3 main trends in the world of AI in 2017
It seems that the whole world (both people and things) is connected to the Internet, and, according to forecasts, by 2020 there will be 6 billion smartphones and 50 billion devices connected to the Internet.
We have already seen impressive experiments in using users' personal information to create an individual approach, making better use of their time and attracting more focused attention. Despite the fact that satisfaction of needs remains the main currency of the digital economy, simply providing an individual approach to the interests of users is no longer enough human experience should be more interesting, faster and more contextually acceptable.
Thanks to the significant advancement in the development of artificial intelligence (AI) and machine training over the past few years, we are already seeing the possibilities of using AI to improve user service in other areas of customer interaction. For example, AI-based chatbots can answer user questions on various topics. AI will be critical in creating an individual approach to users, the next generation approach, and, as a guide for users, sellers must use it to succeed.
Below are the main forecasts for AI for the coming year.
1. Help detect hidden opportunities and increase user satisfaction.
An analysis of the total amount of data that is generated daily by various users creates a unique and complex problem that requires modern technology to solve. As the volume and variety of data sources continues to grow, the need for the development of new methods of analysis, including the development of new algorithms and techniques that increase productivity and allow for deeper analysis, grows accordingly.
AI can take advantage of cloud scaling and unconventional data analysis to process and organize this ever-growing amount of unexpectedly useful information. A machine-learning device can find deeper mathematical relationships that other systems do not take into account. It can also constantly re-evaluate the results obtained by comparing them with emerging new data in order to ignore the “noise” and draw attention to really important and new trends. AI can support data analysis to draw our attention to important things - opportunities for change, including:
• Bridging gaps where users need more services
• Switching resources to increase efficiency
• Changing processes to meet projected growing demand.
In addition, AI can help businesses correlate requests with user stories, identify trends, and draw attention to new patterns.
2. Identification of emerging patterns and adaptation with anticipation of events.
Predicting the future is difficult, and it seems that there are always unexpected side effects of change. AI can help predict user behavior by conducting a variety of data analysis and evaluating alternatives, and then suggesting the best path for the organization.
Looking through the accumulated data, AI-based applications can detect this-then-that patterns that can be used to sort what is happening in your business. With the effective use of your own data to sort behavior patterns, the system can provide forecasts based on previous actions of your organization in the same conditions.
Tracking the results, according to the forecasts made, over time, the AI feedback loop makes the models more perfect and reliable. Having the ability to predict alternative patterns of behavior and quickly switch when changing current data, we will get even faster delivery of the product and services to our users in 2017.
3. Data accumulation for even deeper individual interaction with the user.
Today, any user who has ever connected offers a ton of opportunities to companies that want to take another step towards an individual approach to user satisfaction. User satisfaction, taking into account the context, takes an individual approach to the next level, for example, when you know where your client is currently and what he is doing, you can adjust the message, offer or other exchange of information.
Now users have high expectations of more thoughtful, almost predictive interaction and AI, of course, will help to realize these expectations. The abundance of available data enables higher performance and better machine learning models, creates new levels of productivity and predictability, which ultimately leads to higher levels of user satisfaction.
We expect that in the future, user interaction systems will become as perfect and capable of cooperation as a person. Imagine, for example, a program that recognizes repetitive mechanical tasks and turns on specifically to complete them. Or a program that calibrates the various methods with which you can perform the task, and recommends the most effective technique.