Generative Artificial Intelligence (AI) at NMU - Center for Teaching and Learning

What is Generative AI?

Generative AI refers to a subset of artificial intelligence technologies that can generate new content, including text, images, videos, music, or even code, that is similar to but not identical to existing content. Unlike traditional AI models that focus on understanding or interpreting information, Generative AI models create new data that mimics the characteristics of the data they were trained on. This capability makes them incredibly versatile and useful across various domains, from art and design to software development and beyond.

Click on the above image to access video. This video was generated using the Invideo AI platform. Invideo has an agreement with iStock and other providers to include its stock media library with watermarks for free videos.

How Does It Work?

The core of Generative AI involves machine learning models, particularly neural networks, that have been trained on large datasets. These models learn patterns, styles, and structures from the data they are fed. Once trained, they can produce new, original outputs based on the input they receive, which might be a text prompt, an image, or a set of parameters defining the desired output.

Common Platforms

1. ChatGPT, Claude, Google Gemini: These are examples of large language models that can generate human-like text based on the input they receive. They can write essays, create code, compose emails, and even generate creative fiction.
2. DALL-E, Midjourney, Stable Diffusion: These models are specifically designed for generating images from textual descriptions. It can create detailed and imaginative visuals based on a wide range of prompts.

Generative AI's flexibility and power make it a rapidly evolving field, with new applications and models being developed continuously. It has significant implications for creative industries, content creation, software development, and more, offering tools that can augment human creativity and productivity in unprecedented ways.     

Other Related Platforms

These platforms leverage Generative AI to achieve their objectives, offering valuable tools for users focused on research activities.

1. Perplexity: Perplexity offers a unique approach to online search, allowing users to ask questions directly and receive precise, reliable answers supported by a carefully selected collection of sources. It features a conversational interface that understands context and personalizes responses based on your interests and preferences, gradually adapting to them over time.
2. SciSpace: SciSpace is an AI-driven platform tailored to improve the engagement of researchers, students, and professionals with academic texts. This tool demystifies intricate scientific materials by breaking down complex ideas into understandable terms and equipping users with interactive tools for more effective content engagement. Its main features, including a literature review tool, PDF data extraction capabilities, an AI co-pilot for reading, and a paraphrasing tool, work together to streamline repetitive tasks and enhance both comprehension and collaborative efforts.     

Challenges and Issues with Generative AI

Generative AI poses several challenges to higher education, impacting teaching methodologies, academic integrity, research, and the broader educational ecosystem. Here are some key challenges:

1. Academic Integrity and Plagiarism: Generative AI makes it easier for students to produce essays, reports, and other forms of academic work without engaging in the learning process, challenging traditional notions of authorship and originality. Detecting AI-generated work and distinguishing it from student-generated content becomes increasingly difficult.
2. Assessment and Evaluation: With the capability of AI to produce high-quality work, educators face challenges in assessing student learning and understanding accurately. This necessitates the development of new assessment methods that can effectively measure student engagement, critical thinking, and creativity.
3. Curriculum Development and Teaching Methods: As Generative AI tools become more prevalent, there's a need for curricula to evolve to incorporate digital literacy, critical thinking about AI-generated content, and the ethical use of AI technologies. Teaching methods must adapt to prepare students for a future where AI plays a significant role in the workforce and society.
4. Educational Equity: The varying levels of access to advanced AI tools among students and institutions may exacerbate existing inequalities. Ensuring that all students have equal opportunities to learn about and use AI technologies is a significant challenge.

5. Intellectual Property Issues: The use of Generative AI raises questions about the ownership of AI-generated content, particularly in research, creative works, and collaborative projects. Institutions must navigate the complex legal landscape of intellectual property rights in the context of AI-generated works.

   Note regarding AI-generated imagery: Despite the usefulness and excitement surrounding the creation of generative artwork utilizing Artificial Intelligence, it is essential to point out the ethical concerns. When training the AI models, many AI generative image tools utilize artworks on the internet that legally belong to the artist who created them. Companies use these images for training without permission or compensation for the individual artist. The monetization of these AI generative image tools complicates the issue, as companies are making profits partially off the intellectual property of those artists. These situations create copyright issues and have not yet been fully vetted in the legal system.

   A few companies developing AI generative image tools have implemented restrictions on their training models and even have implemented compensation for the owners of images utilized in training. Moving forward with any of these AI generative artwork tools requires the individual user to research and consider these issues and make personal decisions.

6. Quality Control and Information Accuracy: AI-generated content may contain inaccuracies or biases, leading to misinformation. Educators and students must critically evaluate AI-generated information, requiring a higher level of digital literacy and critical thinking skills.
7. Emotional and Social Learning: The increased use of AI in education may impact the development of social and emotional skills. Finding the right balance between leveraging AI for educational purposes and ensuring that students develop essential interpersonal skills is crucial.
8. Research Integrity and Ethics: In the context of research, Generative AI can produce novel findings and data, but it also raises ethical concerns regarding data provenance, authenticity, and the potential for generating misleading or fabricated results.
9. Professional Development for Educators: Teachers and faculty need training and resources to integrate AI tools into their teaching effectively, understand their implications, and address the challenges they pose. This requires ongoing professional development and support.
10. Long-term Impact on Learning and Cognition: There's an ongoing debate about how the use of AI for educational purposes might affect the way students learn, think, and process information in the long term, including potential impacts on creativity, problem-solving skills, and deep understanding of subjects.

11. Job Displacement and Role Evolution: Generative AI presents both challenges and opportunities for employment within higher education. Administrative roles, content creation, teaching, library services, and technical support are among areas potentially impacted by automation and AI capabilities, leading to job displacement and the need for role evolution. However, this shift also underscores the importance of human skills that AI cannot easily replicate, such as ethical judgment and interpersonal communication. Institutions should focus on retraining and upskilling employees to navigate the evolving educational landscape, highlighting the dual need to prepare students for a future workforce intertwined with AI and to adapt existing roles to new realities, fostering an environment where AI enhances rather than replaces human capabilities.

     

Productive Uses for Generative AI

As well as challenges, Generative AI brings with it many possibilities for responsible use, including:
1. Productivity: Generative AI can increase productivity by quickly generating necessary documents, reports, code, or data visualizations. For example, an AI tool could draft an initial version of a report based on bullet points provided by a user, saving hours of writing and formatting time.
2. Creativity: Generative AI can inspire new designs, art, or music compositions. For instance, an AI could generate several logo designs based on a company's brand values and color preferences, providing a creative starting point for designers.
3. Analysis: Generative AI can analyze data and generate insights or summaries. Imagine an AI system that reads through thousands of product reviews and then generates a summary report highlighting the most common praises and complaints.
4. Diagnostics: In the context of Generative AI, diagnostics might involve generating explanations or visualizations based on data patterns. For instance, an AI could analyze machine performance data and generate a diagnostic report that predicts when parts might fail or need maintenance.
5. Accessibility/Communication: Generative AI helps make information and communication more accessible by simplifying complex texts and enhancing speech recognition for people with disabilities. It also creates more inclusive digital environments by generating accessible content and providing real-time translation, making technology easier to use for everyone.
6. Automation of Routine/Repetitive Tasks: Generative AI can automate the creation of content that would otherwise be monotonous or time-consuming. For example, it could automatically generate social media posts, product descriptions for e-commerce sites, or personalized emails to customers.
7. Simulation/Roleplay: In training and education, Generative AI can create realistic scenarios or problems that learners can solve, providing an interactive learning experience. It might generate a virtual patient with specific symptoms for medical students to diagnose.
8. Entertainment: Generative AI can create new forms of entertainment by generating stories, dialogue, or even entire game worlds. It can craft personalized narratives in video games or generate scripts for movies or plays, sometimes in collaboration with human writers.

Generative AI applications are particularly interesting because they not only streamline existing tasks but also have the potential to create what has never been seen before, pushing the boundaries of human creativity and innovation.
 

Understanding Prompts

A prompt is essentially your input to the AI—a question, statement, or command that tells the AI what kind of information or response you're seeking. Think of it as starting a conversation on a specific topic.

Components of a Basic Prompt

1. Clarity: Be clear about what you're asking. If you're too vague, the AI might not give you the kind of answer you're looking for.
2. Specificity: Include specific details about what you need. The more specific you are, the more likely you'll get a relevant and accurate response.
3. Context: Provide any necessary background information. This helps the AI understand the framework or perspective from which you're asking the question.

Example

Imagine you're studying volcanoes and you have the following task:

• Vague Prompt: "Tell me about volcanoes."
  • This is too broad, and the AI might not know what specific information you're interested in.
• Improved, Specific Prompt: "Explain how a volcano erupts, including the roles of magma and gases, for a high school earth science class."
  • This prompt is clear, provides a specific topic (volcano eruptions), mentions key elements to include (magma and gases), and identifies the audience (high school earth science class), guiding the AI to tailor its response appropriately.

Steps to Crafting a Basic Prompt

1. Identify Your Need: Determine exactly what you want to know or what kind of response you're looking for. Do you want a summary, an explanation, a list, etc.?
2. Add Details: Include relevant details that can help the AI understand and respond accurately. This might include the topic's scope, any particular aspect you're interested in, or the format you want the response in (paragraph, list, etc.).
3. Specify the Audience: If relevant, mention who the information is for (e.g., beginners, experts). This helps in tailoring the complexity of the response.

Click on the above image to access video. Video generated by the Collossyan AI Platform

Prompting Best Practices

When approaching creating prompts for Generative AI, it’s important to be strategic to ensure the output is useful, accurate, and relevant. Here are some tips to remember:

1. Be Specific and Detailed

Generative AI works best with clear, detailed prompts. The more specific you are about what you want, the closer the output will be to your expectations. Instead of a broad prompt like "write an essay on climate change," try "write a 500-word essay discussing the impact of renewable energy sources on global warming in the last decade." 

2. Define the Structure and Style

If you have a particular structure or style in mind, include that in your prompt. For instance, if you’re asking for an essay, mention whether you want it to be argumentative, descriptive, or persuasive. For creative works, you might specify a tone or mood, like "write a short story in a whimsical tone about a robot discovering nature." You can also try using the “Act as a…” style of prompting. This makes the AI roleplay a certain type of communicator and respond accordingly. So, for instance, the prompt, “Act as a motivational coach giving an uplifting speech to someone who has just lost their job,” specifies a supportive and encouraging tone.

3. Use Iterative Prompting

Don’t expect perfection in one go. Use the output as a draft and refine your prompt based on what you receive. This iterative process can help tailor the AI's responses to better suit your needs. For example, if the essay lacks certain details, you could follow up with, "expand on the renewable energy technologies mentioned, focusing on solar and wind power."

4. Incorporate Keywords Wisely

If there are specific terms or concepts you want included, mention them in your prompt. Keywords help guide the AI’s focus and ensure the output aligns with your topic’s requirements. For instance, "include examples of photovoltaic technology and offshore wind farms" can direct the AI to cover those areas.

5. Set Parameters for Length and Complexity

Be clear about the desired length and the complexity level of the content. Indicating word count, paragraph number, or even the reading level can help generate content that fits your needs without additional editing.

6. Clarify the Audience

Understanding the intended audience can significantly impact the tone, style, and complexity of the AI's output. Specify whether the content is for beginners, experts, or a general audience. For example, "write an introduction to quantum computing for high school students" sets a different expectation than "for postgraduate physics students."

7. Check for Bias and Accuracy

Always review the AI-generated content for potential biases and factual accuracy, especially when dealing with sensitive topics. AI models can inadvertently propagate biases present in their training data, so it’s crucial to critically evaluate their output.

Avoiding Hallucinations

Minimizing hallucinations—instances where Generative AI produces false or nonsensical information—in AI-generated content involves crafting prompts that guide the AI towards accuracy, relevance, and clear understanding. Here are strategies to achieve that:

1. Be Specific and Detailed in Your Prompt:

• Precise Information: Include as much relevant detail as possible in your prompt. The more context you provide, the less likely the AI is to "hallucinate."
• Example: Instead of asking, "Tell me about historical events," specify "Describe the key events and their impacts of the French Revolution between 1789 and 1799."

2. Limit the Scope of the Prompt:

• Focused Queries: Narrow down the scope of your question to avoid broad or vague responses that can lead to inaccuracies.
• Example: Rather than prompting, "How do computers work?" you might ask, "What is the function of a CPU in a computer?"

3. Request Sources or Basis for Claims:

• Source Inclusion: Ask the AI to include sources or the basis of its information, understanding that it cannot browse the internet but can refer to its training data up to its last update.
• Example: "Based on your training data, what are the main reasons cited for the fall of the Roman Empire? Please specify the sources of your information."

4. Use Prompts that Encourage Fact-Based Outputs:

• Fact Orientation: Craft prompts that lead the AI to generate outputs based on widely accepted facts or data.
• Example: "Provide a summary of the photosynthesis process in plants, including the chemical equation involved, as understood in current biology textbooks."

5. Incorporate Verification Steps into Your Prompt:

• Self-Check Requests: Ask the AI to perform a self-check on the information it provides, if possible, although this technique has its limitations.
• Example: "Explain the theory of relativity, and please include a brief verification of the key facts you present."

6. Iterative Prompting:

• Refinement Through Feedback: Use the AI's responses to refine your prompt, asking for clarifications or corrections as needed.
• Example: If the initial response to a question about a historical event contains inaccuracies, follow up with, "You mentioned [incorrect detail]. Can you verify this or provide a more accurate description?"

7. Avoiding Leading Questions:

• Neutral Wording: Ensure that the prompts are neutrally worded and do not lead the AI towards a predetermined answer, which might not be accurate.
• Example: Instead of suggesting, "Why is quantum computing considered superior to classical computing?" ask, "Can you compare quantum computing and classical computing?"

8. Stay Updated on AI Developments:

• Continuous Learning: Keep abreast of the latest updates and improvements to AI models and their capabilities to craft better prompts.
• Example: If new versions of the AI are released with improved accuracy or new features, adjust your prompts to leverage these advancements.

By employing these strategies, prompters can significantly reduce the occurrence of hallucinations in AI-generated content, leading to more accurate, reliable, and useful outputs.

Getting Started with Using Generative AI

To get started using Generative AI, you will have to access a Generative AI platform. These are available on the internet, and many are free to use. For the following demonstration, we will look at ChatGPT, which is probably the most common Generative AI platform. Other platforms will work very similarly.

Basic Walk-through of Using ChatGPT

1. Accessing ChatGPT:
   • Open your preferred web browser.
   • Type chat.openai.com in the address bar.
   • Press Enter to navigate to the ChatGPT website.
2. Signing In or Creating an Account:
   • Once on the ChatGPT page, you'll see options to either Log in or Sign up to create a new account.
   • If you're new, select Sign up and follow the prompts to set up your account. You'll likely need to provide an email address and create a password.
   • If you already have an account, simply choose Log In and enter your credentials.

3. Starting a New Conversation:
   • After signing in, you'll be directed to the main ChatGPT interface.
   • Look for a text box typically located at the bottom of the page with a placeholder text that says "Message ChatGPT..." 

• This is where you'll type your questions or prompts.

4. Entering Your Query:
   • Click on the text box and start typing your question or prompt. This could be anything from asking for information, requesting help with a topic, or even asking for a joke.
   • Feel free to make your query as simple or complex as you like.
5. Sending Your Query:
   • Once you've typed your question or prompt, look for the Send button near the text box.

• Click Send to submit your query to ChatGPT.

6. Receiving and Reading ChatGPT's Response:
   • After a short wait, you'll see ChatGPT's response appear on the screen as a conversation bubble.

• ChatGPT might ask follow-up questions to clarify your query. Feel free to continue the conversation by responding in the text box.

7. Ending or Continuing the Conversation:
   • You can end the conversation at any point by simply leaving the website or closing the browser tab.
   • If you wish to ask another question or start a new topic, just type in the text box again and repeat the process.

Example

Let's say you're working on a project about healthy eating habits. Instead of prompting with "Tell me about healthy eating," you could use:

• Specific Prompt: "Provide five tips for maintaining healthy eating habits for college students on a budget, including suggestions for simple, nutritious meals."

This prompt guides the AI to focus on practical advice suitable for college students, emphasizing affordability and simplicity.

Faculty-Specific Uses and Issues

In the rapidly evolving landscape of higher education, the integration of generative Artificial Intelligence (AI) into teaching methodologies is opening up unprecedented opportunities for enhancing critical thinking skills among students. As educators strive to prepare students for a complex world where analytical skills, creativity, and ethical reasoning are paramount, leveraging Generative AI presents a novel and exciting frontier. 

This exploration delves into the multifaceted ways in which Generative AI can be harnessed to cultivate these essential skills, transforming traditional educational environments into dynamic spaces for innovation, personalized learning, and deeper intellectual engagement. Through a range of applications—from facilitating complex problem-solving and encouraging diverse perspectives to developing analytical skills and promoting creative problem-solving—this text outlines strategic approaches for educators to incorporate Generative AI into their pedagogical practices. 

Furthermore, it examines the implications of using Generative AI in classroom activities, assessments, and the streamlining of administrative tasks, thereby enhancing the learning experience and fostering a culture of continuous improvement and ethical consideration. In doing so, it underscores the transformative potential of Generative AI in higher education, paving the way for a future where teaching and learning are more aligned with the demands of the digital age.

Faculty Specific Resources (links will open in a new window)

• Using AI for General Instruction (Designing In)

• Exploring Critical Thinking and Ethics Instruction

• Using Generative AI in Assessment

• Academic Dishonesty Using Generative AI (Cheating, Classifiers, and Humanizers)

• Using Turnitin's AI Detection Tools for Faculty (External Page)

• Creating AI-Resistant Assignments, Activities, and Assessments (Designing Out)

• Streamlining Administrative Tasks with Generative AI

• Creating a Personal AI Course Plan

• Evaluating Generative AI Platforms to Best Meet Your Needs

• Identifying Relevant Domain-Specific AI Platforms

• Sample NMU AI Syllabus Statements

General Knowledge Resources (For All Users)

What is Generative AI?

• Generative AI in Education: Past, Present, and Future. Educause Review.

• AI: What is Generative AI? University of Pittsburgh Center for Teaching and Learning.

General Ethical Issues Concerning Generative AI 

• Generative AI Ethics: 8 Biggest Concerns and Risks. TechTarget.com.

How to Use Generative AI - Getting Started

• Chat GPT: How To Use ChatGPT. Zapier.com.

• Bing Chat (Copilot): What is Bing Chat, and How Can You Use It? Microsoft.com.

• Google Bard/Gemini: Using Gemini. Google Support.

• Claude: How to Use Claude AI (and how it's different from ChatGPT). ZDNet.com.

• Perplexity: Getting Started with Perplexity. Perplexity AI.

Prompt Engineering

• Prompt Engineering. Open AI.

• Prompt Engineering Guide. Promptingguide.ai.

• Getting started with prompts for text-based Generative AI tools. Harvard University

• Prompt Literacy in Academics. University of Michigan.

General AI Terms/Glossary

• AI Glossary by a16z. a16z.com.

• Glossary of LLM and Generative AI. Medium.com.

Career Preparation

• Cengage Group 2023 Graduate Employability Report. Cengage Group.

• Pluralsight AI Skills report. Pluralsight.com.

Faculty Focused Resources

Sample NMU AI Syllabus Statements

• Syllabus Statements Concerning AI Use in Student Work. NMU Center for Teaching and Learning.

Integration of Generative AI in Coursework

• Barnard University’s AI resource page Barnard University Center for Engaged Pedagogy.

• Considerations for AI Tools in the Classroom. Columbia University Center for Teaching and Learning.

• Generative Artificial Intelligence for Education and Pedagogy. Cornell University.

• Incorporating AI In Education: Empowering Educators, Engaging Students. Magna Publications

Challenges and Opportunities

• Benefits, Challenges, and Sample Use Cases of Artificial Intelligence in Higher Education. Inside Higher Ed.

Faculty Experiences and Use Cases

• Incorporating Generative AI in Teaching and Learning: Faculty Examples Across Disciplines. Columbia University.

• Ideas for Experimenting with Generative AI: Use Cases and Things to Keep in Mind. Harvard University. 

AI Detection/Academic Integrity

• Encouraging Academic Integrity. University of Pittsburgh.

• The Trouble With AI Writing Detection. Inside Higher Ed.

• Universities Rethink Using AI Writing Detectors to Vet Students’ Work. Bloomberg.

• How Can Educators Respond to Students Presenting AI-generated Content as their Own? Open AI.

• NMU Student Handbook.  Northern Michigan University

The Technology of Artificial Intelligence (AI) is Evolving Rapidly.

As we continue to explore the possibilities of generative AI, it is crucial to remember that this technology is in a state of constant evolution. At Northern Michigan University, we are dedicated to staying ahead of these advancements, ensuring that our community has access to the latest tools, knowledge, and best practices. We recognize that the integration of AI into our academic and operational frameworks requires ongoing learning, adaptation, and a commitment to ethical standards. By fostering an environment of continuous improvement and responsible innovation, we aim to equip our students, faculty, and staff with the skills and insights necessary to thrive in an AI-enhanced world.

Generative AI holds tremendous potential to transform how we teach, learn, and conduct research. However, with this potential comes the responsibility to use these technologies thoughtfully and ethically. At NMU, we are not only embracing the benefits of generative AI but also critically examining its implications, challenges, and societal impacts. We invite you to join us on this journey, as we navigate the complexities of AI together and work towards a future where these powerful tools are leveraged to create positive, meaningful change in our academic community and beyond.

For those interested in staying informed about the latest developments in generative AI and its applications in education, we recommend exploring the following resources:

(Links will open in a new window)

• EdSurge: AI in Education

• Educause: Artificial Intelligence

• The Chronicle of Higher Education: AI and Higher Ed

• MIT News: Artificial Intelligence

What is AGI?

Artificial General Intelligence (AGI) refers to a type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence. Unlike narrow AI, which is designed to perform specific tasks (e.g., image recognition, language translation), AGI can generalize knowledge and skills to tackle unfamiliar problems and adapt to new situations similarly to a human.

Key Characteristics of AGI

1. Generalization: AGI can apply learned knowledge to a variety of different contexts, much like how humans use their cognitive abilities in diverse scenarios.
2. Learning Ability: AGI can learn from experiences and improve over time without being explicitly programmed for each specific task.
3. Autonomy: AGI can operate independently, making decisions and solving problems without human intervention.
4. Adaptability: AGI can adapt to new and unforeseen challenges by leveraging its general knowledge and learning capabilities.

Differences from Narrow AI

• Scope: Narrow AI is specialized and excels at specific tasks, while AGI is versatile and capable of performing a wide range of activities.
• Flexibility: Narrow AI systems lack the flexibility to adapt to new tasks without significant reprogramming, whereas AGI can adjust and learn on the go.
• Understanding: AGI aims to possess a deeper understanding and reasoning capability, allowing it to tackle complex and abstract problems.

Potential Implications of AGI

• Scientific and Technological Advancements: AGI could accelerate discoveries and innovations by automating research and development processes.
• Economic Impact: The widespread deployment of AGI could transform industries, leading to increased productivity and possibly significant economic shifts.
• Ethical and Safety Concerns: The development and deployment of AGI pose substantial ethical and safety challenges, including ensuring alignment with human values and preventing misuse.

Current State and Future Prospects

As of now, AGI remains a theoretical concept, with significant research and development required to achieve it. Leading AI research organizations, like OpenAI, are actively exploring pathways to AGI, focusing on aligning AI systems with human intent and ensuring their safe and beneficial integration into society​ (OpenAI)​​ (OpenAI)​​ (HT Tech)​.

OpenAI's (ChatGPT) Plans for Attaining Human-Level AI

OpenAI has a comprehensive plan for developing human-level AI, often referred to as Artificial General Intelligence (AGI). The plan is structured around several key initiatives and goals:

Five-Level System for Tracking Progress

OpenAI has introduced a five-level system to monitor their progress toward human-level AI. The levels are designed to measure the increasing capabilities of AI models:

1. Level 1 - Conversational AI: AI capable of interacting in conversational language with people.
2. Level 2 - Reasoners: AI that can perform basic problem-solving tasks, comparable to a human with a doctorate but without access to tools.
3. Level 3 - Agents: AI capable of executing various tasks on behalf of a user for several days.
4. Level 4 - Innovators: AI that can contribute to new inventions and scientific discoveries.
5. Level 5 - Organizational AI: AI capable of performing complex organizational tasks autonomously​ (HT Tech)​​ (HiBit)​.

Superalignment Initiative

OpenAI is dedicating significant resources to solve the problem of aligning superintelligent AI systems with human intent. This includes:

• Scalable Oversight: Using AI systems to assist in evaluating other AI systems and ensuring their outputs are aligned with human values.
• Robustness Testing: Developing automated methods to search for and mitigate problematic behaviors in AI models.
• Adversarial Testing: Deliberately training misaligned models to test the effectiveness of alignment techniques​ (OpenAI)​.

Public Engagement and Governance

OpenAI emphasizes the importance of a global conversation about the governance of AGI, the fair distribution of its benefits, and the sharing of access to advanced AI systems. They advocate for independent audits and public standards to ensure safe development and deployment of AGI. OpenAI also highlights the necessity for governments to have insight into large-scale AI training runs to manage potential risks​ (OpenAI)​.

Long-Term Vision

OpenAI believes that AGI will be a significant milestone, but just a point along a continuum of increasing intelligence. They envision a future where AGI could accelerate scientific progress and bring about profound changes in society. However, they also acknowledge the risks and emphasize the need for careful management to avoid catastrophic outcomes. Coordination among AGI efforts to slow down development at critical junctures may be necessary to allow society to adapt​ (OpenAI)​.