Adam Gibson Interview
summary
AI splits into subfields with production work defaulting to machine learning and deep learning; Skymind focuses on deploying models for Fortune 2000 customers who care about outcomes not novel math. What actually works in production is count and label pipelines and basic forecasting, while many Google papers are proofs of concept that won’t translate immediately. Market education is required because spinning up AWS tutorials doesn’t equal expertise and neural nets are empirical so models need careful tuning and validation.
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