AI is poised to drive the next wave of technological disruption across industries. Like previous technology revolutions in Web and mobile, however, there will be huge dividends for those organizations who can harness this technology for competitive advantage. I spend a lot of time working with customers, many of whom are investing significant time and effort in building AI applications for this very reason. From the outside, these applications couldn't be more diverse – fraud detection, retail recommendation engines, knowledge sharing – but I see a sweeping opportunity across the board: context. Without context (who the user is, what they are searching for, what similar users have searched for in the past, and how all these connections play together) these AI applications may never reach their full potential.
This is a continuation of the three part series on machine learning for product managers. The first note focused on what problems are best suited for application of machine learning techniques. As I had mentioned in Part I, the core skill sets required of a PM do not change whether you work in a machine learning driven solution space or not. Product managers typically use five core skills -- customer empathy/design chops, communication, collaboration, business strategy and technical understanding. Working on ML will continue to leverage these skills.
Machine learning is changing the automotive industry, creating opportunities for dealerships to get more personal and effective in running your businesses. Machine learning can fuel the 4Ps of Automotive Marketing (product, price, place, and person) in some remarkable ways. But the topic is not always easy to understand – so let's break it down for you.
Last week I went to an event hosted by StoryStream, which bills itself as as a'next generation content marketing platform for brands.' To help launch its new Aura platform, more on which in a moment, the company assembled a trio of AI experts to discuss how the technology might impact on marketing in the coming years.
Named Entity Recognition is a process where an algorithm takes a string of text (sentence or paragraph) as input and identifies relevant nouns (people, places, and organizations) that are mentioned in that string. In our previous blog, we gave you a glimpse of how our Named Entity Recognition API works under the hood. In this post, we list some scenarios and use cases of Named Entity Recognition technology.
Datafication is a buzzword of the last several years, that is used actively along Big Data industry. Honestly, if you would search the term'datafication' on the internet you probably won't find that much relative information about it, yet it is a word we are hearing a lot these days. However, after analyzing the topic itself, I could say that many of us understand the meaning of the term, but probably named it another way.