AI model tagged posts

Developers are increasingly relying on large language models (LLMs) for everyday computing tasks such as fixing bugs, explaining code and automating text-processing tasks like filtering logs.

However, it’s not as simple as entering or submitting a question and relying on the model to give you the answer. While humans easily understand these tasks and know exactly what they want, it is difficult to translate them into rigid computer code.

The cloud dilemma
As standard programming is often not up to the task, developers often use AI to handle jobs that are difficult to express as traditional rules...

New work explaining the inner workings of artificial intelligence could provide a way around the threat of AI “model collapse,” potentially averting growing numbers of AI hallucinations in the future.

First coined in 2024, “model collapse” refers to a scenario where an AI model trained on AI-produced data ceases to provide accurate results, instead producing inaccurate “gibberish” because of the poor quality of its training data.

Some have warned that high-quality text data to train systems like Large Language Models (LLMs) is set to run out as early as this year, and so data produced by models themselves has taken a larger training role—inviting the threat of model collapse.

Simple statistical models reveal a fix
Through analysis of a simple yet powerful set of statistical ...

A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81%. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.

The MIT researchers boosted the efficiency of a technique known as federated learning, which involves a network of connected devices that work together to train a shared AI model.

In federated learning, the model is broad...

To diagnose either type 2 diabetes or pre-diabetes, clinicians typically rely on a lab value known as HbA1c. This test captures a person’s average blood glucose levels over the previous few months. But HbA1c cannot predict who is at highest risk of progressing from healthy to prediabetic, or from prediabetic to full-blown diabetes.

Now, scientists at Scripps Research have discovered that artificial intelligence can use a combination of other data—including real-time glucose levels from wearable monitors—to provide a more nuanced view of diabetes risk.

The new model, described in Nature Medicine, uses continuous glucose monitor (CGM) data alongside gut microbiome, diet, ph...