adopt a different approach to learning.”

ENGAGING WITH OUR READERS

Explaining AI as a layman to fellow laymen can be a challenge, but there is a well-known figure who advocates for this approach: Richard Feynman. If you’re not familiar with Feynman and his learning technique, here’s what ChatGPT has to say:

“Richard Feynman was an American theoretical physicist known for his work in the path integral formulation of quantum mechanics, the theory of quantum electrodynamics, and the physics of the superfluidity of supercooled liquid helium. He received the Nobel Prize in Physics in 1965.

The Feynman Technique is a learning strategy that Feynman reportedly used to understand complex concepts. It consists of four steps:

  1. Choose a topic you want to understand and start studying it. Write down everything you know about the topic on a notebook page, and add to that page every time you learn something new about it.
  2. Pretend to teach your topic to a classroom. Make sure you’re able to explain the topic in simple terms.
  3. Go back to the books when you get stuck. The gaps in your knowledge should become apparent. Revisit problem areas until you can explain the topic fully.
  4. Simplify and use analogies. Repeat the process while simplifying your language and connecting facts with analogies to help strengthen your understanding.

We believe that by following the Feynman Technique, we can make complex AI concepts accessible to our readers: “If you want to learn something, teach it.” We’ll be using this approach throughout our article series.

LET’S GET STARTED!

Where can we find high-quality research papers on Generative AI? Our assumption is that both companies that build the technology and universities are good sources. Let’s ask ChatGPT. Here’s the response:

“Universities:

  • Stanford University: Stanford’s Artificial Intelligence Lab has produced significant work in various branches of AI, including Generative AI.
  • Massachusetts Institute of Technology MIT: The Computer Science and Artificial Intelligence Laboratory CSAIL at MIT is known for pioneering research in AI.
  • University of Montreal: The Montreal Institute for Learning Algorithms MILA at the University of Montreal has been particularly influential in the field of deep learning and Generative AI.

Companies:

  • Google: Google’s research teams, particularly Google Brain and DeepMind, have published extensively on deep learning and Generative AI.
  • OpenAI: As an AI research lab, OpenAI has made numerous contributions to the field of Generative AI, including the development of advanced language models like GPT-3.
  • Facebook AI Research FAIR: FAIR is known for conducting and publishing cutting-edge research in various areas of AI, including Generative AI.

Sources:

We assume that this list is not exhaustive, and companies such as Apple for example contribute as well. “By the way, how about Chinese companies?” we thought. However, the list above is a good starting point. Let’s pick Google Research, OpenAI, and Facebook and see what results we get from ChatGPT:

“Google Research:

  • “Transformers: Attention Is All You Need”: This is one of the most influential papers in the natural language processing field, introducing the concept of transformers, which have since formed the basis of many artificial intelligence models.
  • “Large Scale Distributed Deep Networks”: This paper presents Google’s work on deploying large-scale neural networks across distributed infrastructure.
  • “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”: This paper introduces a new scaling method for neural networks, which has improved the performance of many image processing models.

OpenAI:

  • “Language Models are Few-Shot Learners”: This paper presents GPT-3, one of the most powerful language models currently available, capable of generating impressively human-like text.
  • “Dactyl: Solving a Rubik’s Cube with a Robot Hand”: OpenAI demonstrates a groundbreaking use of reinforcement learning to train a robotic hand to solve a Rubik’s cube.

Facebook AI:

  • “DeepFace: Closing the Gap to Human-Level Performance in Face Verification”: This paper presents DeepFace, a deep learning model for face recognition which achieved near-human level performance.
  • “Exploring the Limits of Weakly Supervised Pretraining”: Facebook AI discusses how they trained a model on 3.5 billion publicly available images, significantly improving the state-of-the-art for various image recognition benchmarks.”

We’ll start with the first paper on the list: “Attention Is All You Need” in our next blog post. Stay tuned!

This article was a collaborative effort between us and AI. We provided the foundation, while AI handled the research, polishing, editing, and improving our writing style. We found that Mistral performed the best in comparison to ChatGPT and Gemini. Mistral was able to maintain the original tone of voice, while ChatGPT and Gemini made it sound like the article was written by a chatbot.

Rafael Knuth

ChatGPT Prompts for Instructional Designers – Action Mapping

Action mapping is arguably the most effective method for creating meaningful learning experiences.

“The goal of action mapping is to solve business problems by changing job behavior.”

Cathy Moore

Thanks to action mapping, our team has taken our e-learning offerings to the next level. Being heavily invested in technology and AI, we excessively use various chatbots. For the record, while we utilize publicly available services such as ChatGPT and Google Gemini, we are also exploring enterprise-grade versions. These explorations will be the subject of upcoming blog posts.

Of course, ChatGPT cannot magically create the entire learning experience. However, it proves invaluable in generating ideas, researching e-learning topics, understanding action mapping, and communicating with stakeholders who might not be familiar with it.

1ST PROMPT: WHAT IS ACTION MAPPING

As an Instructional Designer well-versed in the latest methodologies and strategies for effective learning, I seek a comprehensive explanation of “Action Mapping,” a concept developed by Cathy Moore. This explanation should encompass its foundational principles, core objectives, and its practical application in designing engaging and outcome-oriented instructional materials.

Let’s check the response from ChatGPT:

Action Mapping, developed by Cathy Moore, is an instructional design model focused on optimizing the impact of training by linking it directly to the desired business outcomes. Unlike traditional models that often start with the content, Action Mapping begins with the end in mind: the actions that learners need to perform in the real world to achieve organizational goals.

The core of Action Mapping involves four main steps:

  1. Identify Business Outcomes: Begin by clarifying the specific, measurable business goals that the training aims to support. This ensures that the training is directly tied to tangible organizational objectives, rather than abstract educational goals.
  1. Determine the Actions: Once the business outcomes are clear, the next step is to identify the precise actions that employees need to take to achieve these outcomes. This shifts the focus from what learners need to know to what they need to do, emphasizing performance and behavior change over information transmission.
  1. Analyze Practice Activities: After defining the necessary actions, the model recommends designing practice activities that closely simulate real-world tasks. These activities should provide learners with opportunities to practice the desired actions in a controlled, feedback-rich environment, thereby bridging the gap between theory and practice.
  1. Identify the Minimal Information Required: Only after defining the outcomes, actions, and practice activities does the model suggest identifying the essential information learners need to perform the actions. This approach ensures that content is streamlined and focused, reducing cognitive overload and ensuring that every piece of information provided directly supports the desired behaviors.

The practical application of Action Mapping in instructional design involves iterative development and close collaboration with subject matter experts and stakeholders. This ensures that the training materials are not only engaging and interactive but also directly aligned with the strategic objectives of the organization.

By emphasizing real-world actions and tying learning directly to business results, Action Mapping offers a pragmatic and outcome-oriented approach to instructional design. It encourages designers to create more engaging, efficient, and effective learning experiences that have a measurable impact on organizational performance.

Let’s explore a few more prompts (this time, I will not include the answers from ChatGPT).

2ND PROMPT: DEFINING THE BUSINESS PROBLEM

As an instructional designer within [industry/company], I am currently developing a [training/course] focused on [product/service], specifically designed for [target audience]. My goal is to clearly articulate the underlying business problem this training aims to address and the specific behavioral changes we aim to instigate among the participants. To ensure the training content is precisely aligned with our objectives, I invite you to assist me in defining these elements more clearly. Could you please provide detailed insights or pose targeted questions regarding the business challenges we are facing, the exact nature of the behavioral shifts we aspire to achieve through this training, and any other relevant context that would enrich the development process?

3RD PROMPT: DESIRED BEHAVIORAL CHANGE

As an instructional designer committed to employing Cathy Moore’s action mapping framework, I am in the process of developing a [training/course] aimed at achieving [business goal] by facilitating a shift in [target audience]’s behaviors to encompass [behavioral change 1], [behavioral change 2], and [behavioral change 3]. To effectively tailor this learning experience, I need to diagnose the root causes behind the current discrepancy between the [target audience]’s actual and desired actions. In accordance with the principles of action mapping, I seek a detailed analysis that pinpoints potential barriers within their environment, alongside identifying any deficiencies in their skills, knowledge, or motivation that may be hindering the adoption of the targeted behavioral changes.

4TH PROMPT: DRAFTING THE LEARING EXPERIENCE

As an experienced instructional designer, I am seeking your expertise in crafting a structured outline for a [training/course] centered around [topic], specifically tailored for [target audience]. This curriculum should be meticulously designed following Cathy Moore’s action mapping methodology, with a keen focus on achieving the strategic business objective of [business goal]. The ultimate aim is to foster and instill [desired behavioral change 1], [desired behavioral change 2], [desired behavioral change 3] within the participants, thereby directly contributing to our organizational objectives.

To ensure the course is highly effective and relevant, please incorporate strategies that address the unique challenges faced by our [target audience]. These challenges include:

  • Environmental Factors: [specific environmental roadblocks]
  • Skillset Gaps: [particular skills that need development or enhancement]
  • Knowledge Deficiencies: [key areas where knowledge is lacking]
  • Motivational Barriers: [specific motivational or engagement issues to be overcome]

Your outline should detail the sequence of activities, learning modules, practical exercises, and evaluation methods, all constructed to overcome these roadblocks and lead to the desired behavioral transformations. The proposed outline should not only align with our business goals but also engage the [target audience] in a meaningful and impactful learning journey.

CONCLUSION

As you can see, the possibilities of maximizing Cathy Moore’s action mapping methodology with ChatGPT are boundless. What are your thoughts? How do you use generative AI when strategizing on a learning experience for your audience?

Rafael Knuth