OpenAI might be the hottest company in tech right now. The company is behind the popular large language-based model, ChatGPT, and also built the GPT-3, GPT-3.5, and GPT-4 models which several AI coding assistants use, such as GitHub Copilot.
Since it arrived last year, ChatGPT has been shipping new features at high speed
Gergely Orosz asked the question "What is the engineering culture that allows the ChatGPT team to iterate this fast?" to Evan Morikawa
You can find the interview at The Pragmatic Engineer
What resonates with me is that the decisions do not require any magic sauce and I will just mention two of them:
Operating like a small, independent startup: They want the atmosphere of an early-stage startup iterating towards product-market fit. ..So every team member was on-site, and rearranged seating to put people next to each other.
Created a tight integration with Research In most tech companies, there is a “classic” trio of teams referenced as “EPD”: Engineering, Product, and Design. These teams tend to collaborate heavily with one another, and cross-functional teams usually have members of engineering, product, and design within them. ChatGPT brought researchers into product teams.
Suddenly I realize that this is how we are in LATAM.
We are interconnected and very often we cross boundaries between different fields. Our unique genetic makeup may contribute to our success, but we may not fully realize its significance.
The need arise from the need to streamline the analysis of exams performed on patients who may present detectable pathologies through imaging studies, assisting the physician in making a faster and more accurate diagnosis.
Researchers at Sakana.AI, a Tokyo-based company, have worked on developing a large language model (LLM) designed specifically for scientific research.
Competing against ChatGPT Enterprise by OpenAI, now Anthropic released its own Claude for Enterprise.
Cerebras Systems, known for its innovative Wafer Scale Engine (WSE), has received a mix of feedback regarding its processors, particularly compared to traditional GPUs like those from Nvidia.
FruitNeRF: Revolutionizing Fruit Counting with Neural Radiance FieldsHow can we accurately count different types of fruits in complex environments using 3D models derived from 2D images without requiring fruit-specific adjustments?
Mistral AI has announced that you can now fully customize their models like Mistral Large 2 and Codestral