An Army of ChatGPT Killers & Google's Huge Controversy

🏝 TheTechOasis 🏝

Breaking down the most advanced AI systems in the world to prepare you for the future.

5-minute weekly reads.

TLDR:

AI Research of the Week: An Army of ChatGPT Killers

Leaders: Understanding Google’s Racist Controversy

🤩 AI Research of the week 🤩

Predibase, an LLM service company, has announced a set of models that might change the course of Generative AI at the enterprise level.

Named LoraLand, this suite of very small models outperforms ChatGPT in very specific downstream tasks, all the while being able to deploy all models in the same GPU and with an average of 8 dollars spent on fine-tuning, which sounds absolutely outrageous.

A complete revolution in Enterprise Generative AI—which you are about to try—might be among us, so let’s dive in.

The Hardest Decision

The emergence of foundation models, pre-trained models that can be used for a plethora of different tasks, officially ignited the flame of Enterprise AI.

Before that, deploying AI was a challenge, and a very risky one.

You had to train a specific model for your tasks, and the chances of failure were almost 90%, according to Gartner.

But now AI comes with priors, models that we already know are very good, and that we simply need to ground to the task at hand, usually using prompt engineering, which essentially means optimizing the way we interact with the model to maximize performance.

However, at the end of the day, it all boils down to choosing the right LLM for the right task, which is unequivocally one of the hardest decisions in Generative AI today.

But at first, if you’re just starting, the decision might seem quite straightforward.

Proprietary vs open-source

A quick look at the most popular benchmarks and you’ll see that ChatGPT, Gemini, and Claude are the best models.

Also, they are heavily capitalized, offering dirt-cheap prices. However, using these proprietary models comes at a cost, and not an economic one.

You have no control over the model.

This means that it could be updated unexpectedly and force you into reengineering your deployments, and you also need to trust these companies not to use your confidential data being sent to these models.

And, for the sake of explanation, be mindful that these companies have every incentive in the world to use your data to further refine their models.

On the other hand, open-source models like LLaMa or Mixtral8×7B, while offering generally lower quality, allow you to have absolute control over the model.

Also, you have guarantees that your confidential data, the most important asset for companies today, is never compromised.

And adding insult to injury, the quality gap with private models can be closed using fine-tuning.

Data comes first, size second

Even though open-source models indeed lag behind today, with enough fine-tuning on a particular task, you can increase the performance of your open-source model far beyond what ChatGPT can offer.

There are plenty of examples of models that are more than ten or hundred times smaller than…

Enjoyed the introduction? Subscribe or sign in to continue with this edition.