![]() ![]() Finding new methods and strategies for leveraging this customer data into better customer service and new customer experiences will be a focus for many people working in the field of data science during 2022.ĭeepfakes, generative AI, and synthetic data ![]() The pandemic sparked a wave of investment and innovation in online retail technology, for example, as businesses looked to replace the hands-on, tactile experiences of bricks ‘n’ mortar shopping trips. This has also led to a drive to create greater levels of personalization in goods and services being offered to us by businesses. Our interactions with businesses are becoming increasingly digital – from AI chatbots to Amazon’s cashier-less convenience stores - meaning that often every aspect of our engagement can be measured and analyzed for insights into how processes can be smoothed out or made more enjoyable. This could mean cutting down friction and hassle in e-commerce, more user-friendly interfaces and front-ends in the software we use, or spending less time on hold and being transferred between different departments when we make a customer service contact. ![]() This is about how businesses take our data and use it to provide us with increasingly worthwhile, valuable, or enjoyable experiences. In 2022 we will see it appearing in an increasing number of embedded systems – everything from wearables to home appliances, cars, industrial equipment, and agricultural machinery, making them all smarter and more useful. TinyML refers to machine learning algorithms designed to take up as little space as possible so they can run on low-powered hardware, close to where the action is. Self-driving cars, for example, cannot rely on being able to send and receive data from a centralized cloud server when trying to avoid a traffic collision in an emergency situation. It’s closely linked to the concept of edge computing. This is why the concept of “small data” has emerged as a paradigm to facilitate fast, cognitive analysis of the most vital data in situations where time, bandwidth, or energy expenditure are of the essence. This is fine if you’re working on cloud-based systems with unlimited bandwidth, but that doesn’t by any means cover all of the use cases where ML is capable of adding value. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |