<story><title>DeepMind’s work in 2016: a round-up</title><url>https://deepmind.com/blog/deepmind-round-up-2016/?href=</url></story><parent_chain></parent_chain><comment><author>rattray</author><text>I can&amp;#x27;t believe this one passed under my radar:&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;deepmind.com&amp;#x2F;blog&amp;#x2F;deepmind-ai-reduces-google-data-centre-cooling-bill-40&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;deepmind.com&amp;#x2F;blog&amp;#x2F;deepmind-ai-reduces-google-data-ce...&lt;/a&gt;&lt;p&gt;&amp;gt;  Our partnership with Google’s data centre team used AlphaGo-like techniques to discover creative new methods of managing cooling, leading to a remarkable 15% improvement in the buildings’ energy efficiency.&lt;p&gt;Which itself is an understatement of the achievement:&lt;p&gt;&amp;gt; Our machine learning system was able to consistently achieve a 40 percent reduction in the amount of energy used for cooling, which equates to a 15 percent reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies. It also produced the lowest PUE the site had ever seen.&lt;p&gt;Basically, they built an AI that was able to tune the &amp;quot;large industrial equipment such as pumps, chillers and cooling towers&amp;quot; to react to the dynamic, nonlinear interactions that vary within and between datacenters (weather, utilization, etc).&lt;p&gt;They also describe this AI as &amp;quot;General&amp;quot;:&lt;p&gt;&amp;gt; Because the algorithm is a general-purpose framework to understand complex dynamics, we plan to apply this to other challenges in the data centre environment and beyond in the coming months.&lt;p&gt;They seem to imply that this technique could make almost any industrial process more efficient with minimal oversight&amp;#x2F;training&amp;#x2F;customization.</text></comment>
<story><title>DeepMind’s work in 2016: a round-up</title><url>https://deepmind.com/blog/deepmind-round-up-2016/?href=</url></story><parent_chain></parent_chain><comment><author>conistonwater</author><text>&amp;gt; &lt;i&gt;In its ability to identify and share new insights about one of the most contemplated games of all time, AlphaGo offers a promising sign of the value AI may one day provide, and we&amp;#x27;re looking forward to playing more games in 2017.&lt;/i&gt;&lt;p&gt;What I find fascinating is how different AlphaGo&amp;#x27;s impact was from the impact of early chess engines. Once chess engines became decent (not even good—before even Deep Blue), they identified tons of inaccuracies in published chess literature. These were missed tactics, hard-to-see moves, requiring only relatively shallow calculation but a computer&amp;#x27;s precision. These inaccuracies were found even in classical annotations of well-known chess games, as well as standard books about openings. I believe John Nunn was known for this kind of work.&lt;p&gt;AlphaGo hasn&amp;#x27;t achieved nearly the same impact. Have they even tried to identify the same types of inaccuracies in classical Go books? Can you imagine how absolutely cool it would be for a go engine to find errors in &lt;i&gt;Invincible&lt;/i&gt;? Maybe they tried, but didn&amp;#x27;t find any inaccuracies, so now this negative result is sitting in one of their file drawers? I really wish they were more active with this sort of thing.</text></comment>