<story><title>India’s data labellers are powering the global AI race</title><url>https://factordaily.com/indian-data-labellers-powering-the-global-ai-race/</url></story><parent_chain><item><author>snrji</author><text>Unsupervised learning is the dark matter of AI, as LeCun said. We cannot rely on labeled data, which is expensive and scarce.&lt;p&gt;The first company to figure out how to leverage the massive unlabeled data that is available will win the AI arms race.&lt;p&gt;We have already seen impressive progress (language models, GANs...) but much more work remains. Models requiring labeled data or only working with toy data (even if unlabeled) will soon become irrelevant.</text></item></parent_chain><comment><author>bjoernbu</author><text>&amp;gt; The first company to figure out how to leverage the massive unlabeled data that is available will win the AI arms race.&lt;p&gt;I would expect that this will remain a per-task question for a very long time, maybe even forever. For example, I was amazed at how elegant &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;unsupervised-sentiment-neuron&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;unsupervised-sentiment-neuron&amp;#x2F;&lt;/a&gt; is for an unsupervised model for sentiment analysis.&lt;p&gt;I think there are numerous similar application in text understanding and I would not be suprised to eventually seen many sota systems learn in an unsupervised fashion.
Still, there remain many other tasks (in general, but also for text understanding) where this and similar strategies do not seem applicable to me -- or at least where I cannot directly imagine how they should be.&lt;p&gt;I would not be suprised if we end up with many, many successful strategies to apply unsupervised learning -- many completely different, and thousands of problems still being used with the help of supervised learning. Not with a &amp;quot;company to figure out&amp;quot; a general case</text></comment>
<story><title>India’s data labellers are powering the global AI race</title><url>https://factordaily.com/indian-data-labellers-powering-the-global-ai-race/</url></story><parent_chain><item><author>snrji</author><text>Unsupervised learning is the dark matter of AI, as LeCun said. We cannot rely on labeled data, which is expensive and scarce.&lt;p&gt;The first company to figure out how to leverage the massive unlabeled data that is available will win the AI arms race.&lt;p&gt;We have already seen impressive progress (language models, GANs...) but much more work remains. Models requiring labeled data or only working with toy data (even if unlabeled) will soon become irrelevant.</text></item></parent_chain><comment><author>Tenoke</author><text>&amp;gt;Unsupervised learning is the dark matter of AI, as LeCun said. We cannot rely on labeled data, which is expensive and scarce.&lt;p&gt;This sounds very extreme to me. Sure, unsupervised learning is already bearing fruit (most obviously in NLP) but there are domains where labeled data is unlikely to disappear soon - in many cases semi-supervised learning is a more reasonable goal which will reduce the need for labeling (or labeling-related tasks).&lt;p&gt;&amp;gt;The first company to figure out how to leverage the massive unlabeled data that is available will win the AI arms race.&lt;p&gt;This is an even stronger claim. Why are you so sure only one actor will be able to leverage it? Yes, it will be a huge boon but claiming that the first to crack it will &amp;#x27;win&amp;#x27; seems very farfetched.</text></comment>