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Sunday, 21 March 2021

Choosing Problems in Data Science and Machine Learning

Eugene Yan 5 years ago 22

A data science leader discusses how to prioritize which problems a team should tackle by using cost-benefit analysis, considering extent and severity of problems, and evaluating whether solutions are quantifiable. Key metrics include assigning dollar values to benefits (like $1.2 million yearly revenue from a notification system), accounting for development costs (team compensation plus infrastructure), and recognizing that capabilities and learning exercises reduce future costs by acting as multipliers. Teams should balance incremental improvements with disruptive solutions, avoiding pitfalls like resume-driven development where engineers choose technologies based on personal career benefit rather than business value.

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Reducing Toxicity in Language Models

Lil'Log 5 years ago 51

Research addresses methods for detecting and reducing toxicity in large language models trained on internet data, which inevitably acquire unsafe content and biases. Key approaches include collecting annotated datasets through crowdsourcing with quality controls, using semi-supervised learning on unlabeled data to expand training sets, and developing robust detection models through adversarial testing where workers iteratively find ways to fool classifiers. The ultimate goal is to enable safe deployment of pretrained language models in real-world applications by improving toxicity detection accuracy and resilience against adversarial attacks.

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Every AI story that matters — and the intelligence behind it.

Sunday, 21 March 2021

Choosing Problems in Data Science and Machine Learning

Reducing Toxicity in Language Models

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