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Свежий документ Data Governance in Open Source AI [1] от Open Source Initiative про то как публиковать данные для обучения ИИ с открытым кодом. В документе много всего, важно что они промоутируют отход от чистого определения Open Data и говорят о новом (старом) подходе Data Commons с разными моделями доступа к данным.
Дословно в тексте упоминаются, привожу как есть: - Open data: data that is freely accessible, usable and shareable without restrictions, typically under an open license or in the Public Domain36 (for example, OpenStreetMap data); • Public data: data that is accessible to anyone without authentication or special permissions (for example, Common Crawl data). Note that this data can degrade as web content becomes unavailable; • Obtainable data: data that can be obtained or acquired through specific actions, such as licensing deals, subscriptions or permissions (for example, ImageNet data); • Unshareable non-public data: data that is confidential or protected by privacy laws, agreements or proprietary rights and cannot be legally shared or publicly distributed.
С точки зрения многих в открытых данных всё это звучит как размывание открытости, но с точки зрения практики ИИ в этом есть логика.
Свежий документ Data Governance in Open Source AI [1] от Open Source Initiative про то как публиковать данные для обучения ИИ с открытым кодом. В документе много всего, важно что они промоутируют отход от чистого определения Open Data и говорят о новом (старом) подходе Data Commons с разными моделями доступа к данным.
Дословно в тексте упоминаются, привожу как есть: - Open data: data that is freely accessible, usable and shareable without restrictions, typically under an open license or in the Public Domain36 (for example, OpenStreetMap data); • Public data: data that is accessible to anyone without authentication or special permissions (for example, Common Crawl data). Note that this data can degrade as web content becomes unavailable; • Obtainable data: data that can be obtained or acquired through specific actions, such as licensing deals, subscriptions or permissions (for example, ImageNet data); • Unshareable non-public data: data that is confidential or protected by privacy laws, agreements or proprietary rights and cannot be legally shared or publicly distributed.
С точки зрения многих в открытых данных всё это звучит как размывание открытости, но с точки зрения практики ИИ в этом есть логика.
Also in the latest update is the ability for users to create a unique @username from the Settings page, providing others with an easy way to contact them via Search or their t.me/username link without sharing their phone number. The SC urges the public to refer to the SC’s I nvestor Alert List before investing. The list contains details of unauthorised websites, investment products, companies and individuals. Members of the public who suspect that they have been approached by unauthorised firms or individuals offering schemes that promise unrealistic returns On Feb. 27, however, he admitted from his Russian-language account that "Telegram channels are increasingly becoming a source of unverified information related to Ukrainian events." If you initiate a Secret Chat, however, then these communications are end-to-end encrypted and are tied to the device you are using. That means it’s less convenient to access them across multiple platforms, but you are at far less risk of snooping. Back in the day, Secret Chats received some praise from the EFF, but the fact that its standard system isn’t as secure earned it some criticism. If you’re looking for something that is considered more reliable by privacy advocates, then Signal is the EFF’s preferred platform, although that too is not without some caveats. That hurt tech stocks. For the past few weeks, the 10-year yield has traded between 1.72% and 2%, as traders moved into the bond for safety when Russia headlines were ugly—and out of it when headlines improved. Now, the yield is touching its pandemic-era high. If the yield breaks above that level, that could signal that it’s on a sustainable path higher. Higher long-dated bond yields make future profits less valuable—and many tech companies are valued on the basis of profits forecast for many years in the future.
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