In this article, we'll dive into a recent study that uncovers substantial disparities in the tokenization process used by language models across different languages.

It is crucial for business leaders to understand the limitations and potential pitfalls of current approaches to measuring AI capabilities.

To accelerate development of advanced conversational AI applications, Microsoft recently introduced AutoGen, an open-source Python library that streamlines orchestrating multi-agent conversations.

MemGPT, applies OS principles like virtual memory and process management to unlock more powerful applications of LLMs - all while staying within their inherent memory limits.

Rumors are swirling that GPT-4 may use an advanced technique called Mixture of Experts (MoE) to achieve over 1 tr parameters. This offers an opportunity to demystify MoE

"Symbolic knowledge distillation" that automates common sense acquisition for AI.

Enhancing AI's Compositional Language Skills

Building QA Systems that catch knowledge gaps and avoid misleading users.

Researchers are exploring new techniques to improve AI's ability to grasp diverse sentence structures and indirect meaning.

Making Conversational AI More Natural: Helping Systems Understand Indirect References

In their research paper, AI21 Labs demonstrates that frozen LLMs have untapped potential that can match or exceed fine-tuning approaches, without the downsides.

A new perspective paper argues that we must evaluate LLMs along two dimensions: formal linguistic competence and functional linguistic competence.

LLMs have some serious limitations that constrain their usefulness for real-world applications. To overcome these limitations, AI researchers have proposed a new type of AI system architecture called Modular Reasoning, Knowledge and Language (MRKL).

Researchers are exploring how to combine LLMs with neurosymbolic methods that incorporate logical reasoning and structure.

A 2016 study analyzed over a thousand stories to uncover the basic emotional arcs that form the building blocks of narratives.

While machine translation has improved dramatically for major languages, quality translation still lags for thousands of smaller languages.

Data analysis often involves exploring data to unearth insights, then crafting stories to communicate those findings. But converting discoveries into coherent narratives poses challenges. Researchers have developed an AI assistant called Notable that streamlines data storytelling.

Retrieval-augmented generation (RAG) systems learn to retrieve and incorporate external knowledge when generating text.

The researchers found that optimizing the AI for direct human preferences significantly boosted performance compared to just training it to mimic reference summaries.

LLMs struggle with logical reasoning and decision-making when tackling complex real-world problems. Researchers propose an approach called ReAct that interleaves reasoning steps with actions to address this accuracy problem.