<story><title>Major advancements in Deep Learning in 2016</title><url>https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/</url></story><parent_chain><item><author>drcode</author><text>The big question with all this stuff to me is whether we&amp;#x27;ve just figured out a couple of new tricks (primarily around neural nets processing 2D data and word sequences for translation) and are now going to hit a new plateau in machine learning- or whether &amp;quot;this time it&amp;#x27;s different&amp;quot; and we&amp;#x27;re going to similar improvements year after year for the next decade or more...</text></item></parent_chain><comment><author>dswalter</author><text>If the only major impact of current deep learning methods on culture was to squeeze out an additional 1-5% of performance on every task set in front of it, the fact that it has made large-scale speech recognition and image recognition good enough for public use, it would be enough to call it substantial progress.&lt;p&gt;But I think one of the major successful things of the deep learning renaissance has been the ability to embed different &amp;#x27;worlds&amp;#x27; into the same vector space (French and English, text and music, images and text, etc.). By co-locating these different worlds, we gain the ability to perform more accurate search, to create images from text like in the &amp;quot;Generative Adversarial Text to Image Synthesis&amp;quot; paper, and a wide variety of other multi-modal tasks. We can multi-embed almost anything, even things that are nonsensical. You want to make a music-to-font translation system? Or a sneaker-to-handwriting generator? Gather the training data, and the world is now your oyster. The impact of deep learning as differentiable task pipelines has only begun to scratch the surface of what is possible.</text></comment>
<story><title>Major advancements in Deep Learning in 2016</title><url>https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/</url></story><parent_chain><item><author>drcode</author><text>The big question with all this stuff to me is whether we&amp;#x27;ve just figured out a couple of new tricks (primarily around neural nets processing 2D data and word sequences for translation) and are now going to hit a new plateau in machine learning- or whether &amp;quot;this time it&amp;#x27;s different&amp;quot; and we&amp;#x27;re going to similar improvements year after year for the next decade or more...</text></item></parent_chain><comment><author>tshadley</author><text>&amp;gt; &amp;quot;this time it&amp;#x27;s different&amp;quot;&lt;p&gt;It may be different for other reasons but the main difference I note today is the number of opensource AI&amp;#x2F;ML platforms that are trivial to install, use, play, experiment at pretty much near the peak computing capacity of the hardware we use today.  Exploring the vast search space of reality has never been easier and faster than today.</text></comment>