Deep learning, yes. Transformers, maybe. LLMs, no, not directly. Though LLMs could always be a tool available to an intelligent agent.

One of the keys to productivity is quick visual feedback about what you're currently doing. In Keras, you have two fast ways to inspect the model you're working with:

- `model.summary()` prints basic information about model size and components
- `keras.utils.plot_model()`…

Demand for compute will keep growing for the foreseeable future.

These are all true simultaneously:

1. Scaling up deep learning will keep paying off (unlock more applications, or higher performance on existing ones).
2. Scaling up deep learning isn't the path to AGI.
3. We aren't particularly close to AGI, and LLMs did not represent a step…

Now, let's say you want to create a newly initialized model with the same architecture, but where the Embedding and Dense layers are twice larger. No problem -- just use the `clone_function` argument.

You can use this to dynamically grow models over time, by inserting new layers…

Francois Chollet's mental model for LLMs: LLMs are stores of programs. Querying LLMs involves selecting a program from the latent program space and running it on your data. And the ability of LLMs to interpolate between these programs is what makes LLMs so flexible.

I recommend only using writing automation for text that isn't meant to be read (boilerplate), or to fix style/grammar in text you've written yourself. Writing is about communicating your ideas, so if you can't bother writing something, no one should be expected to bother reading…

The real threat to LLM chatbots isn't that adoption levels have stalled -- greater capabilities could reignite adoption. It's the fact that 1.5 years of massive investments haven't resulted in meaningfully better models, that would make a real difference in adoption potential.

A KerasNLP starter notebook for the Automated Essay Scoring competition on Kaggle: kaggle.com/code/awsaf49/a…

Amazing that a chip bricked in 46-yr-old Voyager I, preventing it from sending data, and NASA figured out how to split up and reallocate its functions to other hardware, sending code 15 billion miles away (45 hours round trip!)--and Voyager's back online. blogs.nasa.gov/voyager/2024/0…

More exciting news today -- Gemini 1.5 Pro result is out!

Gemini 1.5 Pro API-0409-preview now achieves #2 on the leaderboard, surpassing #3 GPT4-0125-preview to almost top-1!

Gemini shows even stronger performance on longer prompts, in which it ranks joint #1 with the latest…