In the US housing boom, small and midsize lenders trying to preserve slim profits are an opportunity for AI and machine learning solutions.

KoBold’s unconventional mining approach uses machine learning (ML) algorithms to predict productive mining locations, reportedly for both cost-savings and environmental benefits. How we got here: Cobalt and nickel are important components of EV batteries and consumer electronics.

Requires large amounts of data, machine learning, and natural language processing to imitate human interactions like speech recognition, text input, and language translation.

Machine learning (ML): A branch of AI and method by which computer systems learn, analyze, and interpret data to take actions on their own without programming. Natural language processing (NLP): A branch of AI that enables computers to understand, interpret, and respond to human language.

Incumbent wealth managers can apply AI-powered technologies and techniques—like machine learning and advanced analytics—to their large client data sets to identify patterns that could inform future client behavior. Advisors can then use these insights to personalize their client interactions.

AI and Machine Learning. Chatbots, voice assistants, and voice-enabled devices can provide digital biomarkers of a range of conditions. The Alexa-powered Amazon Echo smart speaker, for example, can be used to detect irregular heart rhythms and monitor infants’ breathing.

Carbon’s machine learning technology allows patients to scan their insurance information on its app before coming in, enabling an expedited check-in process. Carbon also applies machine learning to shape pre-assessment answers given by patients into a structured format for clinicians. 

This approach to data enables a hospital environment to keep on improving and learning to become smarter over time. A smart hospital triggers action. It effectively leverages AI and machine learning to not only learn from the data, but also act on the data by building automation around it.

Our most recent forecast shows that conversational AI adoption is growing. In 2021, 64.2% of US adults between the ages of 25–34 used a voice assistant. Overall, 46.9% of US adults will use a voice assistant in 2022, 48.2% by 2025. Conversational AI is on track to become a mainstream technology in practically every vertical.

More on this: Deliverr leverages predictive analytics and machine learning to allocate third-party sellers’ inventory across different fulfillment centers based on consumer demand. The technology also aims to determine the best shipping method to ensure items quickly arrive at customers’ doors.

In another example, Amwell made a $100 million deal with Google Cloud to integrate more machine learning-powered tools, like conversational AI in virtual waiting rooms, into Amwell’s suite of telehealth services.

Tripadvisor also won a Drum Award for “Most Effective Use of AI/Machine Learning” as a result of this campaign. Where else is it considering AI: Tripadvisor is considering the use of dynamic content tailored to their individual users as well as predictive analytics for better creative output and delivery.

Centralized teams that oversee cloud, data, AI, and machine learning—collectively known as tech enablement platforms—are leading the change. A group of tech advisors conducts quarterly reviews of each product line. The "mini-CEOs"—really, general managers—own the products, are highly knowledgeable about them, and direct incremental updates to them and strategic decisions.

Facebook open-sourced an AI training data set designed to surface age, gender, and skin tone bias in computer vision and audio machine learning models. Google and Snap both addressed the poor ability of their computer vision apps to identify and process dark skin tones. What’s next?