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How reliable are French language AI Chatbots?

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Treść dostarczona przez Somewhere on Earth. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Somewhere on Earth lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.

How reliable are French language AI Chatbots?

French is one of the fastest growing languages in the world. About half of all French speakers live in Africa. It’s the official language in 27 countries. So when you find out that French language AI chatbots give the wrong answer a quarter of the time, that’s a problem given the rising popularity of such bots. The anti-misinformation news organisation NewsGuard have just published a report on this. Ania’s been speaking to Chine Labbé, she’s one of the report’s authors.

Using AI to create AI images to train robots

Imagine teaching a robot to do parkour without ever letting it see the real world. That's exactly what MIT CSAIL researchers have achieved with their new "LucidSim" system, which uses AI-generated imagery to create virtual training environments. By integrating generative AI with physics simulators, the researchers designed scenarios where robots acquire skills through synthetic data instead of relying on real-world experiences. The outcomes of this research are impressive: robots trained in these machine-generated settings achieved a success rate of 88%, in stark contrast to the mere 15% success rate of those trained by human experts. Furthermore, when the team increased the volume of AI-generated training data, there was a consistent improvement in performance, indicating that enhanced virtual practice correlates with superior real-world capabilities. This methodology addresses a significant challenge in robotics: the reliance on extensive real-world training data. It represents a significant advancement toward creating robots that can swiftly adapt to new tasks and environments. To find out more, Gareth speaks to lead researcher Ge Yang at MIT’s Computer Science and Artificial Intelligence Laboratory.

The programme is presented by Gareth Mitchell and the studio expert is Ania Lichtarowicz.

More on this week's stories:

French-Language AI Misinformation Monitor

Learning Visual Parkour from Generated Images

Editor: Ania Lichtarowicz

Production Manager: Liz Tuohy

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Treść dostarczona przez Somewhere on Earth. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Somewhere on Earth lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.

How reliable are French language AI Chatbots?

French is one of the fastest growing languages in the world. About half of all French speakers live in Africa. It’s the official language in 27 countries. So when you find out that French language AI chatbots give the wrong answer a quarter of the time, that’s a problem given the rising popularity of such bots. The anti-misinformation news organisation NewsGuard have just published a report on this. Ania’s been speaking to Chine Labbé, she’s one of the report’s authors.

Using AI to create AI images to train robots

Imagine teaching a robot to do parkour without ever letting it see the real world. That's exactly what MIT CSAIL researchers have achieved with their new "LucidSim" system, which uses AI-generated imagery to create virtual training environments. By integrating generative AI with physics simulators, the researchers designed scenarios where robots acquire skills through synthetic data instead of relying on real-world experiences. The outcomes of this research are impressive: robots trained in these machine-generated settings achieved a success rate of 88%, in stark contrast to the mere 15% success rate of those trained by human experts. Furthermore, when the team increased the volume of AI-generated training data, there was a consistent improvement in performance, indicating that enhanced virtual practice correlates with superior real-world capabilities. This methodology addresses a significant challenge in robotics: the reliance on extensive real-world training data. It represents a significant advancement toward creating robots that can swiftly adapt to new tasks and environments. To find out more, Gareth speaks to lead researcher Ge Yang at MIT’s Computer Science and Artificial Intelligence Laboratory.

The programme is presented by Gareth Mitchell and the studio expert is Ania Lichtarowicz.

More on this week's stories:

French-Language AI Misinformation Monitor

Learning Visual Parkour from Generated Images

Editor: Ania Lichtarowicz

Production Manager: Liz Tuohy

For new episodes, subscribe wherever you get your podcasts or via this link:

https://somewhere-on-earth-the-global-tech-podcast-the-podextra-edition.pod.fan/

Follow us on all the socials:

Join our Facebook group

Instagram

Twitter/X

If you like Somewhere on Earth, please rate and review it on Apple Podcasts

Contact us by email: hello@somewhereonearth.co

Send us a voice note: via WhatsApp: +44 7486 329 484

Find a Story + Make it News = Change the World

Learn more about your ad choices. Visit megaphone.fm/adchoices

  continue reading

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