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Episode Synopsis "TalkML Podcast - Ep One - Fake News"
Hello Listeners :) This is our first podcast where we take an interesting topic - in this case “Fake News” - and provide as many interesting technical and non-technical details to you - our audience. In this podcast, we discuss Fake News and how it can be defined. The fundamentals of fake news and how it spreads like wildfire using the same infrastructure and rails as standard / normal news. It is important to understand these concepts from a ‘norm’, ‘biased feedback loop’ and ‘machine-learning’ perspective as these are foundations to how fake news perpetuates. Aside from the physical infrastructure, we touch upon tech companies (such as the FANG) role to formulate different users into clusters. And a learning algorithm would take over and learn the user behavior in a supervised manner. We also define Deep Fakes and its repercussions to our culture and society at large. Deep Fake, like all emerging technologies, has its flaws too. We can recognize / discern that a video is a deep fake by looking at some clues e.g. eye blinks, facial features contours etc. Fake News Generation and Detection https://github.com/rowanz/grover Deep-Fake Links https://arxiv.org/pdf/2005.05535v4.pdf https://arxiv.org/pdf/1909.12962v4.pdf https://arxiv.org/abs/1406.2661 https://github.com/deepfakes Try it yourself: shorturl.at/bBRT1 Acronyms and Abbreviations FANG: Facebook, Amazon, Netflix, and Alphabet Creators Stefan: https://www.linkedin.com/in/stefan-juang-93b63998/ Abhishek: https://www.linkedin.com/in/aparyani/
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