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This week in AI: Amazon ‘enhances’ reviews with AI while Snap’s goes rogue

TechCrunch

So until an AI can do it for you, here’s a handy roundup of the last week’s stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own. This week in AI, Amazon announced that it’ll begin tapping generative AI to “enhance” product reviews.

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Scaling Media Machine Learning at Netflix

Netflix Tech

We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. We will then present a case study of using these components in order to optimize, scale, and solidify an existing pipeline.

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List of Top 10 Machine Learning Examples in Real Life

Openxcell

But with technological progress, machines also evolved their competency to learn from experiences. This buzz about Artificial Intelligence and Machine Learning must have amused an average person. But knowingly or unknowingly, directly or indirectly, we are using Machine Learning in our real lives.

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Avalo uses machine learning to accelerate the adaptation of crops to climate change

TechCrunch

So when the companies do what are called genome-wide association studies, they end up with hundreds of candidates for genes that contribute to the trait, and then must laboriously test various combinations of these in living plants, which even at industrial rates and scales takes years to do.

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Causal Machine Learning for Creative Insights

Netflix Tech

What if we could use machine learning and computer vision to support our creative team in this process? Utilizing this dataset, we have developed the framework to test creative insights and estimate their causal impact on an artwork’s performance via the dataset generated through our recommendation system.

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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. We’ll review methods for debugging below. Not least is the broadening realization that ML models can fail.

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7 famous analytics and AI disasters

CIO

And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

Analytics 351