Are robots slowly but surely taking over the world, leaving only people who can develop algorithms in employment? At least in areas where face-to-face interaction is still essential, the added value of AI lies in a hybrid approach.
Advances in the field of opinion mining and sentiment analysis has opened up unprecedented access to insights into what people think. On Facebook alone, the most widely used social media platform, 510,000 comments are posted, 293,000 statuses are updated, and 136,000 photos are uploaded every minute.
The trend towards digital advice is sweeping across all sectors. In the investment management business, Robo-advisors, online platforms that use algorithms to recommend investment portfolios, are already widely in use, gaining acceptance and growing rapidly.
The questions you want to answer determine which type of analytics to use
A widely acknowledged, purpose-based classification divides analytics it into four different types: descriptive, diagnostic, predictive or prescriptive analytics.
A widespread view is that structured and semi-structured data constitute only about 10% of the data aggregated by companies. The remaining 90% of all data available is unstructured. Structured data breaks down well into fields and can be stored for example in a data warehouse.
Big data analytics has achieved wide acceptance as a means to generate insights into what clients want and to derive good, evidence-based business decisions from it. However, while confidence in analytic decision-making is growing, there is also evidence to suggest that investments into big data analytics are not yet paying off.
As consumers spread their attention across more and more channels and screen types, content producers are struggling to keep up. Some respond by topping up their content creation capabilities, others try to optimize them.
Many of us might not have noticed the quiet revolution taking place in the world of search. Rather than examining search query terms and matching them to keywords it finds on websites, search engines are now trying to “understand” users’ queries and offer links that provide answers to them.
The internet is often referred to as a massive archive of “human knowledge”. It’s become universally possible to access information on virtually anything – or so it seems. For the average user, finding information can still be a weary process, unless you know how to refine your search queries or have the patience to click through endless links provided by search engines.
Mobile finance is permeating every aspect of our lives. According to a survey Google conducted in the UK, 75% of those surveyed had 1-2 financial apps installed on their phones. 44% used their apps daily whereas the most common activities were checking balances and making payments.
Search engines are becoming increasingly sophisticated. There is also a lot of advanced artificial intelligence (AI) behind personalization, i.e. recommending content to specific users or user groups. Both serve the same purpose: to deliver the right information to the right user at the right time. This goal has also been at the center of many recent changes to search engine algorithms.
The next frontier of artificial intelligence is natural language processing. A lot of effort is now going into improving how machines understand human writing and speech. Anyone who has ever tried Google Translate knows how difficult processing human language can be.
Binzstrasse 23 8045 Zurich Switzerland
+41 43 344 91 89 company@adviscent.com
Generel Terms and Conditions for Interactive Advisor
GetPre-Study
We use cookies to provide a user-friendly experience. By continuing to browse this site, you give consent for cookies to be used.
Contact us to discuss and plan a proof-of-value.