<story><title>All public GitHub code was used in training Copilot</title><url>https://twitter.com/NoraDotCodes/status/1412741339771461635</url></story><parent_chain><item><author>mikewarot</author><text>I have about 300,000 photos that haven&amp;#x27;t been scanned by AI (unless someone at Backblaze did it without permission). I&amp;#x27;m sure there are lots of other photographers out there who miss Picassa, which Google killed off to push everyone&amp;#x27;s data to their service. (It did really well in matching faces, even across age, but the last version has a bug when there are multiple faces in a picture, sometimes it swaps the labels)&lt;p&gt;If there were offline image recognition we could train on our own data privately, &lt;i&gt;could&lt;/i&gt; the results of those trainings be merged to come up with better recognition on average than any one person could do themselves with their own photos?&lt;p&gt;In other words, would it be possible for us to share the results of training, and build better models, without sharing the photos themselves?</text></item><item><author>fleddr</author><text>To me, the particular use case and whether it is fair use or not, is of minor interest. A far more pressing matter is at hand: AI centralization and monopolization.&lt;p&gt;Take Google as an example, running Google Photos for free for several years. And now that this has sucked in a trillion photos, the AI job is done, and they likely have the best image recognition AI in existence.&lt;p&gt;Which is of course still peanuts compared to training a super AI on the entire web.&lt;p&gt;My point here is that only companies the size of Google and Microsoft have the resources to do this type of planetary scale AI. They can afford the super expensive AI engineers, have the computing power and own the data or will forcefully get access to it. We will even freely give it to them.&lt;p&gt;Any &amp;quot;lesser&amp;quot; AI produced from smaller companies trying to compete are obsolete, and the better one accelerates away. There is no second-best in AI, only winners.&lt;p&gt;If we predict that ultimately AI will change virtually every aspect of society, these companies will become omnipresent, &amp;quot;everything companies&amp;quot;. God companies.&lt;p&gt;As per usual, it will be packaged as an extra convenience for you. And you will embrace it and actively help realize this scenario.</text></item></parent_chain><comment><author>mceachen</author><text>Absolutely possible.&lt;p&gt;What I&amp;#x27;m building into PhotoStructure is typically called &amp;quot;transfer learning.&amp;quot;&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Transfer_learning&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Transfer_learning&lt;/a&gt;&lt;p&gt;PhotoStructure is entirely self-hosted, including model training and application: the public domain base models (trained on huge datasets) are fetched and cached locally.&lt;p&gt;By design, none of your data (or even metadata) leaves your server.&lt;p&gt;(I expect to ship this in an upcoming beta next month.)</text></comment>
<story><title>All public GitHub code was used in training Copilot</title><url>https://twitter.com/NoraDotCodes/status/1412741339771461635</url></story><parent_chain><item><author>mikewarot</author><text>I have about 300,000 photos that haven&amp;#x27;t been scanned by AI (unless someone at Backblaze did it without permission). I&amp;#x27;m sure there are lots of other photographers out there who miss Picassa, which Google killed off to push everyone&amp;#x27;s data to their service. (It did really well in matching faces, even across age, but the last version has a bug when there are multiple faces in a picture, sometimes it swaps the labels)&lt;p&gt;If there were offline image recognition we could train on our own data privately, &lt;i&gt;could&lt;/i&gt; the results of those trainings be merged to come up with better recognition on average than any one person could do themselves with their own photos?&lt;p&gt;In other words, would it be possible for us to share the results of training, and build better models, without sharing the photos themselves?</text></item><item><author>fleddr</author><text>To me, the particular use case and whether it is fair use or not, is of minor interest. A far more pressing matter is at hand: AI centralization and monopolization.&lt;p&gt;Take Google as an example, running Google Photos for free for several years. And now that this has sucked in a trillion photos, the AI job is done, and they likely have the best image recognition AI in existence.&lt;p&gt;Which is of course still peanuts compared to training a super AI on the entire web.&lt;p&gt;My point here is that only companies the size of Google and Microsoft have the resources to do this type of planetary scale AI. They can afford the super expensive AI engineers, have the computing power and own the data or will forcefully get access to it. We will even freely give it to them.&lt;p&gt;Any &amp;quot;lesser&amp;quot; AI produced from smaller companies trying to compete are obsolete, and the better one accelerates away. There is no second-best in AI, only winners.&lt;p&gt;If we predict that ultimately AI will change virtually every aspect of society, these companies will become omnipresent, &amp;quot;everything companies&amp;quot;. God companies.&lt;p&gt;As per usual, it will be packaged as an extra convenience for you. And you will embrace it and actively help realize this scenario.</text></item></parent_chain><comment><author>waterheater</author><text>Yes, you&amp;#x27;re talking about federated learning.&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Federated_learning&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Federated_learning&lt;/a&gt;</text></comment>