“The future is now” – this phrase has been repeated like a mantra for a few years. It can be observed with the naked eye that technology and artificial intelligence are developing at an incredibly fast pace. In today’s blog post you will find out what machine learning is and why it’s constantly being developed.
How a computer defeated the champion of the GO game…
One day in 2016, two outstanding opponents faced each other in a fight for a million dollars. The multiple champions of the strategic GO board game confronted the computer – and lost every time they played. Why does it matter? Because GO is a strategic game, similar to chess, and it requires a logical and intuitive approach that is generally assigned to human beings, not machines. Moreover, it’s considered to be the most challenging game in the world.
The program that won the game was called AlphaGo. It was created by experts working for DeepMind, a company acquired by the Mountain View giant, Google.
A year later, the Chinese GO grandmaster, Ke Jie also decided to face the program. However, similarly to his predecessor, he also didn’t manage to win with the computer.
In the meantime, the program was used in other games which contributed to the development of its algorithms. To make them even more effective, it played thousands of games against itself.
Google and AI
AlphaGo is a perfect token of the development of self-learning systems. And what does Google say about machine learning?
Lately, on our blog, we published an article discussing information that Google knows about you. Thanks to reading it you can learn what data Google collects to improve its machine learning (ML).
Machine learning is based on gathering data from multiple sources and then processing it in a way that allows the algorithm to learn and analyze information without any human input. As you can see, machine learning works with the provided examples, not with the instructions introduced by web developers. Thanks to the gained experience, algorithms learn in real-time which elements are correct and which need to be improved.
“Machine learning is using data to answer questions” – Yufeng Guo, Developer and Machine Learning Advocate at Google Cloud.
Google search engine which uses many ML systems is the simplest, yet the most sophisticated example of machine learning. Thanks to the systems, the search engine knows what you mean when you start typing in a query and it tailors the search results to you (the information Google knows about you, your preferences or interests). This can be observed when looking at the layout of the search results – at the top positions, you can see content that potentially may be of interest to you. Artificial intelligence is used for face recognition, fraud detection, recommendation systems, text and voice systems (like Google Assistant).
Learning systems are applied in various fields – from medicine (as in the case of diabetic retinopathy, skin disease detection), transport and communication systems (automatic cars, automatic parking and reversing mechanisms), to sales or marketing in Google Ads.
“The Artificial Intelligence system is designed to facilitate everyday activities such as searching for pictures of your beloved ones, overcoming language barriers with the help of Google Translator, writing emails or performing duties using Google Assistant. Artificial intelligence allows us to look at current problems, ranging from healthcare systems to scientific progress, from a completely new perspective.”
Google encourages the use of artificial intelligence not only for business purposes but also in social, humanitarian and environmental contexts.
Learning systems in a marketing agency
Automated bidding allows you to achieve results that correspond to your objectives. The method is based on learning systems thanks to which the algorithm optimizes the bids for each auction and you can focus on the remaining parts of running campaigns. Bidding strategies take into account a number of factors such as the user’s device, location, time of day, language, operating system and so on.
We can distinguish 6 automated bidding strategies. Each of them serves to achieve a different business objective.
|STRATEGY||OBJECTIVE||HOW DOES IT WORK?|
|Maximize clicks||Increasing the number of visits to the site|
|Target impression share||Increasing the visibility of your website|
|Target CPA||Increasing the number of conversions|
|Target ROAS||Meeting your target return on ad spend|
|Maximize Conversions||Increasing the number of conversions|
|Maximize Conversion Value||Increasing the conversion value|
The application of automated bidding strategies can improve your key performance indicators.
At Delante, we successfully employ advanced Google AI tools. Testing various solutions allows us to select the most appropriate ones that generate the most satisfying KPIs.
To sum up
Machine learning has been developing continuously for a long time. Just a few years ago we believed that self-driving cars or smart home appliances were only futuristic ideas that wouldn’t come true in the next decade or two. Today, there is probably no one who would be surprised that solutions which were considered fantasies some time ago can become a part of our everyday reality even in a few days or months. The future is now and we should benefit from the possibilities it offers us.
As an agency, we can tell you it’s worth using learning systems and automated bidding strategies in Google Ads campaigns. Thanks to them, you’ll be more likely to reach your target group, maximize conversions and consequently improve your results and profits.