AI in Business and Society

The first two weeks of the Generative AI for Everyone went through an introduction of AI terminology and functions and then how AI tools function.

In week 3, Andrew Ng discusses the application of AI tools in real world business and society environments.

He discusses using a general-purpose AI tool as a writing assistant or editor for brainstorming, summarizing text, or writing code.

An interesting point he makes is related to the concern that jobs will be lost. But then he points out that it isn't likely that AI tools will be used to automate jobs but rather to automate tasks. I think this is a great way to think about it, since AI isn't going to cause jobs to go away (or at least very many of them) but it will take certain tasks away. This will cause more people's jobs to change but will not likely actually take the jobs themselves. In particular, AI can be used to augment tasks, which is where the AI assists and makes the job easier, vs. automation which is where a task is actually completely given to the AI to perform. This may be a process in some situations, to augment initially, while keeping a human in the process, and eventually as the system is trained those tasks may be trusted enough to be run automatically.

If you're unsure if an LLM can complete a task, experiment. Ask it to do the task, and see what happens. He pointed out last week that the internet won't blow up if you ask an LLM to perform a task and it can't for any reason. the answer may not be yes or no but not yet, meaning some fine-tuning or training could help it learn how to do so. The question is how much time is taken on the task now and how much value is created by using AI to do it faster, cheaper, or more consistently?

An interesting concept Andrew calls out is that we usually think of the iconic part of a particular job role, such as a lawyer arguing a motion in court or a doctor performing surgery. What we don't think about are all the more mundane tasks that have to be done outside of the most iconic pieces. An AI tool could be used to take a difficult process faster and shorter to do the same basic task. Or it can be used to do a deeper analysis, which may take the same amount of time as it would initially but end up with a better result by doing more thorough testing and analysis. He shares a great quote by Curtis Langlotz, which is that "AI won't replace radiologists. But radiologists that use AI will replace radiologists that don't." In the past, technology has been used to create more jobs than it has destroyed, since the cost savings potential of new technology is limited while the growth potential of new technology is unlimited.

Data on the internet which are used to train data models represent our past and present. We may be hopeful for something different in the future. Fine-tuning and reinforcement learning can be used to create a fairer, less biased, and more just future.

In the conclusion, Andrew calls out how human intelligence is expensive. It takes a lot of time and money to train a wise human being, and thus only the wealthiest people can afford to hire the most intelligent people. Artificial intelligence, however, is much less expensive, so AI can be used to give everyone the ability to hire intelligence at low cost.

AI Applications

This is a follow-up to my previous post on the Generative AI for Everyone course.

In week 2, Andrew Ng's course is a little less conceptual and more application-focused. He shows examples of generating code to do basic tasks such as count up restaurant reviews and count how many are positive vs. negative. The code examples were simple and easy to modify and run. One of my favorite quotes as he talked about building a chat tools was the following, "Having built a number of generative AI projects, I've often been surprised and delighted by the strange and wonderful things that the users will try to do with your system." He then goes through how the system would likely respond that it doesn't know the answer but then those questions that stumped the AI can be gathered and used to provide additional information to help the system be able to answer that type of question in the future.

He then gives a simple example to determine how much a generative AI tool might cost. Based on the number of tokens required to input and output text, it could cost maybe 8 cents to generate enough text that it would take a reader about an hour to get through.

One of the most interesting concepts he discusses is that since a large language model doesn't know everything, it is best used as a reasoning engine. That is, it doesn't store and retrieve all information but rather can help you reason through and process information from other sources. Retrieval Augmented Generation (RAG) uses this approach of taking information from a document or other source you direct it to in order to process a response. They can also be fine-tuned or pre-trained for your particular application.

Generative AI for Everyone

I've been going through Andrew Ng's Coursera course titled Generative AI for Everyone to help think through how to integrate AI concepts into courses I teach. It is intended to take 2-3 weeks to complete. As with most MOOC providers, you can go through the course for free, but if you want to take the quizzes/tests and obtain a certificate of completion at the end, it costs a bit. The following notes come from going through the first week.

Generative AI systems produce high-quality text, images, audio, and video.

Supervised learning is used for labeling things. For example, when an email comes in, an AI spam filter could label that email as spam or not. Or for self-driving cars, inputs from the car's sensors can understand the vector of other cars.

A Large Language Model (LLM) can take a prompt and use it to generate output. Supervised learning outputs the next word based on the first part of the sentence as the input.

An LLM can be used to answer simple knowledge questions or as a writing partner to help refine existing text or to generate new text based on the prompt. It can also be used to create something that has not been already widely done on the internet.

AI is a general-purpose technology, meaning it can be useful for many tasks rather than being limited to only specific situations.

Interfaces may be web-based, similar to how a web search functions, or application-based, where the AI function is integrated into another application.

When considering whether an LLM could do a certain task, consider if a fresh college grad could likely complete the task.

They are limited by knowledge cut-offs where certain information has not been used to train the LLM or hallucinations where it makes up information that it doesn't know anything about. There also may be limitations as to the input and output length.

Embracing the Hybrid Future of Education

The classroom is changing. Technology is transforming education, blending the old with the new.

The Old

Traditional classrooms have a charm and nostalgia. The social interaction, the structure (physical rows of desks but also the schedule of activities and teacher rules), the chalkboard, that cleaning solution smell. But they also have limitations.

The New

Digital tools—online platforms, virtual classrooms—are breaking barriers. They offer flexibility, accessibility, and personalized learning. They don't provide visceral connections or common classmate experiences.

The Integration

The challenge is combining these effectively. Here’s how:

1. Blended Learning: Mix in-person with online. The best of both worlds.
2. Interactive Platforms: Foster engagement and collaboration beyond the classroom.
3. Personalized Learning: Tailor education to individual needs using adaptive technology.

The Mindset

Change can be daunting, but it’s essential. Educators must be learners too, embracing technology to enhance the human elements of teaching.

The future of education is hybrid. It’s about enhancing the learning experience, making it more engaging, accessible, and effective. Let’s embrace it.

Embracing Change: Thriving in a Dynamic Business Environment

Change is constant and inevitable. In business, embracing change is essential for survival and growth. Change leadership is more about mindset than job titles. Here’s how to thrive amidst change:

Understand Change

• Constant: Businesses that don’t adapt risk obsolescence.
• Multidimensional: Impacts all levels—organizational, team, individual.
• Driven by Multiple Factors: External (market trends, tech) and internal (culture, leadership).

Strategies for Success

1. Foster Continuous Learning: Promote upskilling and reskilling. Encourage lifelong learning.
2. Embrace Technology: Use digital tools to enhance productivity and customer experience.
3. Promote Agility: Adopt flexible methodologies. Respond quickly to changes.
4. Empower Leadership: Encourage initiative at all levels. Foster a culture of ownership.
5. Communicate Effectively: Maintain transparency. Address concerns promptly.

Conclusion

Change brings opportunities for growth and innovation. By understanding and embracing it, businesses can not only survive but thrive. Prepare, adapt, and leverage change for success.