Mark Cuban on Why You Need to Study Artificial Intelligence
I&#x27;m going to go against the grain here and (gasp) not hate on deep learning. People should realize that although many older statistical methods and &quot;traditional&quot; machine learning methods such as LDA, SVMs and decision trees may be good enough for business tasks, they are not the cutting edge of AI research. I think people are forgetting exactly how difficult image classification and object detection was before the advent of deep learning. People hating on &quot;stack more layers&quot; forget that &quot;stack more layers&quot; is EXACTLY what improved imagenet performance to such as massive extent. ResNets pushed the limits by figuring out how to stack more layers in a beneficial way.<p>And let&#x27;s take a look at AlphaGo, how would you do that with SVMs or decision trees? Just get over the fact that deep learning provides a level of &quot;intuition&quot; (Go&#x27;s search space is famously greater than the estimated number of stars in the universe).<p>I think that a part of the problem is that older ML PhDs are angry that deep learning is so easy (until the learning rate fails to provide convergence of course...) and would prefer that their preferred methods would still reign supreme.<p>I&#x27;ll end this wall of text on the note that OpenAI&#x27;s Ian Goodfellow notes that all projects at OpenAI use deep learning right now, but they are not dogmatic and will consider other approaches if they work well. I think this is the path that should be taken. On the other hand, I also see a bright future for uniting traditional techniques with deep learning such as attaching a SVM to a CNN and combining decision trees with CNNs both of which have resulted in good results.