Its based model performs better than state-of-the-art models available today.

Search giant Google has completed the ‘critical first step’ toward building its artificial intelligence (AI) model that will support the world’s one thousand most-spoken languages. In a blog post the company released details about its Universal Speech Model (USM).

Google’s announcement is part of the build-up to its annual I/O event where it plans to unveil a slew of products powered by AI.

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It can’t fix the code when it does not work though.

The conversational chatbot from OpenAI, ChatGPT, has attracted the attention of users worldwide. However, the lesser-known tool called Codex from OpenAI has quickly become a top favorite among developers. Codex currently powers the Copilot feature on GitHub.

How does Open AI’s Codex work?

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For those, who are relatively new to the world of programming, GitHub is an open-source community where developers share the code for the software they have written for others to use. Microsoft acquired GitHub over four years ago. Working closely with OpenAI, Microsoft has gained access to ChatGPT and Codex, OpenAI’s ChatGPT-like solution for code.

So why not sidestep this conundrum and use neural tissue directly as a biocomputer?

This month, a team from Johns Hopkins University laid out a daring blueprint for a new field of computing: organoid intelligence (OI). Don’t worry—they’re not talking about using living human brain tissue hooked up to wires in jars. Rather, as in the name, the focus is on a surrogate: brain organoids, better known as “mini-brains.” These pea-sized nuggets roughly resemble the early fetal human brain in their gene expression, wide variety of brain cells, and organization. Their neural circuits spark with spontaneous activity, ripple with brain waves, and can even detect light and control muscle movement.

In essence, brain organoids are highly-developed processors that duplicate the brain to a limited degree. Theoretically, different types of mini-brains could be hooked up to digital sensors and output devices—not unlike brain-machine interfaces, but as a circuit outside the body. In the long term, they may connect to each other in a super biocomputer trained using biofeedback and machine learning methods to enable “intelligence in a dish.”

I can’t help myself. I keep thinking about the 1961 musical Stop the World—I Want to Get Off. After opening in Manchester, England, the show transferred to the West End, London, where it ran for 485 performances.

It’s not that the plot of this extravaganza has anything to do with what we are talking about here. It’s just that the sentiment embodied by the show’s title reflects the way I’m currently feeling about artificial intelligence (AI) and machine learning (ML).

On the one hand, the current state of play with AI and ML is tremendously exciting. On the other hand, I’m starting to think that I’ve enjoyed all the excitement I can stand.