Pretty good examples and simple explanations. I didn't realize Claude 2 was so good at working with PDFs natively. I wonder if they're doing anything special? Is this just due to larger context length they have?
Also, biased opinion on my part: I'm especially interested in watching how these things affect data science and data literacy as a whole. Code interpreter is a game changer in my opinion, the most powerful tool that I think deserves all the press it is getting. Also: I released an open source code-interpreter for data (https://github.com/approximatelabs/datadm) and even though I know how to code and use Jupyter daily, I still find myself doing analysis with it instead.
All in all, it does seem like the different models and agents are gaining "specialization" skill is actually good for the user (rather than just using a single jack of all trades super chat model). Even though GPT-4 takes the language model crown, there's still specialization that matters and improves quality for different tasks as discussed here.
I wonder if in 2-5 years we'll all use "a single" AI chat interface for everything, or every specialization continues to "win at its own vertical" and we just have AI embedded inside of every app
I think they necessarily need to specialize, as certain information is only available in the context of the domain. I think bigger context windows will hit a limit, and you'll need to have actually trained and guided the AI on specifics of the domain to be useful.
At the moment it's only the fact that public documentation is available for so many tools that it's proving useful for so many things. But what about massive, closed source, boutique enterprise systems? You can feed it docs as context, but it would be better if it were trained on docs, support tickets and internal forums then properly aligned.
This will create an excellent search engine but a terrible reasoning machine.
There are a lot of ways to search through docs and support tickets now. The ability of an LLM to draw inferences and summarize all of that information comes from being trained on a very large amount of data with billions of parameters. The data can be highly specialized. There just needs to be several thousand gigs of it for the AI to do things that are rare and useful.
Also, biased opinion on my part: I'm especially interested in watching how these things affect data science and data literacy as a whole. Code interpreter is a game changer in my opinion, the most powerful tool that I think deserves all the press it is getting. Also: I released an open source code-interpreter for data (https://github.com/approximatelabs/datadm) and even though I know how to code and use Jupyter daily, I still find myself doing analysis with it instead.
All in all, it does seem like the different models and agents are gaining "specialization" skill is actually good for the user (rather than just using a single jack of all trades super chat model). Even though GPT-4 takes the language model crown, there's still specialization that matters and improves quality for different tasks as discussed here.
I wonder if in 2-5 years we'll all use "a single" AI chat interface for everything, or every specialization continues to "win at its own vertical" and we just have AI embedded inside of every app