LLMs currently face significant challenges when it comes to truthfulness. Understanding these limitations is essential for any business considering leveraging LLMs.
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.
We explore the rapidly evolving AI policy landscape, with a special focus on the significant policy events of 2023 and the state of AI regulation in the United States and European Union.
We explore the current state of responsible AI, examining the lack of standardized evaluations for LLMs, the discovery of complex vulnerabilities in these models, the growing concern among businesses about AI risks, and the challenges posed by LLMs outputting copyrighted material.
In this article, we'll dive into the key findings of the 2024 AI Index Report, focusing on benchmarks for truthfulness, reasoning, and agent-based systems, and explore their implications for businesses.
The 2024 HAI AI Index Report reveals a rapidly evolving AI landscape characterized by rising training costs, potential data constraints, the dominance of foundation models, and a shift towards open-source AI.
Recently, the Open Web Application Security Project (OWASP), a leading authority on cybersecurity, released their list of the Top 10 security risks for LLM applications. Here is what every executive should know about these critical LLM vulnerabilities.
InterpretML is a valuable tool for unlocking the power of interpretable AI in traditional machine learning models. While it may have limitations when it comes to directly interpreting LLMs, the principles of interpretability and transparency remain crucial in the age of generative AI.
Among the myriad areas AI is transforming, innovation management stands out as a domain ripe for disruption. Innovation management, the art and science of bringing new and creative ideas to life, is critical for any organization aiming to maintain a competitive edge in the digital age.
Crafting a generative AI transformation roadmap is about combining strategic foresight, ethical responsibility, and a commitment to continuous learning and adaptation.
A recent critique calls into question a prominent AI transparency benchmark, illustrating the challenges in evaluating something as complex as transparency.
A recent critique calls into question a prominent AI transparency benchmark, illustrating the challenges in evaluating something as complex as transparency.
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.
How exactly should business leaders navigate the complex intersection between AI creation and existing copyright laws? A new research paper by legal scholar Dr Andres Guadamuz provides an enlightening analysis of this murky terrain.
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
