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Anthropic's Claude, now with a 100K Token Context Window.

 


Claude is an LLM from Anthropic, which now has a neat trick up its sleeve, you can ingest several thousands of words into it's prompt window and ask questions of that document immediately. It's no wonder that the company advertise amongst the use cases for this model legal firms. Two Claude models were launched in May, with two different pricing structures. 

I've not mentioned Anthropic before, and haven't read it's AI Safety framework as yet, which I'll have to rectify. But I not that in their paper, 'Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback', they stated under limitations:

We’ve pragmatically defined an aligned assistant as an AI that is18 helpful, honest, and harmless. We are optimistic that at present capability levels, the techniques we have discussed here provide a reasonable approach to achieving helpfulness and harmlessness. However, although our techniques improve model honesty, we believe we are just scratching the surface of that problem, and that other techniques may more efficiently and effectively produce honest AI models.

Anthropic recently raised $450 million from investors including Alphabet Inc's (GOOGL.O) Google and Spark Capital. Anthropic's total funding will be nearly $1 billion, making it one of the most well-funded AI startups. With Claude it's apparent that their challenge to OpenAI and Google is going to be significant. 

They state on their website:

'The average person can read 100,000 tokens of text in ~5+ hours[1], and then they might need substantially longer to digest, remember, and analyze that information. Claude can now do this in less than a minute. For example, we loaded the entire text of The Great Gatsby into Claude-Instant (72K tokens) and modified one line to say Mr. Carraway was “a software engineer that works on machine learning tooling at Anthropic.” When we asked the model to spot what was different, it responded with the correct answer in 22 seconds.'

There are a large number of use cases where complex documents require quick analysis, from research, law, education, or various business cases. It may make other LLMs with small token windows near irrelevant for professional usage. 

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