I can imagine that the right kind of language model could generate jokes that are super funny and have a unique flavor to them.

When you add more parameters and data, you capture more of the distribution, but it's unclear why it scales so smoothly with those parameters and data.

The subtlety of human expressions, such as the difference between a real smile and a fake smile, is essential to capture in computer graphics, as it conveys deep emotional nuances.

AI is trained on averageness, but I am not average; each of us has our own sensibility.

We haven't yet created a model that can generate an entire song because we haven't perfected the conditional aspects of the LSTM yet, which is crucial for handling long-term musical structures.

The aim of my AI research was to give AI a sense of humor, but it failed because it couldn't understand complex images and humor, often producing only mediocre results rather than deep, meaningful human humor.

When prompting, I try to give examples that are not too similar to the data the model will see, to avoid overly consistent responses.

Live demos involving AI can be unpredictable, as they often yield different results each time.

The video shown was entirely generated by a custom model, illustrating the capabilities of fine-tuning general-purpose video generation models on specific domain data.