Ladies: while you are waiting to be scheduled for the violin straightener surgery, give a thought to temporary ameliorations for your deformity. You can lead an almost normal life, starting today
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BREAKING: The Nasdaq 100 closes +2.3% higher after Trump says the US and Iran have agreed to halt strikes and resume talks
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In Brazil, a 17-year-old girl named Ana received a mysterious cake delivered to her door with a note saying “A little gift for the most beautiful girl I’ve ever seen”
Overjoyed, Ana took one bite — but hours later she became violently ill.
36 hours later she was dead.
After investigation, it was discovered the “secret admirer” cake had been laced with arsenic.
The culprit? Her 17-year-old female friend who had become riddled with jealousy over Ana being a popular girl with boys, well-liked, and outgoing.
She wanted to make Ana suffer.
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Overjoyed, Ana took one bite — but hours later she became violently ill.
36 hours later she was dead.
After investigation, it was discovered the “secret admirer” cake had been laced with arsenic.
The culprit? Her 17-year-old female friend who had become riddled with jealousy over Ana being a popular girl with boys, well-liked, and outgoing.
She wanted to make Ana suffer.
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A debate has erupted in Italy over the 18-year prison sentence handed down to 65-year-old Silvia Del Pino.
Silvia Del Pino was on her way somewhere when a Moroccan Muslim refugee attacked her, brandished a knife to rob her of her handbag, and attempted to flee.
Silvia then got into her Mercedes, drove after the fleeing Moroccan refugee, and ran him over, resulting in the robber's death.
An Italian judge has now stated that since the robber had already robbed her at knifepoint, it was merely a case of robbery; Silvia should not have killed the assailant.
This has sparked a nationwide debate in Italy; furthermore, when people investigated the judge's background, it was revealed that he had previously been a leftist activist.
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Silvia Del Pino was on her way somewhere when a Moroccan Muslim refugee attacked her, brandished a knife to rob her of her handbag, and attempted to flee.
Silvia then got into her Mercedes, drove after the fleeing Moroccan refugee, and ran him over, resulting in the robber's death.
An Italian judge has now stated that since the robber had already robbed her at knifepoint, it was merely a case of robbery; Silvia should not have killed the assailant.
This has sparked a nationwide debate in Italy; furthermore, when people investigated the judge's background, it was revealed that he had previously been a leftist activist.
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Forwarded from Chat GPT
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Meta: We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.
Building on v1, which was published today in Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.
We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.
We trained Brain2Qwerty v2 on ~22,000 sentences from 9 volunteers, each recorded for 10 hours wearing an MEG device while typing.
By using end-to-end deep learning on raw brain signals from MEG devices and fine-tuning LLMs, the system effectively bridges the gap between noisy neural data and coherent language.
The results are promising:
- Avg word accuracy of 61% across participants
- 78% word accuracy and 50%+ of sentences decoded with ≤ 1 word error for the top-performing participant
- Performance scales log-linearly with data volume
To help accelerate neuroscience breakthroughs, we're releasing the full training code for Brain2Qwerty v1 and v2, and our partner, bcbl_, is releasing the v1 dataset.
Building on v1, which was published today in Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.
We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.
We trained Brain2Qwerty v2 on ~22,000 sentences from 9 volunteers, each recorded for 10 hours wearing an MEG device while typing.
By using end-to-end deep learning on raw brain signals from MEG devices and fine-tuning LLMs, the system effectively bridges the gap between noisy neural data and coherent language.
The results are promising:
- Avg word accuracy of 61% across participants
- 78% word accuracy and 50%+ of sentences decoded with ≤ 1 word error for the top-performing participant
- Performance scales log-linearly with data volume
To help accelerate neuroscience breakthroughs, we're releasing the full training code for Brain2Qwerty v1 and v2, and our partner, bcbl_, is releasing the v1 dataset.
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Forwarded from Chat GPT
This is a magnetoencephalography (MEG) machine
Meta trained brain2qwerty v2 by having 9 volunteers wear one while typing for 10 hours each
Meta trained brain2qwerty v2 by having 9 volunteers wear one while typing for 10 hours each
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Forwarded from Chat GPT
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🧠⌨️ Decode language from brain activity without surgery. 🧠⌨️
Brain2Qwerty V1 is officially published in Nature Neuroscience. Today, we're releasing Brain2Qwerty V2.
We achieve unprecedented performance for a non-invasive MEG setup.
We’re happy to announce 2 releases today:
- 🧠Brain2qwerty v1 is published at @NatureNeuro
- 🚀 Brain2Qwerty v2 is now publicly released
Explore how we decode sentences from non-invasive brain recordings:
Website
Brain2Qwerty V1 is officially published in Nature Neuroscience. Today, we're releasing Brain2Qwerty V2.
We achieve unprecedented performance for a non-invasive MEG setup.
We’re happy to announce 2 releases today:
- 🧠Brain2qwerty v1 is published at @NatureNeuro
- 🚀 Brain2Qwerty v2 is now publicly released
Explore how we decode sentences from non-invasive brain recordings:
Website
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Daveigh Chase, star of the Ring and voice of Lilo in Lilo & Stitch, died from AIDS, medical examiner confirms
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Meta says Brain2Qwerty v2 can decode natural sentences from non-invasive brain recordings in real time, reaching 61% word accuracy.
The system was trained on about 22,000 sentences from 9 volunteers, each recorded for 10 hours with MEG while typing.
Meta compares that with 8% word accuracy from prior non-invasive methods. Its best participant reached 78%, with more than half of sentences decoded with one word error or less.
This is still controlled lab research: small participant pool, MEG hardware, active typing data, and company-reported results. Not a clinical communication device yet.
Meta is releasing the training code, while BCBL is releasing the v1 dataset, pushing brain-to-text research further into open neuroscience infrastructure.
I am so hyped for the future.
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The system was trained on about 22,000 sentences from 9 volunteers, each recorded for 10 hours with MEG while typing.
Meta compares that with 8% word accuracy from prior non-invasive methods. Its best participant reached 78%, with more than half of sentences decoded with one word error or less.
This is still controlled lab research: small participant pool, MEG hardware, active typing data, and company-reported results. Not a clinical communication device yet.
Meta is releasing the training code, while BCBL is releasing the v1 dataset, pushing brain-to-text research further into open neuroscience infrastructure.
I am so hyped for the future.
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