Artificial intelligence has come a long way ever since its introduction. While not completely ubiquitous in today’s world, companies like Appier are helping to connect businesses to customers, and further leverage the capabilities of AI technology and big data.
Appier’s main focus lies in enterprise-level applications, analysing Big data into actionable insights, and adding value to people’s lives in a world where we use a number of devices.
e27 speaks to Appier CEO and co-founder Chih-Han Yu to further understand what the technology has in store for us in advertising and marketing, and how easy it would be for businesses to take advantage of AI.
What do you think are the most promising applications of artificial intelligence?
To me there are actually so many. You can divide them into three parts, one being specialist automation. Robots can do specialist tasks for humans, like automation in driving, cooking, cleaning or even assisting.
Another one is personalisation. All experiences in the future will be quite personalised, and this includes social channels and education. Communication with AI is also quite adaptive as the technology can learn your accents and the way you speak.
The last application would be enterprise. How to solve problems that deal with large sets of data, and how we could utilise it for better decision making. This will help with making much more informed and intelligent decisions with a clear objective. I think these three applications are very promising in AI.
What area of AI does Appier focus on? How does the Cross Screen technology work?
Appier focuses on the third aspect – enterprise. We help with making more intelligent decisions; the technology even has predictive capabilities. We are building a very important fundamental utility to better marketing efforts. This technology is being used in e-commerce, gaming and even in building your brand. We’re creating a different marketing experience by optimising ad impressions.
What disadvantages do you see in terms of adopting artificial technologies in various markets?
I don’t think there are any major disadvantages, since every market has a different adoption path. In Asia, some countries are interested in the field of automation, and in some other countries they are solving infrastructure problems. So there are no general difficulties across markets.
How do you use AI to break down Big data?
When it comes to Big Data, machine learning and artificial intelligence, there are three steps.
Big data acts as an ingredient. Like when you are making a cake – the data represents the flour, and the actual process of baking the cake is represented through machine learning. Artificial intelligence will then be the output, or the cake if you will. In the past, there was a lot of emphasis on logic, but now we’re making use of huge sets of data and so we’ll have a much better AI with this output.
How difficult is for businesses to integrate AI technology into their platforms?
It is quite easy. From mobile data to PC data, we have a model that works for every kind of device.
What was the reasoning behind pivoting from robotics to other areas of artificial intelligence?
To me, these areas are quite similar. The main difference is that in robotics the innovation cycle is very long. But what we’re doing now is much faster in terms of the innovation cycle.
We have seen work on robotics for applications like autonomous cars go on for 10-15 years, however with enterprise solutions we’re seeing an impact straight away as it focuses on everyday problems and better decision-making; I find this much more exciting.
Image Credit: Appier