Remove Experimentation Remove Measurement Remove Risk Remove Statistics
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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362
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What you need to know about product management for AI

O'Reilly on Data

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. The need for an experimental culture implies that machine learning is currently better suited to the consumer space than it is to enterprise companies.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

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Understanding Simpson’s Paradox to Avoid Faulty Conclusions

Sisense

This is an example of Simpon’s paradox , a statistical phenomenon in which a trend that is present when data is put into groups reverses or disappears when the data is combined. It’s time to introduce a new statistical term. A new drug promising to reduce the risk of heart attack was tested with two groups.

Testing 104
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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

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Building Smarter Financial Services: The Role of Semantic Technologies, Knowledge Graphs and Generative AI

Ontotext

So, this is a big driver for the outcome because when you are saving money for the business, you can measure it and see its value. Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. How do I make sure I can manage risk?” It’s open.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. It might analyze real-time data from cameras, LiDAR and other sensors to identify objects, assess risks and anticipate environmental changes like sudden weather events or unexpected obstacles.