LLMs

However, a key risk with several of these startups is the potential lack of a long-term moat. It is difficult to read too much into it given the stage of these startups and the limited public information available but it’s not difficult to poke holes at their long term defensibility. For example:

• If a startup is built on the premise of taking base

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Setting up the necessary machine learning infrastructure to run these big models is another challenge. We need a dedicated model server for running model inference (using frameworks like Triton oder vLLM), powerful GPUs to run everything robustly, and configurability in our servers to make sure they're high throughput and low latency. Tuning the... See more

A core research interest of mine is imagining new kinds of interfaces to text documents that are made possible by modern AI and software. I think an interesting place to look for such ideas may be interface designs for reading and writing legal documents .

Legal document-wrangling tools have a handful of properties that make it fertile ground for... See more

Zerox OCR

A dead simple way of OCR-ing a document for AI ingestion. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. The vision models just make sense!

The general logic:

• Pass in a PDF (URL or file buffer)


• Turn the PDF into a series of images


• Pass each image to GPT and ask nicely for Markdown


• Aggregat

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Amplify Partners was running a survey among 800+ AI engineers to bring transparency to the AI Engineering space. The report is concise, yet it provides a wealth of insights into the technologies and methods employed by companies for the implementation of AI products.

Highlights

👉 Top AI use cases are code intelligence, data extraction and workflow... See more

GPT-4 Turbo performs better than our previous models on tasks that require the careful following of instructions, such as generating specific formats (e.g., “always respond in XML”). It also supports our new JSON mode, which ensures the model will respond with valid JSON. The new API parameter response_format enables the model to constrain its... See more

The xAI PromptIDE is an integrated development environment for prompt engineering and interpretability research. It accelerates prompt engineering through an SDK that allows implementing complex prompting techniques and rich analytics that visualize the network's outputs. We use it heavily in our continuous development of Grok.

The context size of the input is too small for when you want to analyse CSV's with 1000's of rows and embedding doesn't really work because it loses context.

My $0.02 is that a lot of the future research/work there will be figuring out how to identify effective sub-graphs to provide additional context, to avoid having to pass in the entire graph. As well as trying to identify ontology-less structures in real-time, which includes NER and RE, as well as named entity/relationship... See more