Their original compute platform for running arbitrary ml workloads will become obsolete as the industry consolidates around LLMs.
OpenAI had a first mover advantage. ChatGPT has a good interface and GPT4 still leads on many benchmarks. But it continues to get worse and worse and others are catching up with significantly lower parameter counts. Their moat is shrinking very quickly.
Llama2 is an open source meta model, btw. Anyscale's ray makes it much easier to leverage the super fast pace of development in the OSS community (or released by large companies as OSS).
FWIW GPT-4 is not one single model as you're aware and it evolves over time. In our experience it has objectively degraded in quality over time.
As for anyscale, ultimately it's more like databricks (and funny enough lots of ex databricks folks there). Anyone can just run hosted spark. Databrick's ecosystem has set them apart and it looks like that's what anyscale is building towards.
1. a model for sentiment analysis
2. a model for summarization
3. ...other NLP tasks
other tasks:
4. a model for object detection in images
5. a model for face recognition in images
Whereas now the LLM does all of the above better than the previous state of the art. This will continue and eat more fields of machine learning. It will happen for images and video. I argue that it will even extend to things like time series analysis
imo the industry will consolidate around LLMs for early prototypes and as part of the workflow for building a corpus to train domain-specific models.
Source: team went through this process. LLM cost ~1% unskilled human labelers, then we finetuned BERT to bring the cost down to < 0.001% (savings $120k/y compared to just using the LLM)
The tech consolidator works by: if it reasonably can, it will.
PS5? Gaming machine, 4k disc player, streaming box, etc.
Smartphone? Internet, phone, texting, camera/photos, storage, shopping, identity, gaming, music, video, directions. Soon we'll add highly useful AI bots to the list.
Windows? Pretty much non-stop consolidated software from the industry into itself, across decades.
Also known as eating the ecosystem. GPT & Co. will eat their best plugins.
The point is that a model can do everything, not that it is the most cost effective or best way to do everything.
There are tradeoffs with all tech, and there always will be.
That's for sure. The main problem is how to put them together. There are several ways. First, tokenizing the media and inserting it in the stream. Second, a media controller which accepts verbal instructions generated by LLM. They can be combined. Interesting variation is controller with (immediate) feedback. It can be anything, for example database access.
> I argue that it will even extend to things like time series analysis
AFAIK transformers have been used for low level robotic control. And LLM for high level have been reported by MS and Google.
Our prices are competitive (starting at 25c per million tokens) because of the tech we've built that maximizes GPU utilization. That's what we do better than anyone else.
I still think GPT-4 is best LLM out there. It's just very expensive and you don't need all that horsepower you can save bucketloads of money.
Ray (https://github.com/ray-project/ray) is available for anyone to use. You can also use Aviary (https://github.com/ray-project/aviary) to serve any of those models yourself.