20 years ago, IBM’s supercomputer Deep Blue defeated world chess champion Gary Kasparov in a historical first victory for artificial intelligence. Today, supercomputers are smart enough to easily beat not only chess players, but also succeed in similarly sophisticated games, like the 3000-year-old Chinese game of Go, and most recently, poker challenges against multiple human pros. These wins against humans were results from artificial intelligence.
Self-learning takes from human learning - then outperforms It.
The goal of artificial intelligence (AI) is to make computers as smart, or even smarter than human beings, by giving them human-like thinking and reasoning abilities. However, there are also many other ways to achieve this.
Several years ago, Deep Blue was taught by using hand-written functions, copying the knowledge and wisdom of top human chess players. By implementing AI, the IBM supercomputer was able to identify things it has seen before, consider all possible moves, predict human responses, and then decide on the best move. This wouldn’t be possible without training it to look at large amounts of data and use algorithms that gave it the ability to perform tasks without any human intervention. This process led to what is now known as “machine learning.”
But it doesn’t end there. It takes even more intelligent systems to defeat human players in more complex games like Chinese Go and especially Poker – there are not only billions of options to foresee, but they require “feeling” or intuition. This is where the process of deep learning comes into play.
Deep learning is a highly innovative branch of machine learning that closely imitates the work of the human brain in processing data and creating patterns of decision making. In other words, it is how humans learn from practice as we try many different approaches before making a final decision. Thanks to that self-learn procedure, it intuits from experience how to play optimally – without adding human input or manually implemented rules.
Deep learning makes computers ultra-precise
Deep learning is one of the most promising subfields of AI research. Today, the technology is finding many applications across any industry that relies on technology.
Ultra-precision offered by deep learning has been also used in the advertising industry. These self-learning algorithms offer highly accurate product recommendations, while also better predict the probability that a user clicks on an ad (conversion potential) or the value of purchase (conversion value). This will allow more efficient advertising activities for brands.
Deep learning in advertising illustrates the use of algorithms on a massive amount of data and act like humans without specific instructions or rules. It can work inter alia in recommendations used not only by e-commerce companies to persuade customers to buy additional products, but also by other companies to suggest music, events or even dating profiles.
The typical approach to targeted ads (personalised retargeting) is as follows: A user sees a banner creative based on a matter of predefined assumptions - if you checked out black shoes with a gold clasp, the recommendations might want to show black boots with buckles from historical information gathered.
With deep learning however, there are no pre assumed rules. Computers learn by practicing what will be the best combination, should it be the next pair of black shoes or a better option – brown sandals, matching bags, etc. The main drive is that no human being has programmed a computer to perform any of the specific actions described above – every display is driven by data and algorithmic learning. In other words, human action is only required at the step of programming the algorithm how to program itself.
What will the future bring?
A study on “Ad Spending in Southeast Asia” by eMarketer and IAB Singapore revealed that digital ad spending share of companies in the region will almost double up to 53.5% by 2020. This growth will be driven by increasing demand for mobile, video, and social media advertising. As consumers seek better experiences from brands, regardless from offline or online service providers, personalisation will be a key differentiator for companies in Southeast Asia.
Deep learning will play an increasingly important role not only in the advertising industry, but also across different sectors. As the volume of data is set to surge, companies should consider implementing these new AI technologies to analyse results and help understand our world better, as the game has just begun.
By Daniel Surmacz, COO, RTB House