Mrs. Goldman then asked the following question:

How did you, Gemini in Google Bard, generate that image based on my prompt above? Can you explain the process?

Gemini gave this response:

While I can't access and share the specific details of the generation process due to proprietary reasons, I can explain the general steps involved when I create an image based on your prompt:

1. Understanding your prompt: I meticulously analyze your prompt, paying close attention to keywords, descriptions, and desired style. In this case, I focused on "Tello drone," "flight," "landing," and "silhouette of a person's hand."

2. Text-to-code conversion: I translate your prompt into a format that a large language model (like me) can understand. This involves breaking down the prompt into smaller components and assigning numerical representations to them.

3. Image generation pipeline: My internal system activates a specific image generation pipeline. This pipeline includes a complex neural network trained on a massive dataset of text and images.

4. Generating candidate images: The neural network processes the numerical representation of your prompt and starts generating candidate images based on its understanding of language and the visual world.

5. Selecting the best image: The pipeline evaluates the generated images against various criteria, such as how well they match the prompt, their overall quality, and their coherence. Ultimately, the image that best fulfills these criteria is chosen.

6. Finalizing the image: The chosen image might undergo post-processing steps to enhance its quality, adjust its style, or address any potential issues.

7. Presenting the image to you: Finally, the best-suited image is delivered to you for your evaluation.

It's important to remember that this is a simplified explanation, and the actual process involves numerous intricate calculations and complex algorithms.

If you have any further questions about the capabilities of Google Bard or image generation in general, I'm happy to help!

Ethics in AI - My Biggest Question

Where did the "massive amounts of data" come from to generate the images people request? Did all the images come from people who gave explicit permission and had knowledge their work would be used in this manner, and if not, how will they be compensated for their work?

That's a great question! The "massive dataset" used for training large language models (LLMs) like Gemini raises important ethical and legal considerations regarding data sources and intellectual property. Here's what we know so far:

Sources of data:

• Publicly available images and text: This includes vast quantities of online content like photos, articles, books, and code, often scraped from the web. While technically publicly available, the question of implicit or explicit consent for use in AI training remains complex.

• Licensed datasets: Companies license specific datasets, often curated for image recognition or natural language processing, with usage agreements outlined.

• Internal data: Sometimes, companies use their own data, like images from their platforms or services, which may raise privacy concerns depending on user agreements.exclamation

Permission and Compensation:

• Unclear consent: For publicly available data, it's often unclear whether creators explicitly consented to their work being used in AI training. Copyright laws are evolving to address this, but the current landscape is murky.

• Limited compensation: Some platforms offer optional opt-out mechanisms for creators, but widespread compensation for data used in AI training is largely absent.

• Emerging ethical frameworks: Discussions are ongoing on ethical frameworks for data use in AI, considering fair compensation and creator rights.

Current discussions and challenges:

• Bias and discrimination: Training on vast, unfiltered data can perpetuate existing biases present in society, raising concerns about fairness and responsible development.expand_more

• Transparency and accountability: The lack of transparency about data sources and algorithms makes it difficult to hold developers accountable for potential harms.