<story><title>The man who killed Google Search?</title><url>https://www.wheresyoured.at/the-men-who-killed-google/</url></story><parent_chain><item><author>potatolicious</author><text>It&amp;#x27;s also a good lesson for the new AI cycle we&amp;#x27;re in now. Often inserting ML subsystems into your broader system just makes it go from &amp;quot;deterministically but fixably bad&amp;quot; to &amp;quot;mysteriously and unfixably bad&amp;quot;.</text></item><item><author>JohnFen</author><text>&amp;gt; where he argued against the other search leads that Google should use less machine-learning&lt;p&gt;This better echoes my personal experience with the decline of Google search than TFA: it seems to be connected to the increasing use of ML in that the more of it Google put in, the worse the results I got were.</text></item><item><author>gregw134</author><text>Ex-Google search engineer here (2019-2023). I know a lot of the veteran engineers were upset when Ben Gomes got shunted off. Probably the bigger change, from what I&amp;#x27;ve heard, was losing Amit Singhal who led Search until 2016. Amit fought against creeping complexity. There is a semi-famous internal document he wrote where he argued against the other search leads that Google should use less machine-learning, or at least contain it as much as possible, so that ranking stays debuggable and understandable by human search engineers. My impression is that since he left complexity exploded, with every team launching as many deep learning projects as they can (just like every other large tech company has).&lt;p&gt;The problem though, is the older systems had obvious problems, while the newer systems have hidden bugs and conceptual issues which often don&amp;#x27;t show up in the metrics, and which compound over time as more complexity is layered on. For example: I found an off by 1 error deep in a formula from an old launch that has been reordering top results for 15% of queries since 2015. I handed it off when I left but have no idea whether anyone actually fixed it or not.&lt;p&gt;I wrote up all of the search bugs I was aware of in an internal document called &amp;quot;second page navboost&amp;quot;, so if anyone working on search at Google reads this and needs a launch go check it out.</text></item></parent_chain><comment><author>ytdytvhxgydvhh</author><text>I think that’ll define the industry for the coming decades. I used to work in machine translation and it was the same. The older rules-based engines that were carefully crafted by humans worked well on the test suite and if a new case was found, a human could fix it. When machine learning came on the scene, more “impressive” models that were built quicker came out - but when a translation was bad no one knew how to fix it other than retraining and crossing one’s fingers.</text></comment>
<story><title>The man who killed Google Search?</title><url>https://www.wheresyoured.at/the-men-who-killed-google/</url></story><parent_chain><item><author>potatolicious</author><text>It&amp;#x27;s also a good lesson for the new AI cycle we&amp;#x27;re in now. Often inserting ML subsystems into your broader system just makes it go from &amp;quot;deterministically but fixably bad&amp;quot; to &amp;quot;mysteriously and unfixably bad&amp;quot;.</text></item><item><author>JohnFen</author><text>&amp;gt; where he argued against the other search leads that Google should use less machine-learning&lt;p&gt;This better echoes my personal experience with the decline of Google search than TFA: it seems to be connected to the increasing use of ML in that the more of it Google put in, the worse the results I got were.</text></item><item><author>gregw134</author><text>Ex-Google search engineer here (2019-2023). I know a lot of the veteran engineers were upset when Ben Gomes got shunted off. Probably the bigger change, from what I&amp;#x27;ve heard, was losing Amit Singhal who led Search until 2016. Amit fought against creeping complexity. There is a semi-famous internal document he wrote where he argued against the other search leads that Google should use less machine-learning, or at least contain it as much as possible, so that ranking stays debuggable and understandable by human search engineers. My impression is that since he left complexity exploded, with every team launching as many deep learning projects as they can (just like every other large tech company has).&lt;p&gt;The problem though, is the older systems had obvious problems, while the newer systems have hidden bugs and conceptual issues which often don&amp;#x27;t show up in the metrics, and which compound over time as more complexity is layered on. For example: I found an off by 1 error deep in a formula from an old launch that has been reordering top results for 15% of queries since 2015. I handed it off when I left but have no idea whether anyone actually fixed it or not.&lt;p&gt;I wrote up all of the search bugs I was aware of in an internal document called &amp;quot;second page navboost&amp;quot;, so if anyone working on search at Google reads this and needs a launch go check it out.</text></item></parent_chain><comment><author>munk-a</author><text>I think - I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations... but we need to be frank about the fact that the business layer above us (with a few rare exceptions) absolutely does not understand the limitations of AI and views it as a magic box where they type in &amp;quot;Write me a story about a bunny&amp;quot; and get twelve paragraphs of text out.  As someone working in a healthcare adjacent field I&amp;#x27;ve seen the glint in executive&amp;#x27;s eyes when talking about AI and it can provide real benefits in data summarization and annotation assistance... but there are limits to what you should trust it with and if it&amp;#x27;s something big-i Important then you&amp;#x27;ll always want to have a human vetting step.</text></comment>