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Bringing new AI into old workflows

1. Bringing new AI into old workflows

Welcome back! In this video, let's explore using generative AI in human workflows.

2. Meeting our "replacement"

Say hello to our replacement, the latest AI. We read articles about such things all the time. But is it true? Human jobs are complicated, AI has limitations, and there are reasons why we might prefer a human over an AI, even if an AI can do certain jobs.

3. Advantages and limitations

As workers, generative AIs have several advantages. First, they can exhibit deep knowledge of their training data, sometimes deeper than human professionals. Second, they compute responses many times faster than even teams of humans. Finally, they are inexpensive compared to human workers. But they also have severe limitations. First, they are prone to making things up, known as hallucination, as well as bias, which we've covered previously. Second, they don't have common sense like humans do, because they don't have the experience of a human environment. Third, there are challenges in implementation. Generative AI is not automatically fit for every workflow. Adaptations in user interface design or how teams work are necessary for integration. Instead of being replaced, we can leverage generative AIs to improve our work. Let's discuss a few ways to do it.

4. Augmentation

The first is augmentation, when generative AI completes parts of a human task. For example, an AI could create a concept video so the human can edit and refine it.

5. Co-creation

Second is co-creation, when humans and AI work together to produce a finished product. For example, a human and an AI can discuss a slide deck they are building together.

6. Replacement

Finally is replacement, when the AI fully automates a task. For example, an AI monitoring ocean temperatures could proactively solve an issue with malfunctioning equipment.

7. A novel implementation

Different parts of a job may be amenable to different styles of integration. Let's consider an example of a novelist. AI might augment their work by suggesting edits that the human can decide to implement or not. If they collaborate in a dialogue to generate and refine a whole novel, this is co-creation. Or the AI might replace the novelist in certain tasks, for instance, if the AI generates and publishes Twitter posts about the book launch. So, what are the steps to creating such collaborations?

8. Identify opportunity

First, identify where AI might help. For example, a 3D game developer can use AI to generate game objects.

9. Decompose the process

Second, decompose the process to understand how AI can help. 3D games start with 2D concept art, which a text-to-image tool can create.

10. Test an AI solution

Third, trial this solution. An art director can ask her team to each generate art concepts to see if it's helpful.

11. Scale up

Finally, implement the solution and track its performance. The art director can use generative AI for concept art on several projects to determine how many times faster concepts can be created as a result.

12. A new way of working

Generative AI creates a new way of working. Instead of seeing these supertools as competitors, we might view the AI as a partner for generating ideas and completing routine tasks for us. With technology evolving rapidly, we must continuously learn and keep updated with the latest generative AI trends in our fields. But don't worry - many AIs are great at teaching and explaining. They can help us learn faster than ever before. Finally, we must be patient. Generative AI's capabilities are vast, but AIs process information differently than humans do. Integrating AI is like onboarding a new teammate - it takes time and shared experiences to collaborate most effectively.

13. Let's practice!

Time to practice what we've learned.

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