You can find map at http://www.wrotescan.com/Map_Only. It will break your document into 12000 character chunks then run the map prompt on each chunk.

Progress comes in a multitude of mysterious ways.

If you don't have issues then definitely don't bother migrating! Something like Metaflow or Modal is much more built for purpose. Airflow was designed for the Hadoop era of data engineering; it's straining under changes that have happened in the Python, container, and ML ecosystems.

I might say we gain general intelligence by creating different models for different tasks and gain experience on when to call which. This has the when to call which, but not the creation of new models.

AGI getting closer everyday

Posts like these are no better

It would be amazing if this supports the whisper package directly instead of whisper in transformers from huggingface. (I know this isn't just for whisper but I do really need to speedup whisper)

Wow, I'm really interested at this history, do you have more detailed literature about this?

I disagree about that. Imagine you invest millions of dollars then someone makes millions of it and you lose millions of dollars.

I wonder if this is the ultimate path to reaching general intelligence. After all, humans evolved by learning to master tools.

It's ChatGPT writing about ChatGPT

Dang it. I was hoping I can get away with not having a Twitter account

r/MachineLearning today has 2.6 million subscribers. The more influx of newcomers the more beginner-friendly posts get upvoted. This is OK—don't get me wrong—it's just a different setting.

Academic discussions were popular back when there were only 50-100K. In fact, I remember in 2017 being in OpenAI offices and every morning, seeing a row of researchers with reddit on their monitor. Discussions mostly happen now on Twitter.

Hah this was a nice surprise - yes the whole point of dfdx is to do just this!

Why is airflow bad and can you suggest alternatives? I'm using airflow now with no issues

Most epic game how many questions would be required?

Not just you, it pivoted with the narrative that existing models will scale and stand the test of time with more data and bigger models.

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> If you're deploying models to production

Airflow is not a good tool for ML development. Leave Airflow back in 2018. Also Modal can do prod model deployment, model pipelines, and inference.

Are you interested in reducing the latency or just cutting down the cost? Can you run the workload on GPUs instead?

For BERT-type models, doing some compression and using inference libraries can easily get you 5-10x speedup. If interested, I'd be happy to share more resources on this.

When is the NFT drop

Discussion in this subreddit is always a bit hit and miss. After all, reddit as a community has almost no gate keeping. While this could be a good thing, there are of course downsides to it.

If you look at this post about batch norm, you see that there are people who brought up interesting insights, and there are a good chunk of people who clearly have never even read the paper carefully. And this post is 5 years ago.

[removed]

Qdrant for the vector database and Kern AI refinery for data labeling!

The following is my opinion; so bias is there. My feeling is the sub was never about academic discussions per se. The papers and academic discussions acted like vessels to carry people towards "(deep learning hype + money flow+ industry jobs)" island. In most of the earlier discussions ,if you follow them closely, you will see that there was never really a push for genuine understanding, rather people looking for easy way to earn "publication currency". Initial impression was having some kinda project or publication could land people a high-paying job. Probably later people realized that actually they don't need to worry about papers and stuff, rather doing some kinda quick LLM based project will help to land high-paying jobs even faster. I mean LLMs are currently at the peak of hype. Thus we have more random looking posts.