Nvidia and Microsoft develop 530 billion parameter AI model, but it still suffers from bias

Nvidia and Microsoft have developed an incredible 530 billion parameter AI model, but it still suffers from bias.

The pair claim their Megatron-Turing Natural Language Generation (MT-NLG) model is the "most powerful monolithic transformer language model trained to date".

For comparison, OpenAI’s much-lauded GPT-3 has 175 billion parameters.

The duo trained their impressive model on 15 datasets with a total of 339 billion tokens. Various sampling weights...

12 October 2021 | Companies

Google’s MUM will start adding more context to searches

Google is getting a little help from its MUM (Multitask Unified Model) to add more context to search results.

MUM was announced at Google I/O in May and aims to transform how the web giant handles complex queries. The model uses the T5 text-to-text framework and is said to be “1,000 times more powerful” than BERT (Bidirectional Encoder Representations from Transformers), which itself was a major breakthrough when it was introduced in 2018.

One example used by Google...

30 September 2021 | Companies

Algorithmia announces Insights for ML model performance monitoring

Seattle-based Algorithmia has announced Insights, a solution for monitoring the performance of machine learning models.

Algorithmia specialises in artificial intelligence operations and management. The company is backed by Google LLC and focuses on simplifying AI projects for enterprises just getting started.

Diego Oppenheimer, CEO of Algorithmia, says:

“Organisations have specific needs when it comes to ML model monitoring and reporting.

For example,...

5 November 2020 | Machine Learning

Microsoft’s new AI auto-captions images for the visually impaired

A new AI from Microsoft aims to automatically caption images in documents and emails so that software for visual impairments can read it out.

Researchers from Microsoft explained their machine learning model in a paper on preprint repository arXiv.

The model uses VIsual VOcabulary pre-training (VIVO) which leverages large amounts of paired image-tag data to learn a visual vocabulary.

A second dataset of properly captioned images is then used to help teach the...

19 October 2020 | Machine Learning