Rapid AI Insights: Edition 11

Hello,

Welcome to this week's Rapid AI Insights. In this edition, we discuss a #ILO report that analyzes the potential impact of #generativeAI on job quality and quantity, highlight a case study of effective #automation in #accounting processes, and delve into why #datascience projects are struggling, despite having rich #data. In ML 101, we look what a machine learning model does and how the model-centric approach works.

AI will augment, not replace jobs?

Amidst the apprehension of job losses due to AI, the ILO released a report that analyzed generative AI's potential effects on job quantity and quality.? The report shows that most jobs and industries are only partly exposed to automation and are more likely to be complemented rather than substituted by the latest wave of generative AI, such as chatGPT. The report predicts that the greatest impact of this technology is likely to not be job destruction but rather the potential changes to the quality of jobs, notably work intensity and autonomy.

• 5.5 percent of total employment in high-income countries is potentially exposed to the automating effects of AI whereas in low-income countries, the risk of automation concerns only some 0.4 percent of employment.

Harmonizing complex disparate data made simple

This week, we’d like to showcase our work with SFR3 Fund, a tech-enabled real estate fund that acquires, renovates, and rents affordable homes. SFR3's objective was to streamline accounting processes and management, leverage data science and AI/ML to reduce cost and human errors, and future proof the overall business process, and particularly the FP&A function with a modern data stack. Their two primary challenges were eliminating manual work and avoiding data loss and fidelity.?

With RapidCanvas, end-to-end automation of the accounting was activated in under 6 weeks, and SFR3 was able to run the process processes for one quarter entirely through the RapidCanvas platform and see consistent results

• 95% accuracy in automation?
• 2 second processing of transactions

Read the entire case study here.?

Why data science projects are struggling

Insurance is a data-rich space and there is a growing interest in using AI and machine learning in areas like claims operations and fraud detection. However,? as a recent article in The Insurer says, there is concern amongst leaders that significant investment in data science teams and infrastructure is not delivering the practical, pragmatic business change or value they would like or expect.

Some of the reasons outlined for this less than optimal data science performance are:?

1. Trading off accuracy and value creation
2. A lack of technical challenge
3. Na?vety
4. Managing massive model real estate
5. Insufficient governance and control

While this article tackles the insurance space, the challenges outlined can be seen across many sectors that are trying to make better use of their data. Real business value can be unlocked by having strategy and clear vision around team structure, what to model, deployment and maintenance, as well as ensuring access to technical expertise to ensure the implementation is robust.?

ML 101: ML model and a model-centric approach?

In past weeks, we’ve been looking at the data side of the AI/ML process. Today, let’s look at the model and what a model-centric approach involves.?

A machine learning model is a program that finds patterns or makes decisions from a previously unseen dataset. An ML model is created from an ML algorithm which is trained using either labeled, unlabeled, or mixed data. Different algorithms can be chosen, depending on the goal of the ML model.?

Learn more about machine learning models in this Coursera guide.?

A model-centric approach to machine learning focuses on producing the best model for a given dataset. This can be done by experimenting to choose the best model architecture and training process from a wide range of possibilities.?

Here is a look at the difference between model-centric and data-centric approaches to AI/ML.

About RapidCanvas

RapidCanvas is a no-code AI platform for business users to go from idea to live enterprise AI solution within hours, reducing time to value by over 90%, when compared to traditional AI build-and-deploy processes. RapidCanvas creates out-of-the-box AI solutions tailored to your needs using our proprietary AutoAI technology. Our data science experts work with you to optimize the results to your satisfaction; we combine the efficiency of algorithms with the experience of human experts. RapidCanvas work with leaders in financial services, retail, renewable energy, and manufacturing.?