At Neurips is what is old new again

At Neurips is what is old new again

The current ex-excrement about large language models is just the latest fraud of the deep learning revolution that started in 2012 (or maybe 2010), but Columbia professor and Amazon-Damn Richard Zemel were there before before. As a Ph.D. -Students at the University of Toronto in the late 80s and early 90s, Zemel wrote his dissertation … Read more

Management of disaster readiness: AIS role in navigating complex climate risks

Management of disaster readiness: AIS role in navigating complex climate risks

As climate change intensive, our ability to predict and respond to cascading and compound disasters increasingly critical. Floods, droughts, fires and extreme storms are no longer isolated events; They interact with ways that defy traditional prediction systems. One way to tackle this challenge is to exploit artificial intelligence (AI) to create integrated, impact -focused early … Read more

Amazon Nova Ai Challenge accelerates the field with generative AI

Amazon Nova Ai Challenge accelerates the field with generative AI

At Amazon, responsible AI development includes collaboration with leading universities to promote breakthrough research. When we acknowledge that many academic institutions lack resources for major studies, we transform the landscape with the Amazon Nova AI challenge. While Amazon Nova Ai Challenge will explore different facets of generative AI (Gen AI), this year’s challenge is centered … Read more

Introduction of Amazon -Contracted AI -Challenge

Introduction of Amazon -Contracted AI -Challenge

Today, Amazon is announcing the Amazon Trusted Ai Challenge, a global university competition to run secure innovation in generative AI technology. This year’s challenge focuses on responsible AI and specifically on the Large Language Model (LLM) Coding Security. “We focus on promoting the capabilities of coding LLMs, exploring new techniques to automatically identify possible vulnerabilities … Read more

Detoxification of large language models via regularized fine tuning

Detoxification of large language models via regularized fine tuning

Large language models (LLMs) have demonstrated impressive benefits across different tasks, but as it has been clear in several cases, they have the risk of producing inappropriate, unsafe or partial output. When generating resorts, a successful trained LLM must comply with a set of policies specified by its creator; For example, the developer may limit … Read more