Cerebras Systems CEO Andrew Feldman explains the impact of open-source large language models (LLM) on the broader AI community.
AI21 Labs chief scientist Yoav Levine explains how retrieval augmented language modeling can solve one of the biggest problems of LLMs.
Optimization by PROmpting (OPRO), a powerful method developed by Google DeepMind, uses large language models (LLM) as optimizers for their own prompts.
The Apple Watch SE emerges as one of the strongest options of any smartwatch product line for affordability without sacrificing the premium feel of today's economic climate demands.
Needle procedures evoke fear and anxiety from your first trip to the doctor’s office. However, VR is changing that and reshaping the very essence of medical treatments.
A science of artificial intelligence based on discredited epistemological foundations is doomed to fail, just as the attempt to build other science on them has failed. Scientific progress depends on better understanding of the phenomena, not just producing slight improvements in some well-defined task.
A new software architecture uses neural development programs (NDP) to self-assemble deep learning models from basic units, like their biological counterparts.
ChatGPT is facing severe competition from other closed- and open-source LLMs. Here is how OpenAI is using network effects to solidify its market share.
Alongside the market for closed-source LLMs like ChatGPT, an impressive array of open-source models has emerged. For enterprises, these language models is becoming increasingly compelling.
Retrieval augmented generation (RAG) enables you to use custom documents with LLMs to improve their precision.
Quantization reduces the size of large language models considerably. GPTQ is a popular quantization method that is supported by Hugging Face and applies to many LLMs.