K
Kathleen Martin
Guest
Amnon Mishor is CTO and Founder. Lead space, Recognized in the industryAn AI-powered buyer data platform used by B2B companies such as Zoom and Salesforce.
In the past About 10 years, the digital revolution has given us surplus Of the data. This is exciting for many reasons, but mainly in terms of how AI can revolutionize businesses further.
But in the B2B world, where I’m deeply involved, there’s still a shortage of data, mainly because of the significantly lower number of transactions compared to B2C. that’s why, AI fulfills its promise to revolutionize businesses, We also need to be able to solve these small data problems. Thankfully, you can.
The problem is that many data scientists turn to bad practices and generate self-fulfilling prophecies that reduce the effectiveness of AI in small data scenarios and ultimately AI’s influence on corporate development. Is to prevent.
The term “self-fulfilling prophecy” is used in psychology, investment, etc., but in the world of data science it can easily be described as “clear prediction”. This is seen when a company predicts what is already working “by design” and finds a model that applies it to different scenarios.
For example, retailers determine that people who fill their carts online are more likely to buy than those who don’t, so they sell in large quantities to that group. They are predicting the obvious!
Continue reading: https://californianewstimes.com/how-to-apply-ai-to-small-data-problems-techcrunch/634675/
In the past About 10 years, the digital revolution has given us surplus Of the data. This is exciting for many reasons, but mainly in terms of how AI can revolutionize businesses further.
But in the B2B world, where I’m deeply involved, there’s still a shortage of data, mainly because of the significantly lower number of transactions compared to B2C. that’s why, AI fulfills its promise to revolutionize businesses, We also need to be able to solve these small data problems. Thankfully, you can.
The problem is that many data scientists turn to bad practices and generate self-fulfilling prophecies that reduce the effectiveness of AI in small data scenarios and ultimately AI’s influence on corporate development. Is to prevent.
The term “self-fulfilling prophecy” is used in psychology, investment, etc., but in the world of data science it can easily be described as “clear prediction”. This is seen when a company predicts what is already working “by design” and finds a model that applies it to different scenarios.
For example, retailers determine that people who fill their carts online are more likely to buy than those who don’t, so they sell in large quantities to that group. They are predicting the obvious!
Continue reading: https://californianewstimes.com/how-to-apply-ai-to-small-data-problems-techcrunch/634675/
