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Using behavioral social science and machine learning “63% of companies plan to increase or maintain AI and machine learning spending in 2023”
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hen asked to cite the top three drivers behind these budget changes, leading factors included changes in business strategy, cost pressures, and inflation.
But have the costs been alleviated yet? According to Harvard Business Review, most ML projects fail to deliver value. There are many reasons for this, but we believe that there are 3 fundamental issues that can lead to high failure rates:
of meaning this adds to the data allows companies to communicate more effectively, to offer more relevant recommendations, and understand what customers truly want.
Reliance on historical data doesn’t provide real-time insight. 58% of successful machine learning projects take more than several months to deploy. Delays happen, which means that at the point of deployment, the data being analysed and predicted on is from the past. As stated, this can’t account for a customers changing behaviour, and crucially, it doesn’t factor in their most recent transactions. Can a deployment based on historical data really predict what customers are going to do in the future? Consider the data from the last
3 years when the Covid pandemic influenced an un-referenceable series of data points. Can we reliably use this data to predict what customers want this year, or the next?
Our low-code platform increases your speed to market by reducing complexity costs The platform has a no-code environment Workbench, as well as low-code environment Notebooks. Designed to be accessible to everyone in a business – from the Businessperson who needs to drive success. To the Data Scientists who require uncomplicated tools to untangle machine learning complexities. As well as Technologists who require flexibility in the form of code-based customizations that are easy to implement and integrate. ecosystem.Ai’s multi-faceted technology provides a single platform that works within your own environment, or in the cloud. It facilitates open collaboration for all team members to work on the same project, and keep track of all deployments.
Engage with people more authentically using behavioural analysis Generic data science answers the “how” through analysis of data points and trends. But ecosystem.Ai use Computational Social Science to take this a step further by asking
Deployment Complexity can skyrocket costs. Putting accurate and successful machine learning projects into production is complex. The tools to create predictions, build recommenders and configure experiments are costly, complicated and require expertise. Moving to a low-code, specialised deployment environment reduces these costs immediately, and gives business users more involvement throughout the lifecycle of the project.
Current predictions don’t account for everchanging human behaviour. Although machine processes are largely designed to target, speak to, and engage with humans; most of the patterns and correlations it identifies don’t tell you much about behavior. Ai needs to advance and factor in human behaviour and context. The extra layer The ecosystem.Ai Platform is built on 3 foundational pillars
This is ecosystem.Ai’s area of expertise. Our revolutionary platform integrates Computational Social Science to bridge the gap between machine and human intelligence. Providing analytics from a human and behavioural context, we help companies build behavioral models in their own environment and get their real-time predictions into market faster. Offering the tools to make money by learning while in production and save money by avoiding complicated implementation costs. 24 SYNAPSE | SHOW EDITION 2023
