LLMs

Google Deepmind used similar idea to make LLMs faster in Accelerating Large Language Model Decoding with Speculative Sampling. Their algorithm uses a smaller draft model to make initial guesses and a larger primary model to validate them. If the draft often guesses right, operations become faster, reducing latency.

There are some people speculating... See more

Disruptive innovation comes in two flavors: (1) New-market disruption, where the company creates and claims a new segment in an existing market by catering to an underserved customer base, or (2) Low-end disruption, in which a company uses a low-cost business model to enter at the bottom of an existing market and claim a segment.

Copilots don’t... See more

The quality of dataset is 95% of everything. The rest 5% is not to ruin it with bad parameters.

• Query the RAG anyway and let the LLM itself chose whether to use the the RAG context or its built in knowledge


• Query the RAG but only provide the result to the LLM if it meets some level of relevancy (ie embedding distance) to the question


• Run the LLM both on it's own and with the RAG response, use a heuristic (or another LLM) to pick the best answer

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A new v0.4.0 release of lm-evaluation-harness is available !

New updates and features include:

• Internal refactoring


• Config-based task creation and configuration


• Easier import and sharing of externally-defined task config YAMLs


• Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource


• More advanced configuration

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Two ways for an AI company to protect itself from competition: (a) depend not just on AI but also deep domain knowledge about a particular field, (b) have a very close relationship with the end users.

𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦: it will improve your LLM performance on given use cases (e.g., coding, extracting text, etc.). Mainly, the LLM will specialize in a given task (a specialist will always beat a generalist in its domain)

𝘤𝘰𝘯𝘵𝘳𝘰𝘭: you can refine how your model should behave on specific inputs and outputs, resulting in a more robust product

𝘮𝘰𝘥𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯:... See more

Unlike consumers, enterprises want control over how their data is used and shared with companies, including the providers of AI software. Enterprises have spent a lot effort in consolidating data from different sources and bringing them in-house (this article Partner integrations + System of Intelligence: Today’s deepest Moat by fellow Medium... See more

We generally lean towards picking more advanced commercial LLMs to quickly validate our ideas and obtain early feedback from users. Although they may be expensive, the general idea is that if problems can't be adequately solved with state-of-the-art foundational models like GPT-4, then more often than not, those problems may not be addressable... See more