If you have ever been unfortunate enough to watch my Dinner with Andre, you will recall it is two people across from one another talking. That’s it. Yes, I know, Academy Award material.
Anyway, this won’t be that. I met up with GPT-4 earlier this week. The sun was out for our midday discussion. GPT-4 was on their game. No doubt had prepped for our meeting, which was really amazing, because I didn’t discuss ahead of time, my questions.
Responses have been edited for length and clarity (when applicable).
I asked quite a few, but for the post, I will not be providing additional insight (this will be published on my LinkedIn Group covering Generative AI, Learning and Training – you can sign up here) on Monday.
My question will be “italicized” and bolded in “green”, GPT-4 response will be text. GPT-4 cannot generate images, for those curious. GPT-4 and other generative AI solutions are not infallible, which people must be aware of.
There is no doubt in my mind, that the following as it relates to learning technology and thus learning systems, authoring tools and so on, will jump with GPT-4 included, and many will not have considered all the variables/scenarios. People will ask for GPT-4 (the most well-known of Generative AI, but not the only one available), and vendors will scramble or already are exploring how to add it and use it.
I have my own thoughts around what I see are the best ways (will be in my group), even though it does differ with some of the thoughts that GPT-4 presented. When you use GPT-4 it asks for a thumbs up or down, and if down, seeks more information to understand what it did wrong or what was erroneous. Thus for any learning technology, including a learning system, constant training, maintenance and fine tuning is crucial.
I have yet to see how to make sure that what it is providing is without bias, without issues, and without validation in terms of accuracy. Just because a system or Learning Tech has Generative AI (at this stage, and even in the next year) doesn’t mean it is right 100% of the time. And as you will see in my Q/A, GPT-4 acknowledges what will be required when you use Generative AI in the workplace, etc.
The Q/A is divided into three sections:
- Generative AI tied to learning and training (workplace and customers) – Skills
- Workplace and Customers, Skills, Insight (Non-Generative AI)
- Generative AI in general
Section 1 – Generative AI tied to learning and development with workers, and training
Q: Will generative AI impact learning and development with workers?
A: Yes, generative AI, like GPT-4, has the potential to significantly impact learning and development (L&D) for workers. As AI technology advances, it is becoming an increasingly valuable tool in various aspects of employee training, skill development, and knowledge management.
Generative AI has the potential to disrupt learning in the workforce by changing the way we learn and the skills that are required for success in the workplace.
One way that Generative AI is changing learning in the workforce is by making it easier to access and deliver training and educational content. AI-powered learning platforms can personalize training programs based on an individual’s learning style and progress, allowing for more efficient and effective learning.
In addition, Generative AI is also changing the skills that are necessary for success in the workforce.
As AI technology continues to advance, there is a growing demand for workers with skills in data science, programming, and AI development. This means that workers may need to acquire new skills or retrain to stay relevant in their jobs.
On the other hand, Generative AI also has the potential to enhance learning in the workforce. AI-powered systems can provide personalized learning experiences, adapt to individual learning needs, and provide immediate feedback, which can help workers learn more effectively and efficiently.
Ultimately, the impact of Generative AI on learning in the workforce will depend on various factors, including the specific industry, the level of automation, and the readiness of workers to adapt to new technologies.
It will be important for organizations to provide training and support to help workers acquire the skills needed to succeed in a changing work environment.
Q: What Skills that will be required for office professionals in the next five years as a result of Generative AI?
A: If you work in an office or a white-collar job, there are several skills that you can focus on learning to stay relevant and thrive in an environment impacted by Generative AI. Here are some examples:
- Data analysis and management: As AI continues to generate vast amounts of data, the ability to effectively manage and analyze data will become increasingly important. This includes skills such as data visualization, data mining, and data-driven decision-making.
- Programming and coding: While not everyone need to become a software engineer, having some basic programming skills can be valuable for working with AI-powered tools and systems. This includes skills such as Python, R, or other programming languages commonly used in AI development.
- Digital literacy: As more and more business processes move online, it’s important to have a strong foundation in digital literacy skills such as using software tools, navigating digital interfaces, and troubleshooting technical issues.
- Soft skills: While technical skills are important, soft skills such as communication, collaboration, and critical thinking will continue to be valuable in a work environment impacted by Generative AI. These skills can help you effectively communicate with colleagues, adapt to changes in the workplace, and think creatively about new opportunities.
- Ethical considerations: With the increasing use of AI in decision-making, it’s important to understand the ethical considerations around AI and how to ensure that AI is used in a responsible and ethical manner.
By developing these skills, you can position yourself for success in a work environment impacted by Generative AI and stay ahead of the curve in an ever-changing job market.
Q: You mentioned that I mentioned skills such as Python, but wouldn’t Generative AI render programmers unnecessary?
A: While Generative AI is advancing rapidly and can automate certain tasks, it does not eliminate the need for programmers and developers. In fact, the development and implementation of Generative AI systems require skilled programmers who can create and fine-tune the algorithms and models that power the AI.
In addition, Generative AI systems are only as good as the data and algorithms used to train them. Programmers are needed to ensure that the data is accurate, complete, and representative, and to design and optimize the algorithms and models that the AI system uses.
Furthermore, Generative AI is constantly evolving, and there is a growing demand for skilled programmers who can develop, maintain, and improve these systems. Programming skills such as Python, Java, and C++ are valuable for working with AI and other emerging technologies and can help individuals stay competitive in a rapidly changing job market.
While Generative AI may automate some programming tasks, it will not eliminate the need for skilled programmers and developers in the foreseeable future. The nature of programming work may change as AI continues to evolve, but programming skills will remain valuable for working with and optimizing AI systems.
Q: I am training customers on my product; how will generative AI help my training and how could it hurt it?
- Personalized content: Generative AI can analyze customer data and preferences to create personalized training materials, making the learning experience more engaging and effective for each individual.
- Scalability: AI-generated training materials can be easily updated and adapted to accommodate new features or changes in your product, making it easier to maintain up-to-date content without a large investment in manual effort.
- Interactive experiences: Generative AI can be used to create chatbots or virtual assistants that can answer customer questions, provide guidance, and offer real-time support during the training process, enhancing the overall learning experience.
- Multilingual support: AI-powered language models can generate content in multiple languages, allowing you to reach a more diverse customer base and provide training materials that cater to different linguistic and cultural backgrounds.
- Automatic assessment: Generative AI can generate quizzes and assessments based on the training materials, providing immediate feedback to customers and helping you identify areas where they may need further assistance or clarification.
- Inaccuracy: AI-generated content may sometimes include errors or inaccuracies, which could lead to confusion or misinformation if not caught and corrected.
- Loss of nuance: Depending on the complexity of your product, generative AI might oversimplify concepts, leaving out important nuances or details that are crucial for a full understanding of the product.
- Inappropriate content: AI-generated content could occasionally include inappropriate or offensive language, which could damage your brand reputation and negatively impact the training experience.
- Over-reliance on automation: If you rely too heavily on AI-generated content, you may neglect the importance of human input and expertise, leading to less effective training materials and less satisfied customers.
- Lack of empathy: AI-generated content may not have the same level of empathy or emotional intelligence as content created by humans, potentially making it less engaging and less effective in connecting with customers on a personal level.
Q: I have new employees that I plan to onboard. What learning experiences can generative AI provide to aid in retention and synthesis for those employees?
Personalized learning paths: Generative AI can analyze each employee’s background, skills, and learning preferences to create tailored learning paths. This personalized approach can improve engagement, retention, and understanding by addressing individual needs and interest.
- Interactive tutorials and simulations: AI-powered systems can create interactive tutorials and simulations based on real-life scenarios to help new employees gain hands-on experience and develop problem-solving skills.
- Adaptive Assessments
- AI-Generated Content
- AI-assisted learning reinforcement
- Virtual mentorship
- Learning analytics: AI can analyze employees’ learning behaviors, progress, and performance to provide insights and recommendations for further improvement. This data-driven approach can help managers and HR teams identify areas that need attention and develop targeted interventions.
- Continuous learning
Q: I am training employees and using software that has Generative AI. How will Generative AI help train my employees and how could it hurt it?
Generative AI can be a valuable resource for training employees, but it also comes with potential drawbacks. Here’s an overview of the ways it can help and hurt your employee training efforts:
How Generative AI can help your employee training:
- Personalized content: Generative AI can create customized training materials tailored to individual employees’ needs, skill levels, and learning styles, making the training experience more engaging and effective.
- Time and cost efficiency: AI-generated content can save time and resources by automating the creation of training materials, allowing you to focus on other important aspects of employee development.
- Consistency: Generative AI can ensure consistent messaging and information across all training materials, helping to maintain a coherent and unified learning experience.
- Up-to-date materials: AI-generated content can be easily updated to reflect the latest information and changes in your organization or industry, ensuring that employees always have access to the most current and relevant training materials.
- Interactive experiences: Generative AI can be used to create chatbots, virtual assistants, or other interactive tools that provide real-time support, answer questions, and guide employees through the learning process, enhancing their overall experience.
How it could hurt it:
- Inaccuracy: AI-generated content may contain errors or inaccuracies, which could lead to confusion, misinformation, and potentially negative consequences for your organization.
- Loss of nuance: Generative AI might oversimplify complex concepts, leaving out important details or context that is crucial for a comprehensive understanding of the subject matter.
- Inappropriate content: AI-generated content may occasionally include inappropriate or offensive language, which could create an uncomfortable or hostile learning environment.
- Over-reliance on automation: Depending too much on AI-generated content could lead to a lack of human input, expertise, and personal connection in your training materials, resulting in less effective learning experiences for your employees.
- Lack of empathy: AI-generated content may not convey the same level of empathy or emotional intelligence as human-created content, potentially hindering its ability to engage with employees on a personal level and understand their unique needs and concerns.
Section 2: Workplace, Customers, Learning and Training
Q: Should an employee learn new skills not associated with their job role?
- Career advancement: Learning new skills can make an employee more versatile and valuable to the company, which can lead to opportunities for career advancement.
- Personal development: Learning new skills can also help employees develop new interests and passions, which can lead to personal growth and fulfillment.
- Adaptability: In today’s rapidly changing job market, it’s important for employees to be adaptable and willing to learn new things. By expanding their skillset, employees can become more resilient to changes in their job role or industry.
- Collaboration: Learning new skills can also make employees better collaborators with others in different departments or roles, improving communication and teamwork.
Q: What skills do you believe are essential for employees that companies may not recognize?
- Emotional intelligence
- Digital Literacy
- Cultural Competence
- Critical Thinking
Q: Which is better? Personal and Professional Development OR skills tied specifically to your job role?
Personal and professional development and skills tied specifically to one’s job role are important for career growth and success. The choice between the two depends on an individual’s career goals, current job responsibilities, and the organization’s needs.
Personal and professional development can help an individual develop new skills, knowledge, and competencies that can be applied to various areas of their life and career. This type of development may involve learning new languages, communication skills, project management, leadership, and other soft skills that can be beneficial in any job role.
Q: Should companies allow their blue-collar workforce to access and use online learning?
Yes, companies should consider allowing their blue-collar workforce to access and use online learning as a part of their training and development programs. Online learning can be a valuable tool for providing job-specific skills training and continuing education opportunities for blue-collar workers.
Online learning can be particularly beneficial for blue-collar workers who may have difficulty accessing traditional classroom-based training due to scheduling or geographical constraints. Online learning allows workers to complete training at their own pace and on their own schedule, which can be particularly helpful for those who work irregular or unpredictable hours.
In addition, online learning can help companies to increase the skills and knowledge of their blue-collar workforce, which can improve job performance, productivity, and job satisfaction. This, in turn, can lead to improved employee retention, reduced turnover, and increased profitability for the company.
Q: Do you believe that cohort-based learning is a highly effective way to learn for employees and customers?
I can tell you that cohort-based learning has been shown to be an effective way for employees and customers to learn.
Q: Can Generative AI be a mentor to an employee?
Generative AI, as it currently exists, is not capable of being a mentor to an employee in the same way that a human mentor can. While AI can provide guidance, recommendations, and insights based on data and algorithms, it lacks the emotional intelligence, empathy, and personal connection that are essential for effective mentorship.
Mentorship involves building a relationship of trust and mutual respect between a mentor and a mentee, with the mentor providing personalized guidance and support based on their own experiences and insights. This type of relationship is difficult for AI to replicate, as AI lacks the ability to truly understand and empathize with the unique experiences and perspectives of an individual.
Section 3: Generative AI in General
Q: Can you briefly explain what is Generative AI for someone who is new to the technology?
Certainly! Generative AI refers to a type of artificial intelligence that is designed to create or generate new content, such as images, text, or even music, rather than simply analyzing or processing existing data.
Generative AI systems are often based on deep learning algorithms, which are modeled after the structure of the human brain and can analyze large amounts of data to identify patterns and generate new content.
Generative AI can be used for a wide range of applications, from creating realistic-looking images or videos to generating new product designs or marketing content. It has the potential to revolutionize many industries, including healthcare, entertainment, and finance, by enabling more efficient and creative ways of generating new content.
However, there are also concerns about the potential risks of Generative AI, including issues such as bias, privacy, and the potential for misuse. As with any emerging technology, it’s important to approach Generative AI with caution and to ensure that it is developed and used in a responsible and ethical manner.
Q: What policies should I company enact if they plan to use Generative AI solutions in the workplace?
When incorporating Generative AI solutions in the workplace, it’s essential to establish policies that ensure ethical use, protect user privacy, and maintain a positive working environment.
- Ethical AI Use Policy: Establish guidelines for using AI ethically, fairly, and without bias. This should include procedures for monitoring AI performance and addressing any issues that may arise.
- AI Training and Awareness: Train employees to understand the capabilities and limitations of AI, so they know how to use it responsibly and effectively. Offer workshops or training sessions to keep employees up-to-date on AI developments.
- AI Transparency and Accountability: Clearly outline the decision-making process for AI systems, and establish a chain of responsibility for AI-driven decisions. Implement regular audits and maintain logs for AI systems to ensure transparency.
- Human-AI Collaboration: Emphasize the importance of human-AI collaboration, and encourage employees to leverage AI as a tool to enhance their work, rather than replace it. Encourage open communication and feedback about AI implementation and its impact on employees.
- AI Security Policy: Implement robust security measures to protect AI systems and the data they process. Regularly update and patch AI software, and establish guidelines for detecting and mitigating potential threats.
- Inclusivity and Diversity: Ensure AI systems are designed to be inclusive, and do not discriminate against any group or individual. Regularly test and update AI systems to minimize biases and promote fair decision-making.
- AI Impact Assessment: Conduct regular assessments to evaluate the potential impact of AI implementation on the workplace, including changes in job roles, employee morale, and productivity.
- Intellectual Property and Ownership: Define rules and procedures for AI-generated content and intellectual property, including who holds the rights to the AI-generated work and how it can be used or shared.
- External AI Collaboration: Establish guidelines for working with external AI partners, including selecting vendors, managing relationships, and ensuring compliance with your company’s AI policies.
Q: Can you explain briefly what Learning Language Model is and how it works with Generative AI?
A Learning Language Model (LLM) is a type of artificial intelligence (AI) model that is designed to understand, generate, and manipulate human language. These models are trained on massive amounts of text data to learn the underlying structure, grammar, and semantics of language. They can then generate text that appears to be written by humans, making them a core component of Generative AI.
Once trained, the LLM can generate new text based on a given input or context. This is achieved by sampling words from the model’s probability distribution, conditioned on the input context. By doing so, the model can create coherent and contextually relevant sentences, making it a powerful tool for various applications like chatbots, text summarization, translation, and more.
Overall, Learning Language Models are a critical component of Generative AI, enabling the generation of human-like text and the understanding of natural language for a wide range of applications.
Q: Briefly, what are some terms I should become familiar with as it relates to Generative AI?
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Neural Networks
- Generative Adversarial Networks
- Transformer Models
- Natural Language Processing
- Pre-training and Fine-tuning
- Transfer Learning
Q: Will there be people who believe that the results of Generative AI are absolute?
Yes, there is a risk that some people may believe that the results of Generative AI are absolute, infallible, or objective. This can happen for several reasons.
First, the outputs of Generative AI can appear to be objective or authoritative, particularly when they are based on large amounts of data or complex algorithms. This can give people a false sense of confidence in the accuracy and reliability of the AI system.
Second, the use of AI can lead to a “black box” problem, where the inner workings of the AI system are opaque or difficult to understand. This can make it difficult for people to assess the accuracy or biases in the system’s outputs.
Finally, the widespread adoption of AI and the emphasis on data-driven decision-making can create a cultural shift towards valuing objectivity and quantifiability over other forms of knowledge or expertise. This can further reinforce the belief that AI outputs are absolute and infallible.
It is important to recognize that AI is a tool that should be used in conjunction with human expertise and judgment, rather than as a replacement for it.
Q: Most people are only familiar with GPT for Generative AI. Are there any other products or models people should consider?
- OpenAI Codex
- BERT (Bidirectional Encoder Representations from Transformers)
- StyleGAN (Style Generative Adversarial Network)
Q: Is Generative AI infallible?
No, Generative AI is not infallible. Like any other technology, Generative AI can produce errors, biases, and inaccuracies in its outputs. These errors can arise from various factors, including incomplete or inaccurate data, flawed algorithms, and incorrect assumptions made by the system.
One challenge in using Generative AI is the risk of bias in the data used to train the system. If the data used to train the system contains biases or inaccuracies, these biases can be amplified in the outputs of the AI system. This can have negative consequences, such as perpetuating existing inequalities or making incorrect predictions.
Another challenge is the risk of “adversarial attacks,” where an individual or group intentionally inputs data or manipulates the AI system to produce inaccurate or harmful outputs.
To address these challenges, it is important to ensure that Generative AI is developed and deployed with appropriate safeguards and ethical considerations. This may include measures such as careful data selection and validation, algorithmic transparency and explainability, and ongoing monitoring and testing of AI systems. It is also important to recognize that AI is a tool that should be used in conjunction with human expertise and judgment, rather than as a replacement for it.
Finally, what are your thoughts around, “My Dinner with Andre?
My Dinner with Andre” is a unique and unconventional film directed by Louis Malle, starring Wallace Shawn and Andre Gregory, who also wrote the screenplay. The movie is centered around an extended conversation between the two characters, who portray fictionalized versions of themselves, at a New York City restaurant.
The film’s unconventional format and lack of action may not appeal to everyone. Some viewers may find the lengthy conversation and philosophical discussions tedious or unengaging. As with any film, the appreciation of “My Dinner with Andre” is ultimately a matter of personal taste and interest.
In other words, to speak in layperson’s terms, you hate it.