Marketing Robots: How Neural Networks Will Change the World of Internet Marketing
We live in a funny time. Technological progress is slowly but inexorably squeezing reality into the world from science fiction films. The world in which robots live among us and perform a huge bunch of tasks.
The latest trend in artificial intelligence is neural networks. They can learn, memorize and process information, and then perform some actions based on their experience. Just like people. Almost.
Google, Microsoft, Yandex, as well as hundreds of institutes and startups around the world are engaged in research and development of neural networks. Something cool comes up almost every month.
To begin with, let’s see what robots have already learned to do by the end of 2016, and what benefit we online marketers can get from this.
Neural networks are looking for information
In early November, Yandex announced a new Palekh algorithm based on neural networks. It allows you to more accurately determine what exactly the user wants to find when entering a non-standard query.
Tell me, dear Yandex, a film in which potatoes were grown on another planet. Yes, I’m exaggerating. But there are similar requests, and they need to be handled somehow.
Palekh indexes the headings of all pages and tries to understand their meaning. Then he writes the meaning in the form of numbers and stores them in a multidimensional matrix. When you enter a query, the algorithm also translates it into a numerical format and looks for the most suitable header in the database. This allows you to make the search more useful and responsive.
The next goal is to capture the meaning of the whole text, not just the title.
Things like that change the way you write texts. We need less and less to think about keys and more and more about the meaning, usefulness and, probably, interesting content.
Neural networks recognize images
By the way, neural networks have been working in Google and Yandex for a long time – in the search for images. The robot determines what is in the image and saves this information. When you enter a request, the network compares it with your notes and gives the most suitable pictures and photos.
The same algorithm uses the Clarifai service. You specify a photo or picture, and the neural network determines what is displayed on it and creates tags. It also shows the color scheme.
Google Photo has a similar thing. You upload your photos for storage, and the service analyzes them and helps you find the ones you need. For example, you can view pictures taken in the mountains, or those that have your friend Maxim.
And here is another interesting service – FindFace. You upload a photo of a person, and the neural network finds his VKontakte profile. Expanse for maniacs!
Profit: search for contacts from Central Asia on photos from events to show them relevant ads. Targetologists rejoice.
Neural networks understand speech
Ok Google, do you understand what I’m saying? Remind me to finish writing this article tomorrow. My smartphone understands me well. Not perfect, but good. Obviously better than sleepy saleswomen in stores.
Do not forget about voice input. English-language Google Docs writes well under dictation. He also begins to understand when “Delete” is a command, and when it’s just a word that needs to be entered. Russian localization lags behind in development by several years. As always.
And here is an article about the LipNet neural network that reads lips. In laboratory conditions, the accuracy of speech recognition was 93.4%. This is 93.4% more than mine. It is clear that in real life it is more difficult to catch the point, but LipNet will continue to develop.
The Tribe video messenger not only recognizes your speech, but also understands it. He can automatically create and enable subtitles or find information about the subject of the conversation. The interlocutor mentioned that he was reading “Texterra”, and the Tribe will immediately find and show you the link. Cool, right?
Neural networks are talking
In September, Google revealed the new WaveNet neural network, which simulates a human voice. Models well. Call me on the phone, I would not have guessed that this is not a person. Listen to examples on the site.
WaveNet can speak English and Chinese, and also models different female and male voices.
Profit: automation of Call centers, dubbing of video clips.