<story><title>Exploring Weight Agnostic Neural Networks</title><url>https://ai.googleblog.com/2019/08/exploring-weight-agnostic-neural.html</url></story><parent_chain><item><author>elamje</author><text>Unpopular quote from my image and video processing professor - “The only problem with machine learning is that the machine does the learning and you don’t.”&lt;p&gt;While I understand that is missing a lot of nuance, it has stuck with me over the past few years as I feel like I am missing out on the cool machine learning work going on out there.&lt;p&gt;There is a ton of learning about calculus, probability, and statistics when doing machine learning, but I can’t shake the fact that at the end of the day, the output is basically a black box. As you start toying with AI you realize that the only way to learn from your architecture and results is by tuning parameters and trial and error.&lt;p&gt;Of course there are many applications that only AI can solve, which is all good and well, but I’m curious to hear from some heavy machine learning practitioners - what is exciting to you about your work?&lt;p&gt;This is a serious inquiry because I want to know if it’s worth exploring again. In the past university AI classes I took, I just got bored writing tiny programs that leveraged AI libraries to classify images, do some simple predictions etc.</text></item></parent_chain><comment><author>amelius</author><text>ML is more like growing crops than it is about &amp;quot;designing stuff&amp;quot;. Growing crops is slow, and you don&amp;#x27;t know beforehand what the result will be. However, you can still throw a lot of science at growing crops (&amp;quot;plant breeding&amp;quot; is a science), and the same holds for engineering.</text></comment>
<story><title>Exploring Weight Agnostic Neural Networks</title><url>https://ai.googleblog.com/2019/08/exploring-weight-agnostic-neural.html</url></story><parent_chain><item><author>elamje</author><text>Unpopular quote from my image and video processing professor - “The only problem with machine learning is that the machine does the learning and you don’t.”&lt;p&gt;While I understand that is missing a lot of nuance, it has stuck with me over the past few years as I feel like I am missing out on the cool machine learning work going on out there.&lt;p&gt;There is a ton of learning about calculus, probability, and statistics when doing machine learning, but I can’t shake the fact that at the end of the day, the output is basically a black box. As you start toying with AI you realize that the only way to learn from your architecture and results is by tuning parameters and trial and error.&lt;p&gt;Of course there are many applications that only AI can solve, which is all good and well, but I’m curious to hear from some heavy machine learning practitioners - what is exciting to you about your work?&lt;p&gt;This is a serious inquiry because I want to know if it’s worth exploring again. In the past university AI classes I took, I just got bored writing tiny programs that leveraged AI libraries to classify images, do some simple predictions etc.</text></item></parent_chain><comment><author>kuu</author><text>For me, the exciting part is to understand how the &amp;quot;blackbox&amp;quot;  can learn and to find a representation of the data that makes this box to learn.&lt;p&gt;For instance, I&amp;#x27;ve been working in users profiling and it&amp;#x27;s been a challenge to find which features and in which representation allow the model to learn. It&amp;#x27;s fantastic when you make a little change in a feature (for instance, use the median instead of the mean) and your model suddenly gets a +5% acc.&lt;p&gt;The field in the real world is not as simple as to make an API call. The data in the wild is really complicated. To get value from this data is the real challenge.</text></comment>