Rapid AI Insights: Edition 8

Hello AI Ace,

Welcome to this week's edition of Rapid AI Insights. This week, we look into how to evaluate engineering applications of #generativeai, what the Q2 earning calls trends look like, where #customerexperience is intersecting with AI, and some crucial ML metrics to understand better.

Using Generative AI for engineering?

Engineering leaders across organizations are currently evaluating the use of ChatGPT, and other large language models (LLMs) for their teams to be able to build better products or services, faster than the competition. Generative AI models no doubt augment several processes but choosing the right applications and tools is critical.?

VentureBeat put together some questions to ask and answer at this point, to choose the most effective places to apply generative AI in your engineering operations.?

Has a simpler approach, like a rules-based algorithm, already been tried for this problem, and what did that approach not achieve that ML might?

Can a human provide several specific examples of what a successful ML algorithm would output?

Is high-quality data readily available?

Is there an analogous problem with a documented ML solution?

Has ‘good enough’ been precisely defined?

AI, AI Everywhere?

Earning calls have long been a way of understanding trends in the market, and the latest trend uncovered from studying Q2 earnings calls is that companies are doubling down on AI, and how. Companies reporting earnings recently have talked up AI even more than in the previous quarter.

According to research done by Reuters, companies have been increasingly vocal about their plans to use AI, indicating that they have actively explored and are continuing to explore the use of generative AI and related technologies across their areas of work.

Personalizing the customer experience?

While it looks as though AI is changing everything in every industry, there is one thing that remains constant: the customer must remain the focus and every interaction should be designed to improve a customer’s perception.?

Customers today share their data to enable more personalized experiences, and the companies that deliver on this reap the benefits. In the banking sector, a recent survey of over 30,000 banking customers revealed that respondents who agreed that their bank personalizes the experience are more likely to reward it with a higher Net Promoter Score (NPS).?

Harvard Business Review discusses the different ways in which AI is being used to strengthen customer connections.?

Machine learning 101: Accuracy, Precision and Recall?

Most of us are familiar with business metrics like ROI, revenue growth, and operating profit. In the area of AI and ML models, accuracy, precision, and recall are the three key metrics commonly used to assess the performance of a model.?

Accuracy shows how often a machine learning model correctly predicts an outcome, overall.?

Precision shows how often an ML model correctly predicts the positive class or any other target class i.e.? the proportion of positive predictions that are actually correct.?

Recall measures how often an ML model identifies positive instances (true positives) from all the actual positive samples in a dataset ie. the proportion of actual positives that were predicted correctly.

Learn more about these metrics and how they are measured in this guide.?

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.?