<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>m_ke</author><text>You&amp;#x27;re wrong. Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social&amp;#x2F;media apps, search ranking for google, translation, speech recognition, finance, fraud detection.&lt;p&gt;In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware &amp;#x2F; software and every other aspect of our lives.&lt;p&gt;EDIT: for people who are downvoting me, here are some examples of how machine learning is and will transform our lives:&lt;p&gt;1. 3D graphics: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.youtube.com&amp;#x2F;watch?v=FlgLxSLsYWQ&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.youtube.com&amp;#x2F;watch?v=FlgLxSLsYWQ&lt;/a&gt; Andrew Price doesn&amp;#x27;t have a technical background but does a decent job summarizing some of the ways deep learning will transform computer graphics. Soon all of the mocap, character design and animation will be driven by deep learning systems.&lt;p&gt;2. Computer Architecture and Traditional Software: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.youtube.com&amp;#x2F;watch?v=TTpKWOuzOxc&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.youtube.com&amp;#x2F;watch?v=TTpKWOuzOxc&lt;/a&gt; There&amp;#x27;s a ton of recent research showing that you can use machine learning to beat human crafted heuristics in hardware, scheduling, compiler design and query planning.</text><parent_chain><item><author>iamaelephant</author><text>I very strongly disagree with #3. Machine learning has not yet proved its value outside of a handful of key areas (image recognition, voice recognition&amp;#x2F;synthesizing, etc). I think it&amp;#x27;s entirely possible we&amp;#x27;ll look back on today as a kind of machine learning mania. I certainly believe there is plenty of room to build sustainable businesses without a machine learning asset (and the associated data collection), and it&amp;#x27;s in no way &amp;quot;table stakes for every tech company, large and small&amp;quot;.</text></item></parent_chain></comment><story><title>What Happened in the 2010s</title><url>https://avc.com/2019/12/what-happened-in-the-2010s/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>CapmCrackaWaka</author><text>I think machine learning masquerading as AI is definitely over-hyped. The real revolution is open source predictive modeling and statistical techniques becoming commonplace. Creating statistical models is much more useful and applicable to 99% of the businesses that have a need for analytics, but it seems to be overshadowed by the AI hype.</text><parent_chain><item><author>iamaelephant</author><text>I very strongly disagree with #3. Machine learning has not yet proved its value outside of a handful of key areas (image recognition, voice recognition&amp;#x2F;synthesizing, etc). I think it&amp;#x27;s entirely possible we&amp;#x27;ll look back on today as a kind of machine learning mania. I certainly believe there is plenty of room to build sustainable businesses without a machine learning asset (and the associated data collection), and it&amp;#x27;s in no way &amp;quot;table stakes for every tech company, large and small&amp;quot;.</text></item></parent_chain></comment><story><title>What Happened in the 2010s</title><url>https://avc.com/2019/12/what-happened-in-the-2010s/</url></story>