oli5679

This is what I also use for rock, paper scissors (mod 3). You can also use second hand of a watch for random samples, e.g splitting 0-30 vs. 31-60.

This can be determined by offline benchmarks if you build a system that takes small sequences of actions, and requires live ab test for long sequences of actions.

The more your human ops team work from documented standard operating procedure, rather than tacit knowledge, the less you need Evals except to capture edge cases

I agree with that Evals are a scarce commodity rn. A business needs to define what good looks like. This is a laborious, and sometimes politically controversial, process.

Given good Evals, frontier llms are a magical tool that can automate tasks and do them more accurately than human ops teams.

Exactly which llm to use depends on the mixture of speed, cost and quality of the output.

Jev makes a claim to expand some regions of the Pareto frontier. I look forward to testing if this is true.

There are many areas of work we can’t automate rn. We cannot create good Evals either because time horizons are too long, or it’s too difficult to create good Evals.

That doesn’t mean there’s anything wrong with building good ai engineering systems in areas where it works magically.

Would this ever be viewed as an abuse of dominant market position, aiming to disadvantage potential entrants.

E.g. map <think> -> THINK <user> -> USER <tool> -> TOOL

If they learn something specific in the chat finetuning stage, this might show LLM its user input text not these tag references.

This is a really nice open source coding agent implementation. The use of async is interesting.

It is believed dense models cram many features into shared weights, making circuits hard to interpret.

Sparsity reduces that pressure by giving features more isolated space, so individual neurons are more likely to represent a single, interpretable concept.

It requires no local gpus, just creating a json and posting to OpenAI

https://platform.openai.com/docs/guides/model-optimization

https://noyb.eu/en/noyb-files-complaint-against-eu-commissio...

https://eur-lex.europa.eu/EN/legal-content/summary/transpare...

This gives the probability that two randomly chosen customers belong to the same firm.

In one micro models of oligopoly, Cournot competition, it lines up directly with the markup firms can sustain.

Outside of theory, it’s an intuitive way to average together the market power of all firms, with increases in market share for bigger players being weighted more heavily.

https://chatgpt.com/share/6897a21b-25c0-8011-a10a-85850870da...

Pretty interesting - some contamination, some better answers, and it failed to write a sentence with all 5-letter-words. I’d have expected it to pass this one!

Simple example: “Every night, dreams swirl swiftly.

There is a big upside potential for high growth companies taking advantage of technology trends.

Today, Google’s revenue is £263.66 Billion. This is nearly 300x the revenue Google generated in 2003 ($961.9 million). The company went public on August 19, 2004, at $85 per share, valuing the company at $23 billion. After the IPO, Google reported $1.47 billion in revenue for fiscal year 2003, with a profit of $105.6 million.

A. below is a list of OpenAI initial hires from Google. It's implausible to me that there wasn't quite significant transfer of Google IP

B. google published extensively, including the famous 'attention is all you need' paper, but open-ai despite its name, has not explained the breakthroughs that enabled O1. It has also switched from a charity to a for-profit company.

C. Now this company, with a group of smart, unknown machine learning engineers, presumably paid fractions of what OpenAI are published, has created a model far cheaper, and openly published the weights, many methodological insights, which will be used by OpenAI.

1. Ilya Sutskever – One of OpenAI’s co-founders and its former Chief Scientist. He previously worked at Google Brain, where he contributed to the development of deep learning models, including TensorFlow. 2. Jakub Pachocki – Formerly OpenAI’s Director of Research, he played a major role in the development of GPT-4. He had a background in AI research that overlapped with Google’s fields of interest. 3. John Schulman – Co-founder of OpenAI, he worked on reinforcement learning and helped develop Proximal Policy Optimization (PPO), a method used in training AI models. While not a direct Google hire, his work aligned with DeepMind’s research areas. 4. Jeffrey Wu – One of the key researchers involved in fine-tuning OpenAI’s models. He worked on reinforcement learning techniques similar to those developed at DeepMind. 5. Girish Sastry – Previously involved in OpenAI’s safety and alignment work, he had research experience that overlapped with Google’s AI safety initiatives.

Researcher at Meta or OpenAI spending hundreds of millions on compute, and being paid millions themselves, whilst not publishing any of their learnings openly, here a bunch of very smart, young Chinese researchers have had some great ideas, proved they work, and published details that allow everyone else to replicate.

    "No “inscrutable wizards” here—just fresh graduates from top universities,    PhD candidates (even fourth- or fifth-year interns), and young talents with a few years of experience."

    "If someone has an idea, they can tap into our training clusters anytime without approval. Additionally, since we don’t have rigid hierarchical structures or departmental barriers, people can collaborate freely as long as there’s mutual interest."

Lendable have built the big three consumer finance products from scratch: loans, credit cards and car finance. Help us improve the best-in-class credit analytics that powers credit-decisioning.

Data Scientist https://jobs.eu.lever.co/lendable/dfdcc454-b226-4d6a-af1c-74...

PHP Engineer https://jobs.eu.lever.co/lendable/a7363938-4313-4f1e-b1ae-47...

Kotlin Engineer https://jobs.eu.lever.co/lendable/121800c7-c2d7-457b-aaf3-7e...

React Native Engineer https://jobs.eu.lever.co/lendable/3a9e02d3-6680-487b-b416-e1...

Security Engineer https://jobs.eu.lever.co/lendable/033ac43c-f3a2-4047-bc5c-b9...

Other jobs https://jobs.eu.lever.co/lendable?lever-via=8wqtPJSbHB&lever...