The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the hundreds of millions. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.
Source: threader.app
A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails), each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class labels. The paths from root to leaf represent classification rules. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No)).
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Speaker diarization, the process of partitioning an audio stream with multiple people into homogeneous segments associated with each individual, is an important part of speech recognition systems. By solving the problem of “who spoke when”, speaker diarization has applications in many important scenarios, such as understanding medical conversations, video captioning and more. However, training these systems with supervised learning methods is challenging — unlike standard supervised classification tasks, a robust diarization model requires the ability to associate new individuals with distinct speech segments that weren’t involved in training.
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Note that this guide was written in November 2018 to complement an in-depth conversation on the 80,000 Hours Podcast with Catherine Olsson and Daniel Ziegler on how to transition from computer science and software engineering in general into ML engineering, with a focus on alignment and safety. If you like this guide, we’d strongly encourage you to check out the podcast episode where we discuss some of the instructions here, and other relevant advice. Technical AI safety is a multifaceted area of research, with many sub-questions in areas such as reward learning, robustness, and interpretability.
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